1(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof document?document.currentScript:void 0,43531,75254,63059,74886,31278,37727,e=>{"use strict";var t=e.i(71645);let o=(...e)=>e.filter((e,t,o)=>!!e&&""!==e.trim()&&o.indexOf(e)===t).join(" ").trim(),s=e=>{let t=e.replace(/^([A-Z])|[\s-_]+(\w)/g,(e,t,o)=>o?o.toUpperCase():t.toLowerCase());return t.charAt(0).toUpperCase()+t.slice(1)};var a={xmlns:"http://www.w3.org/2000/svg",width:24,height:24,viewBox:"0 0 24 24",fill:"none",stroke:"currentColor",strokeWidth:2,strokeLinecap:"round",strokeLinejoin:"round"};let r=(0,t.createContext)({}),n=(0,t.forwardRef)(({color:e,size:s,strokeWidth:n,absoluteStrokeWidth:i,className:c="",children:l,iconNode:p,...d},u)=>{let{size:h=24,strokeWidth:m=2,absoluteStrokeWidth:y=!1,color:g="currentColor",className:f=""}=(0,t.useContext)(r)??{},w=i??y?24*Number(n??m)/Number(s??h):n??m;return(0,t.createElement)("svg",{ref:u,...a,width:s??h??a.width,height:s??h??a.height,stroke:e??g,strokeWidth:w,className:o("lucide",f,c),...!l&&!(e=>{for(let t in e)if(t.startsWith("aria-")||"role"===t||"title"===t)return!0;return!1})(d)&&{"aria-hidden":"true"},...d},[...p.map(([e,o])=>(0,t.createElement)(e,o)),...Array.isArray(l)?l:[l]])}),i=(e,a)=>{let r=(0,t.forwardRef)(({className:r,...i},c)=>(0,t.createElement)(n,{ref:c,iconNode:a,className:o(`lucide-${s(e).replace(/([a-z0-9])([A-Z])/g,"$1-$2").toLowerCase()}`,`lucide-${e}`,r),...i}));return r.displayName=s(e),r};e.s(["default",0,i],75254);let c=i("check",[["path",{d:"M20 6 9 17l-5-5",key:"1gmf2c"}]]);e.s(["Check",0,c],43531);let l=i("chevron-right",[["path",{d:"m9 18 6-6-6-6",key:"mthhwq"}]]);e.s(["ChevronRight",0,l],63059);let p=i("copy",[["rect",{width:"14",height:"14",x:"8",y:"8",rx:"2",ry:"2",key:"17jyea"}],["path",{d:"M4 16c-1.1 0-2-.9-2-2V4c0-1.1.9-2 2-2h10c1.1 0 2 .9 2 2",key:"zix9uf"}]]);e.s(["Copy",0,p],74886);let d=i("loader-circle",[["path",{d:"M21 12a9 9 0 1 1-6.219-8.56",key:"13zald"}]]);e.s(["Loader2",0,d],31278);let u=i("x",[["path",{d:"M18 6 6 18",key:"1bl5f8"}],["path",{d:"m6 6 12 12",key:"d8bk6v"}]]);e.s(["X",0,u],37727)},89539,(e,t,o)=>{t.exports=JSON.parse('[{"id":"s0001","stat":"Wilson Sonsini AI agent: 92% accuracy on commercial contract review","source":"Wilson Sonsini","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Agentic AI contract review accuracy"},{"id":"s0002","stat":"$28.5B ALSP market size","source":"Thomson Reuters ALSP Report","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Total size of the alternative legal service provider sector","url":"https://www.thomsonreuters.com/en-us/posts/legal/alsp-report-2025/","access":"open"},{"id":"s0003","stat":"18% CAGR for ALSPs","source":"Thomson Reuters ALSP Report","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Growth rate of the ALSP sector","url":"https://www.thomsonreuters.com/en-us/posts/legal/alsp-report-2025/","access":"open"},{"id":"s0004","stat":"Over 50% of corporate law departments now rely on ALSPs","source":"Thomson Reuters ALSP Report","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Corporate legal departments using ALSPs for eDiscovery, litigation support, legal ops","url":"https://www.thomsonreuters.com/en-us/posts/legal/alsp-report-2025/","access":"open"},{"id":"s0005","stat":"35% of law firms see GenAI as making ALSPs more attractive","source":"Thomson Reuters ALSP Report","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Law firms viewing GenAI as a driver for ALSP attractiveness","url":"https://www.thomsonreuters.com/en-us/posts/legal/alsp-report-2025/","access":"open"},{"id":"s0006","stat":"40% of corporate legal departments see GenAI as making ALSPs more attractive","source":"Thomson Reuters ALSP Report","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Corporate legal departments viewing GenAI as ALSP driver","url":"https://www.thomsonreuters.com/en-us
1/posts/legal/alsp-report-2025/","access":"open"},{"id":"s0007","stat":"Hill Dickinson: detected 32,000 ChatGPT hits in a single week","source":"Hill Dickinson","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Uncontrolled AI usage at a law firm"},{"id":"s0008","stat":"$50B UK legal tech market","source":"The Lawyer","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Referenced market size for UK legal tech companies targeting the US","url":"https://www.thelawyer.com/tapping-into-a-50bn-market-how-uk-legaltechs-are-taking-on-america/","access":"paywalled"},{"id":"s0009","stat":"Harvey Assistant achieved top scores in 5 out of 6 tasks in Vals benchmarking","source":"Vals AI Benchmarking Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Harvey outperformed other legal AI tools and the lawyer baseline"},{"id":"s0010","stat":"Harvey Assistant scored 94.8% on Document Q&A","source":"Vals AI Benchmarking Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Harvey\'s highest individual task score"},{"id":"s0011","stat":"CoCounsel scores ranged from 73.2% to 89.6% across 4 tasks","source":"Vals AI Benchmarking Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Thomson Reuters CoCounsel benchmarked performance range"},{"id":"s0012","stat":"In 4 tasks, AI surpassed the Lawyer Baseline","source":"Vals AI Benchmarking Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Tasks where AI outperformed human lawyers"},{"id":"s0013","stat":"Axiom: 60% efficiency boost for contract-related legal tasks using AI","source":"Axiom","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"AI-enabled contracting solution efficiency claim"},{"id":"s0014","stat":"64% of executives report AI will significantly enhance productivity","source":"Microsoft Agents of Change Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Executive sentiment on AI productivity impact"},{"id":"s0015","stat":"70% of business leaders expect AI agents to redefine how tasks are completed","source":"Microsoft Agents of Change Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Executive expectations for AI agents"},{"id":"s0016","stat":"Norm AI secured $48M in funding","source":"Norm AI","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Compliance AI agent platform funding"},{"id":"s0017","stat":"92% of students are using AI","source":"Referenced article","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Student AI usage and implications for future lawyers"},{"id":"s0018","stat":"65% of organisations now using GenAI, nearly doubling from prior year","source":"McKinsey State of AI Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Year-on-year growth in GenAI adoption"},{"id":"s0019","stat":"42% of companies cite legal and compliance risks as a major AI concern","source":"McKinsey State of AI Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Companies identifying legal/compliance as AI barrier"},{"id":"s0020","stat":"99% of in-house lawyers believe AI will change their job within the year","source":"Juro State of In-
1House 2025","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Near-unanimous belief AI will transform in-house roles","url":"https://juro.com/general-counsel/state-of-in-house-2025","access":"open"},{"id":"s0021","stat":"90% of in-house lawyers use an AI tool daily or weekly","source":"Juro State of In-House 2025","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Frequency of AI tool usage among in-house teams","url":"https://juro.com/general-counsel/state-of-in-house-2025","access":"open"},{"id":"s0022","stat":"45% of in-house lawyers think their firms offer poor or terrible value for money","source":"Juro State of In-House 2025","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"client","sourceType":"vendor","scope":"legal","context":"In-house sentiment on law firm value","url":"https://juro.com/general-counsel/state-of-in-house-2025","access":"open"},{"id":"s0023","stat":"~70% of professionals using GenAI in some form","source":"Thomson Reuters GenAI Professional Services Report 2025","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Usage rate across legal, tax, and compliance"},{"id":"s0024","stat":"~65% cite concerns about trust and reliability with GenAI","source":"Thomson Reuters GenAI Professional Services Report 2025","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Trust and reliability as top concern"},{"id":"s0025","stat":"Less than 10% of firms feel fully prepared for client GenAI expectations","source":"Thomson Reuters GenAI Professional Services Report 2025","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Readiness gap for AI-enabled service delivery"},{"id":"s0026","stat":"31,000 people surveyed for Microsoft Work Trend Index","source":"Microsoft Work Trend Index 2025","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Sample size of annual workforce survey","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/2025-the-year-the-frontier-firm-is-born","access":"open"},{"id":"s0027","stat":"\\"Frontier firms\\" 60% more likely to see productivity gains from embedded AI","source":"Microsoft Work Trend Index 2025","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Productivity advantage for organisations with deeply embedded AI","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/2025-the-year-the-frontier-firm-is-born","access":"open"},{"id":"s0028","stat":"7 distinct innovation profiles identified across law firms","source":"The Lawyer Innovation Map","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Categorisation framework for law firm innovation"},{"id":"s0029","stat":"70% of executives have implemented or piloted GenAI, more than double from prior year","source":"Bain & Company Survey","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Executive GenAI implementation rate doubling YoY"},{"id":"s0030","stat":"1,200 business leaders surveyed globally by Bain","source":"Bain & Company Survey","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Sample size of global GenAI adoption survey"},{"id":"s0031","stat":"50% say they are not yet seeing material business value from GenAI","source":"Bain & Company Survey","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Gap between GenAI adoption and realised business value"}
1,{"id":"s0032","stat":"Legora raises $80M (Series B)","source":"Legora","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Legal AI platform funding round","url":"https://legora.com/blog/series-b","access":"open"},{"id":"s0033","stat":"Garfield.Law: first SRA-approved purely AI firm; costs from £2 for a letter","source":"Garfield.Law","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"First AI law firm authorised by SRA","url":"https://www.garfield.law/press/garfield-ai-becomes-first-sra-regulated-legal-ai","access":"open"},{"id":"s0034","stat":"Average 39% performance drop moving from single-turn to multi-turn across 15 LLMs","source":"LLMs Get Lost in Multi-Turn Conversation","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"200,000+ simulated conversations; 6 generation tasks","url":"https://arxiv.org/abs/2505.06120","access":"open"},{"id":"s0035","stat":"112% increase in unreliability from single-turn to multi-turn (more than doubling)","source":"LLMs Get Lost in Multi-Turn Conversation","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Dominant component of performance gap; not aptitude loss","url":"https://arxiv.org/abs/2505.06120","access":"open"},{"id":"s0036","stat":"Even top models (Claude 3.7, Gemini 2.5, GPT-4.1) suffer 30-40% degradation in multi-turn","source":"LLMs Get Lost in Multi-Turn Conversation","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"No model is immune regardless of capability","url":"https://arxiv.org/abs/2505.06120","access":"open"},{"id":"s0037","stat":"Unreliability remains at ~30% even when temperature set to 0.0 in multi-turn","source":"LLMs Get Lost in Multi-Turn Conversation","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Temperature reduction ineffective for multi-turn","url":"https://arxiv.org/abs/2505.06120","access":"open"},{"id":"s0038","stat":"SNOWBALL mitigation recovers only 15-20% of multi-turn performance loss","source":"LLMs Get Lost in Multi-Turn Conversation","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Agent-like recapitulation provides limited help","url":"https://arxiv.org/abs/2505.06120","access":"open"},{"id":"s0039","stat":"Wordsmith AI raises $25M Series A","source":"Wordsmith AI / Index Ventures","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Legal AI startup funding for autonomous agents","url":"https://www.indexventures.com/perspectives/wordsmith-ai-raises-25m-series-a-to-deploy-ai-agents-and-transform-legal-roles/","access":"open"},{"id":"s0040","stat":"Flank raises $10M to scale autonomous legal agents","source":"Flank","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Legal AI startup funding","url":"https://blog.flank.ai/flank-raises-10m-to-scale-autonomous-legal-agents-embedded-invisible-and-built-for-the-enterprise/","access":"open"},{"id":"s0041","stat":"Jobs requiring AI skills growing 3.5x faster than average","source":"PwC Fearless Future Jobs Report","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Growth rate of AI-skilled job postings","url":"https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf","access":"open"},{"id":"s0042","stat":"25% wage premium for jobs requiring AI skills","source":"PwC Fearless Future Jobs Report","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Pay premium associated with AI skills","url":"https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf","access":"open"},{"id":"s0043","stat":"Over 500M job postings analysed globally by PwC","source":"PwC Fearless Future Jobs Report","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Scale of PwC\'s global labour market analysis","url":"https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf","access":"open"},{"id":"s0044","stat":"78% of professional service leaders say digital labour can close capacity gaps","source":"Microsoft Work Trend Index 2025","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Professional services leaders endorsing digital labour"},{"id":"s0045","stat":"Harvey raises $300M at $5B valuation","source":"Fortune","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Legal AI platform funding and valuation","url":"https://fortune.com/2025/06/23/harvey-raises-300-million-at-5-billion-valuation-to-be-legal-ai-for-lawyers-worldwide","access":"open"},{"id":"s0046","stat":"Clio to acquire vLex for $1B","source":"vLex / Clio","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Major legal tech acquisition"}
1,{"id":"s0047","stat":"52% AI adoption in in-house legal vs only 7% seeing cost reduction","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Suggesting informal use of general models without governance","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0048","stat":"657 respondents, 30 countries in ACC/Everlaw survey","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Sample size for corporate legal GenAI survey","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0049","stat":"Legal tech raised $5.99B in 2025 featuring fourteen $100M+ rounds","source":"Artificial Lawyer","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Total legal tech funding in 2025","url":"https://www.artificiallawyer.com/2026/01/06/legal-tech-raised-6bn-in-2025-as-ai-boom-shows-divisions/","access":"open"},{"id":"s0050","stat":"Companies using AI in CLM can cut contract review time by 50%","source":"Gartner","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"AI impact on contract review"},{"id":"s0051","stat":"Baker McKenzie employment GenAI tool: 100% substantive accuracy across 45 countries","source":"Baker McKenzie","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"AI legal accuracy benchmark"},{"id":"s0052","stat":"51% of frontline employees use AI regularly","source":"BCG AI at Work 2025","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"10,000+ employees surveyed globally","url":"https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain","access":"open"},{"id":"s0053","stat":"Only 36% of employees feel adequately prepared to use AI","source":"BCG AI at Work 2025","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Training gap as key barrier","url":"https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain","access":"open"},{"id":"s0054","stat":"Over one-third of employees reported unauthorised \\"shadow AI\\" use","source":"BCG AI at Work 2025","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Lack of approved tools driving shadow AI","url":"https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain","access":"open"},{"id":"s0055","stat":"£20,000 stipend offered by Freshfields for legal tech master\'s","source":"Freshfields / The Lawyer","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"workforce","sourceType":"media","scope":"legal","context":"Freshfields funding LLM in Law & Technology at King\'s","url":"https://www.thelawyer.com/freshfields-preps-next-gen-lawyers-by-launching-legal-tech-masters-degree/","access":"open"},{"id":"s0056","stat":"Best AI agents completed only 24-30% of workplace tasks autonomously","source":"TheAgentCompany","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Agents succeed at straightforward tasks but fail on complex workflows","url":"https://arxiv.org/abs/2412.14161","access":"open"},{"id":"s0057","stat":"AI tools slowed experienced open-source developers by 19%","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Field study contradicting productivity assumptions","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s0058","stat":"BBVA deployed over 11,000 GPT-5 licenses","source":"BBVA / OpenAI","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"One of Europe\'s largest banks at enterprise scale","url":"https://www.bbva.com/en/innovation/openai-highlights-its-collaboration-with-bbva-in-the-global-launch-of-gpt-5/","access":"open"},{"id":"s0059","stat":"BBVA users saving 3 hours per week on average with GPT-5","source":"BBVA / OpenAI","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Early productivity results","url":"https://www.bbva.com/en/innovation/openai-highlights-its-collaboration-with-bbva-in-the-global-launch-of-gpt-5/","access":"open"},{"id":"s0060","stat":"Even 10% per-step error rate in multi-step AI workflows results in ~67% overall failure","source":"PromptQL","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Compound error rates in agentic workflows"},{"id":"s0061","stat":"Icertis: 1 in 5 Fortune Global 500, 10M+ contracts, ~$350M ARR","source":"Icertis","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Largest CLM vendor metrics","url":"https://sacra.com/c/icertis/","access":"gated"},{"id":"s0062","stat":"AG ranked 8th among Top 10 Most Innovative Law Firms in Europe","source":"FT Innovative Lawyers Europe 2025","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Sixth consecutive year; Highly Commended for RAG report"},{"id":"s0063","stat":"LegalBenchmarks tested 13 AI platforms alongside in-house lawyers","source":"LegalBenchmarks","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Some AI models outperformed top lawyers on contract drafting reliability"},{"id":"s0064","stat":"Filevine raises $400M across two funding rounds","source":"LawNext","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Major legal tech funding","url":"https://www.lawnext.com/2025/09/filevine-raises-400m-in-two-round-funding-to-scale-ai-powered-legal-platform-i-speak-with-its-ceo.html","access":"open"},{"id":"s0065","stat":">$1B in legal tech funding announced in one month (mid-Sep to mid-Oct)","source":"Legaltech Hub","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Funding spree across Filevine, EvenUp, Eve, Harvey, Spellbook","url":"https://www.legaltechnologyhub.com/contents/over-dollar1b-in-one-month-inside-legal-techs-latest-funding-spree/","access":"paywalled"},{"id":"s0066","stat":"£117M raised by 27 UK legal tech companies in H1 2025","source":"LawtechUK / Legal Futures","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"industry","scope":"legal","context":"UK investment nearly matching all of 2024"},{"id":"s0067","stat":"Axiom claims up to 75% productivity gains pairing lawyers with vetted AI","source":"Axiom","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"AI Tech+Talent model claims"},{"id":"s0068","stat":"Databricks built enterprise agents 90x cheaper with automated prompt optimisation","source":"Databricks","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Significant cost reduction in building agents","url":"https://www.databricks.
1com/blog/building-state-art-enterprise-agents-90x-cheaper-automated-prompt-optimization","access":"open"},{"id":"s0069","stat":"61% of lawyers say they are now using generative AI in their work (up from 46% in January 2025)","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"**CORRECTED 11 Aug 2026** against LexisNexis\'s own published summaries. Previously recorded as \\"61% use GenAI day-to-day\\", which mislabels a breadth figure as an intensity figure: heavy day-to-day use is 11%, and 7% at large and medium firms. Date moved from Oct 2025 (Artificial Lawyer coverage) to Sep 2025 (publication). Full report gated and unread - see market-insights/lexisnexis-ai-culture-clash-2025.md","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s0070","stat":"Only 17% of lawyers say AI is fully embedded in their organisation\'s strategy and operations","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"**CORRECTED 11 Aug 2026.** Unit was wrong: previously recorded as \\"17% of law firms\\", it is 17% of lawyers, and the report says \\"fully embedded\\". Full report gated and unread","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s0071","stat":"39% of firms say \\"we are experimenting but progress is slow\\"","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"WARNING - UNVERIFIED as at 11 Aug 2026. Not found in any public LexisNexis summary of the Culture Clash survey nor in Legal IT Insider coverage; may be in the gated report. Verified alternatives on the same theme: two-thirds say their AI culture is slow or non-existent, and the LexisNexis landing page states \\"4 in 5 say their AI culture is slow, fear-based or non-existent\\". Do not use externally until checked","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s0072","stat":"95% of top 100 UK law firms achieved fee-income growth","source":"PwC Law Firm Survey 2025","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Near-universal growth, though rate slowing","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey.html","access":"open"},{"id":"s0073","stat":"Firms foresee average 16% of hours saved through AI in the near term","source":"PwC Law Firm Survey 2025","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Forward-looking time savings estimate","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey.html","access":"open"},{"id":"s0074","stat":"92% of top 100 UK firms list cyber-risk as key concern (up from 89% in 2024)","source":"PwC Law Firm Survey 2025","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Rising cyber-risk concern","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey.html","access":"open"},{"id":"s0075","stat":"The LegalTech Fund closes $110M Fund II","source":"The LegalTech Fund","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Second fund for early-stage legal tech"},{"id":"s0076","stat":"9% of companies prohibit GenAI, down from 29% in 2024","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Prohibition wall collapsed in a single year","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0077","stat":"Only 2% of respondents have no plans to use GenAI","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Near-universal intent to adopt","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0078","stat":"61% of European respondents already using GenAI vs 52% globally","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Europe ahead of global average","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0079","stat":"78% expect to insource drafting; 71% contract management; 62% research; 29% M&A; 29% litigation","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Work types being brought in-house using AI","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0080","stat":"82% expect cost savings in contract drafting/negotiation; 46% compliance; 45% litigation","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Where cost savings expected over 3 years","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0081","stat":"91% report efficiency gains (up from 86%); 49% better client service (up from 38%)","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Benefits are real and growing","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0082","stat":"59% of in-house teams don\'t know whether their law firms are using GenAI","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Transparency gap between firms and clients","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0083","stat":"59% of clients whose firms use GenAI have seen no clear cost savings","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Law firms not passing through savings","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0084","stat":"Only 24% satisfied with how outside counsel deploy GenAI","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Low client satisfaction with firm AI use","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0085","stat":"61% ready to push for alternative fee arrangements","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Billing model disruption coming","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0086","stat":"58% point to law firms not adjusting pricing for GenAI efficie
1ncies","source":"ACC/Everlaw Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Firms not passing on efficiency gains","url":"https://www.acc.com/resource-library/generative-ais-growing-strategic-value-corporate-law-departments-survey-results","access":"gated"},{"id":"s0087","stat":"EvenUp: $150M Series E, $2B+ valuation, 200,000+ cases, $10B+ in damages","source":"EvenUp","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Plaintiff-side legal AI scale","url":"https://www.evenuplaw.com/blog/evenup-2b-valuation/","access":"open"},{"id":"s0088","stat":"Robin AI: growth stalled at ~$10M revenue despite ~$50M raised; acquired by Scissero","source":"Artificial Lawyer","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"media","scope":"legal","context":"Legal AI cautionary tale"},{"id":"s0089","stat":"88% of organisations use AI in at least one business function","source":"McKinsey State of AI 2025","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Near-universal experimentation but scaling challenge","url":"https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai","access":"open"},{"id":"s0090","stat":"Two-thirds of organisations haven\'t rolled out AI enterprise-wide","source":"McKinsey State of AI 2025","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Gap between experimentation and enterprise deployment","url":"https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai","access":"open"},{"id":"s0091","stat":"AI agents ~88% faster than humans on complex workflows","source":"\\"How Do AI Agents Do Human Work?\\"","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"48 humans vs 4 agent frameworks over 16 tasks","url":"https://arxiv.org/abs/2510.22780","access":"open"},{"id":"s0092","stat":"AI agents 90-96% cheaper than humans on complex workflows","source":"\\"How Do AI Agents Do Human Work?\\"","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"But agents showed worse quality and frequent fabrication","url":"https://arxiv.org/abs/2510.22780","access":"open"},{"id":"s0093","stat":"82% of UK law firms say tech spend expected to increase next year","source":"Briefing-HSBC Law Firm Strategy Survey 2025/26","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Strong spending signal from senior leaders"},{"id":"s0094","stat":"70% of firm leaders say tech-driven service delivery is their main disruptor","source":"Briefing-HSBC Law Firm Strategy Survey 2025/26","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Technology overtaking other disruptors"},{"id":"s0095","stat":"92% active use rate among Harvey users","source":"RSGI/Harvey Impact Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"40 organisations (29 firms, 11 in-house); extremely high utilisation","url":"https://www.harvey.ai/resources/reports/harvey-rsgi-report","access":"gated"},{"id":"s0096","stat":"Harvey power users save 2-3x more time than typical users","source":"RSGI/Harvey Impact Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Significant multiplier effect for heavy users","url":"https://www.harvey.ai/resources/reports/harvey-rsgi-report","access":"gated"},{"id":"s0097","stat":"Gemini app has ~650M monthly users","source":"Google (Gemini 3 launch)","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Google\'s scale at Gemini 3 launch","url":"https://blog.google/products/gemini/gemini-3/","access":"open"},{"id":"s0098","stat":"AI Overviews reaches ~2B monthly users","source":"Google (Gemini 3 launch)","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Google\'s AI Overviews product usage","url":"https://blog.google/products/gemini/gemini-3/","access":"open"},{"id":"s0099","stat":"Just over 40% of UK Top 200 firms now use at least one GenAI tool","source":"Legal IT Insider UK Top 200","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"media","scope":"legal","context":"GenAI moved from niche to mainstream in UK legal","url":"https://legaltechnology.com/2025/11/19/legal-it-insiders-uk-top-200-2025-the-gen-ai-battleground
1/","access":"open"},{"id":"s0100","stat":"Average number of GenAI tools per firm is ~1.8","source":"Legal IT Insider UK Top 200","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"media","scope":"legal","context":"Firms \\"multi-homing\\" across several AI tools","url":"https://legaltechnology.com/2025/11/19/legal-it-insiders-uk-top-200-2025-the-gen-ai-battleground/","access":"open"},{"id":"s0101","stat":"Clifford Chance cuts 10% of London jobs, explicitly linked to AI-driven productivity","source":"The Guardian","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"media","scope":"legal","context":"First Magic Circle firm to publicly link job cuts to AI"},{"id":"s0102","stat":"Deloitte caught using fabricated AI-generated research in a million-dollar government report","source":"Fortune","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"Unchecked AI content in major Canadian public-sector report","url":"https://fortune.com/2025/11/25/deloitte-caught-fabricated-ai-generated-research-million-dollar-report-canada-government/","access":"open"},{"id":"s0103","stat":"96% believe GenAI can help demonstrate legal team\'s business value","source":"ACC/Everlaw Proving Value Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Near-universal belief in AI for proving value"},{"id":"s0104","stat":"83% track outside counsel spend; only 12% track technology ROI; only 9% track business outcomes","source":"ACC/Everlaw Proving Value Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Major metrics gap in in-house teams"},{"id":"s0105","stat":"Clifford Chance: 90% firm-wide AI adoption by late 2025","source":"Clifford Chance","date":"Late 2025","sortKey":20251200,"precision":"approx","year":2025,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Highest confirmed AI adoption rate at a law firm","url":"https://legaltechnology.com/2025/12/23/2025-26-reflections-predictions-paul-greenwood-clifford-chance-ai-adoption-is-at-90/","access":"open"},{"id":"s0106","stat":"Clio: $500M Series G, $5B valuation, 400,000 legal professionals, $400M ARR","source":"Clio","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Legal tech platform financials","url":"https://www.clio.com/about/press/clio-completes-landmark-1b-vlex-acquisition-series-g-5b-valuation/","access":"open"},{"id":"s0107","stat":"37.5M anonymised Copilot interactions analysed","source":"Microsoft AI / Copilot Usage Report 2025","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Scale of dataset for Copilot usage patterns","url":"https://microsoft.ai/news/its-about-time-the-copilot-usage-report-2025/","access":"open"},{"id":"s0108","stat":"95% failure rate for AI initiatives","source":"Impertinent (Substack)","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"Operating model failures, not tech failures"},{"id":"s0109","stat":"M&A DD historically costs 0.5-2% of deal value","source":"Harshith Viswanath (Substack)","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Traditional cost baseline before AI DD tools","url":"https://harshithviswanath.substack.com/p/ai-driven-legal-due-diligence-the","access":"open"},{"id":"s0110","stat":"$4.3B in legal AI investment","source":"The Red Line","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Total VC poured into legal AI","url":"https://theredline.versionstory.com/p/why-cant-43b-in-legal-ai-investment","access":"open"},{"id":"s0111","stat":"58% of legal ops under C-suite pressure to deploy GenAI","source":"2025 LDO Survey Report","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"18th annual. C-suite pressure on legal operations","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0112","stat":"Only 32% of legal ops teams can show cost savings from AI","source":"2025 LDO Survey Report","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Gap between deployment pressure and demonstrated ROI","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0113","stat":"94% of legal ops teams planning to bring more work in-house using AI","source":"2025 LDO Survey Report","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Insourcing trend driven by AI","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0114","stat":"Meta acquires Manus for $2B+","source":"WSJ","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Meta\'s bet on agentic capabilities"},{"id":"s0115","stat":"CMS Harvey: 7,000+ lawyers across 50+ countries; 93% reported productivity gains (~117.9 hours saved/lawyer/year)","source":"CMS","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Largest Harvey deployment in EMEA; quantified productivity","url":"https://cms.law/en/int/news-information/cms-announces-roll-out-of-harvey-across-50-countries","access":"open"}
1,{"id":"s0116","stat":"Harvey: 50 of AmLaw 100 as customers; ARR reportedly $190M by end of 2025","source":"Harvey","date":"End 2025","sortKey":20251200,"precision":"approx","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"US market penetration and revenue"},{"id":"s0117","stat":"Prompt repetition wins 47 of 70 benchmark-model combinations with 0 losses","source":"Prompt Repetition Improves LLMs (Google Research)","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Simple technique: repeat input prompt. 7 models, 7 benchmarks","url":"https://arxiv.org/abs/2512.14982","access":"open"},{"id":"s0118","stat":"Prompt repetition improved Gemini Flash-Lite on NameIndex from 21.3% to 97.3% accuracy","source":"Prompt Repetition Improves LLMs (Google Research)","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Most dramatic single improvement","url":"https://arxiv.org/abs/2512.14982","access":"open"},{"id":"s0119","stat":"No increase in output length or latency from prompt repetition","source":"Prompt Repetition Improves LLMs (Google Research)","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Zero-cost improvement unlike chain-of-thought","url":"https://arxiv.org/abs/2512.14982","access":"open"},{"id":"s0120","stat":"$7 cost to produce a complex legal memo using Midpage AI","source":"LegalEd.AI","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"AI drafted multi-thousand dollar memo for fraction of cost","url":"https://legaled.ai/the-7-associate-how-midpage-ai-drafted-a-multi-thousand-dollar-memo-on-immunity-issues-in-the-ice-shootings/","access":"open"},{"id":"s0121","stat":"Record 2025 profits at law firms masked structural fragility (rising write-offs, declining billable hours)","source":"BigHand Annual Law Firm Finance Report","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"\\"Profitability Inflection Point\\"","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-annual-law-firm-finance-report/","access":"gated"},{"id":"s0122","stat":"Matt Shumer post reached ~50M views","source":"Matt Shumer (Substack)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"cross-sector","context":"Viral post on AI crossing white-collar disruption threshold","url":"https://shumer.dev/something-big-is-happening","access":"open"},{"id":"s0123","stat":"77% of in-house lawyers regularly work beyond contracted hours","source":"Juro State of In-House 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"130+ in-house lawyers across 16 countries","url":"https://juro.com/state-of-in-house-2026","access":"open"},{"id":"s0124","stat":"95.5% of in-house lawyers handle legal tasks while on leave","source":"Juro State of In-House 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Always-on culture and burnout","url":"https://juro.com/state-of-in-house-2026","access":"open"},{"id":"s0125","stat":"36% of in-house teams bringing work back in-house","source":"Juro State of In-House 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Insourcing trend","url":"https://juro.com/state-of-in-house-2026","access":"open"},{"id":"s0126","stat":"Top AI agent score on corporate law tasks is just 25.9%","source":"Mercor APEX-Agents Corporate Lawyer Leaderboard","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Even frontier models struggle with genuine legal reasoning","url":"https://www.mercor.com/apex/apex-agents-leaderboard/corporate-lawyer-agent/pass-1/","access":"open"},{"id":"s0127","stat":"72% of UK lawyers concerned AI is eroding juniors\' reasoning skills","source":"LexisNexis","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Nearly 900 UK lawyers surveyed on mentorship gap","url":"https://www.lexisnexis.co.uk/research-and-reports/the-mentorship-gap.html","access":"open"},{"id":"s0128","stat":"~30% of work never makes it into a timesheet","source":"Laurel AI","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Unrecorded professional time","url":"https://www.laurel.ai/resources-post/signal-work-intelligence","access":"open"},{"id":"s0129","stat":"In-house team cut contract review time by 60% after AI pilot","source":"Legal Futures","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Practical AI case study for c
1ontract review","url":"https://www.legalfutures.co.uk/associate-news/in-house-team-cuts-contract-review-time-by-60-after-ai-pilot-in-microsoft-word","access":"open"},{"id":"s0130","stat":"$285B rout in legal software stocks after Anthropic legal plugin","source":"Legal IT Insider","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Market reaction to Anthropic entering legal vertical"},{"id":"s0131","stat":"IBM stock crashed 13.2% in single day after Anthropic COBOL tool","source":"CNBC","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Worst single-day drop since October 2000","url":"https://www.cnbc.com/2026/02/23/ibm-is-the-latest-ai-casualty-shares-are-tanking-on-anthropic-cobol-threat.html","access":"open"},{"id":"s0132","stat":"24,000 fraudulent accounts used to distil Claude\'s capabilities","source":"Anthropic / TechCrunch","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Anthropic accusing DeepSeek, Moonshot AI, MiniMax of data theft"},{"id":"s0133","stat":"$200M Pentagon contract at stake in Anthropic standoff","source":"The Verge","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"cross-sector","context":"Anthropic refusing \\"any lawful use\\" terms"},{"id":"s0134","stat":"Nuclear weapons deployed in 95% of AI war simulations","source":"King\'s College London","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"21 simulated armed conflict scenarios; no model chose de-escalation"},{"id":"s0135","stat":"Claude recommended nuclear strikes in 64% of war game simulations","source":"King\'s College London","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Highest of any model tested"},{"id":"s0136","stat":"80% of ChatGPT\'s 910M weekly users sent fewer than 1,000 messages annually","source":"The Leverage / Evan Armstrong","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"commentary","scope":"legal","context":"Shallow usage patterns","url":"https://www.gettheleverage.com/p/openai-is-looking-like-icarus","access":"open"},{"id":"s0137","stat":"910M weekly ChatGPT users","source":"The Leverage / Evan Armstrong","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"commentary","scope":"legal","context":"Scale of ChatGPT user base","url":"https://www.gettheleverage.com/p/openai-is-looking-like-icarus","access":"open"},{"id":"s0138","stat":"OpenAI gross margins fell from 46% target to 33%","source":"The Leverage / Evan Armstrong","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"legal","context":"Margin compression","url":"https://www.gettheleverage.com/p/openai-is-looking-like-icarus","access":"open"},{"id":"s0139","stat":"OpenAI compute costs through 2030 projected at $665B","source":"The Leverage / Evan Armstrong","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"legal","context":"Scale of infrastructure spending","url":"https://www.gettheleverage.com/p/openai-is-looking-like-icarus","access":"open"},{"id":"s0140","stat":"Over 95% of enterprise AI pilots fail to reach deployment","source":"ElevenLabs","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Legal and security concerns as primary barriers"},{"id":"s0141","stat":"~90% of executives say AI has had no impact on productivity despite 70% using it","source":"Fortune","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"Survey of nearly 6,000 executives; AI productivity paradox"},{"id":"s0142","stat":"CEOs who use AI average just 1.5 hours per week","source":"Fortune","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"cross-sector","context":"Low CEO engagement"},{"id":"s0143","stat":"Training compute doubles every five months","source":"Stats and Society (Substack)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Rate of AI training compute growth"},{"id":"s0144","stat":"85.7% of Claude conversations involved iterating on earlier responses","source":"Anthropic AI Fluency Index","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"User behaviour patterns","url":"https://www.anthropic.com/research/AI-fluency-index","access":"open"},{"id":"s0145","stat":"Only 1 in 5 legal professionals place high trust in AI-generated
1work","source":"Paragon survey","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"practitioner","scope":"legal","context":"250+ legal professionals surveyed"},{"id":"s0146","stat":"67% of legal professionals have overridden or corrected AI output","source":"Paragon survey","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"practitioner","scope":"legal","context":"Frequency of manual correction"},{"id":"s0147","stat":"47% say AI automation has sparked conflict within their team","source":"Paragon survey","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Internal friction from AI adoption"},{"id":"s0148","stat":"OpenAI finalising $100B+ round at $850B+ valuation","source":"TechCrunch","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Largest venture deal in history","url":"https://techcrunch.com/2026/02/19/openai-reportedly-finalizing-100b-deal-at-more-than-850b-valuation/","access":"open"},{"id":"s0149","stat":"Thomson Reuters CoCounsel hits 1M users","source":"Artificial Lawyer","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Counter-narrative to Anthropic disruption","url":"https://www.artificiallawyer.com/2026/02/24/trs-cocounsel-hits-1-millions-users-despite-claude-crash/","access":"open"},{"id":"s0150","stat":"UK commits £1.6B to AI research","source":"ResultSense","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"cross-sector","context":"UK government investment","url":"https://www.resultsense.com/news/2026-02-20-uk-commits-1-6-billion-to-ai-research-in-first-ever-ukri-strategy","access":"open"},{"id":"s0151","stat":"AI skills command 23% salary premium in UK","source":"World Economic Forum","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"industry","scope":"cross-sector","context":"Pay premium for AI-skilled workers","url":"https://www.weforum.org/stories/2026/02/ai-improving-wages-job-quality/","access":"open"},{"id":"s0152","stat":"82% of organisations not collecting ROI metrics on AI","source":"Thomson Reuters 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Majority not measuring AI returns","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0153","stat":"81 US copyright suits against AI companies","source":"Criminal Law Library Blog","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Scale of copyright litigation","url":"https://www.criminallawlibraryblog.com/u-s-map-of-copyright-suits-v-ai-companies-all-81-cases/","access":"open"},{"id":"s0154","stat":"39% performance drop when LLMs engage in multi-turn conversation","source":"ArXiv","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Degradation over extended conversations"},{"id":"s0155","stat":"EU AI Act August 2026 deadline may slip to December 2027","source":"MetricStream","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"cross-sector","context":"Potential delay in enforcement"},{"id":"s0156","stat":"A&O Shearman: Harvey used by 4,000+ staff across 43 jurisdictions","source":"A&O Shearman","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Largest Harvey law firm deployment"},{"id":"s0157","stat":"A&O ContractMatrix: ~2,000 lawyers daily, saves ~7 hours per contract review (~30% efficie
1ncy)","source":"A&O Shearman","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"AI efficiency at a Magic Circle firm"},{"id":"s0158","stat":"Linklaters Laila: 60,000 prompts weekly","source":"Linklaters","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Proprietary AI chatbot usage at scale","url":"https://www.linklaters.com/en/about-us/news-and-deals/news/2024/december/linklaters-launches-ai-sandbox-following-second-global-ideas-campaign","access":"open"},{"id":"s0159","stat":"Hogan Lovells CRAIG: 4,400+ active users, 50%+ time savings","source":"Hogan Lovells","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Proprietary AI chatbot efficiency"},{"id":"s0162","stat":"Weightmans Litera Kira: cut review time by 90% with 98% accuracy","source":"Weightmans","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"AI document review accuracy and speed"},{"id":"s0163","stat":"80% of in-house legal leaders plan to bring law firm work back in-house within two years","source":"Axiom","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"practitioner","scope":"legal","context":"Insourcing intent"},{"id":"s0164","stat":"Axiom: 850+ AI-skilled lawyers (325% increase YoY from 200)","source":"Axiom","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"ALSP AI talent growth"},{"id":"s0165","stat":"71% of clients prefer flat fees over hourly billing","source":"Lupl","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Client billing preference"},{"id":"s0166","stat":"Flat-fee matters close 2.6x faster and get paid nearly twice as quickly","source":"Various","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"practitioner","scope":"cross-sector","context":"Flat-fee speed advantage"},{"id":"s0167","stat":"Ironclad: surpassed $200M ARR (Feb 2026)","source":"Ironclad","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"CLM challenger revenue"},{"id":"s0168","stat":"Privacy management software market: ~$5B (2025), projected $14-45B by 2030-2034","source":"Multiple research f
1irms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Privacy software market size"},{"id":"s0169","stat":"DSAR volume surged 82% year-on-year in 2025","source":"Data Protection Tools research","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"cross-sector","context":"Subject access request growth"},{"id":"s0170","stat":"Average enterprise has 40-60% more data-processing apps than DPO knows about","source":"Data Protection Tools research","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"practitioner","scope":"cross-sector","context":"Shadow IT data processing gap"},{"id":"s0171","stat":"OneTrust: 3B+ consent transactions weekly; 14,000+ customers; 75% of Fortune 100","source":"OneTrust","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Privacy market leader scale"},{"id":"s0172","stat":"Clyde & Co Newton: reads 99% of documents for claims; auto-valuations for 89% of EL/PL claims","source":"Clyde & Co","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Proprietary AI claims automation"},{"id":"s0173","stat":"AI-native firms: processing in 24-48 hours, flat fees $300-$1,500 per deliverable","source":"Various AI firms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"practitioner","scope":"legal","context":"AI-native law firm pricing"},{"id":"s0174","stat":"Default GPT discovery rate 5x lower than unbiased sampling (5.9% vs 29.5%)","source":"Rational Analysis of Sycophantic AI (Princeton)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"n=557. Unmodified chatbot produces worst discovery rate","url":"https://arxiv.org/abs/2602.14270","access":"open"},{"id":"s0175","stat":"Default GPT behaviour statistically indistinguishable from explicitly sycophantic prompting","source":"Rational Analysis of Sycophantic AI (Princeton)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"TOST equivalence test confirmed. Sycophancy is the baseline","url":"https://arxiv.org/abs/2602.14270","access":"open"},{"id":"s0176","stat":"Users became increasingly confident in wrong answers under default GPT (+5.4 points)","source":"Rational Analysis of Sycophantic AI (Princeton)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Confidence inflated even when users failed to discover truth","url":"https://arxiv.org/abs/2602.14270","access":"open"},{"id":"s0177","stat":"Users rate sycophantic responses as higher quality, trust them more, and rate them as less biased","source":"Rational Analysis of Sycophantic AI (Princeton)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Perverse incentive: users seek out systems that distort their reasoning","url":"https://arxiv.org/abs/2602.14270","access":"open"},{"id":"s0178","stat":"Sycophancy documented in 58.2% of cases across medical and maths queries","source":"Prior work cited in Sycophantic AI paper","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Models changed correct to incorrect in 14.7% after user disagreement"},{"id":"s0179","stat":"40% of orgs using GenAI, up from 22% last year (nearly doubled)","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"1,514 respondents, 27 countries","url":"https://www.thomsonreuters.com/en-us
1/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0180","stat":"82% of GenAI users use tools at least weekly; 29% multiple times daily","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Usage frequency among adopters","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0181","stat":"87% believe GenAI will be central to workflow within 5 years","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Future expectation","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0182","stat":"77% expect agentic AI central to workflow by 2030; only 15% of orgs currently use it","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Agentic AI trajectory","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0183","stat":"Industry-specific tool usage rose 14pp in 2026 - fastest growing category","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Vertical tools outpacing general-purpose","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0184","stat":"Legal GenAI use cases: research 80%, document review 74%, summarisation 73%, drafting 59%","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Legal-specific use case rankings","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0185","stat":"50% of legal professionals view AI as \\"major threat\\" to UPL (up from 36%)","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Biggest YoY threat perception increase","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0186","stat":"65% view AI as \\"somewhat or major threat\\" to legal jobs (up from 59%)","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Rising combined job threat","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0187","stat":"82% either not collecting AI ROI metrics or unsure","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Widespread non-measurement","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0188","stat":"~2/3 of corporate respondents want firms to use AI; <20% mandate through RFPs","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Gap between desire and mandate","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0189","stat":"40% told both to use AI and not to use AI depending on client","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Conflicting client signals","url":"https://www.thomsonreuters.com/en-us
1/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0190","stat":"Only 17% of legal professionals comfortable with AI giving legal advice","source":"TR 2026 AI in Professional Services","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Ethics boundary for AI autonomy","url":"https://www.thomsonreuters.com/en-us/posts/technology/ai-in-professional-services-report-2026/","access":"open"},{"id":"s0191","stat":"GPT-5.4 scores 75.0% on OSWorld-Verified, surpassing human performance at 72.4%","source":"OpenAI","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"First model to exceed human performance on computer-use benchmark","url":"https://openai.com/index/introducing-gpt-5-4/","access":"open"},{"id":"s0192","stat":"GPT-5.4 has 33% fewer false claims vs GPT-5.2","source":"OpenAI","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Factual accuracy improvement","url":"https://openai.com/index/introducing-gpt-5-4/","access":"open"},{"id":"s0193","stat":"Microsoft Copilot Cowork E7 tier at $99/user/month","source":"Microsoft","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"New enterprise AI pricing from May"},{"id":"s0194","stat":"Lawhive backed by Google Ventures with ~£40M funding","source":"The Lawyer","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"AI legal platform that acquired Woodstock Legal Services"},{"id":"s0195","stat":"AI employment law firm negotiated £30K settlement","source":"Legal Futures","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Concrete outcome from AI-powered legal delivery"},{"id":"s0196","stat":"$150K market rate for \\"AI Architect\\" roles in legal","source":"Jamie Tso / LegalQuants","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"commentary","scope":"legal","context":"Argued as undervaluing legal+technical combination","url":"https://tsojamie.substack.com/p/the-origin-story-of-a-legal-quant","access":"open"},{"id":"s0197","stat":"13,000 applicants for Anthropic hackathon, won by a lawyer","source":"Hadley Lab","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"legal","context":"California attorney built AI permit assistant in six days","url":"https://hadleylab.org/blogs/2026-03-22-the-lawyer-who-won","access":"open"},{"id":"s0198","stat":"Goldman Sachs: 70% of S&P 500 discussed AI on earnings calls, only 1% quantified impact","source":"Goldman Sachs / Fortune","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Massive gap between AI discussion and measured results","url":"https://fortune.com/2026/03/03/goldman-earnings-ai-anxiety-no-meaningful-impact-productivity-economy-30-percent-in-2-areas/","access":"open"},{"id":"s0199","stat":"UK creative industries worth £124B and 2.4M jobs at risk from AI copyright","source":"Lords Communications Committee","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"\\"Clear and present danger\\"; do not introduce TDM exception","url":"https://publications.parliament.uk/pa/ld5901/ldselect/ldcomm/267/26702.htm","access":"open"},{"id":"s0200","stat":"McKinsey runs 25,000 AI agents alongside 40,000 human consultants, targeting parity by end of 2026","source":"McKinsey","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Scale of AI agent deployment at major consultancy"},{"id":"s0201","stat":"Deloitte deploying Claude across all 470,000 employees","source":"Deloitte","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Scale of enterprise AI deployment"},{"id":"s0202","stat":"£4.5M for LawtechUK over three years; £12M for MoJ Justice AI Unit","source":"Legal Futures / Lammy","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"legal","context":"UK government funding for legal AI","url":"https://www.legalfutures.co.uk/latest-news/lammy-backs-lawtechuk-for-next-three-years-as-he-sets-out-ai-vision","access":"open"},{"id":"s0203","stat":"iManage: 85% piloting AI, only 17% fully integrated (3,185 respondents, 26 countries)","source":"iManage Knowledge Work Benchmark","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Gap between piloting and integration","url":"https://imanage.com/benchmark-report-2026/","access":"open"},{"id":"s0204","stat":"One-third of iManage respondents reported policy-impacting incident from unregulated AI","source":"iManage Knowledge Work Benchmark","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Risk from ungoverned AI tools","url":"https://imanage.com/benchmark-report-2026/","access":"open"},{"id":"s0205","stat":"49% of jobs face AI disruption (nearly 50,000 employees, 48 countries)","source":"PwC","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Global AI disruption survey"},{"id":"s0206","stat":"CEO confidence in revenue at five-year low; only 30% confident","source":"PwC","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"CEO sentiment declining"}
1,{"id":"s0207","stat":"Legora raises $550M Series D at $5.55B valuation","source":"Legal IT Insider","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Tripled in five months","url":"https://legaltechnology.com/2026/03/10/legora-raises-550m-series-d-at-5-55bn-valuation-interview-with-founder-max-junestrand/","access":"open"},{"id":"s0208","stat":"AI adoption in corporate legal doubled - 87% of GCs now using AI, up from 44% in 2025","source":"FTI/Relativity General Counsel Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Year-on-year doubling","url":"https://www.fticonsulting.com/about/newsroom/press-releases/ai-adoption-in-corporate-legal-departments-doubles-according-to-the-general-counsel-report","access":"open"},{"id":"s0209","stat":"64% of in-house teams expect to depend less on outside counsel because of AI","source":"FTI/Relativity General Counsel Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Insourcing signal from corporate legal","url":"https://www.fticonsulting.com/about/newsroom/press-releases/ai-adoption-in-corporate-legal-departments-doubles-according-to-the-general-counsel-report","access":"open"},{"id":"s0210","stat":"Nearly 70% of legal professionals now use AI, up from 31%","source":"8am Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Legal AI adoption more than doubled","url":"https://www.businesswire.com/news/home/20260305103301/en/8am-2026-Legal-Industry-Report-AI-Adoption-Surges-Through-Turbulence-as-Firms-Push-Forward","access":"open"},{"id":"s0211","stat":"43% of firms have no formal AI policy and no plans to create one","source":"8am Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Individual adoption outpacing institutional governance","url":"https://www.businesswire.com/news/home/20260305103301/en/8am-2026-Legal-Industry-Report-AI-Adoption-Surges-Through-Turbulence-as-Firms-Push-Forward","access":"open"},{"id":"s0212","stat":"McKinsey AI chatbot breach exposed 46.5M messages and 728K files within two hours","source":"The Register","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"Security breach at major consultancy"},{"id":"s0213","stat":"Bloomberg Law trained 3,000 lawyers in GenAI; fewer than 25% recognise LLMs struggle with numbers and dates","source":"Bloomberg Law / Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Lawyer AI literacy gaps","url":"https://news.bloomberglaw.com/business-and-practice/we-trained-3-000-lawyers-in-generative-ai-heres-what-we-learned","access":"paywalled"},{"id":"s0214","stat":"$4.3B in legal AI investment vs $20/month ChatGPT","source":"The Red Line","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Specialist vs general-purpose tools question"},{"id":"s0215","stat":"Harvey raises $200M at $11B valuation","source":"Harvey","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"First legal AI decacorn","url":"https://www.harvey.ai/blog/harvey-raises-at-dollar11-billion-valuation-to-scale-agents-across-law-firms-and-enterprises","access":"open"},{"id":"s0216","stat":"Harvey has 25,000+ custom agents across 1,300+ customers in 60 countries","source":"Harvey","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Scale of platform deployment","url":"https://www.harvey.ai/blog/harvey-raises-at-dollar11-billion-valuation-to-scale-agents-across-law-firms-and-enterprises","access":"open"},{"id":"s0217","stat":"Harvey total funding now past $1B","source":"Harvey","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Cumulative funding","url":"https://www.harvey.ai/blog/harvey-raises-at-dollar11-billion-valuation-to-scale-agents-across-law-firms-and-enterprises","access":"open"},{"id":"s0218","stat":"Relativity files for IPO, potentially raising $750M at ~$4B","source":"LawNext","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"First legal tech IPO since DISCO in 2021","url":"https://www.lawnext.com/2026/03/data-intelligence-company-relativity-confidentially-files-for-ipo-would-be-first-in-legal-tech-since-2021.html","access":"open"},{"id":"s0219","stat":"Thomson Reuters \\"Thomson\\" LLM outperforming general models on 4 of 10 legal benchmarks","source":"Artificial Lawyer","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Legally-trained LLM on open-source models","url":"https://www.artificiallawyer.com/2026/03/24/thomson-is-coming-trs-own-legally-trained-llm/","access":"open"},{"id":"s0220","stat":"Crosby raises $60M Series B; total raised $85.8M","source":"Law.com","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Sequoia\'s autopilot poster child"},{"id":"s0221","stat":"$60B in legal work absorbable by AI autopilots","source":"Sequoia / Artificial Lawyer","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"$36B paralegal/LPO + $20-25B transactional","url":"https://www.artificiallawyer.com/2026/03/30/autopilots-can-absorb-60bn-of-legal-work-sequoia/","access":"open"},{"id":"s0222","stat":"81,000 people interviewed across 159 countries, 70 languages, in one week","source":"Anthropic","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Largest qualitative AI attitudes study ever"},{"id":"s0223","stat":"Top AI hope: professional excellence (18.8%)","source":"Anthropic 81K Interviews","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"People want to be better at jobs, not replaced","url":"https://www.anthropic.com/features/81k-interviews","access":"open"},{"id":"s0224","stat":"Top AI fear: unreliability (26.7%)","source":"Anthropic 81K Interviews","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Confident-sounding wrong answers outranks job loss","url":"https://www.anthropic.com/features/81k-interviews","access":"open"},{"id":"s0225","stat":"Two-thirds globally positive about AI; no country below 60%","source":"Anthropic 81K Interviews","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Global optimism across all regions","url":"https://www.anthropic.com/features/81k-interviews","access":"open"},{"id":"s0226","stat":"AI access at 82.7%, up from 61.2% in 2025","source":"Factor GenAI in Legal Benchmarking 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Near-universal AI access in legal","url":"https://www.factor.law/white-paper/genai-in-legal-benchmarking-report-2026","access":"gated"},{"id":"s0227","stat":"Only 22.1% report high trust in AI outputs","source":"Factor GenAI in Legal Benchmarking 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Trust gap as new bottleneck","url":"https://www.factor.law/white-paper/genai-in-legal-benchmarking-report-2026","access":"gated"},{"id":"s0228","stat":"High-trust teams 3x more likely to report positive ROI","source":"Factor GenAI in Legal Benchmarking 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"The headline linking trust to returns","url":"https://www.factor.law/white-paper/genai-in-legal-benchmarking-report-2026","access":"gated"},{"id":"s0229","stat":"Over 25% of organisations spent $100K-$500K on domain-specific legal AI","source":"Factor GenAI in Legal Benchmarking 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Scale of investment","url":"https://www.factor.law/white-paper/genai-in-legal-benchmarking-report-2026","access":"gated"},{"id":"s0230","stat":"Only 12.1% rate themselves as \\"leading the way\\" on AI","source":"Factor GenAI in Legal Benchmarking 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Self-assessment of AI maturity","url":"https://www.factor.law/white-paper/genai-in-legal-benchmarking-report-2026","access":"gated"},{"id":"s0231","stat":"86% of GCs believe legal significantly contributes; only 17% of C-suite agree","source":"Thomson Reuters 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Perception gap between legal and business leadership","url":"https://www.thomsonreuters.com/en-us
1/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0232","stat":"42% of C-suite said legal contributed \\"little or not at all\\"","source":"Thomson Reuters 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"C-suite perception of legal value","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0233","stat":"AI access at 47% of in-house teams","source":"Thomson Reuters 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"In-house access lagging law firms","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0234","stat":"Technology as strategic priority doubled from 14% to 28%","source":"Thomson Reuters 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Growing strategic importance of tech","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0235","stat":"42% of participating firms say Legora has helped them win new work, directly or indirectly","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Verified against the primary report 28 Jul 2026. Vendor-commissioned, Legora customers only. Read against the same report\'s 58% who have NOT won new work","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s0236","stat":"45% say Legora has helped expand existing client relationships, directly or indirectly","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Verified against the primary report 28 Jul 2026. Read against the same report\'s 55% who have seen no expansion","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s0237","stat":"55% rate Legora\'s impact on taking on more or higher-value work, or supporting new matter types and fee structures, as moderate (3) or large (4) on a 1-5 scale","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"**CORRECTED 28 Jul 2026** against the primary report. Previously recorded from secondary coverage as \\"55% say AI enabled briefs they previously could not scope\\", which overstates a 1-5 rating as a capability claim","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s0238","stat":"Deposition review cut from 20 hours to under 2","source":"Artificial Lawyer coverage (31 Mar 2026) of the Legora ROI Survey","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"**UNVERIFIED - do not use without re-sourcing.** Checked against the primary report 28 Jul 2026 ([[legora-roi-report-law-firms]]) and this example appears nowhere in it. The report\'s comparable examples are 4,500 trade slips (75% saving on a 130-hour project) and a class action (3-5 paralegals for a day to 3 hours)"},{"id":"s0239","stat":"AI accuracy drops from 70% to 55% when info shifts from start to middle of docs","source":"Syntheia (Horace Wu)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Context rot in long documents","url":"https://syntheia.io/blog/silent-but-deadly-context-rot-problems-in-legal","access":"open"},{"id":"s0240","stat":"Commercial RAG tools hallucinate on 17-33% of queries","source":"Syntheia (Horace Wu)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Hallucination rates in commercial RAG","url":"https://syntheia.io/blog/silent-but-deadly-context-rot-problems-in-legal","access":"open"},{"id":"s0241","stat":"Models achieve only 50-65% of advertised token capacity on real legal reasoning","source":"Syntheia (Horace Wu)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Gap between marketed and actual context performance","url":"https://syntheia.io/blog/silent-but-deadly-context-rot-problems-in-legal","access":"open"}
1,{"id":"s0242","stat":"$30K sanctions by Sixth Circuit for AI-fabricated citations","source":"Bloomberg Law","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Escalating judicial penalties"},{"id":"s0243","stat":"$10K fine ($500 per citation) in Oregon for AI hallucinations","source":"Bloomberg Law","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"State-level sanctions"},{"id":"s0244","stat":"1,000+ documented AI hallucination cases in legal proceedings","source":"Damien Charlotin database","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Scale of documented incidents","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s0245","stat":"GPT-5.4 scores 79.4% on contracts vs 73.9% for GPT-5.2; 21% fewer errors","source":"LegalOn","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Model improvement on legal tasks","url":"https://www.legalontech.com/post/smarter-contract-review-with-gpt-5-4","access":"open"},{"id":"s0246","stat":"CoCounsel at 1M+ users across 107 countries","source":"Fortune","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"cross-sector","context":"Thomson Reuters global reach","url":"https://fortune.com/2026/03/04/legal-ai-thomson-retuer
1s-cocounsel-anthropic-claude-cowork-whats-the-difference/","access":"open"},{"id":"s0247","stat":"80-90% faster contracting cycles from Eudia digital twins","source":"Eudia / Artificial Lawyer","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Speed improvement from expert digital twins","url":"https://www.artificiallawyer.com/2026/03/24/eudia-launches-expert-digital-twins-partners-with-servicenow/","access":"open"},{"id":"s0248","stat":"Nippon Life suing OpenAI for $10M; ChatGPT drafted 21 frivolous motions","source":"JD Journal","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"AI providing unlicensed legal advice"},{"id":"s0249","stat":"EU AI Act high-risk timeline extended by up to 16 months","source":"EU Council / Omnibus VII","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"Simplification widening SME exemptions","url":"https://www.consilium.europa.eu/en/press/press-releases/2026/03/13/council-agrees-position-to-streamline-rules-on-artificial-intelligence/","access":"open"},{"id":"s0250","stat":"CLM market worth roughly $1.2-3.0B in 2026, growing at 12-13% CAGR","source":"Multiple research f
1irms","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"CLM market size and growth"},{"id":"s0251","stat":"Gartner predicts ~50% of CLM implementations fail","source":"Gartner","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"CLM implementation failure rate"},{"id":"s0252","stat":"Blackstone wrote Kirkland $101M in 2024, then $50M to fund AI firm to reduce that spend","source":"The Brainyacts","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"media","scope":"legal","context":"Client funding AI that threatens own law firm spend","url":"https://thebrainyacts.beehiiv.com/p/275-when-your-biggest-client-starts-eating-your-firm","access":"open"},{"id":"s0253","stat":"GPT-5.4 matches/exceeds industry professionals in 83% of comparisons (up from 70.9%)","source":"OpenAI GDPval","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"AI vs human professional benchmark","url":"https://openai.com/index/introducing-gpt-5-4/","access":"open"},{"id":"s0254","stat":"2,300+ GC interviews; 86% say legal is significant contributor vs only 17% of C-Suite","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"The perception gap headline stat","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0255","stat":"42% of C-Suite said legal contributed \\"little or not at all\\"","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"C-Suite negative perception","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0256","stat":"28% of GCs cited technology as strategic priority, up from 14% (doubled YoY)","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Technology overtaking cost control","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0257","stat":"86% of GC technology comments explicitly mention AI","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"AI dominates tech conversation","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0258","stat":"48% of GCs cite staffing/resource constraints as top barrier to delivering value","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Top constraint on legal departments","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0259","stat":"67% of corporate counsel don\'t know how outside firms approach AI","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Communication gap on firm AI use","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0260","stat":"~75% of corporate counsel expect outside firms to lead the AI conversation","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Expectation firms should initiate dialogue","url":"https://www.thomsonreuters.com/en-us
1/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0261","stat":"Orgs with formal AI strategy 3x more likely to realise positive ROI","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Strategic approach dramatically improves ROI","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0262","stat":"34% already experiencing efficiency benefits from AI; 43% anticipate within 12 months","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"AI benefits timeline","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0263","stat":"M&A net spend anticipation rose 10-fold Q4 2025 vs Q4 2024","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Dramatic M&A spend increase","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0264","stat":"Companies $50M-$1B revenue: legal spend 1% of revenue, median 3 lawyers","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Legal department resourcing benchmarks","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0265","stat":"UK 43% and Mainland Europe 60% cite technology as strategic priority vs 21% US","source":"TR 2026 State of Corporate Law Dept","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"UK/Europe ahead of US on tech strategy","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-report-2026/","access":"open"},{"id":"s0266","stat":"Harvey harness engineering: scores jumped from 40.8% to 87.7% across 12 tasks","source":"Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Self-improving legal agent research"},{"id":"s0267","stat":"7 of 12 Harvey benchmark tasks exceeded 90%; 1 hit 100%","source":"Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Harness engineering results"},{"id":"s0268","stat":"$145K in AI hallucination sanctions in Q1 2026 alone","source":"NPR / multiple","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"cross-sector","context":"Record quarterly sanctions total","url":"https://www.npr.org/2026/04/03/nx-s1-5761454/penalties-stack-up-ai-spreads-through-legal-system","access":"open"},{"id":"s0269","stat":"1,200+ documented AI hallucination cases globally (~800 from US courts)","source":"Damien Charlotin database","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Updated total; annualised rate approaching 1,400","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s0270","stat":"$109,700 sanctions in Oregon - largest single AI hallucination penalty","source":"NPR / NWSidebar","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"cross-sector","context":"Record individual sanctions case","url":"https://www.npr.org/2026/04/03/nx-s1-5761454/penalties-stack-up-ai-spreads-through-legal-system","access":"open"},{"id":"s0271","stat":"10 AI hallucination cases from 10 different c
1ourts on a single day","source":"Damien Charlotin","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Pace of new cases","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s0272","stat":"Legora hits $100M ARR in 18 months","source":"Legora / BVP / TNW","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Fastest enterprise business to $100M ARR per Bessemer","url":"https://www.bvp.com/atlas/legora-the-fastest-enterprise-business-to-reach-100m-arr","access":"open"},{"id":"s0273","stat":"Legora: 1,000+ customers across 50 markets; 400+ employees","source":"Legora","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Scale of legal AI platform"},{"id":"s0274","stat":"Legora total funding past $815M","source":"Legora / BVP","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Cumulative funding"},{"id":"s0275","stat":"Harvey $11B valuation requires ~$550M ARR at 20x multiple - potentially 65% of legaltech software spend","source":"Lawyer Forward analysis","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"legal","context":"Valuation only makes sense targeting $20-25B outsourced legal services market","url":"https://lawyerforward.substack.com/p/yes-they-are-trying-to-replace-you","access":"open"},{"id":"s0276","stat":"ARC-AGI-3 benchmark: AI scored under 1% on autonomous goal inference; humans solved ~100%","source":"ARC-AGI","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"AI limitation on genuinely novel tasks","url":"https://arcprize.org/blog/arc-agi-3-launch","access":"open"},{"id":"s0277","stat":"PageIndex/Mafin 2.5: 98.7% accuracy on FinanceBench","source":"VectifyAI","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Reasoning-based retrieval vs vector RAG","url":"https://github.com/VectifyAI/PageIndex","access":"open"},{"id":"s0278","stat":"69% of legal professionals personally use general-purpose AI for work (up from 31% in 2025, 27% in 2024)","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=1,300+. More than doubled YoY","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0279","stat":"82% of immigration lawyers use general-purpose AI - highest by practice area","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"High-volume repetitive document prep driving adoption","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0280","stat":"42% personally use legal-specific AI tools","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Distinct from general-purpose tools","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0281","stat":"28% use generative AI every day; 71% at least once per week; only 19% never","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Daily AI usage now routine","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0282","stat":"38% of AI users save 1-5 hours/week; 14% save 6-10 hours; 61% report measurable time savings","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Only 6% report no productivity benefits (down from 16%)","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0283","stat":"46% of firms adopted general-purpose AI; 58% among firms with 20+ lawyers","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Firm-level adoption lags individual (69%)","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0284","stat":"34% of firms adopted legal-specific AI, up from 21% last year","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Notable YoY increase","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0285","stat":"43% of firms have no formal AI policy and no plans to create one; only 9% have enforced policy","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"24% developing. Major governance gap","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0286","stat":"54% of firms provide no AI training and have no plans to; only 11% mandatory","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Major training gap","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0287","stat":"Only 6% said clients requesting AI-related price reductions; 83% said \\"not yet\\"","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Client pressure for AI efficie
1ncy not materialised","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0288","stat":"Only 3% said clients frequently ask to demonstrate AI efficiency; 75% \\"not at all\\"","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Client demand for AI proof minimal","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0289","stat":"29% identified AI as top ROI investment next 3 years; rises to 51% among firms with 21+ lawyers","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"AI ranked highest. Case management second at 25%","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0290","stat":"39% expect fewer paralegal/support roles; 21% expect fewer junior associates","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Mixed views on staffing impact","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0291","stat":"60% expect AI to impact more than 25% of routine legal tasks within 5 years","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"26% predict more than 50% impacted","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0292","stat":"Technical fluency with AI ranked most valuable future skill at 61%","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Above legal judgment (53%) and client management (52%)","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0293","stat":"62% of lawyers agreed the rule of law is under threat in the US","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Only 40% of non-lawyers agreed","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0294","stat":"72% identified cost of legal services as primary barrier to access to justice","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Top factor by wide margin","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s0295","stat":"94% said Legora enables faster client response (31 firms, 14 countries)","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Client responsiveness improvement"},{"id":"s0296","stat":"65% said tasks that used to take hours now take minutes","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Time compression"},{"id":"s0297","stat":"4,500 trade slips reviewed with 75% time savings on 130-hour project","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Specific use case - financial matter"},{"id":"s0298","stat":"Average 4.3 non-billable hours saved per lawyer per week","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Weekly time saving per lawyer"},{"id":"s0299","stat":"Potential $6.9M additional yearly billing per 100 lawyers","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Revenue potential calculation"},{"id":"s0300","stat":"71% said Legora identifies issues they would have missed","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Issue-spotting capability"},{"id":"s0301","stat":"74% rated likelihood of recommending Legora as 9 or 10 out of 10","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"NPS-style score"},{"id":"s0302","stat":"68% of mid-level associates cited as primary time-savings beneficiaries","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Mid-levels benefit most, not juniors"},{"id":"s0303","stat":"42% reported Legora helped win new work","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"New business attribution"},{"id":"s0304","stat":"55% said Legora replaced or reduced need for other tools","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Tool displacement rate"},{"id":"s0305","stat":"Contract review: 15-45 min per contract with Legora vs 60-90 min without","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Per-contract time benchmark"},{"id":"s0306","stat":"39% said Legora enables easier AFA/fixed-fee pricing","source":"Legora ROI Report (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Alternative fee arrangement enablement"},{"id":"s0307","stat":"Only 4% of legal ops teams now have no AI strategy, down from 52%","source":"LDO Survey (18th Annual, FTI/Blickstein)","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Dramatic collapse in AI non-adoption; n=68 companies","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0308","stat":"Fully operational AI in legal ops doubled to 23.1%","source":"LDO Survey (18th Annual, FTI/Blickstein)","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"From ~11% previously","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0309","stat":"93.6% of legal ops teams expect GenAI to enable more insourcing (up from 86.8%)","source":"LDO Survey (18th Annual, FTI/Blickstein)","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Near-universal insourcing expectation","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0310","stat":"ALSPs now represent 9% of outside legal spend","source":"LDO Survey (18th Annual, FTI/Blickstein)","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"ALSP share of wallet growing","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0311","stat":"68 companies surveyed in 18th Annual LDO Survey","source":"LDO Survey (18th Annual, FTI/Blickstein)","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Sample size for legal ops benchmark","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0312","stat":"96% of law firms raised rates in 2025","source":"BigHand Annual Law Firm Finance Report","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Near-universal rate increases;
1 800+ senior leaders surveyed","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-annual-law-firm-finance-report/","access":"gated"},{"id":"s0313","stat":"89% of firms saw higher write-offs in 2025; 88% expect more in 2026","source":"BigHand Annual Law Firm Finance Report","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Rising write-off pressure; profitability warning signal","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-annual-law-firm-finance-report/","access":"gated"},{"id":"s0314","stat":"AFA requests rose from 29% in 2024 to 47% in 2025","source":"BigHand Annual Law Firm Finance Report","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Sharp acceleration in alternative fee arrangements","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-annual-law-firm-finance-report/","access":"gated"},{"id":"s0315","stat":"50% of firms cite aged WIP as top concern (up from 32% last year)","source":"BigHand Annual Law Firm Finance Report","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Aged WIP doubled as primary concern","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-annual-law-firm-finance-report/","access":"gated"},{"id":"s0316","stat":"88% of law firms name AI as the top driver of future change","source":"ILTA 2025 Technology Survey","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"AI overtakes all other technology concerns","url":"https://www.iltanet.org/resources/publications/surveys/ts25","access":"gated"},{"id":"s0317","stat":"Firms with \\"nothing applicable\\" on GenAI dropped 17 percentage points to 13%","source":"ILTA 2025 Technology Survey","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"GenAI abstainers nearly vanished in a year","url":"https://www.iltanet.org/resources/publications/surveys/ts25","access":"gated"},{"id":"s0318","stat":"Sanctioned GenAI tool provision at firms nearly tripled in two years","source":"ILTA 2025 Technology Survey","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Formal tool provision accelerating rapidly","url":"https://www.iltanet.org/resources/publications/surveys/ts25","access":"gated"},{"id":"s0319","stat":"Cloud philosophy now at 88% across law firms","source":"ILTA 2025 Technology Survey","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Cloud-first now the overwhelming norm","url":"https://www.iltanet.org/resources/publications/surveys/ts25","access":"gated"},{"id":"s0320","stat":"Top 51-100 UK firms: fee income growth at 11%, outperforming all other bands","source":"PwC Law Firm Survey 2025","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Mid-tier firms outgrowing Magic Circle on revenue","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey.html","access":"open"},{"id":"s0321","stat":"£5.5bn tied up in WIP and debt across top 100 UK firms","source":"PwC Law Firm Survey 2025","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Working capital pressure at scale","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey.html","access":"open"},{"id":"s0322","stat":"No Top 51-100 UK firm has monetised AI yet","source":"PwC Law Firm Survey 2025","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Monetisation lag despite high adoption intent","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey.html","access":"open"},{"id":"s0323","stat":"GenAI adoption in corporate legal: 44% using, up from 28% and 20% in prior years","source":"The General Counsel Report 2025 (FTI/Relativity)","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=34 interviews + 207 survey; trajectory of rapid growth","url":"https://resources.relativity.com/2025-fti-gc-report-full-lp.html","access":"gated"},{"id":"s0324","stat":"85% of GCs say they are unprepared for GenAI risks","source":"The General Counsel Report 2025 (FTI/Relativity)","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Near-universal unpreparedness despite growing adoption","url":"https://resources.relativity.com/2025-fti-gc-report-full-lp.html","access":"gated"},{"id":"s0325","stat":"41% of GCs cite regulatory compliance as their #1 risk","source":"The General Counsel Report 2025 (FTI/Relativity)","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Top risk category for in-house legal teams","url":"https://resources.relativity.com/2025-fti-gc-report-full-lp.html","access":"gated"},{"id":"s0326","stat":"75% of in-house teams lack an AI/technology roadmap","source":"The General Counsel Report 2025 (FTI/Relativity)","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Strategy gap despite growing adoption","url":"https://resources.relativity.com/2025-fti-gc-report-full-lp.html","access":"gated"},{"id":"s0327","stat":"71% of in-house teams lack a legal ops role","source":"The General Counsel Report 2025 (FTI/Relativity)","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Legal ops function still minority capability","url":"https://resources.relativity.com/2025-fti-gc-report-full-lp.html","access":"gated"},{"id":"s0328","stat":"6.5% law firm rate growth for the third consecutive year (2025)","source":"TR State of Corporate Law Dept 2025","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Sustained rate increases; n=2,485 GC interviews","url":"https://www.thomsonreuters.com/en-us
1/posts/corporates/state-of-the-corporate-law-department-2025/","access":"open"},{"id":"s0329","stat":"77% of in-house matters still billed by the hour","source":"TR State of Corporate Law Dept 2025","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Hourly billing dominant despite AFA growth","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-2025/","access":"open"},{"id":"s0330","stat":"Average in-house lawyer works 49-hour week","source":"TR State of Corporate Law Dept 2025","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Overwork widespread; AI time saving potential","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-2025/","access":"open"},{"id":"s0331","stat":"62% of in-house lawyers say they lack sufficient time","source":"TR State of Corporate Law Dept 2025","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Capacity constraint driving AI appetite","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-2025/","access":"open"},{"id":"s0332","stat":"AI predicted to free 13 hours/week per in-house lawyer within 5 years","source":"TR State of Corporate Law Dept 2025","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Forward expectation of AI time savings","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-2025/","access":"open"},{"id":"s0333","stat":"35% of in-house teams feel their department is too slow on AI","source":"TR State of Corporate Law Dept 2025","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Internal self-criticism on AI pace","url":"https://www.thomsonreuters.com/en-us/posts/corporates/state-of-the-corporate-law-department-2025/","access":"open"},{"id":"s0334","stat":"57% of corporate legal departments use ALSPs; 43% do not","source":"ALSP Report 2025 (Thomson Reuters)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"ALSP usage now majority among corporate legal","url":"https://www.thomsonreuters.com/en-us/posts/legal/alsp-report-2025/","access":"open"},{"id":"s0335","stat":"22% of corporate legal departments plan to cut traditional law firm spend","source":"ALSP Report 2025 (Thomson Reuters)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Spend reduction intent; insourcing and ALSP pressure","url":"https://www.thomsonreuters.com/en-us/posts/legal/alsp-report-2025/","access":"open"},{"id":"s0336","stat":"ALSP confidentiality concerns rose to 44% from 26% (despite rising adoption)","source":"ALSP Report 2025 (Thomson Reuters)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Concern growing alongside usage","url":"https://www.thomsonreuters.com/en-us/posts/legal/alsp-report-2025/","access":"open"},{"id":"s0337","stat":"1-in-6 law firms planning to launch GenAI-enabled services","source":"ALSP Report 2025 (Thomson Reuters)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Law firms entering GenAI services market","url":"https://www.thomsonreuters.com/en-us/posts/legal/alsp-report-2025/","access":"open"},{"id":"s0338","stat":"AI automation produces 67-92% time reduction on specific legal document tasks","source":"State of Legal GenAI in 12 Charts (Litera/Kira)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Meta-analysis of 8 peer-reviewed papers"},{"id":"s0339","stat":"GPT-4 costs $0.25 vs $36 per legal document (vs LPO)","source":"State of Legal GenAI in 12 Charts (Litera/Kira)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"144x cost differential; AI vs legal process outsourcing"},{"id":"s0340","stat":"GPT-4 achieves 78% accuracy on legal reasoning vs GPT-3.5 at 64%","source":"State of Legal GenAI in 12 Charts (Litera/Kira)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Model generation improvement on legal reasoning"},{"id":"s0341","stat":"Commercial legal AI tools hallucinate or fail roughly half the time","source":"State of Legal GenAI in 12 Charts (Litera/Kira)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Aggregate finding across commercial tools"},{"id":"s0342","stat":"61.2% of legal professionals have broad AI access but only 18.9% are confident using it","source":"Factor GenAI in Legal Benchmarking 2025","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"The \\"confidence gap\\" - access far ahead of capability; n=120+ in-house counsel","url":"https://www.factor.law/white-paper/genai-in-legal-benchmarking-report-2025","access":"open"},{"id":"s0343","stat":"29.6% of legal teams stuck in \\"pilot purgatory\\"","source":"Factor GenAI in Legal Benchmarking 2025","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Largest single adoption cohort; can\'t scale beyond trial","url":"https://www.factor.law/white-paper/genai-in-legal-benchmarking-report-2025","access":"open"},{"id":"s0344","stat":"47.5% of legal teams built an internal AI chatbot","source":"Factor GenAI in Legal Benchmarking 2025","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Most common early AI investment","url":"https://www.factor.law/white-paper/genai-in-legal-benchmarking-report-2025","access":"open"}
1,{"id":"s0345","stat":"40.4% bought specialised legal AI tools","source":"Factor GenAI in Legal Benchmarking 2025","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Purpose-built tools gaining ground","url":"https://www.factor.law/white-paper/genai-in-legal-benchmarking-report-2025","access":"open"},{"id":"s0346","stat":"44.4% of legal teams have not yet addressed EU AI Act compliance","source":"Factor GenAI in Legal Benchmarking 2025","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Compliance gap despite looming deadline","url":"https://www.factor.law/white-paper/genai-in-legal-benchmarking-report-2025","access":"open"},{"id":"s0347","stat":"UK legal services market worth £40bn with >300,000 professionals and >9,000 firms","source":"Strategy& UK Legal Services Market Report","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=62 firms; baseline for AI transformation modelling","url":"https://www.strategyand.pwc.com/uk/en/reports/uk-legal-services-market-report-2025.pdf","access":"open"},{"id":"s0348","stat":"UK legal market growing at 5% CAGR to 2029","source":"Strategy& UK Legal Services Market Report","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Baseline growth before AI disruption","url":"https://www.strategyand.pwc.com/uk/en/reports/uk-legal-services-market-report-2025.pdf","access":"open"},{"id":"s0349","stat":"11% of legal hours saveable with AI - equivalent to ~£4bn of work","source":"Strategy& UK Legal Services Market Report","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"AI-addressable work as % of total market","url":"https://www.strategyand.pwc.com/uk/en/reports/uk-legal-services-market-report-2025.pdf","access":"open"},{"id":"s0350","stat":"84% of UK law firms taking AI steps; only 2% have monetised AI","source":"Strategy& UK Legal Services Market Report","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Adoption-to-monetisation gap in UK legal","url":"https://www.strategyand.pwc.com/uk/en/reports/uk-legal-services-market-report-2025.pdf","access":"open"},{"id":"s0351","stat":"Regional legal consolidators: 27% revenue growth, 25% EBITDA margins","source":"Strategy& UK Legal Services Market Report","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Regional PE-backed firms outperforming on growth and margin","url":"https://www.strategyand.pwc.com/uk/en/reports/uk-legal-services-market-report-2025.pdf","access":"open"},{"id":"s0352","stat":"6 PE-backed legal platform deals in 2024 - a record","source":"Strategy& UK Legal Services Market Report","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Private equity consolidation accelerating","url":"https://www.strategyand.pwc.com/uk/en/reports/uk-legal-services-market-report-2025.pdf","access":"open"},{"id":"s0353","stat":"88% of top UK law firms have an internal GPT or AI chatbot","source":"The Lawyer Innovation Map","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"media","scope":"legal","context":"Near-universal chatbot deployment at top firms"},{"id":"s0354","stat":"72% of top UK law firms trialling early-stage technology","source":"The Lawyer Innovation Map","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"media","scope":"legal","context":"Active experimentation majority"},{"id":"s0355","stat":"A&O ContractMatrix: 3,500 users, 220,000 queries in 30 days","source":"The Lawyer Innovation Map","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"media","scope":"legal","context":"Scale of internal AI contract tool at A&O"},{"id":"s0356","stat":"Macfarlanes: 80%+ of lawyers using GenAI regularly","source":"The Lawyer Innovation Map","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"media","scope":"legal","context":"Highest reported regular usage rate at a UK firm"},{"id":"s0357","stat":"Simmons & Simmons: 2,200 daily AI users","source":"The Lawyer Innovation Map","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"media","scope":"legal","context":"Daily active users on AI platform"},{"id":"s0358","stat":"356 lawtech companies active in the UK (254 UK-founded, 102 international)","source":"LawtechUK Ecosystem Tracker","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"UK lawtech ecosystem size as of 2023"},{"id":"s0359","stat":"£1.38bn raised by UK-founded lawtech companies","source":"LawtechUK Ecosystem Tracker","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"UK-origin capital raise"},{"id":"s0360","stat":"£4.26bn raised by international lawtech companies active in UK","source":"LawtechUK Ecosystem Tracker","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"Total capital in UK lawtech ecosystem (inc. international)"},{"id":"s0361","stat":"43% of UK lawtech companies based in London","source":"LawtechUK Ecosystem Tracker","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"London dominance in UK legal tech"},{"id":"s0362","stat":"UK lawtech market grew 4x in a decade (37 companies in 2002 to 356 in 2023)","source":"LawtechUK Ecosystem Tracker","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"Decade of growth in UK lawtech"},{"id":"s0363","stat":"14.3% of UK lawtech companies are female-founded","source":"LawtechUK Ecosystem Tracker","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Gender gap in legal tech founding teams"},{"id":"s0364","stat":"UK ROI satisfaction from legal tech: only 22% vs 51% in the US","source":"Definely LegalTech Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Major UK-US satisfaction gap; n=200+ respondents","url":"https://www.definely.com/guide/the-state-of-legaltech-adoption-is-ai-legaltech-truly-transforming-legal-workflows","access":"open"},{"id":"s0365","stat":"58% of UK lawyers cite data security as their top legaltech concern","source":"Definely LegalTech Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Security as primary barrier to adoption","url":"https://www.definely.com/guide/the-state-of-legaltech-adoption-is-ai-legaltech-truly-transforming-legal-workflows","access":"open"}
1,{"id":"s0366","stat":"46% of UK in-house lawyers feel behind their external law firms on AI","source":"Definely LegalTech Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"In-house teams feel disadvantaged relative to firms","url":"https://www.definely.com/guide/the-state-of-legaltech-adoption-is-ai-legaltech-truly-transforming-legal-workflows","access":"open"},{"id":"s0367","stat":"50% of UK in-house lawyers feel uncomfortable with AI","source":"Definely LegalTech Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Discomfort despite rising adoption pressure","url":"https://www.definely.com/guide/the-state-of-legaltech-adoption-is-ai-legaltech-truly-transforming-legal-workflows","access":"open"},{"id":"s0368","stat":"32% of UK in-house lawyers plan to cut external counsel budgets in next 3 years","source":"Definely LegalTech Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Insourcing intent translating to budget cuts","url":"https://www.definely.com/guide/the-state-of-legaltech-adoption-is-ai-legaltech-truly-transforming-legal-workflows","access":"open"},{"id":"s0369","stat":"Only 4% of UK lawyers say AI integration is straightforward","source":"Definely LegalTech Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Near-universal friction in AI deployment","url":"https://www.definely.com/guide/the-state-of-legaltech-adoption-is-ai-legaltech-truly-transforming-legal-workflows","access":"open"},{"id":"s0370","stat":"43% of law firms are not promoting their legaltech to clients","source":"Definely LegalTech Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Firms failing to communicate AI capability to clients","url":"https://www.definely.com/guide/the-state-of-legaltech-adoption-is-ai-legaltech-truly-transforming-legal-workflows","access":"open"},{"id":"s0371","stat":"23% of US in-house teams already using GenAI; 38% using or actively exploring","source":"ACC/Everlaw GenAI Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"n=475 US in-house professionals"},{"id":"s0372","stat":"86% of in-house teams report efficiency gains from GenAI","source":"ACC/Everlaw GenAI Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Strong majority seeing benefits"},{"id":"s0373","stat":"Only 42% of in-house lawyers feel personally prepared for GenAI","source":"ACC/Everlaw GenAI Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Personal readiness gap"},{"id":"s0374","stat":"73% of in-house teams worry about non-lawyers relying on GenAI","source":"ACC/Everlaw GenAI Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"industry","scope":"legal","context":"UPL and quality concern"},{"id":"s0375","stat":"59% of in-house teams have drafted an AI policy","source":"ACC/Everlaw GenAI Survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"regulatory","sourceType":"industry","scope":"legal","context":"Policy development majority"},{"id":"s0376","stat":"Lexis+ AI hallucination rate 17%; accurate 65% of responses","source":"Stanford/Yale Legal RAG Hallucinations Paper","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"legal","context":"Best-performing commercial legal AI tool tested","url":"https://reglab.stanford.edu/publications/hallucination-free-assessing-the-reliability-of-leading-ai-legal-research-tools/","access":"open"},{"id":"s0377","stat":"Westlaw AI-Assisted Research hallucination rate 33%; accurate 42%","source":"Stanford/Yale Legal RAG Hallucinations Paper","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"legal","context":"Worst-performing commercial tool; higher hallucination than accurate","url":"https://reglab.stanford.edu/publications/hallucination-free-assessing-the-reliability-of-leading-ai-legal-research-tools/","access":"open"},{"id":"s0378","stat":"Ask Practical Law AI: 62% incomplete responses; 19-20% accurate","source":"Stanford/Yale Legal RAG Hallucinations Paper","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"legal","context":"Incomplete rather than hallucinated, but still unreliable","url":"https://reglab.stanford.edu/publications/hallucination-free-assessing-the-reliability-of-leading-ai-legal-research-tools/","access":"open"},{"id":"s0379","stat":"General LLMs without RAG hallucinate 58-82% on legal citation queries","source":"Stanford/Yale Legal RAG Hallucinations Paper","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"legal","context":"Unaugmented models unreliable for legal research","url":"https://reglab.stanford.edu/publications/hallucination-free-assessing-the-reliability-of-leading-ai-legal-research-tools/","access":"open"},{"id":"s0380","stat":"41 of top 100 US law firms using at least one legal AI provider (January 2024)","source":"Stanford/Yale Legal RAG Hallucinations Paper","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Legal AI penetration in top US firms","url":"https://reglab.stanford.edu/publications/hallucination-free-assessing-the-reliability-of-leading-ai-legal-research-tools/","access":"open"},{"id":"s0381","stat":"GPT-5 tops AI productivity benchmark at 67.0% on real professional tasks (law, medicine, IB, consulting)","source":"Mercor APEX Benchmark","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"137 expert annotators; mean task time 2.7 hours","url":"https://www.mercor.com/blog/expanding-the-ai-productivity-index-apex/","access":"open"},{"id":"s0382","stat":"Claude Opus 4.5 ranks #5 overall but #2 on hardest tasks in APEX benchmark","source":"Mercor APEX Benchmark","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Reasoning model advantage on complex tasks","url":"https://www.mercor.com/blog/expanding-the-ai-productivity-index-apex/","access":"open"},{"id":"s0383","stat":"Best AI law score in APEX benchmark: 77.9% (GPT-5)","source":"Mercor APEX Benchmark","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Highest model score on legal professional tasks","url":"https://www.mercor.com/blog/expanding-the-ai-productivity-index-apex/","access":"open"},{"id":"s0384","stat":"Investment banking hardest AI domain in APEX: best score 64.0% (Gemini 3 Pro)","source":"Mercor APEX Benchmark","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"IB most resistant to AI automation","url":"https://www.mercor.com/blog/expanding-the-ai-productivity-index-apex/","access":"open"},{"id":"s0385","stat":"95% of enterprises get zero P&L return from AI despite $30-40B investment","source":"MIT NANDA State of AI in Business","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Fundamental gap between AI investment and financial return"},{"id":"s0386","stat":"Only 5% of custom AI tools built internally reach production","source":"MIT NANDA State of AI in Business","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Build failure rate for enterprise AI"},{"id":"s0387","stat":"90% of employees use personal AI tools vs 40% with official subscriptions","source":"MIT NANDA State of AI in Business","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Shadow AI economy at massive scale"},{"id":"s0388","stat":"External AI partnerships deploy successfully 67% of the time vs 33% for internal builds","source":"MIT NANDA State of AI in Business","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Buy beats build 2-to-1"},{"id":"s0389","stat":"$2.3 trillion in latent US labour automation exposure across 39 million positions","source":"MIT NANDA State of AI in Business","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Scale of AI-addressable labour cost"},{"id":"s0390","stat":"Only 1% of companies are genuinely AI-mature","source":"McKinsey Superagency in the Workplace","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Near-universal immaturity despite high investment","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work","access":"open"},{"id":"s0391","stat":"92% of companies plan to increase AI investment in the next 3 years","source":"McKinsey Superagency in the Workplace","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Investment intent near-universal","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work","access":"open"},{"id":"s0392","stat":"Employees use generative AI 3x more than leaders realise (13% daily vs 4% estimate)","source":"McKinsey Superagency in the Workplace","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Massive leader perception gap","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work","access":"open"},{"id":"s0393","stat":"87% of C-suite expect revenue growth from AI in 3 years; only 19% currently see >5% uplift","source":"McKinsey Superagency in the Workplace","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Expectation-reality gap on AI returns","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work","access":"open"},{"id":"s0394","stat":"Millennials aged 35-44 most AI-capable generation: 62% high expertise vs 22% boomers","source":"McKinsey Superagency in the Workplace","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Generation gap in AI capability","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work","access":"open"},{"id":"s0395","stat":"71% of employees trust their employer most to deploy AI ethically","source":"McKinsey Superagency in the Workplace","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Employer trust exceeds government and AI companies","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work","access":"open"},{"id":"s0396","stat":"72% of all employees are regular AI users but frontline stalled at 51%","source":"BCG AI at Work 2025","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Frontline-office worker adoption gap","url":"https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain","access":"open"},{"id":"s0397","stat":"AI adoption by country: India 92%, Japan 51%, UK 64%","source":"BCG AI at Work 2025","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Wide geographic variance in AI adoption rates","url":"https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain","access":"open"},{"id":"s0398","stat":"Leadership support creates 41 percentage point adoption gap (82% vs 41%)","source":"BCG AI at Work 2025","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Leader behaviour is the single biggest adoption driver","url":"https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain","access":"open"},{"id":"s0399","stat":"Companies redesigning workflows see 55% of employees save 1+ hour/day vs 29% tool-only","source":"BCG AI at Work 2025","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Workflow redesign doubles productivity benefit","url":"https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain","access":"open"},{"id":"s0400","stat":"Only 13% of organisations have AI agents integrated into their workflows","source":"BCG AI at Work 2025","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Agentic AI still nascent despite high awareness","url":"https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain","access":"open"},{"id":"s0401","stat":"77% of employees expect AI agents to be important in 3-5 years","source":"BCG AI at Work 2025","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Near-term agentic AI expectation","url":"https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain","access":"open"},{"id":"s0402","stat":"41% of employees fear losing their job to AI","source":"BCG AI at Work 2025","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Job displacement fear significant but not majority","url":"https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain","access":"open"},{"id":"s0403","stat":"UK is the third largest AI market globally","source":"UK AI Opportunities Action Plan (DSIT)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"market","sourceType":"government","scope":"cross-sector","context":"Government assessment of UK competitive position","url":"https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan","access":"open"},{"id":"s0404","stat":"AI could add £400bn to UK economy by 2030","source":"UK AI Opportunities Action Plan (DSIT)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"market","sourceType":"government","scope":"cross-sector","context":"UK government economic impact estimate","url":"https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan","access":"open"},{"id":"s0405","stat":"UK produced 46,000 AI graduates in 2022 - highest in Europe in absolute terms","source":"UK AI
1Opportunities Action Plan (DSIT)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"workforce","sourceType":"government","scope":"cross-sector","context":"UK AI graduate pipeline","url":"https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan","access":"open"},{"id":"s0406","stat":"Only 22% of UK AI/data science workers are women","source":"UK AI Opportunities Action Plan (DSIT)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"workforce","sourceType":"government","scope":"cross-sector","context":"Gender gap in AI/data science roles","url":"https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan","access":"open"},{"id":"s0407","stat":"AlphaFold saved equivalent of 400 million years of researcher time","source":"UK AI Opportunities Action Plan (DSIT)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"technical","sourceType":"government","scope":"cross-sector","context":"Flagship example of AI scientific impact","url":"https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan","access":"open"},{"id":"s0408","stat":"AI can free up 20% of employee time across sectors","source":"UK AI Opportunities Action Plan (DSIT)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Government estimate of AI productivity potential","url":"https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan","access":"open"},{"id":"s0409","stat":"AI reduces document production time by 20-80%","source":"UK AI Opportunities Action Plan (DSIT)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Range from government case study evidence","url":"https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan","access":"open"},{"id":"s0410","stat":"LinksAI English law benchmark: o1 scores 6.4/10; Gemini 2.0 scores 6.0/10;
1 GPT-4 scores 3.2/10","source":"LinksAI English Law Benchmark (Linklaters)","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Scores out of 10 across 7 English law practice areas"},{"id":"s0411","stat":"Fictitious case citations dropped from ~33% (GPT-3/4) to 9% (Gemini 2.0/o1)","source":"LinksAI English Law Benchmark (Linklaters)","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Hallucination improvement over model generations"},{"id":"s0412","stat":"Best English law practice area for AI: real estate 7.0/10; worst: corporate 3.9/10","source":"LinksAI English Law Benchmark (Linklaters)","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Practice area variance in AI legal accuracy"},{"id":"s0413","stat":"80% of law firm professionals believe GenAI will have high or transformative impact","source":"TR Future of the Law Firm","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Near-universal belief in AI significance"},{"id":"s0414","stat":"74% of lawyers believe AI is a force for good; 64% have personally tried AI tools at work","source":"TR Future of the Law Firm","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Personal and values-level AI engagement"},{"id":"s0415","stat":"AI Leaders: 94% already seeing benefits; AI Followers: 51%","source":"TR Future of the Law Firm","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Almost double the benefit realisation for AI leaders"},{"id":"s0416","stat":"AI projected to cut billable hours by up to 40% on classic work; up to 80% on commoditised work","source":"TR Future of the Law Firm","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Structural threat to hourly billing model"},{"id":"s0417","stat":"Up to 20% of legal services could become highly commoditised by AI","source":"TR Future of the Law Firm","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Commoditisation risk to standard legal work"},{"id":"s0418","stat":"New AI-enabled premium services priced 25-40% higher than standard","source":"TR Future of the Law Firm","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Premium pricing opportunity for AI-differentiated work"},{"id":"s0419","stat":"Law firm leverage ratio expected to drop from ~3:1 to ~2:1 as AI absorbs associate work","source":"TR Future of the Law Firm","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Structural change to partnership pyramid"},{"id":"s0420","stat":"FY2024 legaltech capital raised: $8.31bn (up 120%), 322 companies, 647 investors","source":"Legal Tech Funding Report 2024-25 (Legalcomplex)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Total legaltech capital; strong growth despite fewer companies","url":"https://www.legalcomplex.com/2025/01/07/2024-saw-record-legal-tech-funding-but-far-fewer-deals/","access":"open"},{"id":"s0421","stat":"Debt finance was 61.5% ($5.09bn) of 2024 legaltech total; VC was $3.22bn (up 61.9%)","source":"Legal Tech Funding Report 2024-25 (Legalcomplex)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Debt vs equity split; VC growing fast","url":"https://www.legalcomplex.com/2025/01/07/2024-saw-record-legal-tech-funding-but-far-fewer-deals/","access":"open"}
1,{"id":"s0422","stat":"Median 2024 legaltech venture round: $7M (up 27.3%)","source":"Legal Tech Funding Report 2024-25 (Legalcomplex)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Round size inflation signal","url":"https://www.legalcomplex.com/2025/01/07/2024-saw-record-legal-tech-funding-but-far-fewer-deals/","access":"open"},{"id":"s0423","stat":"Q1 2025 legaltech VC: $1.16bn ex-debt (up 279% year-on-year)","source":"Legal Tech Funding Report 2024-25 (Legalcomplex)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Q1 2025 VC surge","url":"https://www.legalcomplex.com/2025/01/07/2024-saw-record-legal-tech-funding-but-far-fewer-deals/","access":"open"},{"id":"s0424","stat":"2024 legaltech M&A: $422M deal value (down 84%); 62 deals (down 36%)","source":"Legal Tech Funding Report 2024-25 (Legalcomplex)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"M&A collapsed while VC surged","url":"https://www.legalcomplex.com/2025/01/07/2024-saw-record-legal-tech-funding-but-far-fewer-deals/","access":"open"},{"id":"s0425","stat":"GRC sector is 4x larger than legaltech by capital: $30.96bn in 2024 (up 31%)","source":"Legal Tech Funding Report 2024-25 (Legalcomplex)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Compliance/risk far bigger than legal tech proper","url":"https://www.legalcomplex.com/2025/01/07/2024-saw-record-legal-tech-funding-but-far-fewer-deals/","access":"open"},{"id":"s0426","stat":"North America received $2.18bn; Europe $824M of legaltech VC in 2024","source":"Legal Tech Funding Report 2024-25 (Legalcomplex)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Geographic split of legaltech investment","url":"https://www.legalcomplex.com/2025/01/07/2024-saw-record-legal-tech-funding-but-far-fewer-deals/","access":"open"},{"id":"s0427","stat":"1H2025 global legaltech VC: £1,328M vs £520M in 1H2024 (+155%)","source":"European Legal Tech Services Insight (Raymond James)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"First-half 2025 surge","url":"https://www.raymondjames.com/-/media/rj/dotcom/files/corporations-and-institutions/investment-banking/industry-insight/european-legal-tech-services-insight.pdf","access":"open"},{"id":"s0428","stat":"Harvey raised $300M Series D in February 2025 and $300M Series E in June 2025","source":"European Legal Tech Services Insight (Raymond James)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Two mega-rounds in six months","url":"https://www.raymondjames.com/-/media/rj/dotcom/files/corporations-and-institutions/investment-banking/industry-insight/european-legal-tech-services-insight.pdf","access":"open"},{"id":"s0429","stat":"Harvey: 337 clients, 53 countries, 340 employees (plans to double headcount) at Series E","source":"European Legal Tech Services Insight (Raymond James)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Harvey scale at Series E close","url":"https://www.raymondjames.com/-/media/rj/dotcom/files/corporations-and-institutions/investment-banking/industry-insight/european-legal-tech-services-insight.pdf","access":"open"},{"id":"s0430","stat":"Clio: $300M ARR, $900M Series F at $3bn valuation","source":"European Legal Tech Services Insight (Raymond James)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Clio financials before vLex acquisition","url":"https://www.raymondjames.com/-/media/rj/dotcom/files/corporations-and-institutions/investment-banking/industry-insight/european-legal-tech-services-insight.pdf","access":"open"},{"id":"s0431","stat":"Noxtua: â¬81M Series B; 55 million training documents","source":"European Legal Tech Services Insight (Raymond James)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"European legal AI specialist","url":"https://www.raymondjames.com/-/media/rj/dotcom/files/corporations-and-institutions/investment-banking/industry-insight/european-legal-tech-services-insight.pdf","access":"open"},{"id":"s0432","stat":"Eudia: $105M Series A (General Catalyst)","source":"European Legal Tech Services Insight (Raymond James)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Expert digital twin legal AI","url":"https://www.raymondjames.com/-/media/rj/dotcom/files/corporations-and-institutions/investment-banking/industry-insight/european-legal-tech-services-insight.pdf","access":"open"},{"id":"s0433","stat":"Laurel: $100M Series C; valuation doubled to $510M","source":"European Legal Tech Services Insight (Raymond James)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"AI timekeeping platform","url":"https://www.raymondjames.com/-/media/rj/dotcom/files/corporations-and-institutions/investment-banking/industry-insight/european-legal-tech-services-insight.pdf","access":"open"},{"id":"s0434","stat":"ABA: lawyers using AI grew from 11% (2023) to 30% (2024)","source":"European Legal Tech Services Insight (Raymond James)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"US lawyer AI adoption nearly tripled in 12 months","url":"https://www.raymondjames.com/-/media/rj/dotcom/files/corporations-and-institutions/investment-banking/industry-insight/european-legal-tech-services-insight.pdf","access":"open"},{"id":"s0435","stat":"AI improves legal work quality by up to 28% across 6 legal tasks (randomised controlled trial)","source":"European Legal Tech Services Insight (Raymond James)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"analyst","scope":"legal","context":"Minnesota/Michigan RCT using o1-preview and Vincent AI","url":"https://www.raymondjames.com/-/media/rj/dotcom/files/corporations-and-institutions/investment-banking/industry-insight/european-legal-tech-services-insight.pdf","access":"open"},{"id":"s0436","stat":"RELX electronic revenue: 22% of total in 2000, now 75% in 2024; subscriptions 53% of revenue","source":"European Legal Tech Services Insight (Raymond James)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"TR/RELX digital transformation benchmark","url":"https://www.raymondjames.com/-/media/rj/dotcom/files/corporations-and-institutions/investment-banking/industry-insight/european-legal-tech-services-insight.pdf","access":"open"},{"id":"s0437","stat":"JPMorgan COiN: saves 360,000 hours of manual legal document review annually","source":"PwC Agentic AI Playbook","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"NLP/AI on contracts; most cited legal AI productivity stat","url":"https://www.pwc.com/m1/en/publications/documents/2024/agentic-ai-the-new-frontier-in-genai-an-executive-playbook.pdf","access":"open"},{"id":"s0438","stat":"Hogan Lovells (Kira Systems): AI increased document review speed by 40%","source":"PwC Agentic AI Playbook","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Legal AI contract review speed benchmark","url":"https://www.pwc.com/m1/en/publications/documents/2024/agentic-ai-the-new-frontier-in-genai-an-executive-playbook.pdf","access":"open"},{"id":"s0439","stat":"Unilever: AI reduced hiring time by 75%; saved over $1M annually in recruitment costs","source":"PwC Agentic AI Playbook","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"AI in HR screening","url":"https://www.pwc.com/m1/en/publications/documents/2024/agentic-ai-the-new-frontier-in-genai-an-executive-playbook.pdf","access":"open"}
1,{"id":"s0440","stat":"McKinsey analysed 50+ agentic AI builds; some companies already rehiring where agents failed","source":"McKinsey Agentic AI Lessons","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Agentic AI retrenching; not all deployments stick","url":"https://www.mckinsey.com/capabilities/quantumblack/our-insights/one-year-of-agentic-ai-six-lessons-from-the-people-doing-the-work","access":"open"},{"id":"s0441","stat":"Reusable agent components on centralised platform eliminate 30-50% of non-essential work","source":"McKinsey Agentic AI Lessons","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Platform approach to agentic AI","url":"https://www.mckinsey.com/capabilities/quantumblack/our-insights/one-year-of-agentic-ai-six-lessons-from-the-people-doing-the-work","access":"open"},{"id":"s0442","stat":"$1 trillion in unmet legal demand projected to unlock as AI reduces cost and friction","source":"TLTF/Harvey Pathways Report","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Horizon 3 (2040) projection; workshop-based foresight"},{"id":"s0443","stat":"AI-first dispute prevention could cut litigation in half by 2040 (scenario projection)","source":"TLTF/Harvey Pathways Report","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Foresight scenario; not a statistical forecast"},{"id":"s0444","stat":"OpenAI proposes Public Wealth Fund modelled on Alaska Permanent Fund, seeded by AI companies","source":"OpenAI Industrial Policy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Structural alternative to UBI; citizens as shareholders in AI economy","url":"https://openai.com/index/industrial-policy-for-the-intelligence-age/","access":"open"},{"id":"s0445","stat":"OpenAI proposes robot/automation taxes to replace eroding payroll tax base","source":"OpenAI Industrial Policy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"AI hollowing out Social Security funding via reduced payroll tax revenue","url":"https://openai.com/index/industrial-policy-for-the-intelligence-age/","access":"open"},{"id":"s0446","stat":"OpenAI proposes 32-hour/four-day workweek pilots with no pay cut, as \\"efficiency dividend\\" from AI","source":"OpenAI Industrial Policy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Productivity gains redistributed as time rather than profit","url":"https://openai.com/index/industrial-policy-for-the-intelligence-age/","access":"open"},{"id":"s0447","stat":"OpenAI proposes automatic safety net triggers activated when AI displacement metrics hit thresholds","source":"OpenAI Industrial Policy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Time-bound, proportional support that phases out when conditions stabilise","url":"https://openai.com/index/industrial-policy-for-the-intelligence-age/","access":"open"},{"id":"s0448","stat":"OpenAI offering research grants up to $100K + $1M API credits for industrial policy research","source":"OpenAI Industrial Policy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Fellowship programme announced alongside paper","url":"https://openai.com/index/industrial-policy-for-the-intelligence-age/","access":"open"},{"id":"s0449","stat":"Frontier AI systems advanced from minute-level tasks to hour-level tasks; month-level projected","source":"OpenAI Industrial Policy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"OpenAI\'s own capability trajectory framing","url":"https://openai.com/index/industrial-policy-for-the-intelligence-age/","access":"open"},{"id":"s0450","stat":"OpenResearch UBI study: 3,000 participants, $1,000/month for 3 years (largest US experiment)","source":"OpenResearch","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"RCT across Texas and Illinois; ages 21-40, income under $30K","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"},{"id":"s0451","stat":"UBI recipients worked 1.3 fewer hours/week (~8 fewer days annually)","source":"OpenResearch","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Modest employment reduction; 2pp less likely to be employed","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"},{"id":"s0452","stat":"UBI recipients 10% more likely to be job searching but more selective about roles","source":"OpenResearch","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Agency not laziness; prioritised meaningful/interesting work","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"},{"id":"s0453","stat":"UBI recipients 14% more likely to pursue education/job training","source":"OpenResearch","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Strongest for under-30s and lowest-income recipients","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"},{"id":"s0454","stat":"Black UBI recipients 26% more likely to start a business by year three","source":"OpenResearch","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Capital access as the barrier, not motivation","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"},{"id":"s0455","stat":"Female UBI recipients 15% more likely to start a business vs control","source":"OpenResearch","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Entrepreneurship gender gap narrows with unconditional cash","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"},{"id":"s0456","stat":"UBI recipients: 20% decrease in problematic drinking; 10% increase in dental care","source":"OpenResearch","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Health improvements from financial stability","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"}
1,{"id":"s0457","stat":"UBI recipients: 26% increase in hospitalisations; no measurable physical health improvement","source":"OpenResearch","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Increased healthcare utilisation, not necessarily better health","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"},{"id":"s0458","stat":"UBI recipients: no measurable improvement in children\'s academic or behavioural outcomes","source":"OpenResearch","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Parenting practices improved but short-run child outcomes unchanged","url":"https://www.openresearchlab.org/findings/key-findings-parenting-and-child-outcomes","access":"open"},{"id":"s0459","stat":"UBI recipients saw work as more important, not less - countering \\"people will stop working\\" narrative","source":"OpenResearch","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Political attitudes study; intrinsic value of work increased","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"},{"id":"s0460","stat":"UBI spending increase: ~$310/month, mainly food, rent, and transportation","source":"OpenResearch","date":"Aug 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Basic needs, not discretionary spending","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"},{"id":"s0461","stat":"Household income with UBI transfer: ~$6,100 higher; without: $2,500-$4,100 lower","source":"OpenResearch","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Net positive with transfer; employment reduction partially offsets","url":"https://www.openresearchlab.org/studies/unconditional-cash-study/study","access":"open"},{"id":"s0462","stat":"Harvey power users (law firms) save 36.9 hours/month vs 15.7 hours for standard users","source":"RSGI/Harvey Impact Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Power users = >30 prompts/week; typically 20-30% of users","url":"https://www.harvey.ai/resources/reports/harvey-rsgi-report","access":"gated"},{"id":"s0463","stat":"Harvey power users (in-house) save 28.3 hours/month vs ~12 hours for standard users","source":"RSGI/Harvey Impact Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"In-house power user time savings","url":"https://www.harvey.ai/resources/reports/harvey-rsgi-report","access":"gated"},{"id":"s0464","stat":"Two-thirds of Harvey study organisations saw measurable benefits within first 3 months","source":"RSGI/Harvey Impact Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Speed to value; n=40 organisations","url":"https://www.harvey.ai/resources/reports/harvey-rsgi-report","access":"gated"},{"id":"s0465","stat":"100% of in-house Harvey participants agreed it enables faster advice delivery to business","source":"RSGI/Harvey Impact Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Top benefit for in-house teams","url":"https://www.harvey.ai/resources/reports/harvey-rsgi-report","access":"gated"},{"id":"s0466","stat":"9 out of 10 in-house Harvey participants agreed it lets them do more work in-house","source":"RSGI/Harvey Impact Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"client","sourceType":"vendor","scope":"legal","context":"Insourcing capacity; 4 of 11 teams reduced external spend","url":"https://www.harvey.ai/resources/reports/harvey-rsgi-report","access":"gated"},{"id":"s0467","stat":"Some Harvey power users reported 20-45% monthly time savings (43-98 hours/month)","source":"RSGI/Harvey Impact Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Upper range individual productivity gains","url":"https://www.harvey.ai/resources/reports/harvey-rsgi-report","access":"gated"},{"id":"s0468","stat":"51% of Harvey law firm participants said it accelerated junior lawyers\' expertise acquisition","source":"RSGI/Harvey Impact Report","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Junior development signal; 15 neutral","url":"https://www.harvey.ai/resources/reports/harvey-rsgi-report","access":"gated"},{"id":"s0469","stat":"80% of global workforce say they lack enough time or energy to do their work","source":"Microsoft Work Trend Index","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"The capacity gap; 31,000 knowledge workers, 31 countries"},{"id":"s0470","stat":"82% of leaders are confident they will use AI agents to expand workforce capacity in next 12-18 months","source":"Microsoft Work Trend Index","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Agent adoption intent at leadership level"},{"id":"s0471","stat":"Employees are interrupted every 2 minutes during core hours - 275 times per day","source":"Microsoft Work Trend Index","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"M365 telemetry; fragmentation of knowledge work"},{"id":"s0472","stat":"71% of Frontier Firm employees say their company is thriving vs only 37% globally","source":"Microsoft Work Trend Index","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Frontier Firm = AI-powered human-agent teams"},{"id":"s0473","stat":"Frontier Firm workers are 55% able to take on more work vs 20% globally","source":"Microsoft Work Trend Index","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Capacity advantage of AI-mature organisations"},{"id":"s0474","stat":"33% of leaders are considering headcount reductions in the next 12-18 months","source":"Microsoft Work Trend Index","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Job displacement signal; counterbalanced by 32% planning to increase headcount"},{"id":"s0475","stat":"AI-native startups grew headcount 20.6% year-on-year vs Big Tech at 10.6%","source":"Microsoft Work Trend Index","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"LinkedIn data; AI sector hiring outpacing Big Tech"},{"id":"s0476","stat":"Wells Fargo AI agent: 35,000 bankers, 4,000 branches; 75% of searches now via agent; response time cut from 10 minutes to 30 seconds","source":"Microsoft Work Trend Index","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Case study - 95% reduction in response time"},{"id":"s0477","stat":"Bayer Crop Science: researchers each save up to 6 hours per week using AI agents","source":"Microsoft Work Trend Index","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Agent productivity case study"},{"id":"s0478","stat":"90% of vertical AI use cases remain stuck in pilot mode; fewer than 10% ever reach production","source":"McKinsey Seizing the Agentic AI Advantage","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Pilot purgatory at scale","url":"https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage","access":"open"},{"id":"s0479","stat":"Fewer than 30% of companies report their CEO directly sponsoring the AI agenda","source":"McKinsey Seizing the Agentic AI Advantage","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"CEO sponsorship gap as root cause of scaling failure","url":"https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage","access":"open"},{"id":"s0480","stat":"Call centre with AI assistance but no redesign: 5-10% resolution time reduction; with full reinvention: 60-90% reduction","source":"McKinsey Seizing the Agentic AI Advantage","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"The 6-12x multiplier from workflow redesign vs tool adoption","url":"https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage","access":"open"}
1,{"id":"s0481","stat":"AI skills wage premium jumped from 25% to 56% in a single year","source":"PwC Fearless Future","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Rapid acceleration of AI skills premium; analysis of ~1B job postings","url":"https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf","access":"open"},{"id":"s0482","stat":"Skills required in most AI-exposed jobs changing 66% faster than least-exposed (was 25% faster prior year - 2.5x acceleration)","source":"PwC Fearless Future","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Skills disruption accelerating; core skills now lasting 12-18 months not 4-6 years","url":"https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf","access":"open"},{"id":"s0483","stat":"Most AI-exposed industries productivity growth: 9.0% (2022) â 15.0% (2023) â 27.0% (2024)","source":"PwC Fearless Future","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Post-ChatGPT acceleration compounding year on year","url":"https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf","access":"open"},{"id":"s0484","stat":"In every country analysed, more women than men work in AI-exposed roles","source":"PwC Fearless Future","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Gender exposure to AI disruption higher for women","url":"https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf","access":"open"},{"id":"s0485","stat":"Hospitality agentic AI case study: brand standards review times reduced by up to 94%","source":"PwC Fearless Future","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Extreme end of workflow automation potential","url":"https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf","access":"open"},{"id":"s0486","stat":"Only 3% of LPPM professionals see GenAI as a competitive threat today; 38% expect it to be their chief rival within 5 years","source":"LPPM Survey 2024 (4th Annual, Blickstein/LVN)","date":"Aug 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=69 Am Law 200 firms; threat perception trajectory","url":"https://mailchi.mp/2d3abb2266e9/2sqdhw6ct8","access":"gated"},{"id":"s0487","stat":"More than half of survey participants added the legal operations function within the past five years","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"83 companies surveyed","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0488","stat":"Only 7% of respondents introduced the legal operations role in 2024","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Of 83 companies surveyed","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0489","stat":"Nearly 40% of legal departments have between two and five full-time employees dedicated to legal operations","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0490","stat":"22% of legal departments have up to 20 people in the legal operations function","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0491","stat":"Average of 13 years of legal operations leadership among respondents","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0492","stat":"90% of respondents do not split their time between legal operations and other responsibilities","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0493","stat":"More than 60% of legal operations professionals report directly to the general counsel","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0494","stat":"One-third of respondents expected their team to grow, compared to only 18% in the previous year\'s report","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Year-on-year c
1omparison","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0495","stat":"46% of respondents are involved in enterprise-wide strategic initiatives frequently or very frequently","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0496","stat":"44% are frequently involved in cross-functional objectives, and an additional 18% stated very frequently","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0497","stat":"42% of respondents stated generative AI will be one of their top three challenges in managing legal department functions in the coming year","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0498","stat":"52% of survey respondents stated that their AI strategy is either not sophisticated or they do not have an AI strategy at all","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0499","stat":"45% are developing a technology strategy, while 11% do not have and are not working on one","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0500","stat":"Pre-execution contract management tools rated 6 or higher (out of 10) by 54% of respondents","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0501","stat":"Post-execution contract management tools rated 6 or higher (out of 10) by 61% of respondents","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0502","stat":"34% plan to add or replace their contract management technology in the coming year","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0503","stat":"Only 38% of respondents said their CLM was integrated with other enterprise systems","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Shows significant room for improvement beyond repository use","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0504","stat":"51% stated their metrics are being used effectively in the legal department; 49% stated no","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0505","stat":"54% stated their organisation\'s business units are not using the insight gleaned from legal metrics effectively","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0506","stat":"72% believe GenAI will become an essential part of the legal profession","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=64 for this question","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0507","stat":"50.8% of LDO professionals are primarily responsible for GenAI tools, with another 33.3% having some influence","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=64","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0508","stat":"Only 6.3% have no plans to use GenAI at all","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=63","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0509","stat":"81.3% reported that the number one priority with their GenAI strategy is efficie
1ncy/performance","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=64; cost savings ranked second at 42.2%","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0510","stat":"42.2% said their GenAI strategy is driven by cost savings","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=64","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0511","stat":"37.5% of this year\'s respondents said their law firms are innovative - a record, up from 25.5% last year","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Year-on-year increase","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0512","stat":"43% agreed that law firms are leveraging technology to deliver legal services more effectively and cost-efficiently, compared to only 32% last year","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Year-on-year comparison","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0513","stat":"More than three out of four (78%) are encouraging their law firms to use GenAI, compared with 63% last year","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Year-on-year comparison; N=58","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0514","stat":"Number of organisations forbidding their firms from using GenAI halved from 10% to 5% year-on-year","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"N=58","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0515","stat":"28% discouraged outside counsel from using GenAI last year; now only 17% still discouraging it","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Year-on-year comparison","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0516","stat":"96% agree - more than half strongly agree - that they would prefer law firms approach them with new service delivery models","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"N=64","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0517","stat":"Nearly three-quarters believe the value of legal services provided by law firms will go up if GenAI/LLM is integrated into the service delivery model","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=64","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0518","stat":"About a third of respondents don\'t believe GenAI will actually lower law firm spend","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Jevons Paradox cited as possible explanation","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0519","stat":"86.7% agreed GenAI will enable them to bring more work in-house, up significantly from 66.7% last year","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Year-on-year c
1omparison; N=64","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0520","stat":"More than half of respondents are using GenAI for legal research, document analysis, document review, and document summarisation","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=64","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0521","stat":"More than 40% are using GenAI for eDiscovery, document drafting, and contract clause analysis","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=64","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0522","stat":"37% of respondents say their teams frequently use AI for pre-execution contract management, up from just 19% last year","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Year-on-year comparison","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0523","stat":"Post-execution AI adoption climbed to 35% from 9% year-on-year","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Year-on-year comparison","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0524","stat":"60% of respondents reported being frequently or very frequently involved in cross-functional objectives","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0525","stat":"Nearly 40% of respondents measure CLM programme success based on commercial outcomes like revenue and cost savings","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0526","stat":"Another 40% measure efficiency metrics like headcount saved and speed of business","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0527","stat":"77.4% of legal departments are committed to creating and managing durable post-negotiation contract management processes","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0528","stat":"85.2% of legal departments are focused on using standardised terms and clauses","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0529","stat":"Nine out of 10 respondents experienced law firm rate increases in the last 12 months","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=55","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0530","stat":"41.8% saw a rate increase of more than 5%; 49.1% saw an increase of less than 5%","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=55","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0531","stat":"One in five respondents expect rates to remain flat in the next 12 months; 43.6% believe increases will be less than 5%","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=55","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0532","stat":"29.7% cited security and privacy concerns as the biggest barrier to integrating GenAI","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"legal","context":"N=64","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0533","stat":"Only 15.6% say trust in the reliability of AI-generated content is a concern, down significantly from prior year","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"legal","context":"N=64","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0534","stat":"80% of respondents believe the hierarchical staffing model at traditional law firms impedes efficie
1ncy and increases costs","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0535","stat":"Nearly two out of three legal operations professionals are concerned that their law firms are over-lawyering on their behalf","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0536","stat":"92% of respondents say they are responsible for legal department technology solutions yet 30% expect push-back for funding new initiatives","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0537","stat":"Three out of four LDO professionals are actively trying to cut costs by bringing more work in-house; 86% would like to if they had the bandwidth","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0538","stat":"37% of respondents have increased their spend on ALSPs; only 5.5% have seen a decrease","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=54","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0539","stat":"40% expect to spend more on ALSPs in the next 12 months","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=52","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0540","stat":"29% of respondents indicated that finance departments are becoming more involved in contract language and operations","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0541","stat":"In 10 contracts in a typical enterprise-sized organisation gets completely lost","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Referenced via Agiloft commentary","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0542","stat":"15 unique metrics are tracked by at least 25% of respondents to measure department performance","source":"Blickstein Group 17th Annual LDO Survey","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=83","url":"https://www.blicksteingroup.com/law-department-operations-survey","access":"gated"},{"id":"s0543","stat":"81% of LPPM survey respondents have taken the survey before","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Lends consistency to year-on-year comparisons","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0544","stat":"Half of respondents reported annual firm revenue of more than $750 million with 751 or more attorneys; another 15% reported $500M-$750M","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Am Law 200 firms; N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0545","stat":"Nearly half of respondents (48%) had C-suite titles","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0546","stat":"59% sit in the finance/accounting department; 16% in practice management; 13% in legal operations","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0547","stat":"41% of LPPM respondents\' largest clients either discourage or outright forbid use of GenAI (30% discourage, 11% forbid)","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"N=69; 58% encourage","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0548","stat":"90% of LPPM respondents agreed that for work augmented with GenAI, firms will increase their use of AFAs/modified billing","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Strongly agree 37.1%, Agree 53.2%","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0549","stat":"61% of LPPM respondents agreed that their clients really want them to be innovative in 2024; 9.7% strongly agreed","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Up from 63.5% agree-only in 2023 (no strongly agree option)","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0550","stat":"71% of LPPM respondents agreed or strongly agreed (29% disagree) that their clients want them to be innovative in 2024","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Vs 61% in 2023 - 10pp improvement","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0551","stat":"Only 25% of LDO respondents agreed their law firms are innovative (2023 LDO survey) - down from 41% in the prior year","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Client-side view; demonstrates disconnect","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0552","stat":"3.3% of LPPM respondents view GenAI as the chief rival to sourcing and maintaining business now","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0553","stat":"37.7% of LPPM respondents expect GenAI to be their chief rival to maintaining business in five years","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Five-year outlook; N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0554","stat":"In-house law departments are the chief rival cited by 26.2% now; only 14.8% see them as the chief rival in five years","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0555","stat":"59.7% of LPPM respondents agree and 11.3% strongly agree that most law departments will be using AI substantially in the next three years","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"LPPM view; LDO view is 85.1% agree","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0556","stat":"64.5% agree and 9.7% strongly agree that most law firms will be using GenAI substantially in the next three years","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"LPPM view; LDO view is 85.1% agree","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0557","stat":"LDO respondents show 85.1% agree both that law departments AND law firms will be using AI substantially in three years","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Comparison of LDO vs LPPM respondents","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0558","stat":"22.6% of LPPM respondents strongly agree and 61.3% agree they are leveraging technology to deliver legal services more efficiently","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Firm self
1-assessment; clients disagree (68% of LDO say firms are not)","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0559","stat":"68% of LDO respondents disagreed that law firms are leveraging technology to deliver legal services more effectively (2023) - up from 51% in 2022","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Client-side view; growing client dissatisfaction","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0560","stat":"More than 90% of LPPM respondents agree their role is to shepherd change regardless of title","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0561","stat":"Nearly three out of four LPPM respondents agree their role is primarily change management","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0562","stat":"More than 90% of LPPM respondents said the recent emergence of more sophisticated AI capabilities has increased demand for their innovation team\'s time and focus at least somewhat","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0563","stat":"47% saw innovation team demand increase somewhat; 44% increase substantially following new AI capabilities","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0564","stat":"Top challenge for LPPM function: increasing client pushback on rates (59.4% cite as top three) - 89% in prior year","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0565","stat":"53.1% cite meeting client demands for value while maintaining profitability as top-three challenge","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0566","stat":"46.9% cite increasing partner pushback to raising rates as a top-three challenge","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0567","stat":"Fewer than one in three law firm respondents agree that their largest clients share the metrics on which they evaluate the firm","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Strongly agree 1.6%, Agree 27.4%","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0568","stat":"Only around 50% of largest clients schedule regular status meetings to discuss the overall relationship","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0569","stat":"87.1% of LPPM respondents agree that most clients don\'t know how to effectively evaluate or structure AFAs","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Up from 77.8% in 2023","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0570","stat":"84% of firms take client payment behaviour into account when determining whether to work with a client","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0571","stat":"87% of firms factor how quickly clients pay into their negotiations","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0572","stat":"Peer firm rate benchmarking data is the top factor in setting standard rack rates, used by 89% of LPPM respondents","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0573","stat":"Market trends/demand dynamics cited by 72% as a top-three rate-setting factor","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0574","stat":"Half of firms now get at least 16% or more of their revenue from AFAs","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0575","stat":"25.8% strongly agree and 58.1% agree that measuring the value of legal services is more art than science","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0576","stat":"Number of clients supported (60.7%) and YOY revenue increase of average billing attorney rate increase (55.7%) are the top pricing performance metrics","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0577","stat":"Number of clients/matters supported by LPM (55.7%) and number of matters with active budget monitoring (54.1%) are top LPM metrics","source":"Blickstein Group / Legal Value Network 4th Annual LPPM Survey","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=69","url":"https://blicksteingroup.com/publications/","access":"gated"},{"id":"s0578","stat":"Four out of five lawyers (82%) are currently using or have plans to use generative AI","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"803 respondents, UK and Ireland","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0579","stat":"AI users in the UK legal sector jumped from 11% in July 2023 to 41% in September 2024","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Almost quadrupled in just over a year","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0580","stat":"Lawyers planning to use AI also jumped from 28% to 41% over the same period","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"July 2023 to September 2024","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0581","stat":"Number of lawyers with no plans to adopt AI dropped from 61% to 15%","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"July 2023 to September 2024","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0582","stat":"71% of lawyers cited delivering work faster as the top benefit of AI","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"N=803; followed by improved client service (54%) and competitive advantage (53%)","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0583","stat":"54% cited improved client service as a key benefit of AI","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0584","stat":"53% cited competitive advantage as a key benefit of AI","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"}
1,{"id":"s0585","stat":"2/3 of organisations in the legal sector have made a change to implement generative AI","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0586","stat":"60% of the legal industry have made at least one internal change to implement generative AI","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0587","stat":"The most common change was offering an AI-powered product to staff, which rose from 15% in January 2024 to 36% by September 2024","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Year-within-year comparison","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0588","stat":"Developing AI use policies grew from 11% to 24% within 2024","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"January to September 2024","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0589","stat":"Providing AI-related staff training grew from 11% to 18% within 2024","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"January to September 2024","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0590","stat":"Large law firms are most likely to have made a change to drive AI forward, at 78%","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0591","stat":"Corporate in-house legal teams making changes to drive AI jumped from 47% to 74% within 2024","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"January to September 2024","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0592","stat":"Bar adoption of AI rose from 8% to 32% throughout 2024","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"January to September 2024","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0593","stat":"41% of lawyers said keeping pace with new technology is one of the top three challenges facing their firm in the next 12 months","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Second biggest challenge after legal talent","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0594","stat":"71% of in-house teams expect their external counsel to use generative AI","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"client","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0595","stat":"39% of private practice lawyers now expect to adjust their billing practices due to AI, up from only 18% in January 2024","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"legal","context":"More than doubled within 2024","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0596","stat":"Only 17% of those in private practice think AI will end the billable hour model","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"legal","context":"40% believe hourly billing will remain; 42% uncertain","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0597","stat":"40% of private practice lawyers believe the billable hour model will remain in place despite AI","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0598","stat":"Three-quarters (76%) of UK legal professionals are concerned about inaccurate or fabricated information from public-access generative AI platforms","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"critical","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0599","stat":"72% said they would feel more confident using a GenAI tool grounded in legal content with verifiable citations, up from 65% in January 2024","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Within-year c
1omparison","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0600","stat":"50% of lawyers are concerned about over-reliance on AI","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"critical","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0601","stat":"49% cite leaking confidential information as a concern","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"critical","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0602","stat":"43% cite accidental bias as a concern","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"critical","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0603","stat":"77% of in-house counsel expect law firms to inform them when they\'re using generative AI","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"client","sourceType":"vendor","scope":"legal","context":"N=803","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0604","stat":"Only 59% of firms believe their clients would want to know when they use GenAI - an 18pp gap","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"client","sourceType":"vendor","scope":"legal","context":"Expectation mismatch between clients and firms","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0605","stat":"Nearly half of the 1,200+ staff at Charles Russell Speechlys are using AI monthly","source":"LexisNexis","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Qualitative reference from CRS Director of Innovation","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"open"},{"id":"s0606","stat":"97% of all firms surveyed posted UK fee income growth in FY2024, including 100% of the Top 50","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Top 100 UK law firms surveyed","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0607","stat":"Just over half of Top 50 firms posted double-digit fee income growth in FY2024","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Against average inflation of approximately 5%","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0608","stat":"81% of the Top 100 reported increases in profit in FY2024, compared to 56% last year","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Year-on-year comparison","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0609","stat":"94% of Top 25 firms recorded fee income growth; 44% achieved double-digit growth","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Top 25 UK-headquartered global firms","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0610","stat":"Top 10 firms increased global revenues by 8.0%; Top 11-25 by 9.0%","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Global fee income","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0611","stat":"Top 10 fee income growth of 65% rate-driven and 35% volume-driven","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Top 11-25 split was roughly 50%/50%","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0612","stat":"Global profit margin for Top 10 firms improved 0.1pp to 39.6%; Top 11-25 reduced 0.2pp to 35.2%","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Global figures","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0613","stat":"UK offices: Top 10 actual growth 11.6% vs expected 8.7%; Top 11-25: 9.8% vs 7.9%; Top 26-50: 11.7% vs 10.7%; Top 51-100: 12.5% vs 9.4%","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"All exceeded expectations set a year ago","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0614","stat":"UK profit growth exceeded fee income growth for Top 50 firms in FY2024","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Top 10: 13.1% profit vs 11.6% fee income; Top 11-25: 13.6% vs 9.8%","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0615","stat":"Average rate per hour in the UK increased between 3.7% and 6.9% across bandings compared to prior year","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Only Top 51-100 pushed rate beyond average UK inflation of 5.2% (at 6.9%)","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"}
1,{"id":"s0616","stat":"Rate per hour growth over five years (2019-2024): Top 10 up 39.9%; Top 11-25 up 14.0%; Top 26-50 up 22.8%; Top 51-100 up 23.5%","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Significant divergence between Top 10 and rest","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0617","stat":"Top 10 net profit margin (UK): 41.2%, up 0.5pp; Top 11-25: 27.7%, down 0.2pp; Top 26-50: 25.2%, flat; Top 51-100: 23.5%, down 0.2pp","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"UK offices","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0618","stat":"Top 11-25 fixed share equity partners increased by 40.7% year-on-year (to average 76)","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Significant structural shift; Top 10 flat at 41","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0619","stat":"Top 10 total headcount up 2.6%; fee-earner headcount up 1.3%; business support staff up 4.2%","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"FY2024 vs FY2023","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0620","stat":"95% of Top 100 firms use chargeable hours performance to determine bonus payments","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Followed by firmwide performance (70%), BD (67%), behaviours/values (63%)","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0621","stat":"Top 10 staff turnover fell 7.6% in FY2024 following sustained rises in 2023","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Top 11-25 up 0.7%; Top 26-50 down 2.1%; Top 51-100 down 1.2%","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0622","stat":"Female representation at full equity partner level in Top 10 firms increased 4.8pp to 29.8%","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"2.8pp on a like-for-like basis","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0623","stat":"Minority ethnic representation at full equity partner level: Top 10 grew from 8.8% to 9.0%","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Top 26-50: 4.5% to 5.5%; Top 51-100: 6.3% to 7.4%","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0624","stat":"Over 85% of firms monitor social mobility, disability and sexual orientation as diversity dimensions","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Just under 40% also monitor neurodiversity","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0625","stat":"90% of Top 100 firms are either extremely or somewhat concerned about cyber risk (up from 85% in 2023)","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Top concern across all firm bandings","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0626","stat":"Top 10 firms increased cyber security spend by 21% year-on-year; Top 11-25 by 43%","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Average Top 100 cyber spend £7.41m (up from £6.14m, +20.7%)","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0627","stat":"Average cyber security spend across Top 100 firms grew 20.7% to £7.41m","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Up from £6.14m prior year","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0628","stat":"Approximately 55% of Top 100 firms are extremely or somewhat concerned about macroeconomic volatility and geopolitical instability","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Down from 87% in 2023","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0629","stat":"Just over 50% of Top 100 firms are extremely or somewhat concerned that speed of technological change will stop them meeting future ambitions","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Down from 63% in 2023","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0630","stat":"39% view clients reducing demand through automation of legal work as either extremely or somewhat concerning","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"New concern option added to 2024 survey","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0631","stat":"A significant majority (80%) of Top 100 firms believe at least 6% of existing chargeable work could be automated","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"A third believe at least 16% could be automated","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0632","stat":"A third of Top 100 firms believe at least 16% of existing chargeable work could be automated","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Would represent unprecedented scale of disruption","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0633","stat":"Just under half of Top 100 firms expect GenAI to generate savings in chargeable hours of at least 10%","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=Top 100","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0634","stat":"83% of Top 10 firms believe they will be able to use increased productivity gains from GenAI to do more work for the same clients","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Positive framing of GenAI impact","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0635","stat":"Over 50% of Top 11-100 firms expect GenAI adoption to have no impact on work volume, but expect it to reduce pricing","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Negative framing; approximately 20% believe demand will reduce as clients adopt AI","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0636","stat":"Almost 90% of Top 100 firms have now implemented or trialled GenAI tools, compared to only 55% in 2023","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Year-on-year c
1omparison","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0637","stat":"Microsoft Copilot is the most widely implemented tool: 30% of Top 100 have implemented it; 45% are trialling it","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"75% implemented or trialling","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0638","stat":"63% of Top 100 are either trialling or considering Harvey, CaseText, Spellbook; only 9% have implemented","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Legal-specific GenAI tools","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0639","stat":"23% of Top 100 firms have developed a proprietary GenAI tool using their own data","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Vast majority among Top 50","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0640","stat":"Only 19% of Top 100 firms have reached the point where they are realising some productivity gains from GenAI","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"67% still trialling; only 1 firm has monetised","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0641","stat":"67% of Top 100 are still trialling GenAI applications and are yet to achieve productivity gains","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"N=Top 100","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0642","stat":"IT spend per user grew in all bandings except Top 26-50; Top 10 up 11.4% to £16.6k; Top 51-100 up 11.0% to £10.1k","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Top 26-50 consistent at £9.8k per user","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0643","stat":"53% of Top 100 firms plan IT transformation exercises in the next 1-2 years; 47% Finance","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Most active functions for transformation","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0644","stat":"60% of Top 100 firms plan CRM-focused projects in the next 1-2 years; 100% of Top 10","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"PMS (49%) and HR (42%) also targeted","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0645","stat":"Approximately half of Top 100 firms that had implemented CRM were unsatisfied with the business outcomes","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Adoption and change management cited as key ga
1ps","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0646","stat":"About one third of Top 100 firms hold at least 50% of their data on-premises","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Cloud modernisation is top IT strategic priority","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0647","stat":"Planned data strategy spend over next three years: weighted average 1.6% of revenue; ranges from £1m (Top 51-100) to £23.6m (Top 10)","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Likely biggest area of new spend over coming period","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0648","stat":"Average lock up days (Top 10): 153 days in 2024 (up 1 from 2023); Top 11-25: down 8 to 137; Top 26-50: up 5 to 147; Top 51-100: up 5 to 145","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Little progress on working capital improvement","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0649","stat":"Top 10 has the largest gap between year-end and average lock up at 30 days; average lock up nearly 25% greater than year-end","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Shows reliance on year-end billing heroics","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0650","stat":"77% of the Top 100 include improved working capital as one of their top two future plans to fund basis period reform","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Reform is a live cash pressure for UK law firms","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0651","stat":"77% of Top 100 GHG emissions targets in place; 72% gender representation targets; 61% race and ethnicity","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Up from 64%, 70%, 55% respectively last year","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0652","stat":"In the Top 50, 49% have a well-developed ESG policy embedded in their activities; 44% have a largely formulated but not fully implemented policy","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Compares to 28% and 28% for Top 51-100","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0653","stat":"60% of Top 100 expect travel policies to have significant influence on ESG over next five years; 49% supplier selection; 42% recruitment","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"N=Top 100","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0654","stat":"79% of Top 100 are actively pursuing improvements in client and account management planning; 18% considering","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=Top 100","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0655","stat":"Lateral hires are the number one inorganic growth option - 79% actively pursuing, 19% considering","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"N=Top 100","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0656","stat":"11% of firms are considering minority investment from private capital investors, up from 9% last year","source":"PwC Law Firms\' Survey 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Slow growth in appetite for external investment","url":"https://www.pwc.co.uk/industries/legal-professional-business-support-services/law-firms-survey/2024.html","access":"open"},{"id":"s0657","stat":"Survey garnered responses from 493 professionals across the legal sector","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"market","sourceType":"industry","scope":"legal","context":"Primarily North America and UK; law firms 52%, vendors/suppliers 20%, consulting 11%","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0658","stat":"AI initiatives engaged 85% of respondents in 2024, up significantly from 70% in 2023","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"15 percentage point year-on-year increase - largest shift in any category","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0659","stat":"AI-related work (NLP, LLM, GenAI) is set to engage 91% of respondents in 2025","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"8.3% increase from 2024 planned work","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0660","stat":"Process improvement was the second most common focus area in 2024, at 55% (slightly down from 58% in 2023)","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Stable alongside AI growth","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0661","stat":"Selling KM and Innovation internally grew from 43% to 51%, becoming the third most common activity","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Reflects growing need to drive adoption internally","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0662","stat":"Document assembly/automation rose from 39% to 44% in 2024","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Possibly reflects AI integration","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0663","stat":"Data analytics remains stable at 43% in 2024","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0664","stat":"Forms and precedents/knowledge bank work at 42%, up from 40%","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0665","stat":"Collaboration systems at 36%, down from 39%","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Slight decline","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0666","stat":"Client-facing KM at 30%, down from 33%","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0667","stat":"Data analytics is the largest planned growth area for 2025 - up 10.2 percentage points to 53%","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Largest year-on-year increase for any category in planned work","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0668","stat":"Client-facing KM and APIs show notable increases of 9.6% for 2025","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Reflects growing emphasis on external service delivery","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0669","stat":"Process improvement planned at a steady 54% for 2025","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Stable foundational activity","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0670","stat":"Selling KM and fostering internal innovation planned at 52% for 2025, up 1.0%","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0671","stat":"Collaboration systems and document assembly/automation both decreasing by 3.6% for 2025","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Possible maturation or absorption into broader AI initiatives","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0672","stat":"Artificial intelligence leads discussion interest at 84% for SKILLS 2025","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Followed by data analytics and process improvement (both 43%)","url":"https://skills.law/the-surveys","access":"open"}
1,{"id":"s0673","stat":"Forms and Precedents/Knowledge Bank shows a -19% differential between planned work and discussion interest","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Most significant gap - essential but unexciting","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0674","stat":"Innovation portfolio management and Expert systems/low code are the only areas where discussion interest exceeds planned work (+1% each)","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"All other categories show more planned work than discussion interest","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0675","stat":"Client-facing KM shows 35% discussion interest with only a -3% gap to planned work","source":"SKILLS.law 2025 Survey","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Close alignment between interest and intention","url":"https://skills.law/the-surveys","access":"open"},{"id":"s0676","stat":"Legal AI software market valued at $548.44 million, projected to grow 17% over next five years to $2.6 billion","source":"LexCheck (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"vendor","scope":"legal","context":"Current market size and growth forecast for legal AI software"},{"id":"s0677","stat":"Global legal AI software market projected to reach $4.83 billion by 2033 at a CAGR of 20.3% from 2024-2033","source":"Business Research Company (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Longer-term market growth projection"},{"id":"s0678","stat":"Global legal AI market expected to grow at CAGR of 17.8% from 2023-2030, reaching USD 3.89 billion by 2030","source":"Grand View Research (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Alternative market sizing from Grand View Research"},{"id":"s0679","stat":"At least 35% of law firms are now using AI for legal tasks, with an additional 42% planning to adopt AI within the next year","source":"LexisNexis (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Snapshot of AI adoption rate in law firms"},{"id":"s0680","stat":"The number of lawyers using generative AI doubled in six months between July 2023 and January 2024","source":"LexisNexis (cited in Genie AI)","date":"Feb 2024","sortKey":20240200,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Rapid acceleration of GenAI adoption among lawyers","url":"https://www.lexisnexis.co.uk/research-and-reports/lawyers-cross-into-the-new-era-of-generative-ai.html","access":"gated"},{"id":"s0681","stat":"73% of lawyers (in-house and law firms) surveyed in November 2023 expected to integrate generative AI into their legal work in 2024","source":"Wolters Kluwer (cited in Genie AI)","date":"Nov 2023","sortKey":20231100,"precision":"month","year":2023,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Forward-looking adoption intent"},{"id":"s0682","stat":"26% of legal professionals using AI monthly, rising to 32% in large firms","source":"LexisNexis (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Usage frequency breakdown by firm size"},{"id":"s0683","stat":"50% of law firms using some form of AI","source":"Oxford University study (cited in Genie AI)","date":"Dec 2021","sortKey":20211200,"precision":"month","year":2021,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Historical baseline for comparison"},{"id":"s0684","stat":"89% of law firms using or planning to use AI","
1source":"CBRE (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Near-universal intent to use AI in law firms"},{"id":"s0685","stat":"AI software can reduce total document review hours by up to 40%","source":"Deloitte (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"technical","sourceType":"analyst","scope":"legal","context":"Document review time reduction potential","url":"https://www.genieai.co/en-us/blog/legal-ai-automated-due-diligence-reporting","access":"open"},{"id":"s0686","stat":"Global e-discovery market expected to reach $17.1 billion by 2027","source":"MarketsandMarkets (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"E-discovery market growth projection","url":"https://www.marketsandmarkets.com/Market-Reports/e-discovery-market-11881863.html","access":"gated"},{"id":"s0687","stat":"AI-driven legal research platforms can reduce research time by up to 70% vs traditional methods","source":"WiFi Talents (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Legal research time savings from AI"},{"id":"s0688","stat":"76% of respondents already use contract management software","source":"Thomson Reuters (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Contract management software penetration"},{"id":"s0689","stat":"60% of financial institutions are already using AI for risk management","source":"UK Finance (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"AI adoption in financial services risk management"},{"id":"s0690","stat":"27% of legal professionals who had not tried AI technologies cited ethics concerns as their reason","source":"EY (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Barriers to AI adoption - ethics as a barrier"},{"id":"s0691","stat":"AI could automate up to 23% of lawyers\' work hours by 2030","source":"McKinsey Global Institute (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Automation impact on legal work hours"},{"id":"s0692","stat":"45% of Chief Legal Officers will invest in new technology solutions to boost operational efficiency in 2024 - highest since 2021","source":"ACC (cited in Genie AI)","date":"Jan 2024","sortKey":20240100,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"CLO tech investment intentions"},{"id":"s0693","stat":"AI-assisted document review reduced review time by 80% compared to manual methods","source":"Deloitte (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"analyst","scope":"legal","context":"DD document review time saving"},{"id":"s0694","stat":"67% of legal departments using AI for compliance reported improved efficiency and decision-making","source":"Thomson Reuters (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Compliance AI effectiveness"},{"id":"s0695","stat":"AI can identify cybersecurity threats with 90% accuracy","source":"EY (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"analyst","scope":"legal","context":"AI accuracy in cyber threat detection"},{"id":"s0696","stat":"AI-powered risk assessment tools reduced costs in risk, legal and compliance departments by 33%, and increased revenue by 62%","source":"McKinsey & Company (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"analyst","scope":"legal","context":"AI impact on legal/compliance department finances"}
1,{"id":"s0697","stat":"Deloitte: 25% efficiency saving using GenAI vs traditional AI-enabled due diligence; 75% efficiency saving vs manual review","source":"Deloitte (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"technical","sourceType":"analyst","scope":"legal","context":"GenAI vs baseline in DD document review","url":"https://www.genieai.co/en-us/blog/legal-ai-automated-due-diligence-reporting","access":"open"},{"id":"s0698","stat":"54% of M&A professionals using AI for due diligence reported a reduction in transaction closing times","source":"Bain & Co (cited in Genie AI)","date":"Apr 2024","sortKey":20240400,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"AI impact on M&A transaction timelines"},{"id":"s0699","stat":"78% of M&A AI users cited reduced manual effort as most compelling benefit","source":"Bain M&A Practitioners\' 2024 Outlook Survey (cited in Genie AI)","date":"Apr 2024","sortKey":20240400,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Most cited GenAI benefit for M&A","url":"https://www.bain.com/insights/generative-ai-m-and-a-report-2024/","access":"open"},{"id":"s0700","stat":"Almost two-thirds of legal professionals use GenAI for legal writing/communications","source":"Bloomberg 2023 State of Practice (cited in Genie AI)","date":"Dec 2023","sortKey":20231200,"precision":"month","year":2023,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Legal writing as leading GenAI use case"},{"id":"s0701","stat":"56% of lawyers see drafting documents as the top potential use case for generative AI","source":"LexisNexis International Legal GenAI Report (cited in Genie AI)","date":"Aug 2023","sortKey":20230800,"precision":"month","year":2023,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Top potential use case, not measured current use","url":"https://www.lexisnexis.com/community/pressroom/b/news/posts/lexisnexis-international-legal-generative-ai-survey-shows-nearly-half-of-the-legal-profession-believe-generative-ai-will-transform-the-practice-of-law","access":"open"},{"id":"s0702","stat":"World Economic Forum estimates lawyers will gain back 46% of their time through use of generative AI","source":"WEF (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Time recaptured by lawyers using GenAI"},{"id":"s0703","stat":"77% of businesses using NLP expect to increase their investment in NLP","source":"EY (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"analyst","scope":"legal","context":"NLP investment intentions"},{"id":"s0704","stat":"78% of law firms now use cloud-based storage solutions for client data","source":"ILTA (cited in Genie AI)","date":"Sep 2023","sortKey":20230900,"precision":"month","year":2023,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Cloud storage adoption in law firms"},{"id":"s0705","stat":"73% of state bar associations have issued guidelines on ethical use of AI in legal practice","source":"American Bar Association (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"Bar association AI ethics guidelines"},{"id":"s0706","stat":"AI-powered contract review tools achieved 94% accuracy vs 85% for human lawyers","source":"LawGeex (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"technical","sourceType":"vendor","scope":"legal","context":"AI vs human accuracy comparison in contract review"},{"id":"s0707","stat":"AI contract review can complete tasks in 26 seconds that would take human lawyers an average of 92 minutes","source":"SuperLegal (cited in Genie AI)","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Speed comparison for AI vs human contract review"},{"id":"s0708","stat":"76% of legal departments are already using contract management software solutions","source":"Thomson Reuters LDO Index (cited in Genie AI)","date":"Sep 2023","sortKey":20230900,"precision":"month","year":2023,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Contract management software penetration in legal depts"},{"id":"s0709","stat":"AI-powered contract management systems can accelerate contract review cycles by as much as 40%","source":"Icertis (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Contract review cycle time reduction"},{"id":"s0710","stat":"36% of law departments predicted an increase in regulatory spend","source":"Thomson Reuters (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Growing focus on compliance spend"},{"id":"s0711","stat":"68% of legal industry respondents cited data security concerns as a barrier to AI adoption; 62% cited privacy and confidentiality","source":"(cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Top barriers to AI adoption in legal"},{"id":"s0712","stat":"Over 60% of large law firms were exploring new generative AI systems as of late 2023; a third of small firms","source":"SRA (cited in Genie AI)","date":"Nov 2023","sortKey":20231100,"precision":"month","year":2023,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"BigLaw vs small firm GenAI exploration rates","url":"https://www.sra.org.uk/sra/research-publications/artificial-intelligence-legal-market/","access":"open"},{"id":"s0713","stat":"About one-quarter of law firm and corporate legal department respondents had used public AI tools like ChatGPT","source":"Thomson Reuters (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Actual usage rate vs exploration rate","url":"https://www.thomsonreuters.com/en/reports/2024-generative-ai-in-professional-services","access":"open"},{"id":"s0714","stat":"74% cited potential for inaccurate responses as barrier to GenAI adoption","source":"EY (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Top barrier to GenAI adoption - inaccuracy risk"},{"id":"s0715","stat":"57% cited hallucinations as biggest hurdle to GenAI adoption (LexisNexis survey)","source":"LexisNexis (cited in Genie AI)","date":"January 2024","sortKey":20240100,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Hallucinations as primary adoption barrier"},{"id":"s0716","stat":"55% cited security concerns and 55% said current technology not trustworthy enough","source":"LexisNexis (cited in Genie AI)","date":"January 2024","sortKey":20240100,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Security and trust barriers to GenAI"},{"id":"s0717","stat":"48% of non-legal desk workers would be uncomfortable admitting to their manager they used AI for workplace tasks","source":"Slack Fall 2024 Workforce Index (cited in Genie AI)","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Shadow AI / AI usage discomfort","url":"https://slack.com/blog/news/the-fall-2024-workforce-index-shows-executives-and-employees-investing-in-ai-but-uncertainty-holding-back-adoption","access":"open"},{"id":"s0718","stat":"40% of solo lawyers and 35% of small-firm lawyers planning to adopt AI within next six months vs 24% of larger firm lawyers","source":"Cli
1o Survey (cited in Genie AI)","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"AI adoption velocity difference by firm size"},{"id":"s0719","stat":"A&O Shearman claim 30% productivity gain from AI in document reviews","source":"A&O Shearman (cited in Genie AI)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"vendor","scope":"legal","context":"BigLaw AI productivity outcome"},{"id":"s0720","stat":"Clifford Chance partnership with Orbital Witness saved 350 hours on one project","source":"Clifford Chance (cited in Genie AI)","date":"Apr 2024","sortKey":20240400,"precision":"month","year":2024,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Real-world BigLaw AI time saving example","url":"https://www.cliffordchance.com/insights/resources/blogs/innovation-insights/2024/04/ai-innovation-drives-welcome-change-for-how-clifford-chance-is-approaching-real-estate-diligence-for-complex-cases.html","access":"open"},{"id":"s0721","stat":"Gartner predicts half of procurement contract management will be AI-enabled by 2027","source":"Gartner (cited in Genie AI)","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"CLM AI adoption forecast"},{"id":"s0722","stat":"38% of lawyers excited about AI\'s ability to reduce inaccuracies due to human error","source":"Thomson Reuters Future of Professionals Report (cited in Genie AI)","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Positive lawyer sentiment towards AI accuracy"},{"id":"s0723","stat":"AI can halve NDA review times for multinational companies","source":"LexCheck (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"technical","sourceType":"vendor","scope":"legal","context":"NDA-specific time saving from AI"},{"id":"s0724","stat":"Harvey raised $80M Series B, EvenUp $50M Series B, Darrow $35M Series B, Robin AI $22M+$25M, Spellbook $27M Series A, Genie AI $17.8M Series A","source":"Stanford GenAI Legal Landscape 2024 (cited in Genie AI)","date":"Jan 2023-Feb 2024","sortKey":20240200,"precision":"month","year":2024,"theme":"market","sourceType":"academic","scope":"legal","context":"Key legaltech funding rounds","url":"https://law.stanford.edu/publications/generative-ai-legal-landscape-2024-2/","access":"open"},{"id":"s0725","stat":"Thomson Reuters acquired Casetext for $650M; Clio raised $900M","source":"(cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"vendor","scope":"legal","context":"Major legaltech M&A and fundraising"},{"id":"s0726","stat":"KPMG plans to invest \\"tens of millions of dollars\\" to help corporate legal departments implement GenAI","source":"Bloomberg Law (cited in Genie AI)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"vendor","scope":"legal","context":"Non-law firm competition entering legal AI market"},{"id":"s0727","stat":"Initial literature review identified 22 Legal AI use cases; expanded to 34 use cases after 18 semi-structured interviews","source":"TUM research (primary)","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"legal","context":"Scope of identified Legal AI use cases","url":"https://portal.fis.tum.de/en/publications/legal-ai-use-case-radar-2024-report","access":"gated"},{"id":"s0728","stat":"18 semi-structured interviews conducted with legal professionals in German-speaking region for Round 1","source":"TUM research (primary)","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Scale of primary interview research","url":"https://portal.fis.tum.de/en/publications/legal-ai-use-case-radar-2024-report","access":"gated"},{"id":"s0729","stat":"23 Experience Reports produced from Round 2 interviews with Legal AI solution providers and users","source":"TUM research (primary)","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Real-world case studies captured","url":"https://portal.fis.tum.de/en/publications/legal-ai-use-case-radar-2024-report","access":"gated"},{"id":"s0730","stat":"Document Classification rated highest relevance (4.90) and academic interest (4.80) among legal AI use cases in category survey","source":"TUM research (primary)","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"legal","context":"Highest-rated Legal AI use case by relevance and interest","url":"https://portal.fis.tum.de/en/publications/legal-ai-use-case-radar-2024-report","access":"gated"},{"id":"s0731","stat":"Content Lifecycle Management rated highest relevance (4.99) among all document management use cases","source":"TUM research (primary)","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"legal","context":"Content lifecycle management as the most legally relevant use case","url":"https://portal.fis.tum.de/en/publications/legal-ai-use-case-radar-2024-report","access":"gated"},{"id":"s0732","stat":"German law firm with 350+ employees has legal tech department of only 6 employees driving digital transformation","source":"TUM ER1 interview (primary)","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Scale of internal legal tech resource"},{"id":"s0733","stat":"Large German accounting firm with 15,000+ employees using ContractPodAI\'s Leah for document summarisation in daily production","source":"TUM ER7 interview (primary)","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Enterprise-scale CLM AI deployment in production"},{"id":"s0734","stat":"German law firm M&A department (100+ employees) testing Henchman for clause import and contract drafting","source":"TUM ER5 interview (primary)","date":"April-May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"German M&A practice AI adoption case"},{"id":"s0735","stat":"LegalTechTalk 2024 brought together 300+ speakers and 2,500 attendees from 65 countries across 90+ panels, fireside chats, and keynotes","source":"LegalTechTalk (primary)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"media","scope":"legal","context":"Scale and reach of the conference"},{"id":"s0736","stat":"Approximately 71% of nearly 3,000 lawyers surveyed had anxiety - a 5% increase from 2022","source":"ALM and Law.com Compass mental health survey (cited in LegalTechTalk)","date":"May 2023","sortKey":20230500,"precision":"month","year":2023,"theme":"workforce","sourceType":"media","scope":"legal","context":"Lawyer mental health - anxiety prevalence","url":"https://www.law.com/americanlawyer/2023/05/18/mental-health-by-the-numbers-an-infographic-mapping-the-legal-industrys-wellbeing/","access":"paywalled"},{"id":"s0737","stat":"24% rise in number of people contacting LawCare, the legal mental health charity, in 2023","source":"LawCare (cited in LegalTechTalk)","date":"Oct 2023","sortKey":20231000,"precision":"month","year":2023,"theme":"workforce","sourceType":"media","scope":"legal","context":"Legal mental health crisis - helpline demand","url":"https://www.lawcare.org.uk/latest-news/world-mental-health-day-10-october-2023/","access":"open"},{"id":"s0738","stat":"11 million people across the UK have an unmet legal need annually","source":"(cited in LegalTechTalk)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"client","sourceType":"media","scope":"legal","context":"Scale of unmet legal need in the UK"},{"id":"s0739","stat":"Around 83% of people in England and Wales do not get access to legal services they need","source":"(cited in LegalTechTalk)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"client","sourceType":"media","scope":"legal","context":"Access to justice gap in England and Wales"},{"id":"s0740","stat":"Only around 25% of businesses who have a legal need actually get access to a legal service","source":"(cited in LegalTechTalk)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"client","sourceType":"media","scope":"legal","context":"Access to justice gap for businesses"},{"id":"s0741","stat":"Software M&A forms about 75% of all M&A transactions","source":"(cited in LegalTechTalk)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"media","scope":"legal","context":"Tech sector dominance in M&A volumes"},{"id":"s0742","stat":"SRA data (2023): 53% of lawyers were women; 19% BAME lawyers; 6% disabled lawyers","source":"SRA (cited in LegalTechTalk)","date":"Dec 2023","sortKey":20231200,"precision":"month","year":2023,"theme":"workforce","sourceType":"government","scope":"legal","context":"Legal profession diversity snapshot","url":"https://www.sra.org.uk/news/news/press/2023-press-releases/2023-firm-diversity-data-pay-gaps/","access":"open"},{"id":"s0743","stat":"LGBTQ+ lawyers (lesbian, gay, bisexual, or other) grew from 3% in 2015 to 4.4% in 2023","source":"SRA (cited in LegalTechTalk)","date":"Dec 2023","sortKey":20231200,"precision":"month","year":2023,"theme":"workforce","sourceType":"government","scope":"legal","context":"LGBTQ+ representation trend in law","url":"https://www.sra.org.uk/news/news/press/2023-press-releases/2023-firm-diversity-data-pay-gaps/","access":"open"},{"id":"s0744","stat":"Women now at 53% of lawyers but only 32% of full-equity partners vs 62% of solicitors","source":"SRA (cited in LegalTechTalk)","date":"Dec 2023","sortKey":20231200,"precision":"month","year":2023,"theme":"workforce","sourceType":"government","scope":"legal","context":"Gender seniority gap in law firms","url":"https://www.sra.org.uk/news/news/press/2023-press-releases/2023-firm-diversity-data-pay-gaps/","access":"open"},{"id":"s0745","stat":"Lawyers from professional socio-economic background decreased from 60% in 2019 to 57% in 2023; lawyers from independent/fee-paying schools still 65% at largest firms","source":"SRA (cited in LegalTechTalk)","date":"Dec 2023","sortKey":20231200,"precision":"month","year":2023,"theme":"workforce","sourceType":"government","scope":"legal","context":"Social mobility trends in legal profession","url":"https://www.sra.org.uk/news/news/press/2023-press-releases/2023-firm-diversity-data-pay-gaps/","access":"open"},{"id":"s0746","stat":"Research shows winning more work from existing clients is 6-7 times cheaper than winning new clients","source":"(cited in LegalTechTalk)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"client","sourceType":"media","scope":"legal","context":"Client retention economics"}
1,{"id":"s0747","stat":"About half of code written at Leya (legaltech startup) is now written by AI","source":"Leya (cited in LegalTechTalk)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"vendor","scope":"legal","context":"AI code generation in legal tech product development"},{"id":"s0748","stat":"At least 90% of lawyers will have a generative AI workflow in their day-to-day within a few years","source":"Olivia Vaughan, RobinAI (cited in LegalTechTalk)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Forward projection on GenAI lawyer workflow adoption"},{"id":"s0749","stat":"Draftable gained 700+ customers globally in less than two years, including Allens, Brodies, and Jones Walker","source":"Draftable (cited in LegalTech Diaries)","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Document comparison tool adoption velocity"},{"id":"s0750","stat":"Draftable runs over 4 million document comparisons a year on its general (non-legal) platform","source":"Draftable (cited in LegalTech Diaries)","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Scale of document comparison activity"},{"id":"s0751","stat":"Freshfields developed an AI-powered Dynamic Due Diligence (D3) tool that automates document review in a one-click process","source":"Freshfields (cited in LegalTech Diaries)","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"BigLaw proprietary AI DD tool - real example","url":"https://www.freshfields.com/en/our-thinking/news/news-search/2025/04/freshfields-and-google-cloud-accelerate-legal-innovation-through-strategic-ai-collaboration2","access":"open"},{"id":"s0752","stat":"Xapien\'s partnership with Pinsent Masons automates 90% of the client onboarding process","source":"Xapien/Pinsent Masons (cited in LegalTech Diaries)","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"AI onboarding automation percentage - AML due diligence"},{"id":"s0753","stat":"NRG Lawyers has worked with over 400 NRG students and graduates","source":"NRG Lawyers (cited in LegalTech Diaries)","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Scale of non-Russell Group legal diversity initiative"},{"id":"s0754","stat":"84% of legal professionals globally are already using generative AI","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Across all legal professional categories surveyed across 36 countries","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0755","stat":"90% of legal professionals believe generative AI significantly supports their professional activities","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Both generic and specific AI tools","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0756","stat":"Average generative AI experience rating is 7.1/10 in 2024, up from 6.8 in 2023","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Scale of 1-10; weekly users rate experience 7.3/10","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0757","stat":"53% of legal professionals use generative AI daily (specific tools); 47% use generic tools daily","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Law firms show higher daily usage than global average","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0758","stat":"78% of legal professionals use generative AI tools at least weekly","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Calculated from daily + weekly figures","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0759","stat":"ChatGPT is the most used tool at 47%, followed by Microsoft Copilot (27%) and Claude (10%)","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Claude leads on user satisfaction ratings","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0760","stat":"40% of legal professionals use specific (purpose-built) legal AI tools","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Vs 60% relying primarily on generic tools","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0761","stat":"Top use cases: document summarisation (19%), document analysis/data extraction (17%), drafting (17%), translation (17%)","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Shift from 2023 where curiosity (29%) and non-legal text production (25%) were primary motivations","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0762","stat":"Only 38% of organisations have an AI policy in place","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"regulatory","sourceType":"industry","scope":"legal","context":"Of those with policies: 19% permit internal use for any purpose, 28% restrict external AI use to vetted tools, 2% strictly prohibit GenAI","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"}
1,{"id":"s0763","stat":"Budget constraints and data/integration issues each cited by 18% as the biggest adoption challenge; regulatory and compliance by 16%","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"market","sourceType":"industry","scope":"legal","context":"Followed by lack of expertise (15%), hallucinations/performance (15%), resistance to change (13%)","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0764","stat":"27% of organisations report no budget allocated for generative AI; only 8% have a dedicated GenAI budget","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"market","sourceType":"industry","scope":"legal","context":"71% plan to invest, primarily up to â¬50,000; only 9% expect to exceed â¬100,000","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0765","stat":"86% of legal professionals find generative AI \\"very\\" or \\"somewhat helpful\\" in 2024","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Up from 77% in 2023 who believed AI would significantly help; only 5% view it as unhelpful","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0766","stat":"Only 20% of legal professionals fully trust AI to generate legal documents; 50% remain uncertain; 30% say no","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Down from 38% saying \\"no\\" in 2023, showing gradual increase in willingness","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0767","stat":"47% of organisations uncertain about ROI from generative AI; only 6% expect less than 10% ROI; 16% foresee 20-30%","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"market","sourceType":"industry","scope":"legal","context":"32% aim for 10-40% ROI","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0768","stat":"Only 6% of legal professionals agree AI might fully replace them; 40% disagree","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"workforce","sourceType":"industry","scope":"legal","context":"24% believe AI threatens certain parts of their job; 44% agree AI will significantly help their work","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0769","stat":"72% of legal professionals anticipate new roles will be created to manage AI; 81% believe AI will aid in role transitions","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"workforce","sourceType":"industry","scope":"legal","context":"But 75% express concern about job displacement","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0770","stat":"Economic disruption and job displacement concern rose sharply to 70% rating it high importance, up from 30% in 2023","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Privacy and data security rated high importance by 50% (down from 57% in 2023)","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0771","stat":"76% of respondents expect at least a significant impact from GenAI on the legal profession already or in the next 3-5 years","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"market","sourceType":"industry","scope":"legal","context":"59-62% say \\"it is already happening\\"","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0772","stat":"78% believe AI should be included in law school curricula","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Only 4% consider formal education the most beneficial learning method","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0773","stat":"Law firm respondents show 93% GenAI usage (vs 84% global average); 92% find AI supportive of their work","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Law firms: 49% daily use, 36% weekly use - both above global averages","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0774","stat":"Legal departments show 90% GenAI usage (above global 84%); CLM usage notably higher at 23% vs global 14%","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Legal departments have 44% with AI policies (vs 38% globally)","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0775","stat":"97% of ALSPs have tried generative AI; 83% use it at least weekly; 73% have used it for over a year","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"ALSPs integrate GenAI faster and more efficiently than law firms and in-house departments","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0776","stat":"For specific legal AI tools: legal research (23%), document management (21%), CLM (14%) are top use cases","source":"ELTA Global Survey 2024","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Among those using specific (purpose-built) legal AI tools","url":"https://elta.org/lana-download/generative-ai-global-report-2024/","access":"gated"},{"id":"s0777","stat":"Survey covered 203 law firms representing nearly 100,000 employees including approximately 50,000 lawyers globally","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"Respondents were managing partners or Chief Innovation/Information/Knowledge Officers","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0778","stat":"Average proportion of mundane/repetitive tasks across surveyed law firms is 38.2%","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"industry","scope":"legal","context":"In litigation firms, repetitive tasks replaceable by AI rise to 52%","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0779","stat":"79.7% of law firm respondents identify legal research as a task that could be streamlined by AI","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Followed by document review (72.1%) and contract drafting (55.8%)","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0780","stat":"Over 40% of respondents see AI potential in due diligence, case management, compliance monitoring, contract analysis and proofreading","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Key task categories identified for AI streamlining","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0781","stat":"51% of surveyed law firms have already implemented AI-based tools; 12% are currently exploring options","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Only 37% are neither implementing AI nor exploring it","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0782","stat":"68.5% of law firms declared one of the two highest levels of openness to AI adoption (4 or 5 out of 5)","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Median openness score was 4/5; most frequently declared value was 5/5","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0783","stat":"24.5% average percentage of lawyers dealing with AI in their work, with median of 15.2%","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Significant variation by firm size and geography","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0784","stat":"79.6% of law firms cite data security and privacy as a key factor in selecting AI tools","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"Followed by cost (75.5%) and ease of use (61.7%)","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0785","stat":"68.8% of lawyers gain AI knowledge through networking and contact with other professionals","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"workforce","sourceType":"industry","scope":"legal","context":"AI events cited by 59.3%; academic conferences by only ~30%","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0786","stat":"Document automation (39%) and legal research (34%) are the top areas for AI tool implementation in law firms","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"industry","scope":"legal","context":"E-discovery use is almost exclusively by US-based firms","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0787","stat":"59.2% of law firms using AI tools have been doing so for less than a year (as of 2023 survey)","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Only 5.8% have used AI tools for more than five years","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0788","stat":"58% of law firms piloted AI solutions before full adoption; 50% are monitoring implementation very closely","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"industry","scope":"legal","context":"49.5% require AI technology to be pre-approved by firm leadership","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0789","stat":"43% of law firms have an AI innovation department, with a median size of 2-3 people","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"13% employ dedicated AI specialists such as legal engineers or prompt engineers","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"}
1,{"id":"s0790","stat":"80.2% of lawyers cite legal issues (liability, regulation) as a top challenge in the AI era","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"regulatory","sourceType":"industry","scope":"legal","context":"Followed by privacy and security (66.8%) and AI accuracy and reliability (63.9%)","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0791","stat":"87% of law firms agree implementing AI will streamline repetitive tasks and improve productivity","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"industry","scope":"legal","context":"56.5% see AI as an opportunity to reduce human error","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0792","stat":"53.2% expect AI tools to become a core part of lawyers\' workflows within 3 years; same proportion expect paralegal tasks to be automated","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"workforce","sourceType":"industry","scope":"legal","context":"38.8% foresee high demand for AI-specialist lawyers","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0793","stat":"61.7% of law firms say developing internal AI policies and guidelines is a key resource need","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"regulatory","sourceType":"industry","scope":"legal","context":"52.2% also need additional education and training for both legal and non-lawyer teams","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0794","stat":"McKinsey estimates generative AI could impact legal industry at nearly $100 billion annually - around 15-20% of legal services spending","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"McKinsey also estimates $180-260 billion additional value potential in the legal industry","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0795","stat":"Above the Law 2023 survey of 275 lawyers: 71% say GenAI could replace document review lawyers within the next decade; 68% say same for law librarians","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"workforce","sourceType":"industry","scope":"legal","context":"41% say paralegals could be replaced in 10 years; 80%+ agreed GenAI would create \\"transformative efficiencies\\" in research and routine tasks","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0796","stat":"Generative AI funding reached $12 billion in the first five months of 2023; VC investment in GenAI grew at 74% CAGR from 2017 to 2022","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"Total funding figure for Jan-May 2023 only","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0797","stat":"AmLaw 100 reported 9% decline in demand for transactional work in Q4 2022; law firm profitability (PPEP) declined in 2022 for first time since 2009","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"market","sourceType":"industry","scope":"legal","context":"Backgroun
1d context on law firm market pressures driving AI adoption","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0798","stat":"Law firms experienced a sharp decline in average monthly hours worked per employee to 119 in 2022 - a record low","source":"LLI First Global Report on AI in Legal Practice","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"workforce","sourceType":"industry","scope":"legal","context":"More than 40% of young lawyers reluctant to return to offices in 2022; up to 37% reported professional burnout","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s0799","stat":"Approximately half of all professionals in legal, tax/accounting/audit, corporate risk and government use GenAI in some fashion","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Survey of 1,702 respondents; US (42%), Canada (15%), UK (12%), Australia (12%)","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0800","stat":"41% of professionals personally use publicly available tools such as ChatGPT; 17% use industry-specific GenAI tools","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Only 9% use GenAI within proprietary systems","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0801","stat":"Organisations actively using GenAI nearly doubled year-over-year: 22% in 2025 vs 12% in 2024","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Additional 50% are creating plans to use or deciding whether to","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0802","stat":"Proportion of organisations with no plans to use GenAI dropped from 45% in 2024 to 28% in 2025","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Decline in scepticism across all professional services sectors","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0803","stat":"Only 13% say GenAI is currently central to their organisation\'s workflow; but 29% believe it will be within the next year","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"95% of all respondents believe GenAI will be central to their workflow within the next five years","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0804","stat":"55% of professionals categorise their sentiment towards GenAI as excited or hopeful - an 11-percentage-point increase vs 2024","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Hesitant/concerned/fearful sentiment fell 12 percentage points year-over-year","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0805","stat":"89% of professionals see a use case for GenAI within their own work - up from 81% in 2024","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Consistent across legal (89%), tax (89%), corporate legal (90%), corporate tax (92%)","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0806","stat":"62% of professionals believe GenAI actively should be used for work in their industry","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Law firms at 59%, tax firms at 71%, corporate tax at 75%, government at 48%","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0807","stat":"Tax firm respondents supporting GenAI use rose from 52% in 2024 to 71% in 2025 - a 19-percentage-point jump","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Largest year-over-year improvement of any professional sector","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0808","stat":"More than half (59%) of organisations are not measuring ROI for GenAI tools; only 20% are","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Of those measuring ROI: internal
1cost savings (79%), employee usage (64%), employee satisfaction (51%), client satisfaction (38%)","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0809","stat":"52% of respondents believe their organisations have no GenAI policies; 64% have received no GenAI training","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Number without policies dropped 14 percentage points in one year, but majority still lack them","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0810","stat":"More than 60% of organisations have already begun a wide-scale GenAI rollout or plan to do so within the next year","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"In 2024, only 12% had conducted wide-scale rollout; in 2025 this rose to 26%","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0811","stat":"Document review and legal research remain the top two GenAI use cases for legal professionals; six separate use cases cited by 50%+ of active legal users","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Legal: document review (74%), legal research (73%), document summarisation (72%), brief/memo drafting (59%), contract drafting (51%), correspondence drafting (50%)","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0812","stat":"For active GenAI users: over 40% use it at least daily; under three-quarters use it at least weekly","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Law firm respondents use GenAI least frequently: 69% weekly vs higher in corporate and tax settings","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0813","stat":"57% of corporate legal clients want their outside firms to be using GenAI - up from 51% in 2024","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"71% of law firm clients and 59% of tax firm clients do not know whether their outside firms are using GenAI","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0814","stat":"36% of respondents who use GenAI at law firms are already doing so on a wide-scale basis - above the overall 26% average","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Law firms are ahead of corporate tax, risk and government in wide-scale rollout","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0815","stat":"Only 2% of organisations make GenAI knowledge a requirement in current hiring practices; 24% treat it as \\"nice to have\\"","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"33% of law firm respondents do not know whether their firm considers GenAI skills in hiring","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0816","stat":"40% of law firm respondents expect use of alternative fee arrangements to increase as a result of GenAI","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"46% believe GenAI will not have an impact on their rates - a 7-percentage-point drop from 2024","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0817","stat":"36% of legal professionals see unauthorised practice of law as a major GenAI threat; 15% see major threat to jobs","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Fewer than 20% of legal professionals see GenAI as a threat to their jobs or firm revenue","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0818","stat":"GenAI training provision has increased 12 percentage points in one year, but still under one-third of organisations train on GenAI","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Of those receiving training, one-third do so less than annually or only as needed","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0819","stat":"83% of corporate legal clients and 82% of corporate tax clients have not instructed outside firms either to use or not use GenAI","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Client-side governance of firm GenAI use is almost entirely absent","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0820","stat":"70% of tax firm respondents report no GenAI policy at their organisation","source":"Thomson Reuters Institute","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Legal has a higher policy adoption rate but still a majority without formal GenAI governance","url":"https://www.thomsonreuters.com/en/reports/2025-generative-ai-in-professional-services-report","access":"open"},{"id":"s0821","stat":"37% of SRA-regulated firms are currently using legal technology; 24% are not using but planning to","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Based on survey of 891 SRA-authorised firms in April 2021","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0822","stat":"Over half (55%) of surveyed firms improved or increased use of existing technology in the last 12 months; 35% introduced new technology","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"adoption","sourceType":"academic","scope":"legal","context":"COVID-19 pandemic was a major driver of technology adoption in the prior 12 months","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0823","stat":"COVID-19 pandemic led 51% of firms to increase technology use to manage or process work; 48% to interact with clients; 26% to attract new clients","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"adoption","sourceType":"academic","scope":"legal","context":"90% of firms experiencing any of these changes said they were likely to be permanent","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0824","stat":"Top 5 legal technologies in use: videoconferencing with clients (86%), cloud storage (66%), practice management software (62%), legal research software (50%), e-verification/e-signature (37%)","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Chatbots/virtual assistants only used by 6.2%; contract review software by 7.3%; AI data analytics by 5.1%","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0825","stat":"Technologies with planned use exceeding current use: online portals for matter status (21% planning vs 15% using), interactive legal document websites (20% planning vs 10% using), chatbots (14% planning vs 6% using)","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Strongest planned growth in interactive, client-facing technology","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0826","stat":"Top purposes for legal technology adoption: improve service quality (72%
1), improve workflow efficiency (71%), allow flexible working (44%), reduce cost (33%), increase security and compliance (22%)","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Future users rate \\"increasing demand for services\\" higher than cost reduction","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0827","stat":"Lack of financial capital to invest is the top barrier to technology adoption, cited by 58% of technology adopters and 51% of non-adopters","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"market","sourceType":"academic","scope":"legal","context":"Lack of staff expertise cited by 50%; regulatory uncertainty by 45%","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0828","stat":"44% of legal technology adopters and 35% of non-adopters cite regulatory barriers","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Top regulatory barriers: client confidentiality/data protection, professional indemnity insurance requirements, and uncertainty about whether regulations allow new approaches","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0829","stat":"ABSs are more than twice as likely to have introduced new services as non-ABSs (31% vs 13%) and more likely to have introduced new technology (53% vs 33%)","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"market","sourceType":"academic","scope":"legal","context":"Majority of ABS firms are in PeopleLaw (wills, probate, conveyancing)","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0830","stat":"B2C (PeopleLaw) share of total law firm revenue in England and Wales declined from approximately 50% in 1997-98 to 20% in 2016-17","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"market","sourceType":"academic","scope":"legal","context":"Indicates sharp structural divergence between BigLaw and consumer legal services over two decades","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0831","stat":"Only 1-2% of legal job postings in the UK require lawtech skills for lawyers (solicitors, barristers, judges); 5-15% for non-lawyer legal sector roles (2014-2020)","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"workforce","sourceType":"academic","scope":"legal","context":"UK solicitors with lawtech skills earn 13% more on average; paralegals with lawtech skills earn 25% more","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0832","stat":"UK lawtech startup total funding was $853 million vs $5.98 billion in the US; average per-venture funding $9M (UK) vs $28M (US)","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"market","sourceType":"academic","scope":"legal","context":"104 UK lawtech startups vs 256 in the US identified via Crunchbase and Legal Technology Hub","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0833","stat":"Only 18% of UK lawtech ventures (and US) have at least one female founder; within UK BigLaw startups only 8% have a female founder","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"market","sourceType":"academic","scope":"legal","context":"63% of UK PeopleLaw startups have at least one female founder - sharp gender contrast between segments","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0834","stat":"24 acquisitions of UK-based lawtech startups since 2012, compared to 77 acquisitions of US-based startups","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"market","sourceType":"academic","scope":"legal","context":"Growth of new lawtech ventures accelerated until 2017, after which growth rate declined - possibly due to sector consolidation","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0835","stat":"PeopleLaw startups received only 3.2% of total lawtech funding; BigLaw ventures receive 75 UK funding rounds vs 23 for PeopleLaw","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"market","sourceType":"academic","scope":"legal","context":"Almost all venture funding is concentrated in BigLaw; PeopleLaw startups underserved by capital markets","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0836","stat":"66.3% of SRA-regulated firms consider themselves at least \\"somewhat innovative\\" with respect to new or improved services; 74.6% for delivery of services; 48.8% for marketing","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Self-perception of innovativeness; larger firms and ABSs score higher across all three types","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0837","stat":"Two-thirds (67%) of firms introducing new services also introduced new technology; one-third did not","source":"Oxford/SRA Technology and Innovation in Legal Services","date":"Jul 2021","sortKey":20210700,"precision":"month","year":2021,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Innovation and technology adoption are linked but not identical","url":"https://www.sra.org.uk/sra/research-publications/technology-innovation-in-legal-services/","access":"open"},{"id":"s0838","stat":"60-75% of all innovation efforts in top-50 UK law firms are directed at \\"transforming the core\\" - improving internal
1capabilities and workflows rather than creating new offerings","source":"LexisNexis / Judge Business School - Innovation in the Legal Sector","date":"Aug 2020","sortKey":20200800,"precision":"month","year":2020,"theme":"market","sourceType":"vendor","scope":"legal","context":"Estimate from senior partners in the research sample; reflects the inward focus of law firm innovation"},{"id":"s0839","stat":"Two-thirds of failed technology-driven innovation initiatives in law firms can be traced to underestimating the challenge of getting lawyers to adopt new technology","source":"LexisNexis / Judge Business School - Innovation in the Legal Sector","date":"Aug 2020","sortKey":20200800,"precision":"month","year":2020,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Technology and systems experts adopt an execution-oriented view rather than a change management view"},{"id":"s0840","stat":"Highly innovative law firms show higher profit margins over time compared to moderately innovative firms and those maintaining the status quo","source":"LexisNexis / Judge Business School - Innovation in the Legal Sector","date":"Aug 2020","sortKey":20200800,"precision":"month","year":2020,"theme":"market","sourceType":"vendor","scope":"legal","context":"Qualitative/initial analysis; extracting value requires moving on multiple innovation fronts simultaneously, not just one initiative"},{"id":"s0841","stat":"75% of global knowledge workers use AI at work","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"n=31,000 across 31 countries. Survey of full-time knowledge workers globally","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0842","stat":"46% of AI users at work started using it less than 6 months ago","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Indicates generative AI at work nearly doubled in six months","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0843","stat":"78% of AI users are bringing their own AI tools to work (BYOAI)","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"BYOAI is even more common at SMEs (80%); spans all generations","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0844","stat":"Gen Z BYOAI rate: 85%; Millennials: 78%; Gen X: 76%; Boomers+: 73%","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Shows BYOAI is not just a younger worker phenomenon","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0845","stat":"90% of AI users say it saves them time; 85% say it helps them focus on most important work; 84% say it makes them more creative; 83% say they enjoy work more","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Self-reported benefits from AI users","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0846","stat":"79% of leaders agree their company needs to adopt AI to stay competitive","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"But 59% worry about quantifying the productivity gains","url":"https://www.microsoft.com/en-us
1/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0847","stat":"60% of leaders worry their organisation lacks a plan and vision to implement AI","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Pressure to show immediate ROI is creating inertia","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0848","stat":"68% of people say they struggle with the pace and volume of work; 46% feel burned out","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Overload is driving employees to AI without waiting for guidance","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0849","stat":"85% of emails are read in under 15 seconds; knowledge workers read ~4 emails for every 1 they send","source":"Microsoft 365 productivity signals (rolling 28-day period, March 2024)","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Email overload persists; in M365 apps users spend 60% of time on comms","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0850","stat":"Top 5% of Teams users summarised 8 hours of meetings using Copilot in March 2024 (the equivalent of a full workday)","source":"Microsoft Copilot usage data, March 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Copilot meeting summarisation among heaviest commercial users"},{"id":"s0851","stat":"52% of people who use AI at work are reluctant to admit using it for their most important tasks","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Employees hide AI use amid concerns about perception","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0852","stat":"53% of people who use AI at work worry that using it on important tasks makes them look replaceable","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Fear of replacement drives concealment of AI use","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0853","stat":"55% of leaders are concerned about having enough talent to fill roles in the year ahead; rises to 60%+ for cybersecurity, engineering, and creative design","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Hidden talent shortage - leaders worried despite fear of job loss","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0854","stat":"66% of leaders say they would not hire someone without AI skills","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"AI aptitude now a baseline hiring requirement for many leaders","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0855","stat":"71% of leaders say they would hire a less experienced candidate with AI skills over a more experienced one without","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"AI skills overtaking experience as hiring criterion","url":"https://www.microsoft.com/en-us
1/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0856","stat":"77% of leaders say early-in-career talent will be given greater responsibilities because they can delegate more to AI","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"AI is reshaping career progression expectations","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0857","stat":"45% of US executives are not currently investing in AI tools or products for employees","source":"LinkedIn Executive Confidence Index (unpublished)","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Training deficit persists alongside adoption pressure"},{"id":"s0858","stat":"Only 39% of people globally who use AI at work have received AI training from their employer","source":"LinkedIn Workplace Learning Report 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Massive gap between use and formal employer training","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0859","stat":"Only 25% of companies plan to offer training on generative AI this year","source":"LinkedIn Workplace Learning Report 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Most companies are not keeping pace with employee AI adoption","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0860","stat":"76% of people say they need AI skills to remain competitive in the job market","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Professionals are self-motivated to skill up on AI","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0861","stat":"69% say AI can help them get promoted faster; 79% say AI skills will broaden their job opportunities","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"AI skills seen as career accelerator by employees","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0862","stat":"LinkedIn Learning courses designed to build AI aptitude spiked 160% in past six months among non-technical professionals","source":"LinkedIn data","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Non-technical workers leading the AI skilling charge","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0863","stat":"142x increase in LinkedIn members adding AI skills like ChatGPT and Copilot to their profiles","source":"LinkedIn data","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Rapid signalling of AI capability in the labour market","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0864","stat":"Global skills projected to change by 50% by 2030 (from 2016 baseline); generative AI expected to accelerate this change to 68%","source":"LinkedIn Economic Graph","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"AI is a major accelerator of skills disruption","url":"https://www.microsoft.com/en-us
1/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0865","stat":"\\"Head of AI\\" role tripled over past five years and grew by more than 28% in 2023","source":"LinkedIn data","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Emerging leadership function as organisations build AI governance","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0866","stat":"12% of recruiters say they are already creating new roles tied specifically to the use of generative AI","source":"LinkedIn data","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"AI-native roles emerging across industries","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0867","stat":"More than two-thirds (68%) of this year\'s LinkedIn Jobs on the Rise (fastest-growing US roles) didn\'t exist 20 years ago","source":"LinkedIn Jobs on the Rise data","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"AI accelerating creation of entirely new job categories"},{"id":"s0868","stat":"LinkedIn job posts mentioning AI or generative AI have seen 17% greater application growth over past two years","source":"LinkedIn data","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"AI signals in job ads are a recruitment advantage","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0869","stat":"54% of early-in-career and individual contributor employees say access to AI would influence their choice of employer","source":"LinkedIn research","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"AI access is becoming a talent attraction tool"},{"id":"s0870","stat":"22% of recruiters already say they are updating job descriptions to reflect usage of generative AI","source":"LinkedIn data","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"AI reshaping how roles are defined and advertised","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0871","stat":"Within 5 years, 41% of leaders who are \\"extremely familiar\\" with AI expect to redesign business processes from ground up; 42% expect to orchestrate and train AI bot teams; 47% expect ethical AI oversight to be core to their job","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Senior leaders with AI familiarity anticipate deep transformation","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0872","stat":"AI power users save more than 30 minutes per day; are 56% more likely to use AI to catch up on missed meetings; 51% more likely to use it to analyse information","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Power user segment defined by frequency, savings, and impact","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0873","stat":"AI power users are 68% more likely to frequently experiment with different ways of using AI - the #1 predictor of becoming a power user","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Experimentation is the key differentiator for heavy AI users","url":"https://www.microsoft.com/en-us
1/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0874","stat":"AI power users are 61% more likely to hear from their CEO about the importance of using AI; 53% more likely to receive encouragement from leadership","source":"Microsoft/LinkedIn Work Trend Index","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Organisational culture and senior leadership drive power user emergence","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part","access":"open"},{"id":"s0875","stat":"Copilot study (n=3,000 across 60 companies): users read 11% fewer emails; spent 4% less time on email overall; heaviest users 25-45% less time on email","source":"Microsoft randomised control trial, 6-month study","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"First mass-scale observation of AI in natural work environment","url":"https://www.microsoft.com/en-us/research/publication/generative-ai-in-real-world-workplaces/","access":"open"},{"id":"s0876","stat":"Copilot study: users edited 10% more Word/Excel/PowerPoint documents; companies with largest impact saw 20% increase","source":"Microsoft randomised control trial, 6-month study","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Suggests time saved on comms is redirected to creation work","url":"https://www.microsoft.com/en-us/research/publication/generative-ai-in-real-world-workplaces/","access":"open"},{"id":"s0877","stat":"Study panel: 187,489 distinct developers observed weekly from July 2022 to July 2024, generating millions of developer-week observations","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Natural experiment using GitHub Copilot free access eligibility","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0878","stat":"16% of top developers used Copilot at least once during the general access period (July 2022-July 2023)","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Uptake was low partly because GitHub did not actively advertise free access","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0879","stat":"On average, top developers use Copilot for approximately 8 days over the observation period; 3% weekly exposure","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Low average masks strong effects near the eligibility threshold","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0880","stat":"Developers with free Copilot access: coding activities as share of all activity increased by 5.4 percentage points (12.37% relative to baseline)","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Core causal result: AI shifts work composition towards core tasks","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0881","stat":"Project management activities as share of all activity dropped by 10 percentage points (24.93% relative to baseline) for developers with Copilot access","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"AI reduces time on managerial/coordination tasks, not just boosts coding","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0882","stat":"The average developer allocates 44% of engagement towards coding and 37% towards project management","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Baseline task allocation before AI introduction","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0883","stat":"Copilot eligible developers work in repositories with 17 fewer peers relative to a baseline of 22 peers (79.3% reduction in peer group size)","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"AI reduces need for collaborative interaction, enabling more autonomous work","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0884","stat":"Copilot eligible developers engage with an additional 15 new repositories on average relative to ineligible peers","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"AI encourages exploration of new projects and technologies","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0885","stat":"Copilot eligible developers increase cumulative exposure to new programming languages by 21.79% relative to baseline","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"AI broadens the range of technologies developers engage with","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0886","stat":"Copilot access induces developers to experiment with programming languages that command a 1.41% higher salary (an increase of $1,683 above a $119,371 baseline)","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"AI prompts upskilling toward higher-value, better-paid technologies","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0887","stat":"Back-of-the-envelope: extrapolating to all 300,000 top GitHub developers, Copilot could improve aggregate value of labour market potential by ~$468 million per year","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"Net of $120/year Copilot subscription cost ($1,563 net per developer)","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"}
1,{"id":"s0888","stat":"96% of corporate codebases contain some open source code","source":"Synopsys 2023 (cited in paper)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Illustrates the scale of OSS as critical infrastructure"},{"id":"s0889","stat":"AI adoption rates in the study ranging from 61% to 321% depending on measurement definition, all with F-values substantially beyond the threshold of 10","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Strong first-stage results confirming treatment effect validity","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0890","stat":"Peak treatment effects in third quarter of study: coding activity up to 10%, project management reduction up to 27% in absolute terms","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Effects stabilise in year two around 2.5% coding / 8% project management","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0891","stat":"45.51% of Copilot usage happens in weeks where no public OSS activity is observed, implying extensive private repository use","source":"HBS Working Paper 25-021","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Estimated ~50% of top developer activity occurs in private repositories, suggesting published effects are underestimates","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=66593","access":"open"},{"id":"s0892","stat":"AI systems improved from solving 4.4% of coding problems to 71.7% on SWE-Bench from 2023 to 2024","source":"Maslej et al. 2025 AI Index (cited in paper)","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Benchmark for software engineering capability improvement","url":"https://hai.stanford.edu/ai-index/2025-ai-index-report","access":"open"},{"id":"s0893","stat":"AI can match or outperform up to 47% of industry professionals on economically valuable tasks","source":"Patwardhan et al. 2025 (cited in paper)","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Benchmark study of AI performance on professionally relevant tasks"},{"id":"s0894","stat":"LLM adoption at work among US survey respondents above age 18 reached 46% by June/July 2025","source":"Hartley et al. 2025 (cited in paper)","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Near-half of working US adults using LLMs at work"},{"id":"s0895","stat":"Nearly 40% of the US population age 18-64 reported using generative AI by late 2024; 23% using it for work weekly; 9% daily","source":"Bick et al. 2024 (cited in paper)","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Rapid diffusion data from late 2024 US survey","url":"https://www.nber.org/papers/w32966","access":"gated"},{"id":"s0896","stat":"Employment for software developers aged 22-25 declined nearly 20% compared to its peak in late 2022, by September 2025","source":"Brynjolfsson/Chandar/Chen - ADP payroll data (3.5-5 million workers per month)","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"One of the most striking individual occupation findings"},{"id":"s0897","stat":"Workers aged 22-25 experienced a 6% decline in employment in the most AI-exposed occupations from late 2022 to September 2025","source":"ADP payroll data","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Compared to a 6-9% increase for older workers in same occupations"},{"id":"s0898","stat":"For workers aged 22-25, firm-time fixed effects analysis shows a 15 log-point decline in relative employment for the most AI-exposed quintiles compared to least exposed","source":"ADP payroll data","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Poisson event study. Robust to firm-level and industry-level shocks as alternative explanations"},{"id":"s0899","stat":"Employment growth for workers in bottom 3 AI exposure quintiles: 5-13% across all age groups from late 2022 to September 2025","source":"ADP payroll data","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"No differential pattern by age in less-exposed occupations"},{"id":"s0900","stat":"Close to 70% of occupations in the lowest AI exposure quintile saw rising early career employment from Oct 2022-Sep 2025, c
1ompared to less than half in the highest quintile","source":"ADP payroll data","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Broad-based divergence, not confined to a few occupations"},{"id":"s0901","stat":"Dario Amodei (Anthropic CEO) predicted AI could eliminate roughly 50% of all entry-level white-collar jobs within five years","source":"Morris 2025 Fortune (cited in paper)","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"media","scope":"cross-sector","context":"Cited as context for the public debate the paper addresses","url":"https://fortune.com/2025/05/28/anthropic-ceo-warning-ai-job-loss","access":"open"},{"id":"s0902","stat":"Nearly 40% of Americans report using generative AI at home or work - outpacing early diffusion of the personal computer and the internet","source":"Bick et al. cited in paper","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Context for the scale of generative AI adoption"},{"id":"s0903","stat":"Study analysed 200,000 anonymised Copilot conversations sampled representatively from 9 months of US usage in 2024","source":"Microsoft Research (Tomlinson et al.)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Largest real-world usage dataset of this type for occupational analysis"},{"id":"s0904","stat":"Eloundou et al. predict 80% of the US workforce could have at least 10% of their tasks affected by LLMs; 19% could have 50% of their tasks affected","source":"Eloundou et al. 2024 (cited in paper)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Standard prediction used as benchmark comparison for actual usage data","url":"https://www.science.org/doi/10.1126/science.adj0998","access":"paywalled"},{"id":"s0905","stat":"In 40% of Copilot conversations, the user goal and AI action IWAs (work activities) are completely disjoint","source":"Microsoft Research (Tomlinson et al.)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"AI often performs different activities from what the user is trying to accomplish - a key asymmetry"},{"id":"s0906","stat":"Correlation between AI applicability score (actual usage) and Eloundou et al. occupation-level predictions: r=0.73 (r=0.91 at broadest occupation group level)","source":"Microsoft Research (Tomlinson et al.)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"High validation of prior predictions against actual usage data"},{"id":"s0907","stat":"Interpreters and Translators are the #1 most AI-applicable occupation by score (0.49), with 98% of work activities overlapping with frequent Copilot tasks","source":"Microsoft Research (Tomlinson et al.)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Top-ranked occupation by AI applicability scoring"},{"id":"s0908","stat":"Customer Service Representatives (2.86 million employed): applicability score 0.44; Sales Representatives of Services (1.14 million): 0.46","source":"Microsoft Research (Tomlinson et al.)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Large-employment occupations with high AI applicability"},{"id":"s0909","stat":"SOC major groups with highest AI applicability scores: Sales and Related (0.32), Computer and Mathematical (0.30), Office and Administrative Support (0.29)","source":"Microsoft Research (Tomlinson et al.)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Occupations involving information, communication, and knowledge work are most exposed"},{"id":"s0910","stat":"Legal occupations have an AI applicability score of 0.13 - below the middle of the 22 major groups","source":"Microsoft Research (Tomlinson et al.)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Legal ranks 14th of 22 major groups, below management (0.14)"},{"id":"s0911","stat":"All common IWAs have a positive feedback share of 50% or higher - users find Copilot helpful across all work activities measured","source":"Microsoft Research (Tomlinson et al.)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Baseline satisfaction signal: no IWA is net-negative"},{"id":"s0912","stat":"Writing/editing and research tasks have highest positive feedback; data analysis and visual design have lowest","source":"Microsoft Research (Tomlinson et al.)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"AI is better at writing and researching than analysis and creative/visual components"},{"id":"s0913","stat":"Copilot usage distribution differs substantially from Claude: Copilot has considerably less focus on computer and mathematical tasks","source":"Microsoft Research (Tomlinson et al.)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Different AI platforms attract different usage patterns - no single tool captures the full picture"},{"id":"s0914","stat":"Large language models include the correct diagnosis in their differential in 88% of c
1ases, comparable to clinician performance","source":"Levine et al. 2023 (cited in paper)","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Used to illustrate near-substitution level in structured cognitive domains"},{"id":"s0915","stat":"AI achieves 80% precision in visual skin condition identification when combining text and images","source":"Zhou et al. 2024 (cited in paper)","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Further evidence of AI performance at clinician level in specific tasks"},{"id":"s0916","stat":"IAS 38 accounting standards exclude most internally developed AI systems from balance sheets unless they meet strict capitalization criteria","source":"International Accounting Standards Board, IAS 38 (cited)","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"regulatory","sourceType":"academic","scope":"cross-sector","context":"Highlights why AI-driven productivity gains are invisible in financial statements"},{"id":"s0917","stat":"~$400bn in AI capex in 2025 for the big four hyperscalers alone (Alphabet, Amazon, Meta, Microsoft)","source":"Benedict Evans / company reports","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Global telecoms capex for comparison is ~$300bn"},{"id":"s0918","stat":"Big Tech planned 2025 capex almost doubled during 2025 itself - revised estimate ~$350bn then pushed higher","source":"Benedict Evans / company reports","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Capex surge driven by FOMO and competitive pressure among hyperscalers"},{"id":"s0919","stat":"US data centre construction has overtaken office construction as share of total US construction value","source":"Benedict Evans / US Census","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Data after July 2025 delayed by US government shutdown"},{"id":"s0920","stat":"OpenAI announced commitments for 30GW+ of capacity at $1.4tr","source":"Benedict Evans / OpenAI","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Oct 28 2025. OpenAI aspiration: 1GW/week of new construction at $20bn/GW = ~$1tr annually","url":"https://www.ben-evans.com/s/2025-Autumn-AI.pdf","access":"open"},{"id":"s0921","stat":"OpenAI\'s announced capacity aspiration is equivalent to 2/3 of total current global data centre base, every year","source":"Benedict Evans / OpenAI","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Scale of ambition versus existing global infrastructure","url":"https://www.ben-evans.com/s/2025-Autumn-AI.pdf","access":"open"},{"id":"s0922","stat":"Nvidia has $72bn of trailing twelve-month free cash flow","source":"Benedict Evans / Nvidia","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Context for \\"circular revenue\\" dynamic in AI infrastructure ecosystem","url":"https://www.ben-evans.com/s/2025-Autumn-AI.pdf","access":"open"},{"id":"s0923","stat":"ChatGPT reached ~800m weekly active users by late 2025, but reportedly only ~5% are paying","source":"Benedict Evans / OpenAI","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"OpenAI reports WAU not DAU - engagement gap noted as significant","url":"https://www.ben-evans.com/s/2025-Autumn-AI.pdf","access":"open"},{"id":"s0924","stat":"Consumer generative AI chatbot daily use in USA: still well below monthly use as of 2025 (Deloitte surveys, June annually)","source":"Benedict Evans / Deloitte","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Far more people use chatbots occasionally than make them part of daily lives"},{"id":"s0925","stat":"A quarter of CIOs have already launched at least one LLM project in production; 40% don\'t plan anything until at least 2026","source":"Benedict Evans / Morgan Stanley CIO Survey (Sep 2025)","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Deployment is polarised - early movers vs significant laggards","url":"https://www.ben-evans.com/s/2025-Autumn-AI.pdf","access":"open"},{"id":"s0926","stat":"Enterprise workloads in public cloud: ~30% today despite cloud being 10+ years old","source":"Benedict Evans / Goldman Sachs CIO Survey","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Used to contextualise how slowly enterprise tech adoption actually moves","url":"https://www.ben-evans.com/s/2025-Autumn-AI.pdf","access":"open"},{"id":"s0927","stat":"Typical large enterprise in the USA now uses 4-500 apps (SaaS era)","source":"Benedict Evans / Productiv","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Historical context for new platform shifts and tool proliferation","url":"https://www.ben-evans.com/s/2025-Autumn-AI.pdf","access":"open"},{"id":"s0928","stat":"Y Combinator AI startups: from negligible in 2015 to majority of cohort by S 2025","source":"Benedict Evans / Y Combinator","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"AI startups now dominate YC batches - platform shift visible in startup formation"},{"id":"s0929","stat":"Grocery barcodes (UPCs) launched in 1974 enabled US supermarkets to expand from ~10,000 to 50,000+ SKUs","source":"Benedict Evans / FMI","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Analogy: automation alone can be transformative even without AI"},{"id":"s0930","stat":"AI agent use by business function (where GenAI already used): mostly still in experiment/pilot phase, not deployed, as of June 2025","source":"Benedict Evans / McKinsey","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"\\"Agentic is 2025\'s buzzword, but deployment takes longer\\"","url":"https://www.ben-evans.com/s/2025-Autumn-AI.pdf","access":"open"},{"id":"s0931","stat":"Global data centre capacity estimates for 2030: range from ~100GW to 300GW across analyst firms (Omdia, Bloomberg BNEF, Goldman Sachs, Morgan Stanley, Bain, BCG, McKinsey)","source":"Benedict Evans / multiple analyst firms","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Wide variance reflects genuine uncertainty; some \\"bragawatts\\" may be performative","url":"https://www.ben-evans.com/s/2025-Autumn-AI.pdf","access":"open"},{"id":"s0932","stat":"Weekly active users of generative AI tools as
1share of population (June 2025): ChatGPT leads across USA, UK, France, Denmark, Japan; Meta AI and Copilot much lower","source":"Benedict Evans / Reuters Institute","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"adoption","sourceType":"media","scope":"cross-sector","context":"Brand/distribution advantage not model quality - models are near-commodities"},{"id":"s0933","stat":"72% of enterprise decision-makers report using Gen AI at least once a week in 2024 (up from 37% in 2023)","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"n=802 senior decision-makers, US enterprises 1,000+ employees, July 2024. Near-doubling of weekly usage in 12 months","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0934","stat":"Gen AI spending increased by 130% since 2023","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"Compared to IT spend growing at only half the rate of inflation (per IBM IBV cited data)","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0935","stat":"72% say Gen AI budgets will increase in the next year; majority expect growth to slow (+1-10%)","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"Investment intent strong but expectations for pace are tempering","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0936","stat":"55% of respondents currently use Gen AI across business functions on average","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Of those, 58% rated the performance of those use cases as \\"Great\\"","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0937","stat":"Top Gen AI uses in enterprise (2024): Document/proposal writing/editing (64%), Data analysis/analytics (62%), Document/meeting summarisation (59%), Marketing content creation (58%), Personalised marketing (58%), Internal support/help desk (58%), Customer service support (58%)","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Legal contract generation ranks at 41% - one of the lower use cases","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0938","stat":"Legal contract generation used by 41% of enterprise respondents","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Towards lower end of enterprise use cases despite high AI performance scores in this area","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0939","stat":"Gen AI weekly usage by function (2024): Total 72%, HR 35%, IT/BI 75%, Marketing/Sales 66%, Operations 75%, Product Dev/Engineering 75%, Purchasing/Procurement 20% (note: some figures approximate from chart), Management 50%, Finance/Accounting 40%, Legal 78%","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Legal professionals show high weekly usage rate (78%)","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0940","stat":"Smaller companies ($50M-$250M revenue): 80% use Gen AI at least weekly; Mid-sized ($250M-$2B): 78%; Younger individuals (18-34): 80%","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"High usage not limited to large enterprises or young workers","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0941","stat":"46% of companies now have a Chief AI Officer (CAIO) role: Large ($2B+): 41%; Mid-sized ($250M-$2B): 48%;
1 Small ($50M-$250M): 49%","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"CAIO role has proliferated rapidly; smaller companies actually ahead of large enterprises","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0942","stat":"Gen AI strategy kept in-house: 47% rely on multiple existing teams (up from 34% in 2023); only 5% rely primarily on external consultants (down from 7%)","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"Gen AI strategy is being driven internally, not by consultants","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0943","stat":"\\"Amazed\\" and \\"Curious\\" perceptions of Gen AI decreased since 2023; \\"Pleased\\" (29% to 40%) and \\"Excited\\" (25% to 48%) increased; \\"Worry\\" down (19% to 10%) and \\"Skepticism\\" down (23% to 23%)","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Sentiment shift from amazement/curiosity to satisfaction/excitement","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0944","stat":"90% of decision-makers believe Gen AI will enhance employees\' skills in some tasks (up from 80% in 2023, a statistically significant 10pp increase)","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Growing acceptance that AI enhances rather than replaces","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0945","stat":"72% believe Gen AI will replace employees\' skills in some tasks (down slightly from 75% in 2023)","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Replacement concern softening but still majority view","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0946","stat":"Gen AI perceived as \\"high impact\\" by function (2024): IT 58%, Business Intelligence 46%, Customer Service 45%, Marketing 44%, Operations 43%, Product Development 41%; Legal only 28% high impact","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"IT is the only function where a majority see high impact; Legal lowest at 28%","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0947","stat":"About half (51%) of enterprises have few or no restrictions on Gen AI usage at work","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"More restrictions exist in larger organisations; only 15% of $2B+ firms allow unrestricted use","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0948","stat":"Only 15% of enterprises with $2B+ annual revenue allow Gen AI use without any restrictions","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Larger organisations impose more restrictions - additional risk scrutiny","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0949","stat":"9% of enterprises completely ban Gen AI for work purposes","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Prohibition is a minority position but present","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0950","stat":"Gen AI spending planned to increase 2-5 years out: Total increase substantially (>10%): 25%; increase somewhat (1-10%): 57%; remain same: 14%","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"Spending growth continues but majority expect moderate not aggressive increase","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0951","stat":"Knowledge of Gen AI increased +23 percentage points overall since 2023; Marketing/Sales saw largest increase (+49pp)","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Familiarity growing rapidly especially in functions that lagged in 2023","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0952","stat":"Microsoft/Azure seen as future Gen AI leader (3-5
1years) by 50%; Google/GCP by 62% (Google has lost perception share); Amazon/AWS by 36%; OpenAI by 28%; Apple by 21% (down 12pp)","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"Google overtakes Microsoft in future leadership perception; Apple loses significant ground","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0953","stat":"800 enterprise senior decision-makers surveyed (n=802), US only, enterprises with 1,000+ employees, July 2024","source":"AI at Wharton / GBK Collective (n=802)","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"Study methodology: online quantitative tracking survey, 15-minute","url":"https://ai.wharton.upenn.edu/focus-areas/human-technology-interaction/2024-ai-adoption-report/","access":"open"},{"id":"s0954","stat":"5,000 executives surveyed across 24 countries and 25 industries","source":"IBM Institute for Business Value / Oxford Economics","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Survey methodology: December 2023 to March 2024"},{"id":"s0955","stat":"Average AI ROI hit 13% in 2022, boosted to 31% in 2023 driven by early generative AI wins","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"5,000 executives, 24 countries, 25 industries. Early generative AI pilots driving strong but small-scale ROI numbers","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0956","stat":"Operating profit gains directly attributable to AI doubled to nearly 5% from 2022 to 2023","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Executives expect that figure to hit 10% by 2025","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0957","stat":"77% of executives say generative AI is \\"market ready\\" (up from 36% in 2023)","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Dramatic shift in confidence in a single year","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0958","stat":"62% of executives now say generative AI is \\"more reality than hype\\" (up from 33% in 2023)","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Scepticism rapidly declining among senior leaders","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0959","stat":"More than three-quarters (77%+) of executives say they need to adopt generative AI quickly to keep up with competitors","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Competitive pressure driving urgency","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0960","stat":"72% of the highest-performing CEOs say competitive advantage depends on who has the most advanced generative AI","source":"IBM IBV 2024 CEO study","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Cited from separate IBM IBV CEO study (n not specified here)"},{"id":"s0961","stat":"64% of CEOs say succeeding with generative AI will depend more on people\'s adoption than the technology itself","source":"IBM IBV CEO study","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Culture change flagged as critical, not just technology deployment","url":"https://newsroom.ibm.com/2024-05-16-IBM-Study-As-CEOs-Race-Towards-Gen-AI-Adoption,-Questions-Around-Workforce-and-Culture-Persist","access":"open"},{"id":"s0962","stat":"One in three companies pause an AI use case after the pilot phase; two in three do not","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Failure rate in pilots, but majority do proceed to deployment","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0963","stat":"Roughly two-thirds of executives say their organisations are adopting generative AI in customer service (70%), IT (65%), and product development (65%)","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"These are the \\"low-risk\\" starting functions consistent with 2023 findings","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0964","stat":"AI luminaries (most mature AI adopters): at least 60% have implemented generative AI across most functions","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"n=5,000, segmented. AI luminaries contrast with \\"generative AI opportunists\\" who focu
1s on 3 key functions","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0965","stat":"85% of companies that have adopted generative AI in marketing are using it to summarise market intelligence","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Most common marketing GenAI use case","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0966","stat":"80% of companies adopting generative AI in supply chain use it to generate operations documents","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Leading supply chain GenAI use case","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0967","stat":"77% of companies that have adopted generative AI in IT are using it to generate code","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Code generation as primary IT GenAI use case","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0968","stat":"75% of organisations are at least piloting generative AI in five or more functions","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Broad experimentation across functions rather than deep deployment in one area","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0969","stat":"Top challenges for generative AI: Accuracy/bias concerns (45%), Insufficient proprietary data (42%), Inadequate expertise (42%), Inadequate financial justification (42%), Privacy/confidentiality concerns (40%)","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Top five barriers; limited access to technology (40%) and requires too much investment (36%) also significant","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0970","stat":"80% of organisations have a separate part of their risk function dedicated to AI/GenAI risks","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Risk management structures are being built even as pilots run","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0971","stat":"81% conduct regular risk assessments to identify potential security threats introduced by generative AI","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Security assessment is near-universal among surveyed organisations","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0972","stat":"78% maintain robust documentation to enhance explainability of how generative AI models work","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Explainability practices widespread in this executive sample","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0973","stat":"76% establish clear organisational structures, policies and processes for generative AI governance","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Governance frameworks being put in place","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0974","stat":"More than half of executives say generative AI will enable entirely new types of work in the next three years","source":"IBM IBV (n=5,000)","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Beyond efficie
1ncy - belief in genuinely new work categories emerging","url":"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/scale-generative-ai","access":"open"},{"id":"s0975","stat":"IBM and NASA geospatial foundation model: 15% improvement in accuracy vs state-of-the-art deep learning for mapping floods and burn scars, using half as much labelled data","source":"IBM Research","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Trained using 5,000+ GPU hours on IBM Research\'s Vela supercomputer","url":"https://research.ibm.com/blog/geospatial-models-nasa-ai","access":"open"},{"id":"s0976","stat":"Top-ranked Gen AI use case in 2025: Therapy/companionship (up from #2 in 2024) - Reach score: 9/10, Usefulness: 7/10","source":"Marc Zao-Sanders, 2025 Top-100 Gen AI Use Case Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Shift toward personal wellbeing use cases from productivity/technical in 2024","url":"https://learn.filtered.com/hubfs/The%202025%20Top-100%20Gen%20AI%20Use%20Case%20Report.pdf","access":"open"},{"id":"s0977","stat":"#2 use case: Organise my life (new entry, not ranked in 2024) - Reach: 9/10, Usefulness: 8/10","source":"Marc Zao-Sanders, 2025 Top-100 Gen AI Use Case Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"New category reflecting personal organisation as a major use case","url":"https://learn.filtered.com/hubfs/The%202025%20Top-100%20Gen%20AI%20Use%20Case%20Report.pdf","access":"open"},{"id":"s0978","stat":"#3 use case: Find purpose (new entry) - Reach: 8/10, Usefulness: 6/10","source":"Marc Zao-Sanders, 2025 Top-100 Gen AI Use Case Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Existential/purpose use cases newly prominent in 2025","url":"https://learn.filtered.com/hubfs/The%202025%20Top-100%20Gen%20AI%20Use%20Case%20Report.pdf","access":"open"},{"id":"s0979","stat":"#4: Enhance learning (was #8 in 2024) - Reach: 8/10, Usefulness: 5/10","source":"Marc Zao-Sanders, 2025 Top-100 Gen AI Use Case Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Learning use case risen in rank","url":"https://learn.filtered.com/hubfs/The%202025%20Top-100%20Gen%20AI%20Use%20Case%20Report.pdf","access":"open"},{"id":"s0980","stat":"#5: Generate code (pros) (was #47 in 2024) - Reach: 3/10, Usefulness: 7/10","source":"Marc Zao-Sanders, 2025 Top-100 Gen AI Use Case Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Dramatic rise for professional code generation; low reach but high usefulness","url":"https://learn.filtered.com/hubfs/The%202025%20Top-100%20Gen%20AI%20Use%20Case%20Report.pdf","access":"open"},{"id":"s0981","stat":"#6: Generate ideas (was #1 in 2024) - Reach: 10/10, Usefulness: 8/10","source":"Marc Zao-Sanders, 2025 Top-100 Gen AI Use Case Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Previously #1 use case drops to #6 as personal/wellbeing uses rise above it","url":"https://learn.filtered.com/hubfs/The%202025%20Top-100%20Gen%20AI%20Use%20Case%20Report.pdf","access":"open"},{"id":"s0982","stat":"#10: Healthier living (was #75 in 2024) - Reach: 4/10, Usefulness: 6/10","source":"Marc Zao-Sanders, 2025 Top-100 Gen AI Use Case Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"One of the biggest risers in the ranking year-on-year","url":"https://learn.filtered.com/hubfs/The%202025%20Top-100%20Gen%20AI%20Use%20Case%20Report.pdf","access":"open"},{"id":"s0983","stat":"Use case data sourced primarily from Reddit forums using expert-driven curation methodology","source":"Marc Zao-Sanders, 2025 Top-100 Gen AI Use Case Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Qualitative approach capturing authentic real-world use rather than survey responses","url":"https://learn.filtered.com/hubfs/The%202025%20Top-100%20Gen%20AI%20Use%20Case%20Report.pdf","access":"open"},{"id":"s0984","stat":"Research methodology consistent with 2024 report but with broader and deeper dataset","source":"Marc Zao-Sanders, 2025 Top-100 Gen AI Use Case Report","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Year-on-year c
1omparability maintained","url":"https://learn.filtered.com/hubfs/The%202025%20Top-100%20Gen%20AI%20Use%20Case%20Report.pdf","access":"open"},{"id":"s0985","stat":"71% of executives say GenAI is leveraging data in decision making","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Impact on business model; 225 C-suite and senior leaders at $1B+ revenue organisations","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0986","stat":"52% say GenAI is shaping competitive positioning","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Impact on business model; same survey cohort","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0987","stat":"47% say GenAI is opening new revenue opportunities","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Impact on business model; same survey cohort","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0988","stat":"83% of respondents say their GenAI investments will increase over the next 3 years","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Forward-looking investment intent","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0989","stat":"78% are confident they will see ROI on planned GenAI investments over the next 1-3 years","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Includes 11% with high confidence","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0990","stat":"61% plan to expand the scope of current GenAI initiatives in next 1-3 years","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Next steps in GenAI journey","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0991","stat":"55% plan to introduce GenAI into new business functions in next 1-3 years","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Next steps in GenAI journey","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0992","stat":"Only 16% of organisations have a workforce that is highly equipped for GenAI utilisation","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Despite investment in training and hiring","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0993","stat":"78% say their teams are moderately equipped for GenAI","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Reflects increases in training and hiring","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0994","stat":"69% of organisations are investing in upskilling / training current employees for GenAI (up from 12% in 2023)","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Significant year-on-year increase","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0995","stat":"61% are hiring new talent to close GenAI skills gaps (up from 24% in 2023)","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Significant year-on-year increase","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0996","stat":"37% of C-suite respondents cite revenue growth as the main initial objective for GenAI","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"C-suite priority vs. overall respondent priority of productivity","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0997","stat":"Increasing productivity ranked #1 objective by all respondents overall","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Contrasts with C-suite priority of revenue growth","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0998","stat":"37% of those without a dedicated CAIO plan to assign one within the next 12 months","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Leadership and accountability structures","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s0999","stat":"50% of organisations buy/lease GenAI from vendors","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Primary approach to managing GenAI initiatives","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1000","stat":"29% use a mix of building, buying, and partnering","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Blended approach","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1001","stat":"12% build GenAI solutions in-house","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Small minority building internally","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1002","stat":"76% associate data privacy and security risk with engaging GenAI partners","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Top concern for third-party risk","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1003","stat":"58% cite dependence on partner expertise and resources as a risk","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Second-ranked third-party risk","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1004","stat":"52% cite risk of non-compliance with regulations as a partner risk","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Third-ranked third-party risk","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1005","stat":"56% consider risk mitigation of high significance in GenAI implementation","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Overall risk focus","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"}
1,{"id":"s1006","stat":"43% cite data privacy and security as a key risk and legal challenge in GenAI implementation","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Top internal risk category","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1007","stat":"13% cite regulatory uncertainty as a key risk in 2024 (down from 24% in 2023)","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Regulatory uncertainty decreasing as a limiting factor","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1008","stat":"39% anticipate AI regulations to have a high impact on GenAI implementation","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Forward-looking regulatory concern","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1009","stat":"63% anticipate more stringent data privacy and security measures as a result of AI regulation","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Anticipated regulatory impact","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1010","stat":"54% anticipate increased compliance costs as a result of AI regulation","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Anticipated financial impact of regulation","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1011","stat":"60% are reviewing and updating data handling practices to prepare for AI regulations","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Compliance preparation steps","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1012","stat":"52% are implementing technical measures for AI transparency and fairness","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Compliance preparation steps","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1013","stat":"51% are increasing focus on transparency and explainability in AI models","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Compliance preparation steps","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1014","stat":"50% are updating protocols to align with new regulations","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Compliance preparation steps","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1015","stat":"64% of Industrial Markets respondents use GenAI for inventory management","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Top sector-specific use case","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"}
1,{"id":"s1016","stat":"57% of Healthcare and Life Sciences respondents use GenAI for healthcare document assessment","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Sector-specific use case","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1017","stat":"45% of IT/Tech respondents use GenAI for IT query support and assistance","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Top function-specific use case","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1018","stat":"42% of Product Development respondents use GenAI for design and prototyping","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Function-specific use case","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1019","stat":"30% of Financial Services respondents use GenAI for customer service and chatbots","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Sector-specific use case","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1020","stat":"19% of Consumer and Retail respondents use GenAI for customer service chatbots","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Sector-specific use case","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1021","stat":"Survey conducted June 21-July 12, 2024, with 225 C-suite and senior business leaders from organisations with annual revenue of $1 billion or more","source":"KPMG GenAI Survey 2024","date":"August 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Survey scope and sample","url":"https://kpmg.com/us/en/media/news/gen-ai-survey-august-2024.html","access":"open"},{"id":"s1022","stat":"A US company used GenAI to identify duplicate terms across contract types and reduced legal templates from 1,000 down to 250","source":"PwC NewLaw / Financial Times (cited)","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Contract management and template rationalisation; cited from FT report on GenAI in legal"},{"id":"s1023","stat":"PwC has 24 offices across 12 countries in the Middle East with around 10,300 people","source":"PwC NewLaw","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"About the publisher"},{"id":"s1024","stat":"PwC is a network of firms in 152 countries with over 328,000 people","source":"PwC NewLaw","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"About the publisher"},{"id":"s1025","stat":"78% of organisations surveyed are using AI in at least one business function","source":"McKinsey State of AI (cited in Tech Trends Outlook 2025)","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Referenced in the AI chapter of the report","url":"https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-how-organizations-are-rewiring-to-capture-value","access":"open"},{"id":"s1026","stat":"92% of executives are planning to invest more in AI over the next three years","source":"McKinsey Superagency in the Workplace report (cited)","date":"January 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Referenced in the AI chapter","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work","access":"open"},{"id":"s1027","stat":"Only 1% of leaders say their companies are fully mature in AI deployments","source":"McKinsey Superagency in the Workplace report (cited)","date":"January 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Highlights the gap between adoption and maturity","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work","access":"open"},{"id":"s1028","stat":"Agentic AI equity investment reached $1.1 billion in 2024","source":"McKinsey Technology Trends Outlook 2025 / PitchBook","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Private and public market capital raises","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech","access":"open"},{"id":"s1029","stat":"Job postings in agentic AI grew +985% between 2023 and 2024","source":"McKinsey Technology Trends Outlook 2025","date":"2023-24","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Fastest-growing talent demand across all 13 trends","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech","access":"open"},{"id":"s1030","stat":"AI equity investment reached $124.3 billion in 2024","source":"McKinsey Technology Trends Outlook 2025 / PitchBook","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Includes private and public market capital raises","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech","access":"open"},{"id":"s1031","stat":"AI job postings grew +35% between 2023 and 2024","source":"McKinsey Technology Trends Outlook 2025","date":"2023-24","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Strong hiring growth despite earlier pullback","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech","access":"open"},{"id":"s1032","stat":"In Q1 2025, AI companies raised $52 billion, including the SoftBank-led $40 billion investment in OpenAI - the largest VC funding deal ever recorded","source":"McKinsey Tech Trends Outlook 2025 / CNBC (cited)","date":"Q1 2025","sortKey":20250300,"precision":"quarter","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Record-breaking quarter for AI capital raising"},{"id":"s1033","stat":"Google, Alphabet, Amazon, Meta, and Microsoft are each projected to spend $70 billion to more than $100 billion on AI-related capital expenditure in 2025","source":"McKinsey Tech Trends Outlook 2025 / CNBC (cited)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Data centre expansions and custom silicon development"},{"id":"s1034","stat":"Tech megacaps collectively directing hundreds of billions of dollars annually toward AI infrastru
1cture, models, and deployments","source":"McKinsey Tech Trends Outlook 2025","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Characterised as exceeding early cloud investment levels","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech","access":"open"},{"id":"s1035","stat":"Gartner projects that by 2027, 40% of GenAI solutions will be multimodal, up from just 1% in 2023","source":"Gartner (cited in McKinsey Tech Trends Outlook 2025)","date":"September 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Rapid trajectory of multimodal adoption","url":"https://www.gartner.com/en/newsroom/press-releases/2024-09-09-gartner-predicts-40-percent-of-generative-ai-solutions-will-be-multimodal-by-2027","access":"open"},{"id":"s1036","stat":"AI inference costs are declining between 9x and 900x a year, with a median of 50x annually (across benchmarks and performance thresholds)","source":"Epoch AI (cited in McKinsey Tech Trends Outlook 2025)","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Dramatic cost reduction across the AI stack","url":"https://epoch.ai/data-insights/llm-inference-price-trends","access":"open"},{"id":"s1037","stat":"McKinsey agentic workflow for credit memo drafting increased credit analyst productivity by up to 60%","source":"McKinsey QuantumBlack Labs (cited in Tech Trends Outlook 2025)","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Real-world pilot at a bank using multiagent systems","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech","access":"open"},{"id":"s1038","stat":"46% of leaders cite skill gaps in workforces as a major barrier to AI adoption","source":"McKinsey State of AI / Superagency report (cited)","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Talent gap remains the primary adoption blocker"},{"id":"s1039","stat":"More than 20% of employees surveyed report minimal training in AI","source":"McKinsey Superagency in the Workplace report (cited)","date":"January 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Training investment not reaching all employees","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work","access":"open"},{"id":"s1040","stat":"Equity investment increased in 10 of 13 technology trends in 2024","source":"McKinsey Technology Trends Outlook 2025","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Broad recovery after 2023 market dip"},{"id":"s1041","stat":"Python is required in 57% of agentic AI job postings","source":"McKinsey Technology Trends Outlook 2025","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Most in-demand skill for agentic AI roles"},{"id":"s1042","stat":"Machine learning skills required in 49% of agentic AI job postings","source":"McKinsey Technology Trends Outlook 2025","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Second most demanded skill"},{"id":"s1043","stat":"Python talent available at only 0.5x ratio relative to demand for agentic AI roles","source":"McKinsey Technology Trends Outlook 2025","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Significant skills shortage; TensorFlow at 0.9x (better supplied)"},{"id":"s1044","stat":"Machine learning talent available at only 1.2x ratio relative to demand (broadly balanced)","source":"McKinsey Technology Trends Outlook 2025","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Better supply-demand balance than Python"},{"id":"s1045","stat":"Ally Bank implemented GitLab and increased ability to update and deploy software by 55%","source":"GitLab / McKinsey Tech Trends Outlook 2025 (cited)","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Real-world AI-enabled DevSecOps outcome","url":"https://about.gitlab.com/blog/gitlab-receives-ally-technology-partner-award-for-operational-excellence/","access":"open"},{"id":"s1046","stat":"Regrello claims to reduce manual tasks by up to 45% using AI-generated
1workflow applications","source":"Regrello / McKinsey Tech Trends Outlook 2025 (cited)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"analyst","scope":"legal","context":"AI-first ERP platform generating workflow applications in minutes"},{"id":"s1047","stat":"MBTL algorithm is between 5x and 50x more efficient than standard reinforcement learning approaches","source":"MIT (cited in McKinsey Tech Trends Outlook 2025)","date":"November 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Training efficiency improvement for complex reasoning tasks"},{"id":"s1048","stat":"Agentic AI adoption score: 2 out of 5 (Experimentation) - few companies scaling or fully scaled","source":"McKinsey Technology Trends Outlook 2025","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Despite high interest, real-world deployment remains limited","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech","access":"open"},{"id":"s1049","stat":"AI adoption score: 4 out of 5 (Scaling in progress) - organisations scaling deployment across enterprises","source":"McKinsey Technology Trends Outlook 2025","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Most mature of the tech trends assessed","url":"https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-top-trends-in-tech","access":"open"},{"id":"s1050","stat":"A2A (Agent2Agent) protocol supported by more than 50 partners","source":"Google / McKinsey Tech Trends Outlook 2025 (cited)","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Open standard for secure AI agent collaboration across vendors","url":"https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/","access":"open"},{"id":"s1051","stat":"Over 70% of McKinsey\'s 45,000 employees use Lilli (internal AI tool)","source":"McKinsey (cited in Business Insider)","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Lilli synthesises 100 years and over 100,000 documents/interviews of IP","url":"https://www.businessinsider.com/consulting-ai-mckinsey-bcg-deloitte-pwc-kpmg-chatbots-ai-tools-2025-4","access":"open"},{"id":"s1052","stat":"McKinsey employees who use Lilli do so approximately 17 times a week","source":"McKinsey (cited in Business Insider)","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"High frequency of use by active users","url":"https://www.businessinsider.com/consulting-ai-mckinsey-bcg-deloitte-pwc-kpmg-chatbots-ai-tools-2025-4","access":"open"},{"id":"s1053","stat":"McKinsey reports workers saved 30% of their time using Lilli","source":"McKinsey case study (cited in Business Insider)","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Lilli ROI claim from internal case study"},{"id":"s1054","stat":"Just one hour of training improved employee engagement with Lilli","source":"McKinsey (cited in Business Insider)","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Addresses initial \\"prompt anxiety\\" barrier","url":"https://www.businessinsider.com/consulting-ai-mckinsey-bcg-deloitte-pwc-kpmg-chatbots-ai-tools-2025-4","access":"open"},{"id":"s1055","stat":"McKinsey acquired QuantumBlack (data analytics and design) in 2015; now its AI consulting arm employs 7,000 tech experts across 50 countries","source":"Business Insider","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"cross-sector","context":"McKinsey\'s AI heritage and capability scale"},{"id":"s1056","stat":"About 40% of McKinsey\'s work is analytics-related or AI-related, with much moving to GenAI","source":"McKinsey senior partner Ben Ellencweig (cited in Business Insider)","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Revenue mix shift toward AI-adjacent work","url":"https://www.businessinsider.com/consulting-ai-mckinsey-bcg-deloitte-pwc-kpmg-chatbots-ai-tools-2025-4","access":"paywalled"},{"id":"s1057","stat":"McKinsey has built an ecosystem of alliances with 19 AI companies and completed over 400 GenAI client projects","source":"Bu
1siness Insider","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"cross-sector","context":"McKinsey\'s external AI practice scale"},{"id":"s1058","stat":"BCG\'s 33,000 employees have built over 18,000 custom GPTs since rolling out ChatGPT Enterprise in 2023","source":"BCG (cited in Business Insider)","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Internal AI tool proliferation at BCG"},{"id":"s1059","stat":"BCG has developed 8 or 9 internal generative AI tools","source":"BCG Scott Wilder (cited in Business Insider)","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Range of bespoke internal tooling","url":"https://www.businessinsider.com/consulting-ai-mckinsey-bcg-deloitte-pwc-kpmg-chatbots-ai-tools-2025-4","access":"open"},{"id":"s1060","stat":"About 40% of BCG associates use Deckster (AI slideshow editor) weekly","source":"BCG (cited in Business Insider)","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Deckster trained on 800-900 slide templates"},{"id":"s1061","stat":"BCG estimates employees reinvest about 70% of the time they save using AI into higher value activities","source":"BCG (cited in Business Insider)","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Framing of AI as augmentation not replacement"},{"id":"s1062","stat":"PwC has built more than 250 internal AI agents in the past 18 months","source":"PwC (cited in Business Insider)","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Context for launching agent OS platform"},{"id":"s1063","stat":"Deloitte invested billions in AI and unveiled Zora AI fleet of AI agents in March 2025","source":"Business Insider","date":"March/April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Zora AI trained on specific subjects like finance and marketing","url":"https://www.businessinsider.com/consulting-ai-mckinsey-bcg-deloitte-pwc-kpmg-chatbots-ai-tools-2025-4","access":"open"},{"id":"s1064","stat":"Regulatory uncertainty as a key risk for GenAI has decreased from 24% to 13% between 2023 and 2024 (referenced via KPMG data)","source":"KPMG (cited in context)","date":"Aug 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Referenced as supporting context for regulatory landscape discussion"},{"id":"s1065","stat":"Inference cost declining significantly across the industry (qualitative reference; no specific figure cited)","source":"RF Best Practices Enterprise LLM Adoption","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"legal","context":"Referenced directionally; paper pre-dates Epoch AI cost data"},{"id":"s1066","stat":"36% of UK consumers (18 million people aged 16-75) have used a Generative AI tool â up 10pp from 26% in 2023","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"n=4,150; tools include ChatGPT, Google Gemini, Midjourney","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1067","stat":"40% of UK consumers not aware of Generative AI","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Down from higher figures in 2023","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1068","stat":"14% of UK consumers have used Gen AI at work = 7 million people â up 66% YoY from 4 million","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Up from approximately 4 million (8%) in May 2023","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1069","stat":"41% of Gen AI users use it less than monthly","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Of those who have used Gen AI (n=1,473)","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1070","stat":"26% of Gen AI users have only tried it once or twice","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"\\"Experimented but saw little utility\\"","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1071","stat":"36% of Gen AI users use weekly or daily = 13% of all UK people","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Of those who have used Gen AI","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1072","stat":"4% of all UK people use Gen AI daily (vs 3% in 2023)","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Of full UK population aged 16-75","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1073","stat":"Top barrier to more frequent use: \\"Don\'t find it helpful\\" (23%)","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Of low-frequency users (less than monthly)","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1074","stat":"19% of low-frequency users not satisfied with answers Gen AI gave","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Of those using Gen AI less than monthly","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1075","stat":"17% cite data privacy concerns as barrier","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Of low-frequency Gen AI users","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1076","stat":"13% say Gen AI gives inaccurate answers","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Of low-frequency Gen AI users","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1077","stat":"13% think AI infringes on copyright and IP","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Of low-frequency Gen AI users","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1078","stat":"Top Gen AI work tasks: generating ideas (44%), looking up information (41%), creating written content (39%), writing/editing emails (38%), summarising text (37%)","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Of those using Gen AI for work (n=571)","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1079","stat":"18% of Gen AI work users report using it for writing code","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Report notes less than 1% of people can code","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"}
1,{"id":"s1080","stat":"74% of Gen AI work users say it boosts productivity a great deal (28%) or fair amount (46%)","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Of those who have used Gen AI at work (n=571)","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1081","stat":"Only 27% of employed adults aware of Gen AI say their company actively encourages its use","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Of employed adults aware of Gen AI (n=1,575); 46% disagree","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1082","stat":"59% would be less inclined to trust an email known to be created using Gen AI","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Of those aware of Gen AI (n=2,476); only 14% disagree","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1083","stat":"56% would be less inclined to use customer services if they knew they were conversing with a Gen AI assistant","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Of those aware of Gen AI","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1084","stat":"75% of consumers have seen online misinformation â up from 69% in 2023","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"87% of 16-24 year olds report seeing misinformation in the last year","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1085","stat":"65% say companies overstate their eco-credentials","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Greenwashing scepticism","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1086","stat":"1 in 3 UK consumers do not plan to purchase any device in the next 12 months","source":"Deloitte Digital Consumer Trends 2024","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Device market plateauing; no breakthrough new form factors","url":"https://www.deloitte.com/uk/en/Industries/tmt/research/digital-consumer-trends.html","access":"open"},{"id":"s1087","stat":"700 million weekly active users on ChatGPT consumer plans by July 2025 = ~10% of global adult population","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Consumer plans only (Free, Plus, Pro); excludes Business/Enterprise/Education","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1088","stat":"18 billion messages sent to ChatGPT per week by July 2025 (~2.5 billion/day)","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Consumer plans only","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1089","stat":"ChatGPT message volume grew by more than 5x between July 2024 and July 2025","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Consumer plans","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1090","stat":"Non-work ChatGPT messages grew from 53% to 73% of all usage (June 2024 to June 2025)","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Work share fell from 47% to 27%","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1091","stat":"Daily work-related ChatGPT messages: 213 million (Jun 2024) â 716 million (Jun 2025)","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Still strong absolute work growth despite declining share","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1092","stat":"Daily non-work ChatGPT messages: 238 million (Jun 2024) â 1,911 million (Jun 2025)","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"8x growth in non-work daily messages in one year","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1093","stat":"~80% of ChatGPT usage falls into three categories: Practical Guidance, Seeking Information, Writing","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Based on ~1.1 million sampled conversations","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1094","stat":"Writing = most common work-related use case: ~40% of work-related messages in June 2025","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Writing dominates across nearly all occupational groups","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1095","stat":"~2/3 of all Writing conversations ask ChatGPT to modify user text (edit, translate, critique) not create from scratch","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"AI augmenting rather than replacing human writing","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1096","stat":"Computer programming = only 4.2% of ChatGPT messages","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Contrast with 33% of Claude work conversations â different user bases","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1097","stat":"Relationships and Personal Reflection = only 1.9% of ChatGPT messages","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Challenges claims that therapy/companionship is the dominant use case","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1098","stat":"In early months ~80% of active ChatGPT users had typically masculine first names; by June 2025 this fell to 48%","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Gender gap has substantially closed or reversed","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1099","stat":"Nearly half of all adult ChatGPT messages from users under age 26","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Young adults are the dominant user group by message volume","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1100","stat":"49% of ChatGPT messages \\"Asking\\", 40% \\"Doing\\", 11% \\"Expressing\\"","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Asking = seeking information; Doing = performing tasks","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1101","stat":"Work messages: 56% Doing, ~35% Asking â skews toward task completion","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Three-quarters of Doing work messages are Writing tasks","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1102","stat":"Consumer surplus from GenAI estimated at at least $97 billion/year in the US","source":"Collis & Brynjolfsson 2025 (cited in NBER paper)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"Based on willingness-to-pay choice experiments","url":"https://www.wsj.com/opinion/ais-overlooked-97-billion-contribution-to-the-economy-users-service-da6e8f55","access":"paywalled"},{"id":"s1103","stat":"ChatGPT usage grew relatively faster in low- and middle-income countries ($10-40K GDP per capita) over the last year","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Significant global diffusion beyond high-income countries","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1104","stat":"Educated users and highly-paid professional occupations substantially more likely to use ChatGPT for work","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"37% work-related for non-college users vs 48% for graduate-educated","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1105","stat":"Up to $40 billion spent on enterprise AI globally","source":"MIT report (cited in Bloomberg Law)","date":"July 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"academic","scope":"legal","context":"Referenced as context for in-house legal ROI pressure"}
1,{"id":"s1106","stat":"95% of organisations are not getting any return on their AI investment","source":"MIT report (cited in Bloomberg Law)","date":"July 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"legal","context":"Driving C-suite and board scrutiny of legal AI spend"},{"id":"s1107","stat":"Some contracts previously taking six weeks to turn around can now be completed in a few days using AI-assisted contract management","source":"Bloomberg Law (Gabe Saunders, Exos / LegalSifter)","date":"December 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"client","sourceType":"media","scope":"legal","context":"Former head of legal ops at Exos â anecdote not a controlled study","url":"https://news.bloomberglaw.com/legal-ops-and-tech/ai-spending-surge-prompts-companies-to-wrestle-with-its-worth","access":"paywalled"},{"id":"s1108","stat":"As of June 2024, Futurice had worked on 48 Gen AI focused projects with clients, including 7 production implementations, 6 AI strategy projects, and 4 AI transformation programmes","source":"Futurice GenAI Working Paper v2","date":"June 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Plus 100+ data & AI transformation projects from before gen AI","url":"https://www.futurice.com/downloads/generative-ai-working-paper","access":"gated"},{"id":"s1109","stat":"Front-runner developers at Futurice report 50%+ increase in development speed using Gen AI coding assistants","source":"Futurice GenAI Working Paper v2","date":"June 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Self-reported â not a controlled study","url":"https://www.futurice.com/downloads/generative-ai-working-paper","access":"gated"},{"id":"s1110","stat":"FutuCortex (internal RAG knowledge tool): average unique weekly user count more than tripled since adding Gen AI natural language search","source":"Futurice GenAI Working Paper v2","date":"June 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Now includes almost all salespeople","url":"https://www.futurice.com/downloads/generative-ai-working-paper","access":"gated"},{"id":"s1111","stat":"Customer service AI: up to 2/3 (~66%) of cases can be resolved automatically with LLM-based self-service bots","source":"Futurice GenAI Working Paper v2","date":"June 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Multiple production cases","url":"https://www.futurice.com/downloads/generative-ai-working-paper","access":"gated"},{"id":"s1112","stat":"Customer service AI: 40% reduction in average handle time while improving customer satisfaction","source":"Futurice GenAI Working Paper v2","date":"June 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Via AI assistants giving agents better knowledge access","url":"https://www.futurice.com/downloads/generative-ai-working-paper","access":"gated"},{"id":"s1113","stat":"Futurice\'s \\"Dog and Pony Show\\" demo shown to 100+ conferences, leadership teams, and boards","source":"Futurice GenAI Working Paper v2","date":"June 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Internal Gen AI demo programme","url":"https://www.futurice.com/downloads/generative-ai-working-paper","access":"gated"},{"id":"s1114","stat":"400% growth in Futurice\'s data and AI transformation work over the last 18 months (to June 2024)","source":"Futurice GenAI Working Paper v2","date":"June 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Self-reported growth driven by Gen AI demand","url":"https://www.futurice.com/downloads/generative-ai-working-paper","access":"gated"},{"id":"s1115","stat":"Sales: AI-based tender processing reduced proposal lead time from 3 weeks to 1 day","source":"Futurice GenAI Working Paper v2","date":"June 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Client production outcome","url":"https://www.futur
1ice.com/downloads/generative-ai-working-paper","access":"gated"},{"id":"s1116","stat":"Management: AI-enabled analysis cycles reduced from a week or more to a day or less â \\"10-20x faster management clock speed\\"","source":"Futurice GenAI Working Paper v2","date":"June 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Construction site management example","url":"https://www.futurice.com/downloads/generative-ai-working-paper","access":"gated"},{"id":"s1117","stat":"67% of legal leaders at high-growth companies feel \\"buried in low-value work\\"","source":"Juro Annual Tech GC Report (cited in eBook)","date":"Nov 2020","sortKey":20201100,"precision":"month","year":2020,"theme":"client","sourceType":"vendor","scope":"legal","context":"Overwhelming majority of that low-value work was routine contracts and related process"},{"id":"s1118","stat":"25% of legal departments in large organisations have no metrics to measure the value they deliver","source":"Acritas (cited in Juro eBook)","date":"Nov 2020","sortKey":20201100,"precision":"month","year":2020,"theme":"client","sourceType":"analyst","scope":"legal","context":"Cited in context of in-house legal teams struggling to demonstrate impact to finance leadership"},{"id":"s1119","stat":"Only 1 in 4 (24%) enterprise employee respondents fully understand who owns, stores, and manages contracts","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"76% of enterprise employees lack clarity on contract ownership"},{"id":"s1120","stat":"Only 3 in 10 (30%) legal respondents believe their teams fully know who owns contracts","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Even legal teams themselves lack internal clarity on contract ownership"},{"id":"s1121","stat":"9 in 10 (91%) operations respondents do not fully understand contract ownership and accountability","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Operations teams - typically the administrative backbone - have lowest contract ownership awareness"},{"id":"s1122","stat":"85% of legal professionals report their involvement in approving contracts, but only 67% of enterprise employees say the legal team is included","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"18-percentage-point gap between legal\'s self-reported involvement and enterprise employees\' experience"},{"id":"s1123","stat":"More than half (50%) of Legal and 49% of enterprise employee respondents believe they lose money from unintended auto-renewals","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Most severe in Sales and Marketing (60%) and Finance (49%)"},{"id":"s1124","stat":"1 in 5 (23%) enterprise employee respondents admit revenue loss from contract management issues","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Top material consequence of poor contract management is missed/lost revenue"},{"id":"s1125","stat":"41% of all respondents (including Legal) believe contract processes are too slow","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Spikes to 79% among R&D and Engineering staff who report slower innovation and product delivery"},{"id":"s1126","stat":"2 in 3 enterprise employee respondents (63%) expect Legal to be the de facto enforcer of contracts regardless of whether it was involved in approvals","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Legal has accountability without authority - expected to enforce what it wasn\'t included in"},{"id":"s1127","stat":"84% of legal respondents and 63% of non-legal respondents say the legal department is responsible for ensuring contract terms are enforced","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Legal is the dominant perceived owner of contract enforcement"},{"id":"s1128","stat":"70% of shared drives are used to store contracts by legal respondents; 50% by non-legal respondents","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Contracts also stored in CRMs (62%/53%), desktops (48%/26%), HCM systems (48%/33%), ERP (48%/32%), email (46%/29%), paper (45%/23%)"},{"id":"s1129","stat":"More than half of Legal and non-Legal respondents use unintegrated channels such as email, Slack, and Teams to manage contract processes","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Siloed conversations lead to fragmented decisions and lack of audit trails"},{"id":"s1130","stat":"48% of legal respondents and 55% of enterprise employee respondents do not use a contract management system","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Majority of organisations still relying on unstructured contract storage and process"},{"id":"s1131","stat":"About 9 in 10 (89%) legal respondents believe contract terms are overlooked in their companies; 14% say this occurs constantly","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Contract term oversight failure is near-universal in enterprise"},{"id":"s1132","stat":"Only 1 in 4 (25%) legal respondents and 1 in 3 (33%) enterprise employees track contracts relevant to their roles","source":"Contract Intelligence Index Report (n=1,250)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Active contract tracking is the exception not the rule across both legal and business teams"},{"id":"s1133","stat":"In the last 10 completed responsiveness reviews by Syllo in live litigations, the lowest estimated Recall was 93.4%","source":"Syllo empirical validation (live litigation data)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Recall measures completeness - percentage of truly relevant documents successfully identified","url":"https://syllo.ai/white-paper-2025/","access":"gated"},{"id":"s1134","stat":"In the last 10 completed responsiveness reviews, the average estimated Recall was 97.8%","source":"Syllo empirical validation (live litigation data)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Average performance across 10 live litigation responsiveness reviews","url":"https://syllo.ai/white-paper-2025/","access":"gated"},{"id":"s1135","stat":"In the last 10 completed responsiveness reviews, the median estimated Recall was 99.4%","source":"Syllo empirical validation (live litigation data)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Median performance across 10 live litigation responsiveness reviews","url":"https://syllo.ai/white-paper-2025/","access":"gated"},{"id":"s1136","stat":"4 out of 10 completed responsiveness reviews by Syllo achieved an estimated Recall of 100%","source":"Syllo empirical validation (live litigation data)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"40% of reviews achieved perfect recall","url":"https://syllo.ai/white-paper-2025/","access":"gated"},{"id":"s1137","stat":"In the last 10 completed responsiveness reviews, median estimated Precision was 85.9%","source":"Syllo empirical validation (live litigation data)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Precision measures accuracy - percentage of identified documents that are actually relevant","url":"https://syllo.ai/white-paper-2025/","access":"gated"},{"id":"s1138","stat":"In the last 10 completed responsiveness reviews, average estimated Precision was 79.7%","source":"Syllo empirical validation (live litigation data)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Average precision across 10 live litigation responsiveness reviews","url":"https://syllo.ai/white-paper-2025/","access":"gated"},{"id":"s1139","stat":"Courts and litigants generally accepted 80-85% estimated Recall as acceptable and legally defensible for predictive coding (TAR) in large cases","source":"Bolch Judicial Institute / Sedona Conference TAR Guidelines (cited)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"technical","sourceType":"academic","scope":"legal","context":"Legal benchmark being exceeded by Syllo\'s agentic approach (93.4-100% range)","url":"https://www.thesedonaconference.org/sites/default/files/publications/Tar-Case-Law-Primer-2nd-Edition-2023.pdf","access":"open"},{"id":"s1140","stat":"Human review teams in complex cases achieve average estimated Recall rates of roughly 60%, with upper-bound of roughly 80%","source":"Academic studies cited in Syllo white paper","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Human baseline Recall rate that agentic AI is being compared against","url":"https://syllo.ai/wp-content/uploads/2025/07/Agentic-Document-Review-White-Paper-3-21-2025.pdf","access":"open"},{"id":"s1141","stat":"Syllo has completed more than 80 agentic document reviews in active litigation since 2023","source":"Syllo (live litigation data)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Datasets spanning from thousands of documents to more than 2 million documents","url":"https://syllo.ai/white-paper-2025/","access":"open"},{"id":"s1142","stat":"In a Ballard Spahr case with 100,000+ documents and 25+ issue codes, Syllo achieved estimated Precision of 95.56% and estimated Recall of 99.4%","source":"Ballard Spahr / Syllo (live litigation)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Set up took approximately 3 hours of human time; no prompt iteration performed","url":"https://syllo.ai/white-paper-2025/","access":"open"},{"id":"s1143","stat":"In a head-to-head comparison at RCCB, Syllo achieved estimated Recall of 93.44% vs. a managed review team\'s estimated Recall below 67%","source":"Royer Cooper Cohen Braunfeld / Syllo (live litigation)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Managed review cost exceeded $2.00 per document due to multiple correction rounds; Syllo achieved 93.44% Recall and 69.81% Precision"},{"id":"s1144","stat":"In a Quinn Emanuel matter (30,000+ documents, 20+ issue codes), Syllo achieved estimated Recall above 98% and estimated Precision above 74%","source":"Quinn Emanuel / Syllo (live litigation)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Review set up in less than 2 months before trial; Syllo also reviewed 40,000+ opposing party documents against 40 issue codes","url":"https://syllo.ai/white-paper-2025/","access":"open"},{"id":"s1145","stat":"In an Outten & Golden employment matter (12,543 documents, 28 issue tags), Syllo achieved Precision of 84.09% and estimated Recall of 100%","source":"Outten & Golden / Syllo (live litigation)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Zero documents in the null-set sample found to be responsive during elusion testing","url":"https://syllo.ai/white-paper-2025/","access":"open"},{"id":"s1146","stat":"In a Mayer Brown matter (400,000+ documents, 8 million+ pages), Syllo a
1pplied 15 issue codes in less than one week","source":"Mayer Brown / Syllo (live litigation)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"More than $300,000 had already been spent on managed document review before Syllo cross-check; Syllo identified 50+ previously missed hot documents, representing ~20% of total hot documents selected","url":"https://syllo.ai/white-paper-2025/","access":"gated"},{"id":"s1147","stat":"In a Quinn Emanuel hot documents matter, Syllo achieved estimated Recall of 98.69% and estimated Precision of 92.83% across 40,000+ documents and 30+ issue codes (zero-shot review)","source":"Quinn Emanuel / Syllo (live litigation)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Zero-shot means no prompt refinement; Syllo identified 200+ key documents not previously found","url":"https://syllo.ai/white-paper-2025/","access":"open"},{"id":"s1148","stat":"Syllo identified 454 documents as very responsive that had not been flagged by human reviewers in a Pillsbury cross-check review (78,000 documents, 15 issue codes)","source":"Pillsbury / Syllo (live litigation)","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Human reviewers frequently over- and under-coded; Syllo correctly identified miscodings in both directions"},{"id":"s1149","stat":"35% of legal teams have already integrated generative AI into their routine legal processes","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"n=112. 13% expect to complete integration within 6 months; 24% within a year; 15% say 1-2 years","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1150","stat":"43% of law firm participants feel pressure to adopt GenAI from their leadership","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"36% feel pressure from clients; 25% from partners; 18% from associates; 42% feel no external pressure","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1151","stat":"64% of in-house legal participants feel pressure to adopt GenAI from their leadership","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Corporate pressure is more unidirectional than law firms - mainly from leadership","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1152","stat":"79% of law firm participants are adopting GenAI to gain efficiencies","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"52% to differentiate as innovators; 50% to improve client outcomes; 39% to meet rising client demand; 36% to reduce costs","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1153","stat":"89% of in-house legal participants are motivated to adopt GenAI primarily to gain efficiencies","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"61% to reduce costs; 59% to stay current with technology; 32% to understand dispute facts/risks faster","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1154","stat":"70% of law firm respondents identified privacy and security as the biggest obstacle to adopting GenAI","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"36% cited malpractice/ethical concerns; 32% cited prohibition of AI in client engagement agreements; 25% cited lack of understanding","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1155","stat":"68% of corporate legal departments cite data security and privacy as top adoption concern","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"59% cite accuracy of outputs concerns; 38% cite lack of understanding; 38% cite budget issues","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1156","stat":"80% of participants work in organisations that have an existing AI policy","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"11% expect to have a policy within the next year; only 1% have no policy and no plans","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1157","stat":"70% of participants said increased efficie
1ncy is the top benefit of GenAI implementation","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"58% cited better data analysis and insights; 40% cited faster evidence gathering; 34% cited cost savings","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1158","stat":"59% of participants say collaboration platforms like Google Drive and Microsoft 365 have the biggest eDiscovery impact","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"55% cite messaging services (Slack/Teams); 37% cite text messages; 33% cite email","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1159","stat":"47% of participants say eDiscovery workloads are increasing; 36% say stable; 4% say decreasing","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Data volume explosion and new data source complexity primary drivers","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1160","stat":"39% of respondents report the duration of complex litigation is growing; of those, 64% say it is prompting investment in GenAI for disputes","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Growing litigation duration is directly driving AI investment decisions","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1161","stat":"71% expect to use GenAI in their most complex litigation; 74% believe it will speed up document review; 67% believe it will enable faster fact understanding","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"53% expect better strategy planning; 47% expect better deposition analysis; 33% expect predictive analytics capability","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1162","stat":"53% of participants rated their confidence in using GenAI for document review at 4 or 5 out of 5 (vs manual review); 79% rated it 3 or above","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"High confidence in GenAI for document review relative to manual methods","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1163","stat":"32% are already experiencing cost savings from GenAI; 55% expect to see them","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Expectation of savings significantly exceeds current realisation","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1164","stat":"52% of participants say they are implementing additional technology to handle new and emerging data types extending the litigation lifecycle","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Driven by proliferation of collaboration platform data, messaging, mobile data","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1165","stat":"46% of respondents say GenAI will simplify routine tasks and free more time for complex legal analysis","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"22% say it will allow more time for strategic work; 22% say legal professionals must adopt technology to succeed","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1166","stat":"66% of law firm survey participants are involved in commercial litigation","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"82% of law firm participants involved in commercial litigation per methodology section","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1167","stat":"63% of participants cite data security and privacy as implementation concerns; 55% cite accuracy fears; 50% cite lack of understanding about GenAI","source":"DISCO / Ari Kaplan Advisors survey","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Budgeting close behind at 46%; lack of clear regulations at 33%; professional resistance at 29%","url":"https://csdisco.com/pressrelease/disco-study-highlights-rapid-pace-of-legal-generative-ai-adoption-key-barriers-for-lawyers","access":"open"},{"id":"s1168","stat":"Only 18% of private practice lawyers believe their firm is fast or very fast at implementing new technology (especially AI)","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"70% believe their firm is adequate, slow or very slow at implementing new technology","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1169","stat":"Only 28% of lawyers at larger firms say their firm is fast or very fast at implementing new technology","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Even in the largest firms, technology implementation speed is considered inadequate by most lawyers","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1170","stat":"Two-thirds (67%) of legal professionals said their firm is adequate, slow or very slow at responding to change","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"More than half (58%) said the same about data and analytics capabilities","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1171","stat":"More than half (52%) of private practice lawyers rated their firm\'s legal research capability as adequate, slow or very slow","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Drafting and reviewing legal documents rated slow by 45%; legal work delivery generally slow by 35%","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1172","stat":"45% of private practice lawyers rate their firm as adequate, slow or very slow at drafting and reviewing legal documents","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Sluggish processes are holding firms back from meeting client demands","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1173","stat":"More than a third (35%) of lawyers say their firm is adequate, slow or very slow at delivering legal work in general","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"1 in 3 firms failing at the most basic speed benchmark","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1174","stat":"More than half (58%) of in-house lawyers rated their department\'s legal research process as adequate, slow or very slow","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"client","sourceType":"vendor","scope":"legal","context":"43% said the same of drafting and document reviewing; only 14% said their department is fast or very fast at implementing new technology","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1175","stat":"Only 14% of in-house legal departments are fast or very fast at implementing new technology","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"client","sourceType":"vendor","scope":"legal","context":"In-house teams face the same innovation speed challenge as private practice","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1176","stat":"One in five (19%) large law firm lawyers said they would consider leaving if their firm didn\'t invest in AI","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"At firm level overall, 11% would consider leaving; 25% of all lawyers say failure to embrace AI would negatively impact their careers","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1177","stat":"25% of all lawyers say failure to invest in AI would negatively impact their careers","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Rises to 36% among lawyers at larger firms saying it would negatively impact their career","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1178","stat":"36% of lawyers at larger firms say failure to invest in AI would negatively impact their career; 19% say they would consider leaving","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Large firm lawyers face greater career r
1isk from AI under-investment than those at small or mid-sized firms","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1179","stat":"In-house legal counsel\'s top three requirements from external counsel: cost-effectiveness (74%), responsiveness and agility (67%), specialist legal expertise (48%)","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"client","sourceType":"vendor","scope":"legal","context":"In-house teams face compliance regulations (51%), law changes (50%), and rising costs (45%) as their top three challenges in the next 12 months","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1180","stat":"Law firm lawyers\' view of what clients need from them: cost-effectiveness (73%), responsiveness and agility (68%), specialist expertise (54%)","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"client","sourceType":"vendor","scope":"legal","context":"Strong alignment between what in-house lawyers say they need and what law firm lawyers believe clients need","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1181","stat":"AI has revolutionised business strategies in IT and tech (28%), marketing and BD (18%), client experience (18%), and product/service offerings (17%) within law firms","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"AI impact currently concentrated in support and business development functions rather than core legal delivery","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1182","stat":"In the next 1-3 years, 57% of private practice lawyers expect firms to become more reliant on AI for legal research and document review","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"47% expect lawyers will need strong tech skills; 45% expect more flexible career paths","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1183","stat":"65% of larger firm respondents expect a shift towards more specialised or niche practice areas in the next 1-3 years","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"53% of larger firm respondents expect greater adoption of alternative billing models; 53% expect increased importance of technology skills","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1184","stat":"Keeping pace with new technology is more of a challenge for large law firms (52%) than medium-sized firms (36%) or small and solo practices (33%)","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"AI may be levelling the playing field for smaller firms who can move faster on technology adoption","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1185","stat":"Attracting and retaining clients is a top challenge for 43% of large firm lawyers vs 37% of medium-sized and 34% of small/solo firms","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Client retention pressure is disproportionately concentrated at the largest firms","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"},{"id":"s1186","stat":"AI adoption has penetrated four-fifths of the legal sector in a little over two years","source":"LexisNexis survey (n=887, UK and Ireland)","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Framing of near-universal adoption intent across UK private practice","url":"https://www.artificiallawyer.com/2025/02/20/give-us-legal-ai-or-were-leaving-survey/","access":"open"}
1,{"id":"s1187","stat":"5,765 Copilot licences distributed across 56 Australian Government agencies in 6-month trial","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"First trial of a generative AI tool in the Australian Government","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1188","stat":"Over 2,000 trial participants from more than 50 agencies contributed to the evaluation","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Surveys: 1,556 pre-use, 1,159 pulse, 831 post-use respondents","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1189","stat":"Only 1 in 3 trial participants used Copilot daily","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Moderate use consistent across APS classifications and job families","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1190","stat":"Over 70% used Microsoft Teams and Word during the trial, mainly for summarising and re-writing content","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Teams and Word met participants\' needs; Excel and Outlook hampered by access issues","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1191","stat":"77% were optimistic about Copilot at end of trial","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Despite moderate use, overall sentiment positive","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1192","stat":"86% of trial participants wished to continue using Copilot","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"SES staff (93%) and Corporate roles (81%) highest positive sentiment","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1193","stat":"75% of participants who received 3+ forms of training were confident using Copilot - 28pp higher than those with one form of training","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Training is the single biggest lever for adoption and confidence","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1194","stat":"69% of survey respondents agreed Copilot improved the speed at which they could complete tasks","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Quality gains more subdued than efficiency gains","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1195","stat":"61% agreed Copilot improved the quality of their work","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Up to 7% reported Copilot added time to activities","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1196","stat":"40% of survey respondents reported reallocating their time to higher-value activities (mentoring, strategic planning, stakeholder engagement)","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"government","scope":"cross-sector","context":"AI enabling time reallocation even at government scale","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1197","stat":"~65% of managers observed an uplift in productivity across their team","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"APS 3-6 and EL1 classifications and ICT roles experienced highest time savings","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1198","stat":"Perceived time savings of ~1 hour/day for summarisation, preparing a first draft, and information searches","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Self-reported; up to 7% of participants said Copilot added time","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1199","stat":"Up to 7% of trial participants reported Copilot added time to their activities","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"Unpredictability and lack of contextual knowledge required verification and editing, negating some efficiency gains","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1200","stat":"61% of managers in the pulse survey could not confidently identify Copilot outputs","source":"DTA Australian Government Copilot Evaluation","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"Significant governance concern â managers cannot tell what is human vs AI work","url":"https://www.digital.gov.au/initiatives/copilot-trial","access":"open"},{"id":"s1201","stat":"65% of ~500 practitioners surveyed say using GenAI in meetings leads to more innovative brainstorming","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"From Gardner & Co. study with
1nearly 500 global business practitioners","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1202","stat":"55% believe GenAI can close gaps in knowledge and perspectives in real time during meetings","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Cross-industry study of legal, consulting, corporate functions","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1203","stat":"40% of participants do not yet use GenAI in discussions or meetings","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Majority optimistic but significant non-adoption in meetings specifically","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1204","stat":"90% of respondents would see added value in GenAI providing functional perspectives in meetings","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Addresses gap of absent/unavailable stakeholders â IT (52%), Biz Dev (51%), Marketing (51%) cited most","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1205","stat":"37% of respondents would appreciate having GenAI act as an \\"outside voice\\" (client, regulator, stakeholder) in real-time discussions","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Specific use case for legal work: simulate regulator/client perspective","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1206","stat":"Heineken legal dept: GenAI saves ~45 minutes per presentation created","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Specific time savings from PowerBot (GPT-4o based internal tool) in Heineken legal department","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1207","stat":"Heineken legal dept: GenAI saves at least 30 minutes per day on email writing (across 5 emails)","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Daily individual savings from Heineken legal team","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1208","stat":"Heineken legal dept: 15 minutes per day on brainstorming/inspiration","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Daily individual savings from Heineken legal team","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1209","stat":"Heineken legal dept: 10 minutes per day on summarisation","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Daily individual savings from Heineken legal team","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1210","stat":"Heineken legal dept: 30 minutes per week on translation tasks","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Weekly individual savings from Heineken legal team","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1211","stat":"Heineken legal dept: 15 minutes per week on meeting notes and follow-up","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Weekly individual savings from Heineken legal team","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1212","stat":"Heineken General Counsel Ernst van de Weert: GenAI \\"saves me easily one to two hours in a day\\" across all tasks combined","source":"Gardner & Co. GenAI Collaboration Study / Heineken Case Study","date":"July 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Anecdotal senior leadership evidence; each individual saving \\"sounds trivial\\" but aggregates significantly","url":"https://www.gardnerandco.co/wp-content/uploads/2024/07/Heinekens-Use-of-GenAI-Powerbot_Gardner-Co-Case-Study-July-2024.pdf","access":"open"},{"id":"s1213","stat":"Three-stage GenAI adoption roadmap proposed for banking: Stage 1 = language processing / rout
1ine automation; Stage 2 = chat-to-agent; Stage 3 = chat-to-execution (autonomous)","source":"Kodex AI / Deutsche Bank Banking WP","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Framework for financial services GenAI adoption â not quantified, but widely cited in FS context","url":"https://corporates.db.com/publications/White-papers-guides/adopting-generative-ai-in-banking","access":"open"},{"id":"s1218","stat":"43% of law firm IT departments spend only 0-20% of their time on value-add and innovation (rest on \\"keeping the lights on\\")","source":"Legal IT Insider / CTS Future Shape of Law Firm IT","date":"Jul 2022","sortKey":20220700,"precision":"month","year":2022,"theme":"adoption","sourceType":"media","scope":"legal","context":"Innovation time constrained across the sector regardless of firm size","url":"https://legaltechnology.com/2022/07/04/the-future-shape-of-law-firm-it-major-report-out-now/","access":"open"},{"id":"s1219","stat":"37% of law firm IT departments spend 20-40% of their time on innovation","source":"Legal IT Insider / CTS Future Shape of Law Firm IT","date":"Jul 2022","sortKey":20220700,"precision":"month","year":2022,"theme":"adoption","sourceType":"media","scope":"legal","context":"Most common \\"aspirational\\" band â below the recommended level","url":"https://legaltechnology.com/2022/07/04/the-future-shape-of-law-firm-it-major-report-out-now/","access":"open"},{"id":"s1220","stat":"14% of law firm IT departments spend 40-60% on value-add/innovation","source":"Legal IT Insider / CTS Future Shape of Law Firm IT","date":"Jul 2022","sortKey":20220700,"precision":"month","year":2022,"theme":"adoption","sourceType":"media","scope":"legal","context":"Only achievable where IT infrastructure outsourced or heavily automated","url":"https://legaltechnology.com/2022/07/04/the-future-shape-of-law-firm-it-major-report-out-now/","access":"open"},{"id":"s1221","stat":"4% of law firm IT departments spend 60-80% of time on innovation/value-add","source":"Legal IT Insider / CTS Future Shape of Law Firm IT","date":"Jul 2022","sortKey":20220700,"precision":"month","year":2022,"theme":"adoption","sourceType":"media","scope":"legal","context":"Outliers, typically firms with fully outsourced core IT (e.g. Seddons)","url":"https://legaltechnology.com/2022/07/04/the-future-shape-of-law-firm-it-major-report-out-now/","access":"open"},{"id":"s1222","stat":"Law firms invest ~1% of revenue on innovation and R&D vs ~5% typical for other businesses","source":"Legal IT Insider / CTS Future Shape of Law Firm IT","date":"Jul 2022","sortKey":20220700,"precision":"month","year":2022,"theme":"adoption","sourceType":"media","scope":"legal","context":"Per LawTechUK director Jenifer Swallow: \\"law firms and legal service providers are generally not achieving their strategic potential\\"","url":"https://legaltechnology.com/2022/07/04/the-future-shape-of-law-firm-it-major-report-out-now/","access":"open"},{"id":"s1223","stat":"Clifford Chance has 320 legal technology-focused analysts embedded across its global practice teams","source":"Legal IT Insider / CTS Future Shape of Law Firm IT","date":"Jul 2022","sortKey":20220700,"precision":"month","year":2022,"theme":"adoption","sourceType":"media","scope":"legal","context":"Not IT first â \\"people with legal services expertise...who can help deliver niche solutions to each practice area\\"","url":"https://legaltechnology.com/2022/07/04/the-future-shape-of-law-firm-it-major-report-out-now/","access":"open"},{"id":"s1225","stat":"LawTechUK sandbox data: 80-90% of contracts shared by firms and said to be \\"bespoke\\" were in fact the same (anonymised SPAs)","source":"Legal IT Insider / CTS Future Shape of Law Firm IT","date":"Jul 2022","sortKey":20220700,"precision":"month","year":2022,"theme":"critical","sourceType":"media","scope":"legal","context":"Fundamental insight: legal work is far more standardisable than lawyers believe","url":"https://legaltechnology.com/2022/07/04/the-future-shape-of-law-firm-it-major-report-out-now/","access":"open"},{"id":"s1226","stat":"Lovable (prompt-based web app builder) achieved >$80M revenue in 7-8 months in 2025","source":"Building for Production / Ma & Mandal (CodeX)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"Cited as example of unique go-to-market strategy (GitHub visibility + multi-channel viral strategy) creating a defensive moat","url":"https://law.stanford.edu/publications/building-for-production-a-revised-playbook-for-enterprise-ready-generative-ai-solutions/","access":"open"},{"id":"s1227","stat":"100% of GCs interviewed reported regretting engaging one of their law firms in the preceding 12 months","source":"O Shaped Industry Report","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"citing Axiom research. \\"Every GC interviewed regretted engaging one of their law firms\\" â Axiom research cited in report","url":"https://online.flippingbook.com/view/681466963/","access":"open"},{"id":"s1228","stat":"4.9/10 â the score clients give when rating their experience of external
1counsel marketing their services","source":"O Shaped Industry Report","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Survey of GCs and Heads of Legal Operations","url":"https://online.flippingbook.com/view/681466963/","access":"open"},{"id":"s1229","stat":"45% of external counsel marketing and BD efforts have no or a negative impact on clients","source":"O Shaped Industry Report","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Majority of law firm BD activity is wasted or counterproductive","url":"https://online.flippingbook.com/view/681466963/","access":"open"},{"id":"s1230","stat":"1/3 of clients are either frustrated or annoyed by external counsel attempts to cross-sell","source":"O Shaped Industry Report","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Cross-selling is law firms\' desire to sell more, not a client need identification exercise","url":"https://online.flippingbook.com/view/681466963/","access":"open"},{"id":"s1231","stat":"Top influence on GC buying decisions: Referral from other in-house legal teams (4.25/5)","source":"O Shaped Industry Report","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Ranking of 11 BD approaches by GCs and Heads of Legal Operations; out of 5","url":"https://online.flippingbook.com/view/681466963/","access":"open"},{"id":"s1232","stat":"Innovation as a selling point rated 4.22/5 by GCs â second-highest ranked BD approach","source":"O Shaped Industry Report","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"\\"Previous experience\\" third at 4.21/5; Technical Legal Expertise fifth at 3.75/5","url":"https://online.flippingbook.com/view/681466963/","access":"open"},{"id":"s1233","stat":"Cross-selling rated 2.82/5 by GCs â lowest-rated BD approach by substantial margin","source":"O Shaped Industry Report","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"industry","scope":"legal","context":"Ranking: Thought Leadership 3.67/5, Rankings 3.25/5, Cross-selling 2.82/5 (bottom three)","url":"https://online.flippingbook.com/view/681466963/","access":"open"},{"id":"s1234","stat":"41% of large law firms have deployed public LLMs (OpenAI/Copilot/Other-Public) â highest deployment rate of all categories","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"n=143 law firms (>200 lawyers); 30% still \\"piloting or experimenting\\"; only 13% \\"considering\\" (down 13pp from 2023)","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1235","stat":"78% of large law firms actively engaging with public LLMs (deployed + licensed + piloting combined)","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Decisive shift from experimental to strategic; multi-platform strategy now standard","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1236","stat":"66% of large law firms actively engaging with firm-developed LLM solutions","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"28% deployed, 11% licensed but not rolled out; firms pursuing customised implementations","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1237","stat":"65% of large law firms actively engaging with other legal-specific LLMs","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Legal-specific LLMs lead in practice-area deployments (33 firms citing \\"across one or more practices\\")","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1238","stat":"55% of large law firms actively engaging with Westlaw LLM; 32% already deployed","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Relatively low piloting rate â shorter, more streamlined pilot phases for legal AI tools","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1239","stat":"37% of large law firms actively engaging with LexisNexis; only 13% deployed, 42% still \\"considering\\"","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"LexisNexis shows highest \\"considering\\" rate and lowest deployment rate of major platforms","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1240","stat":"On-premises LLM hosting fell from 10 firms (2023) to just 1 firm (2024)","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"\\"Two or more hosting locations\\" (32 responses) now most popular; private cloud instances (27 responses) second","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1241","stat":"Summarization is the #1 current use case: 62 respondents ranked it as their top choice","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Consistent with 2023 findings â remains the most mature and trusted LLM application in large law firms","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1242","stat":"Code creation rose from one of the lowest-ranked use cases in 2023 to second-highest in 2024 â 23 respondents ranking it #1","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"\\"Becoming a key use case of LLM in law firms too\\" â rapid discovery and validation of coding use cases","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1243","stat":"Answering legal questions: 13 respondents ranking it #1 (third place for current use case)","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Growing confidence in LLMs for legal reasoning tasks","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1244","stat":"2027 forecast: Document drafting projected to be #1 use case (27 top rankings), overtaking summarization (26);
1 code creation third (24)","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Firms expect increasingly sophisticated LLM applications over next 3 years","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1245","stat":"33% of firms report discussions about LLM business impact but without consensus; 29% see improved productivity without fundamental change; 26% anticipate transformative impact; 12% no discussions","source":"SKILLS 2025 AI Survey","date":"2024/2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Distribution largely unchanged from 2024 â stable views despite another year of deployment experience","url":"https://skills.law/the-surveys","access":"open"},{"id":"s1246","stat":"Leading USA-based LLM (ChatGPT): 800 million weekly active users by April/May 2025","source":"Bond Trends AI / BOND Capital","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"company disclosures. Consumer plans; from chart sourced to company disclosures"},{"id":"s1247","stat":"Big Six US tech CapEx (Apple, NVIDIA, Microsoft, Alphabet, Amazon AWS, Meta): $212B in 2024, up +63% YoY","source":"Bond Trends AI / BOND Capital","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Capital IQ / Morgan Stanley. Big Six = Apple, NVIDIA, Microsoft, Alphabet, Amazon (AWS only), Meta Platforms"},{"id":"s1248","stat":"USA AI IT jobs: +448% from January 2018 to April 2025; non-AI IT jobs: -9% in the same period","source":"Bond Trends AI / BOND Capital","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"University of Maryland UMD-LinkUpAIMaps / Outrigger Group. AI IT job postings index set to 100 at Jan 2018 baseline; non-AI IT jobs declining while AI IT jobs surge"},{"id":"s1249","stat":"Leading USA LLM reached 90% of users outside North America at Year 3; internet took until ~Year 23 to reach equivalent global diffusion","source":"Bond Trends AI / BOND Capital","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"UN/ITU, Sensor Tower. LLM data = monthly active mobile app users; internet comparison from UN/ITU"},{"id":"s1250","stat":"China LLM desktop user share: ~21% of non-China global LLM desktop share as of early 2025","source":"Bond Trends AI / BOND Capital","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"YipitData. Share measured across 6 leading global LLMs; Chinese LLMs gaining ground on US competitors"},{"id":"s1251","stat":"Autonomous taxi provider (Waymo) reached ~27% gross bookings market share in San Francisco operating zone vs ride-share","source":"Bond Trends AI / BOND Capital","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"YipitData. Physical world AI ramp: autonomous driving disrupting ride-share market. Original stats database entry of 34% was a misread from BOND chart (34% was Lyft\'s pre-Waymo share)."},{"id":"s1252","stat":"KPMG: -29% Big Four UK graduate intake 2023-2025 (1,399 â 942 trainees)","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"cross-sector","context":"Accountancy Age, The Finance Story. Specific service lines â not total graduate headcount across all KPMG UK"},{"id":"s1253","stat":"Deloitte: -18% graduate intake (1,700 â 1,400)","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"cross-sector","context":"Big Four \\"Junior Cull\\" driven by AI deployment and economic conditions combined"},{"id":"s1254","stat":"EY: -11% graduate intake (1,800 â 1,600)","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"cross-sector","context":"Pattern consistent across all Big Four; economic cover used to make structural changes permanent"},{"id":"s1255","stat":"PwC: -6% graduate intake (1,600 â 1,500)","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"cross-sector","context":"PwC explicitly cites \\"Acceleration Centres\\" alongside AI as primary drivers"},{"id":"s1256","stat":"McKinsey \\"Lilli\\" platform: 75% of 43,000 employees using it monthly by mid-2025","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"cross-sector","context":"Entrepreneur, Fanatical Futurist. Platform creates PowerPoint slides from prompts, modifies tone, synthesises proprietary knowledge"},{"id":"s1257","stat":"AI tools estimated to save approximately 200 billable hours per lawyer annually (4 hrs/week)","source":"Thomson Reuters Future of Professionals 2024","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"2024 report says 200 hours (4 hrs/week). 2025 report updated to 240 hours (5 hrs/week). ~$20B revenue impact estimate applies to US market only, not US/UK combined."},{"id":"s1258","stat":"96% of GCs acknowledge that ROI influences their technology investment decisions","source":"EY Law General Counsel Study","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Broken Ladder misrepresented this as \\"96% believe AI should reduce legal spend\\" - the actual finding is about ROI measurement generally, not AI-specific cost reduction. Separately, 87% cite cost reduction as a top priority and only 25% prioritise GenAI.","url":"https://www.eylaw.be/insights/2025-ey-law-general-counsel-study","access":"open"},{"id":"s1259","stat":"Only 25% of GCs prioritise GenAI among their technology investments","source":"EY Law General Counsel Study","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Despite broad AI hype, three-quarters of GCs are focused on other technology priorities. 87% cite cost reduction as a top priority.","url":"https://www.eylaw.be/insights/2025-ey-law-general-counsel-study","access":"open"},{"id":"s1260","stat":"Clifford Chance: 90% AI adoption rate across the firm by late 2025","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Legal IT Insider, December 2025. \\"AI is no longer a specialist tool but a universal mandate\\" â Paul Greenwood, Clifford Chance"},{"id":"s1261","stat":"AI hallucinations and reliability issues cost enterprises an estimated $67.4 billion globally in 2024","source":"AllAboutAI AI Hallucination Report","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Modelled estimate, not documented losses. AllAboutAI is a review/comparison site, not a research institution. Korra.ai cited this in a marketing blog but did not conduct the research. Timeframe is 2024 only, not 2024-2025."},{"id":"s1262","stat":"Stanford Digital Economy Lab: 13% relative decline in employment for workers aged 22-25 in AI-exposed occupations by late 2025","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"cross-sector","context":"IntuitionLabs / Stanford. Burden of adjustment disproportionately falling on youngest entrants"},{"id":"s1263","stat":"SparkCo: projected 15% slower hiring velocity for Q1 2026 for generalist associate roles due to budget reallocation","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"cross-sector","context":"SparkCo. Hiring freeze morphing into hiring redesign â unlike previous downturns, rebound unlikely"},{"id":"s1264","stat":"People Matters (July 2025): automation handles 60% of traditional junior analysis work","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"cross-sector","context":"People Matters. Remaining 40% requires high-cognition synthesis â creates \\"cognitive leap\\" problem for junior staff"},{"id":"s1265","stat":"Linklaters launched a 20-strong global team of \\"AI Lawyers\\" in November 2025","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Linklaters press release. Qualified lawyers trained in prompt engineering and workflow architecture â new professional identity"},{"id":"s1266","stat":"Vanguard predicts UK economic growth of just 0.8% in 2026, driven by tighter fiscal policy and tax rises","source":"The Broken Ladder / Treat Digital","date":"January 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"practitioner","scope":"cross-sector","context":"Vanguard economic outlook. Macroeconomic lethargy acting as confounding factor alongside AI-driven structural changes"},{"id":"s1267","stat":"74% of firms report successful AI model implementation","source":"Harbor CINO Sounding Board","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Firms have made significant progress deploying AI models"},{"id":"s1268","stat":"11% of firms made substantial progress staging documents for AI access (down from 27% last year)","source":"Harbor CINO Sounding Board","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Fewer firms advanced in staging documents for AI compared to previous year"},{"id":"s1269","stat":"7% of firms focusing on applying contextual information for AI access (up from 5% last year)","source":"Harbor CINO Sounding Board","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Contextual enrichment efforts remain relatively stable"},{"id":"s1270","stat":"83% of law departments anticipate increase in overall legal demand","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Legal demand expected to remain strong in 2025","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1271","stat":"17% of law departments anticipate no change in legal demand","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Minority expect flat demand","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1272","stat":"0% of law departments anticipate decrease in legal demand","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"No departments expecting a decrease","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1273","stat":"34% of law departments plan to automate routine tasks (up 9% from last year)","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Growing focus on automation to manage rising demand","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1274","stat":"20% of law departments intend to invest in AI (up 8% from last year)","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Increasing AI investment intent","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1275","stat":"36% plan to increase use of current technology","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Method for handling growing demand","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1276","stat":"36% plan to reengineer/standardise work processes","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Method for handling growing demand","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1277","stat":"39% plan to increase workload of existing legal resources","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Most common response to rising demand","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1278","stat":"33% plan to increase use of outside counsel","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Method for handling growing demand","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1279","stat":"29% plan to increase number of in-house lawyers","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Method for handling growing demand","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1280","stat":"7% plan to use Alternative Legal Service Providers (ALSPs)","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"client","sourceType":"analyst","scope":"legal","context":"Lowest-ranked method for handling demand","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1281","stat":"56% of law departments use AI for contract drafting and review","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"t
1heme":"adoption","sourceType":"analyst","scope":"legal","context":"Top AI use case for law departments","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1282","stat":"47% use AI for document drafting and review","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Second-ranked AI use case","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1283","stat":"33% use AI for legal research","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Third-ranked AI use case","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1284","stat":"21% use AI for metadata tagging","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Fourth-ranked AI use case","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1285","stat":"19% exploring legal chatbots","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Fifth-ranked AI use case","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1286","stat":"72% of law departments have core legal systems implemented","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Core systems = e-billing, matter management, DMS, reporting/analytics","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1287","stat":"34% of law departments have adopted emerging technology tools (up from 30% last year)","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Emerging tech = AI, intake, workflow automation, RFP tools","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1288","stat":"29% of all survey participants have AI implemented and in use","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Overall AI implementation status","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1289","stat":"54% considering AI implementation within next 1-2 years","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Over half considering AI implementation","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1290","stat":"17% not considering AI in the immediate future","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Small minority not considering AI","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1291","stat":"47% of Biotech/Pharma/Med Device sector have AI implemented (leading sector)","
1source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Leading sector for AI implementation","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1292","stat":"42% of Technology sector have AI implemented","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Second-highest implementing sector","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1293","stat":"40% of Financial Services sector have AI implemented","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Third-highest implementing sector","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1294","stat":"61% of firms have informal program to engage clients on AI when appropriate","source":"Harbor CINO Sounding Board","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Most common approach to client AI outreach"},{"id":"s1295","stat":"17% have formal program targeting limited number of clients","source":"Harbor CINO Sounding Board","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Formal but limited client engagement"},{"id":"s1296","stat":"11% have formal program for broad client base","source":"Harbor CINO Sounding Board","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Formal broad client engagement"},{"id":"s1297","stat":"11% only engage clients reactively or upon request","source":"Harbor CINO Sounding Board","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Reactive-only engagement"},{"id":"s1298","stat":"83% of companies have either implemented or are focused on optimising/implementing tighter enforcement of outside counsel guidelines","source":"Harbor Law Department Survey / CLOC","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Strong focus on OCG governance around AI","url":"https://harborglobal.com/news-releases/majority-of-law-departments-look-to-ai-as-managing-workloads-remains-top-challenge/","access":"open"},{"id":"s1299","stat":"LLM accuracy in commercial contract reviews increased from 74% to 95% on average through optimised RAG approach","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Core finding - optimisation can achieve near-human accuracy without fine-tuning","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1300","stat":"50% total time saved on diligence review during foundational ML era (2013-2020)","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Historical baseline for ML-era savings","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1301","stat":"80% reduction in manual review process during ML era","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"ML tools reduced manual review substantially but savings didn\'t scale","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1302","stat":"Best performing RAG configuration: F1 score of 0.95 (Follow Up Prompt) and 0.92 (First Prompt)","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"RAG 10 - GPT4-32K - Improved Prompt configuration","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1303","stat":"Machine Learning Extraction Tool achieved average F1 score of 0.86","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Traditional ML tool comparison benchmark","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1304","stat":"GenAI Contract Review Tool achieved average F1 score of 0.72","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Worst performer - general non-customisable tool","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1305","stat":"Follow Up Prompt gives average improvement of 9.2% across all configurations","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Prompt sequencing technique improves all scenarios","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1306","stat":"Follow Up Prompt led to F1 score increase of up to 30-35% in certain cases","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Maximum impact of follow-up prompting on Cap on Liability provisions","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1307","stat":"Optimised retrieval approach increases effectiveness of identifying relevant provisions by ~15% on average (Scenario 1)","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Recall@10 across all 510 CUAD contracts","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1308","stat":"Optimised retrieval improves correct chunk identification by ~20% on average (Scenario 2
1: contracts with 20+ chunks)","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"More challenging scenario with larger documents","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1309","stat":"Optimised retrieval improves identification by ~14% on average (Scenario 3: contracts with 40+ chunks, Recall@20)","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Large document scenario","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1310","stat":"Improvement in retrieval accuracy of between 14% and 22% from optimised components","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Overall retrieval optimisation impact range","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1311","stat":"Chunking Strategy 2 achieved best Recall@1 weighted average of 36.62% and Recall@K of 65.17%","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"3,500 characters with 700 character overlap was optimal","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1312","stat":"Exclusivity Provision saw retrieval improvement of at least 43% from optimisation","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Most improved provision through retrieval optimisation","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1313","stat":"Keyword attention instruction improved recall by up to 16% on some Provisions","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Adding keyword-attention instructions to prompts","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1314","stat":"Cap on Liability and Exclusivity each achieved 10% F1 score increase with Improved Prompt","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Provision-specific prompt improvement","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1315","stat":"Audit Rights achieved 4% F1 score increase with Improved Prompt","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Provision-specific prompt improvement","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1316","stat":"6% increase in F1 score transitioning from Full In-Context to RAG 20 (First Prompt, all Provisions)","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"RAG outperforms full in-context approaches","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1317","stat":"8% increase in F1 score from Full In-Context to RAG 20 (excluding easy Provisions)","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"RAG advantage more pronounced for harder extractions","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1318","stat":"GPT4-32K outperformed GPT4-Turbo by 4% (First Prompt) and 1% (Follow Up Prompt) average F1 scores","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Model comparison for provision extraction","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1319","stat":"GPT4-32K achieved higher F1 score in 11 of 18 paired comparisons, same in 4, lower in only 3","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Consistent superiority of GPT4-32K for this task","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1320","stat":"510 CUAD contracts used for retrieval testing","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Test dataset size","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1321","stat":"167 CUAD documents larger than 20 chunks; 71 larger than 40 chunks","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Document size distribution in test set","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1322","stat":"More than 40 Provisions tested during the research","source":"AG RAG Report","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Scope of provision testing","url":"https://www.addleshawgoddard.com/en/insights/insights-briefings/2024/technology/harnessing-power-large-language-models-legal-document-review/","access":"open"},{"id":"s1323","stat":"127 law students completed the randomised controlled trial","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Study participants from University of Minnesota and University of Michigan law schools","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1324","stat":"153 students formally enrolled; 127 completed","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"83% completion rate","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1325","stat":"250+ students expressed interest in participating","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Strong interest in AI experimentation among law students","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1326","stat":"AI tools improved quality in 4 of 6 tested legal assignments","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Both Vincent AI and o1-preview improved quality in majority of tasks","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1327","stat":"Vincent AI yielded productivity boosts of approximately 38% to 115%","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Statistically significant productivity gains across 5 of 6 tasks","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1328","stat":"o1-preview increased productivity by 34% to 140%","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Particularly strong effects in complex tasks like drafting persuasive letters and analysing complaints","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1329","stat":"o1-preview quality improvements ranged from ~10% to 28% (10.2%, 20.9%, 23.6%, 28.1%)","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Significant at 1% level for 3 assignments and 5% level for the fourth","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1330","stat":"Vincent AI quality improvements ranged from 8% to 15% (8%, 12%, 13.5%, 15.1%)","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Significant at 10% level across 4 assignments","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1331","stat":"3 total hallucinations in Vincent AI-assisted assignments","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Fewer than no-AI group (4 total)","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1332","stat":"11 total hallucinations in o1-preview-assisted assignments","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Substantially higher than both Vincent AI and no-AI groups","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1333","stat":"4 total hallucinations in no-AI assignments","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Baseline hallucination rate without AI","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1334","stat":"18 total hallucinated citations across all treatment groups on 768 tasks","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Overall hallucination rate was very low","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1335","stat":"Speed improvements of 14-37% for Vincent AI","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Range across legal tasks where speed improvements were identified","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1336","stat":"Speed improvements of 12-28% for o1-preview","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Range across legal tasks where speed improvements were identified","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1337","stat":"Vincent AI achieved average 21% reduction in completion time across 6 assignments","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Comparable to GPT-4\'s 22% average reduction in prior study","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1338","stat":"o1-preview achieved average 19% reduction in completion time across 6 assignments","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Similar speed gains to prior-generation AI","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1339","stat":"GPT-4 reduced completion time by average of ~22% across 4 assignments (prior study)","source":"Choi, Monahan, & Schwarcz (prior study)","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"legal","context":"Baseline comparison from prior RCT"},{"id":"s1340","stat":"o1 ranked 89th percentile on competitive programming questions","source":"OpenAI","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Benchmark performance of reasoning models","url":"https://openai.com/index/learning-to-reason-with-llms/","access":"open"},{"id":"s1341","stat":"o1 placed among top 500 students in USA Mathematical Olympiad qualifier","source":"OpenAI","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Reasoning model performance on complex tasks","url":"https://openai.com/index/learning-to-reason-with-llms/","access":"open"},{"id":"s1342","stat":"GPT-4 grew from 117 million to 175 billion parameters","source":"OpenAI","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Scale of parameter growth in LLMs","url":"https://openai.com/index/learning-to-reason-with-llms/","access":"open"},{"id":"s1343","stat":"General-purpose LLMs hallucinated 58-82% of the time on certain law-related queries","source":"Dahl et al.","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"legal","context":"Prior study on hallucination rates (queries designed to induce hallucinations)","url":"https://academic.oup.com/jla/article/16/1/64/7699227","access":"open"},{"id":"s1344","stat":"RAG-enabled tools hallucination rate: 17-33% on certain queries","source":"Magesh et al.","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"legal","context":"Lower than general-purpose but still significant (queries included false premises)","url":"https://arxiv.org/abs/2405.20362","access":"open"}
1,{"id":"s1345","stat":"Prior study: 90% of legal aid professionals reported increased productivity with AI; 75% planned to continue using AI","source":"Kim & Chien","date":"Mar 2024","sortKey":20240300,"precision":"month","year":2024,"theme":"adoption","sourceType":"academic","scope":"legal","context":"~100 legal aid professionals studied","url":"https://digitalcommons.lmu.edu/llr/vol57/iss4/2/","access":"open"},{"id":"s1346","stat":"Harvey raised $100M Series C at $1.5B valuation (July 2024)","source":"Harvey press release","date":"Jul 2024","sortKey":20240700,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"legal","context":"From Google Ventures, OpenAI, Kleiner Perkins, Sequoia Capital","url":"https://www.harvey.ai/blog/harvey-raises-series-c","access":"open"},{"id":"s1347","stat":"Thomson Reuters acquired Casetext for $650 million","source":"Reuters","date":"Jun 2023","sortKey":20230600,"precision":"month","year":2023,"theme":"market","sourceType":"media","scope":"legal","context":"Major legal AI acquisition","url":"https://www.thomsonreuters.com/en/press-releases/2023/june/thomson-reuters-corporation-signs-definitive-agreement-to-acquire-casetext","access":"open"},{"id":"s1348","stat":"Consultants using GPT-4 completed 12.2% more tasks, worked 25.1% faster, produced 40% higher-quality results (prior study)","source":"Dell\'Acqua et al.","date":"Sep 2023","sortKey":20230900,"precision":"month","year":2023,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"\\"Jagged frontier\\" study - benefits did not extend to all tasks"},{"id":"s1349","stat":"o1-preview improved analytical depth in 3 of 6 assignments tested","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Statistically significant improvement in legal analysis quality - first evidence of this kind","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1350","stat":"Draft NDA was the only assignment where neither AI tool improved quality or speed","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Transactional tasks with templates showed no AI benefit","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1351","stat":"114 of 127 participants completed post-experiment survey","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"90% survey response rate","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1352","stat":"6 realistic legal assignments tested (client email, legal memo, complaint analysis, NDA, motion to consolidate, persuasive letter)","source":"Schwarcz et al.","date":"Mar 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Tasks designed with practicing attorneys for junior associate level","url":"https://jle.aals.org/context/home/article/2514/viewcontent/5._20Choi_20_26_20Schwarcz.pdf","access":"open"},{"id":"s1353","stat":"Prior GPT-4 RCT: no statistically significant improvements in overall quality across all 4 assignments","source":"Choi, Monahan, & Schwarcz (prior study)","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"legal","context":"Key contrast - newer AI tools now do improve quality"},{"id":"s1354","stat":"Pre-GenAI tools like Kira and Luminance wouldn\'t have taken you seriously if your annual budget was EUR 10,000","source":"Ask Q","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"legal","context":"Pre-GenAI contract review tools required very large budgets"},{"id":"s1355","stat":"Now you can use powerful AI tools for EUR 150-250 a month","source":"Ask Q","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"legal","context":"Post-GenAI contract review pricing has collapsed dramatically"},{"id":"s1356","stat":"ChatGPT can be used for as little as EUR 20 a month","source":"Ask Q","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"market","sourceType":"vendor","scope":"legal","context":"Floor price for AI-based contract review via general-purpose LLMs"},{"id":"s1357","stat":"Tools promise to make users work 40% more efficiently, but the end result is they have to work 40% longer to accommodate the tools\' workflows","source":"Ask Q","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Poor UX/UI in legal tech tools negates efficie
1ncy gains"},{"id":"s1358","stat":"Best-performing agent (Gemini 2.5 Pro) completes 30.3% of tasks autonomously","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Benchmark of 175 diverse professional tasks in simulated software company","url":"https://the-agent-company.com/","access":"open"},{"id":"s1359","stat":"Gemini 2.5 Pro achieves 39.3% score with partial completion credits","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Partial credit metric that rewards subtask completion","url":"https://the-agent-company.com/","access":"open"},{"id":"s1360","stat":"Claude 3.7 Sonnet achieves 26.3% full completion, 36.4% partial score","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Second-best performing model on the benchmark","url":"https://the-agent-company.com/","access":"open"},{"id":"s1361","stat":"Claude 3.5 Sonnet achieves 24.0% full completion, 34.4% partial score","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Earlier Claude model performance","url":"https://the-agent-company.com/","access":"open"},{"id":"s1362","stat":"GPT-4o achieves only 8.6% full completion, 16.7% partial score","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"OpenAI\'s model underperforms Claude and Gemini significantly","url":"https://the-agent-company.com/","access":"open"},{"id":"s1363","stat":"Gemini 2.0 Flash achieves 11.4% completion at cost of less than $1 per task","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Very cost-efficient model at $0.6 per task vs $4.2 for Gemini 2.5 Pro","url":"https://the-agent-company.com/","access":"open"},{"id":"s1364","stat":"Average cost per task for Gemini 2.5 Pro is $4.2, requiring 27.2 steps","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Cost and computational expense of top-performing agent","url":"https://the-agent-company.com/","access":"open"},{"id":"s1365","stat":"Claude 3.7 Sonnet costs $4.1 per task with 27.8 average steps","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Comparable cost to Gemini but lower success rate","url":"https://the-agent-company.com/","access":"open"},{"id":"s1366","stat":"175 diverse, realistic professional tasks in the benchmark","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Benchmark covers SDE, PM, DS, Admin, HR, Finance roles","url":"https://the-agent-company.com/","access":"open"},{"id":"s1367","stat":"Llama 3.1 405B (open-weight) achieves 7.4% success, nearly matching GPT-4o (8.6%)","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Open models approaching closed model performance","url":"https://the-agent-company.com/","access":"open"},{"id":"s1368","stat":"Llama 3.3 70B achieves 6.9% success rate - comparable to Llama 3.1 405B despite being much smaller","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Smaller models catching up in agent performance","url":"https://the-agent-company.com/","access":"open"},{"id":"s1369","stat":"OWL RolePlay multi-agent achieves only 4.0% with GPT-4o vs 8.6% for OpenHands single-agent","source":"TheAgentC
1ompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Multi-agent collaboration underperforms single-agent approaches","url":"https://the-agent-company.com/","access":"open"},{"id":"s1370","stat":"20 computer science students/engineers spent approximately 3,000 person-hours over 2 months creating the benchmark","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Scale of effort required to build realistic agent benchmarks","url":"https://the-agent-company.com/","access":"open"},{"id":"s1371","stat":"Some complex tasks took more than 10 hours each to design, implement, test, and verify","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Difficulty of creating high-quality benchmark tasks","url":"https://the-agent-company.com/","access":"open"},{"id":"s1372","stat":"12 large language model backbones tested","source":"TheAgentCompany","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Breadth of model evaluation","url":"https://the-agent-company.com/","access":"open"},{"id":"s1373","stat":"52% of UK workers feel they are doing more work than is possible for one person","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Survey of 1,440 UK employees","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1374","stat":"36% of UK leaders claim to do the work of at least two members of staff","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Survey of 1,480 UK leaders","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1375","stat":"25% of UK employees claim to do the work of at least two members of staff","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Survey of 1,440 UK employees","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1376","stat":"60% of UK leaders feel their current workload is more than that of one person","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Leaders under even more pressure than employees","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1377","stat":"72% of UK leaders expect AI agents to be fully integrated across their operations, delivering significant value","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Strong leader expectation for agentic AI integration","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1378","stat":"39% expect full agentic integration within the next two years","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Near-term expectation for agentic AI","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1379","stat":"21% expect agentic transformation within the next 12 months","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Imminent integration expectation","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1380","stat":"8% expect agentic integration within 6 months","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Most aggressive timeline expectation","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1381","stat":"69% of leaders say AI agents will have a positive impact on innovation in their industry in the next five years","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Industry-level innovation impact expectation","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"}
1,{"id":"s1382","stat":"53% believe AI agents will benefit the nation\'s economy overall","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"National economic benefit expectation","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1383","stat":"Microsoft is investing GBP 2.5 billion in UK AI infrastructure, skilling and education","source":"Microsoft","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Microsoft\'s UK AI investment commitment"},{"id":"s1384","stat":"Microsoft has trained more than 1 million people in the UK so far","source":"Microsoft","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Scale of Microsoft UK AI training"},{"id":"s1385","stat":"Only 20% of UK organisations have advanced and scaled AI adoption, up from 9% in 2023","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Still low but doubled in two years","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1386","stat":"50% of UK leaders say there is a gap between AI ambition and action in their company","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Half of leaders acknowledge an ambition-action gap","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1387","stat":"41% of UK leaders admit they \\"don\'t know where to start\\" with AI","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Significant proportion of leaders lacking AI direction","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1388","stat":"Only 40% say their business is using AI to strengthen cybersecurity","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Low adoption for security use case","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1389","stat":"46% of UK leaders say their organisation has a formal AI strategy in place, up from 29% in 2024","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Notable increase but still under half","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1390","stat":"22% of organisations are struggling to identify clear AI use cases aligned with business needs (IDC data)","source":"IDC via Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Use case identification as barrier"},{"id":"s1391","stat":"38% of UK C-suite leaders say employees using AI respond faster to customer needs and are more productive and creative (LinkedIn 2025 Work Change report)","source":"LinkedIn via Microsoft/Goldsmiths","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Cross-referenced LinkedIn data on AI worker productivity"},{"id":"s1392","stat":"31% say AI-augmented workers are happier and better satisfied in their jobs","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Employee satisfaction correlation with AI use","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1393","stat":"67% of leaders believe workers would be more productive if AI could autonomously handle time-consuming tasks","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Leader belief in agentic AI productivity potential","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1394","stat":"46% of employees believe they would be more productive if AI handled repetitive tasks","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Employee belief in AI productivity potential","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1395","stat":"87% believe that transforming the workplace by speeding up AI adoption is important over the next year","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Near-universal agreement on urgency","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1396","stat":"71% of UK organisations are looking for ways to create cost reductions through automation","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Top strategic priority for AI","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"}
1,{"id":"s1397","stat":"64% seek efficiency and productivity gains through AI-led workflows and processes","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Second strategic priority","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1398","stat":"58% would welcome agentic assistance for strategic planning","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Leadership use case demand","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1399","stat":"56% would welcome agentic assistance for decision-making","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Leadership use case demand","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1400","stat":"Public sector frontline workers (doctors, nurses, police, teachers) could save 23 million hours through generative AI tools","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Public sector productivity potential","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1401","stat":"64% of UK leaders point to regulatory uncertainty as a barrier, up from 44% in 2024","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"regulatory","sourceType":"vendor","scope":"cross-sector","context":"Significant increase in regulatory concerns","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1402","stat":"52% of leaders worry agentic AI increases cybersecurity risks","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Security concern as adoption barrier","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1403","stat":"49% of organisations report a lack of AI governance talent","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Governance talent gap","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1404","stat":"Businesses incorporating AI into security strategy might suffer 20% lower financial losses after a successful attack","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"cited research. AI-enhanced cybersecurity ROI","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1405","stat":"51% of UK leaders say their organisation wants to harness AI\'s competitive advantage but is not yet ready","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Readiness gap","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"}
1,{"id":"s1406","stat":"Ability to measure AI impact has risen from 33% in 2023 to 49% in 2025","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Improving but still under half can measure AI impact","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1407","stat":"19% of leaders report measurable ROI on AI investments so far","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Early ROI evidence from experimentation phase","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1408","stat":"57% of UK leaders report a productivity gap between employees who use AI and those who do not","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"AI divide emerging in workforces","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1409","stat":"36% of leaders say frequent AI users are more likely to be promoted","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Career advantage for AI-literate workers","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1410","stat":"Only 18% of UK workers say they have received training on how to use AI in their job","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Very low AI training provision","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1411","stat":"Only 40% of leaders say their organisation is currently harnessing AI for competitive edge","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Low competitive deployment","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1412","stat":"UK is the third largest AI market in the world","source":"UK Government (AI Opportunities Action Plan)","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"market","sourceType":"government","scope":"cross-sector","context":"UK market positioning","url":"https://www.gov.uk/government/publications/ai-opportunities-action-plan","access":"open"},{"id":"s1413","stat":"74% of businesses and 64% of public organisations are looking to AI agents to automate repetitive workflows and reduce operating costs","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Private vs public sector demand comparison","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1414","stat":"60% of private companies and 56% of public organisations feel AI agents can personalise customer interactions","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Personalisation use case","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"}
1,{"id":"s1415","stat":"63% of employees hope agentic AI will catch errors in their work","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Employee desire for AI quality assurance","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1416","stat":"50% of employees hope AI will help them better prioritise tasks","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Task management use case","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1417","stat":"40% hope AI can uncover ways to seek promotion","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Career development use case","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1418","stat":"48% believe AI agents can fill skills gaps","source":"Microsoft/Goldsmiths","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Skills augmentation","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1419","stat":"Taylor Wimpey: 92% Copilot adoption rate from 300 to 1,000 licenses","source":"Taylor Wimpey via Microsoft","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Construction company case study"},{"id":"s1420","stat":"Taylor Wimpey: average daily time saving of 28 minutes, rising to 2 hours for some users","source":"Taylor Wimpey via Microsoft","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Measured time savings from Copilot"},{"id":"s1421","stat":"Linklaters: more than 2 million prompts submitted across 7,400 unique users of Laila (GenAI chatbot)","source":"Linklaters via Microsoft","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Law firm GenAI adoption scale"},{"id":"s1422","stat":"Linklaters: \\"pretty much everyone in the firm, globally, has used Laila at least once\\"","source":"Linklaters via Microsoft","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Firm-wide adoption of internal AI tool"},{"id":"s1423","stat":"Balfour Beatty: AI tool achieves around 80% accuracy of a construction director with 25 years\' experience for defect categorisation","source":"Balfour Beatty via Microsoft","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"AI performance vs expert benchmark in construction"},{"id":"s1424","stat":"Survey sample: 1,480 UK leaders and 1,440 UK employees, conducted via YouGov between 21 January and 3 February 2025","source":"Microsoft/Goldsmiths/YouGov","date":"March 2025","sortKey":20250300,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Research methodology","url":"https://news.microsoft.com/source/emea/features/ai-divide-across-uk-organisations-risks-growth-for-many-new-microsoft-research-reveals/","access":"open"},{"id":"s1425","stat":"Developers forecast AI would reduce completion time by 24% before starting tasks","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Pre-study developer expectations vs reality","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1426","stat":"After completing the study, developers estimated AI reduced completion time by 20% - still believing AI helped despite being slowed down","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Post-hoc developer self-assessment was wrong","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1427","stat":"Economics experts predicted AI would reduce completion time by 39%","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Expert forecasts drastically overestimated AI benefit","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1428","stat":"Machine learning experts predicted AI would reduce completion time by 38%","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Even ML experts dramatically overestimated AI benefit","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1429","stat":"16 developers completed 246 tasks averaging 2.0 hours each","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Study scale and task duration","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1430","stat":"Repositories averaged 23,000 GitHub stars","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Scale of repositories studied","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1431","stat":"Developers had an average of 5 years experience on their specific repository, representing 59% of the repository\'s lifetime","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Ver
1y high developer familiarity with codebases","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1432","stat":"Developers had made an average of 1,500 commits to their repos","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Extremely experienced contributors","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1433","stat":"Repositories averaged 1,100,000 lines of code, 4,900 forks, 20,000 commits, and 710 committers","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Very large and mature codebases","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1434","stat":"Repositories averaged approximately 10 years old","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Mature, well-established projects","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1435","stat":"93% of developers have previously used LLMs","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Not AI-naive participants","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1436","stat":"Only 44% had prior experience using the Cursor IDE","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Moderate familiarity with specific tool used","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1437","stat":"143 hours of screen recordings manually labelled (29% of total developer hours)","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Rich qualitative data collection","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1438","stat":"Developers completed 136 issues with AI-allowed and 110 issues with AI-disallowed","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Balanced randomised design","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1439","stat":"Developers accept less than 44% of AI-generated code suggestions","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Low AI reliability/acceptance rate","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1440","stat":"75% of developers report reading every line of AI-generated code","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"High review overhead for AI outputs","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1441","stat":"56% of developers report often needing to make major changes to clean up AI code","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Significant AI code quality issues","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1442","stat":"100% of developers report needing to modify AI-generated code","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Universal need for human correction","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1443","stat":"9% of developer time spent reviewing and cleaning AI-generated outputs when AI is allowed","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Measurable overhead from AI assistance","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1444","stat":"4% of developer time spent waiting on AI generations","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Latency cost of AI tool usage","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1445","stat":"Developers write 47% more lines of code per hour on AI-allowed issues (not statistically significant, p=0.16)","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"More verbose but not necessarily more productive code","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1446","stat":"34 economics experts and 54 machine learning experts provided speedup forecasts","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Expert forecast sample sizes","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1447","stat":"Developers paid $150/hour to participate","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Study design and incentivisation","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1448","stat":"Prior literature found 56% speedup (Peng et al.), 21% speedup (Paradis et al.), 65% increase in task requirements satisfied (Weber et al.) - all on synthetic or non-fixed outcome tasks","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"literature review. Contrast with prior positive findings","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1449","stat":"Prior literature found 14-51% increases in output metrics using non-fixed outcome measures (natural experiments and RCTs)","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"literature review. Previous studies used less rigorous outcome measures","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1450","stat":"20% of surveyed lawyers under 40 were thinking about leaving the profession entirely","source":"IBA Global Survey of Lawyers","date":"Feb 2022","sortKey":20220200,"precision":"month","year":2022,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Citing work-life balance as key cause for concern","url":"https://www.ibanet.org/New-IBA-report-reveals-significant-numbers-of-young-lawyers-want-to-leave-their-current-job","access":"open"},{"id":"s1451","stat":"71% of lawyers aged 25 and under expressed concern about work-life balance","source":"IBA Global Survey of Lawyers","date":"Feb 2022","sortKey":20220200,"precision":"month","year":2022,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Highest concern bracket among young lawyers","url":"https://www.ibanet.org/New-IBA-report-reveals-significant-numbers-of-young-lawyers-want-to-leave-their-current-job","access":"open"},{"id":"s1452","stat":"44% of respondents knew colleagues who left to take in-house positions","source":"Bloomberg Law State of Practice Survey","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"media","scope":"legal","context":"Second most popular departure destination for attorneys was in-
1house"},{"id":"s1453","stat":"70.2% of lawyers cited workplace promoting a healthy work-life balance as important","source":"Artificial Lawyer survey","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"media","scope":"legal","context":"Top factor in job attractiveness"},{"id":"s1454","stat":"50.9% cited variety of work as important","source":"Artificial Lawyer survey","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"media","scope":"legal","context":"Job attractiveness factor"},{"id":"s1455","stat":"34.8% cited opportunities to travel or work abroad","source":"Artificial Lawyer survey","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"media","scope":"legal","context":"Job attractiveness factor"},{"id":"s1456","stat":"32% cited flexible working schemes","source":"Artificial Lawyer survey","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"media","scope":"legal","context":"Job attractiveness factor"},{"id":"s1457","stat":"29.1% cited location of the workplace","source":"Artificial Lawyer survey","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"media","scope":"legal","context":"Job attractiveness factor"},{"id":"s1458","stat":"22.2% cited salary","source":"Artificial Lawyer survey","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"media","scope":"legal","context":"Job attractiveness factor - notably low priority"},{"id":"s1459","stat":"22.1% cited high staff retention rates","source":"Artificial Lawyer survey","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"media","scope":"legal","context":"Job attractiveness factor"},{"id":"s1460","stat":"21.6% cited regular career or professional development support","source":"Artificial Lawyer survey","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"media","scope":"legal","context":"Job attractiveness factor"},{"id":"s1461","stat":"7 out of 10 lawyers say long hours gravely impact their mental health","source":"Cited in report (uncredited)","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Mental health impact of legal profession"},{"id":"s1462","stat":"35% of UK law firm partners are women","source":"SRA 2021 Report","date":"2021","sortKey":20210000,"precision":"year","year":2021,"theme":"workforce","sourceType":"government","scope":"legal","context":"Gender imbalance in partnership"},{"id":"s1463","stat":"35.8% of young lawyers expected to leave their workplace in the next year due to poor work-life balance","source":"IBA Young Lawyers Report","date":"Feb 2022","sortKey":20220200,"precision":"month","year":2022,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Direct link between work-life balance and attrition"},{"id":"s1464","stat":"54% of IBA-surveyed young lawyers \\"somewhat likely\\" or \\"highly likely\\" to move to a new workplace","source":"IBA Report","date":"Feb 2022","sortKey":20220200,"precision":"month","year":2022,"theme":"workforce","sourceType":"industry","scope":"legal","context":"3,000 young lawyers (aged 40 and under) surveyed worldwide"},{"id":"s1465","stat":"33% of IBA-surveyed young lawyers wanted to switch to a different area of the legal profession","source":"IBA Report","date":"Feb 2022","sortKey":20220200,"precision":"month","year":2022,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Career mobility within profession"},{"id":"s1466","stat":"9 out of 10 Belgian lawyers experience stress about their work","source":"Belgian bar research","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"academic","scope":"legal","context":"Mental health in legal profession"},{"id":"s1467","stat":"~30% of Belgian lawyers consider work stress problematic","source":"Belgian bar research","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"academic","scope":"legal","context":"Subset who find stress problematic vs. normal"},{"id":"s1468","stat":"After 10 years, almost 50% of female lawyers have left the bar","source":"Belgian bar research","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"academic","scope":"legal","context":"Gender-specific attrition from legal profession"},{"id":"s1469","stat":"German Big Law salary increases up to EUR 160k to attract talent","source":"Dr. Nadine Lilienthal observation","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"commentary","scope":"legal","context":"Salary as primary but insufficient talent weapon"},{"id":"s1470","stat":"2,000+ billable hour targets referenced as standard at US firms","source":"Chrissie Wolfe","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"commentary","scope":"legal","context":"Context for work-life balance impossibility"},{"id":"s1471","stat":"1,750 billable hours referenced as standard target","source":"Jana Blount (DLA Piper)","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Annual billable hour target lawyers must hit"},{"id":"s1472","stat":"UK trialling four-day work week with 30 organisations","source":"The Guardian","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"workforce","sourceType":"media","scope":"legal","context":"Potential disruption to law firm working model"},{"id":"s1473","stat":"Law firms\' most significant expense increased by more than 10% almost overnight due to the pay war for legal talent","source":"William Woodford (Avantech Law)","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"market","sourceType":"commentary","scope":"legal","context":"Impact of talent competition on firm economics"}
1,{"id":"s1474","stat":"Inflation at highest pace in 40 years impacting law firm cost base","source":"William Woodford (Avantech Law)","date":"2022","sortKey":20220000,"precision":"year","year":2022,"theme":"market","sourceType":"commentary","scope":"legal","context":"Economic pressures on the billable hour model"},{"id":"s1475","stat":"Only 7% of C-suite executives are digitally literate","source":"Brian Solis (ServiceNow)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"AI literacy among leadership likely even lower"},{"id":"s1476","stat":"90% of the US population lives within 10 miles of a Walmart store","source":"David Rogers (Columbia Business School)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Walmart\'s unique proximity advantage for e-commerce innovation"},{"id":"s1477","stat":"Meaningful revenue from strategic innovation emerges within 5 years, with billion-dollar business units forming in 10 years","source":"Gina O\'Connor (Babson College)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Timeline for breakthrough innovation capability maturity"},{"id":"s1478","stat":"42% of CEOs surveyed by PwC believe their companies won\'t survive the next 10 years if they don\'t change course","source":"PwC CEO Survey","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Existential threat perception among corporate leaders","url":"https://www.pwc.com/gx/en/news-room/press-releases/2025/pwc-2025-global-ceo-survey.html","access":"open"},{"id":"s1479","stat":"Life expectancy of corporations has plummeted from 75 years to just 15","source":"Ed Ross (Mach49)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Accelerating pace of corporate disruption"},{"id":"s1480","stat":"Unicorn startups collectively valued at nearly $4 trillion","source":"Ed Ross (Mach49)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Scale of startup-driven value creation"},{"id":"s1481","stat":"Mach49 team responsible for over $60 billion in market capitalisation","source":"Ed Ross (Mach49)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Venture building track record"},{"id":"s1482","stat":"Only 1 in 30 company startups builds a new $50 million business; 29 out of 30 attempts fail","source":"Frank Mattes (Lean Scaleup)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Corporate venture building failure rate","url":"https://www.leanscaleup.com/book/","access":"open"},{"id":"s1483","stat":"80% chance that growth \\"outside the box\\" is a top-5 priority in your company","source":"Frank Mattes (Lean Scaleup)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Near-universal priority but low delivery rate","url":"https://www.leanscaleup.com/book/","access":"open"},{"id":"s1484","stat":"91% of innovation departments confirmed the phase of launching visible but non-performance-focused actions is ending","source":"ITONICS benchmark","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Shift to results-driven innovation","url":"https://www.itonics-innovation.com/blog/corporate-innovation-trends","access":"open"},{"id":"s1486","stat":"63% of innovation teams are working on creating standardised innovation playbooks","source":"ITONICS benchmark","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Push for consistency across innovation functions","url":"https://www.itonics-innovation.com/blog/corporate-innovation-trends","access":"open"},{"id":"s1487","stat":"32% of innovation teams are prioritising cultural \\"acculturation\\" in 2025","source":"ITONICS benchmark","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Stakeholder misalignment as leading cause of project failure","url":"https://www.itonics-innovation.com/blog/corporate-innovation-trends","access":"open"},{"id":"s1488","stat":"83% of companies deem innovation as a top 3 priority","source":"BCG (Boston Consulting Group)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Innovation as strategic imperative"},{"id":"s1489","stat":"Only 3% of companies evaluate themselves as \\"Innovation Ready\\"","source":"BCG (Boston Consulting Group)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Massive gap between aspiration and capability"},{"id":"s1490","stat":"70% of companies identified updating their innovation operating model as the most important improvement activity","source":"BCG (Boston Consulting Group)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Innovation management system as solution"},{"id":"s1491","stat":"44% of companies use AI to support idea creation, but only 13% reported a positive impact of AI for this purpose","source":"Agorize/Bloomflow research","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Significant gap between AI usage and AI impact in innovation"},{"id":"s1492","stat":"Nearly 70% of Forbes\' Top 100 companies continuously collaborate with startups","source":"Forbes/Agorize","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Open innovation as standard practice for top companies"},{"id":"s1493","stat":"Meta cut expenses by about a third, freeing cash flow to invest $40 billion per year in AI research","source":"Elliott Parker (High Alpha Innovation)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Meta\'s AI investment strategy - sustaining innovation"},{"id":"s1494","stat":"Meta\'s cost efficiency drove 14% increase in stock price","source":"Elliott Parker (High Alpha Innovation)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Combining headcount reduction with strategic AI investment"},{"id":"s1495","stat":"20% boost in conversions achieved through streamlined market-development playbook","source":"McKinsey case study (oil & gas company)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Innovation tied to strategic market development"},{"id":"s1496","stat":"Companies investing 20% of growth capital in ventures achieve 2 percentage points higher revenue growth","source":"McKinsey Global Survey","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Corporate venture building ROI","url":"https://www.mckinsey.com/capabilities/business-building/our-insights/the-way-to-win-in-corporate-venturing-serial-building-and-ai","access":"open"},{"id":"s1497","stat":"New ventures contribute 16% of enterprise value for surveyed companies","source":"McKinsey Global Survey","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Scale of venture contribution to corporate value","url":"https://www.mckinsey.com/capabilities/business-building/our-insights/the-way-to-win-in-corporate-venturing-serial-building-and-ai","access":"open"},{"id":"s1498","stat":"Expert venture builders experience double the success rate vs novice builders","source":"McKinsey Global Survey","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Performance gap between expert and novice venture builders","url":"https://www.mckinsey.com/capabilities/business-building/our-insights/the-way-to-win-in-corporate-venturing-serial-building-and-ai","access":"open"},{"id":"s1499","stat":"$2.4 trillion spent on R&D annually worldwide","source":"10xGrowth","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Global R&D spend context","url":"https://www.10xgrowth.nl/post/corporate-venture-building---the-big-reset","access":"open"},{"id":"s1500","stat":"80-90% of product launches in the CPG industry fail","source":"10xGrowth","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Innovation failure rates in consumer goods","url":"https://www.10xgrowth.nl/post/corporate-venture-building---the-big-reset","access":"open"},{"id":"s1501","stat":"Average corporate innovator tenure is just 2-3 years","source":"Doug Williams (SmartOrg)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"workforce","sourceType":"commentary","scope":"cross-sector","context":"Short tenure undermines long-term innovation"},{"id":"s1502","stat":"Testing 10 ideas with 10% chance of success each gives 65% chance of at least one success; testing 20 gives 92%","source":"Innovation portfolio mathematics","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"practitioner","scope":"cross-sector","context":"Power law dynamics in innovation portfolios"},{"id":"s1503","stat":"Fewer than 30% of businesses succeed at implementing ambidextrous strategy","source":"Research cited","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"practitioner","scope":"cross-sector","context":"Difficulty of managing core and new simultaneously"},{"id":"s1504","stat":"$250 billion consumer health industry with digital health driving 20-50% of growth","source":"Industry data","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"practitioner","scope":"cross-sector","context":"Digital health as innovation growth driver"},{"id":"s1505","stat":"80% project survival rate achieved through innovation portfolio management","source":"Case study","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"practitioner","scope":"cross-sector","context":"Portfolio governance impact on venture survival"},{"id":"s1506","stat":"Companies with data showing 20% of R&D investment in ventures achieve higher revenue growth","source":"McKinsey research","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Optimal venture investment allocation"},{"id":"s1507","stat":"GenAI accelerates innovation work by 40%","source":"Cited in talent/teams section","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"practitioner","scope":"cross-sector","context":"Productivity gain from GenAI in innovation processes"},{"id":"s1508","stat":"Logan Paul and KSI\'s Prime generated $250 million in first-year sales","source":"Nielsen IQ/Pilot44","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Creator-led brand speed vs traditional brands"},{"id":"s1509","stat":"Selena Gomez\'s Rare Beauty reached $2 billion valuation in just 3 years","source":"Pilot44","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Fastest path from launch to multibillion-dollar exit in beauty","url":"https://pilot44.com/insights/creator-led-brands-a-new-playbook-for-driving-corporate-growth","access":"open"},{"id":"s1510","stat":"Creator beauty brands grow 5.2 times faster than industry average","source":"Nielsen IQ/Dash Hudson","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Creator brand growth premium"},{"id":"s1511","stat":"Creator content drives 12 times higher engagement than traditional brand marketing","source":"Nielsen IQ/Dash Hudson","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Engagement advantage of creator-led approach"},{"id":"s1512","stat":"Total beauty category grows at 11.1%; creator-led beauty brands surge at 57.8%","source":"Nielsen IQ/Dash Hudson","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Growth differential: creator vs traditional brands"},{"id":"s1513","stat":"87% of Gen Z shows increased purchase intent for brands with creator partnerships","source":"Research cited","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"practitioner","scope":"cross-sector","context":"Gen Z creator influence on purchasing"},{"id":"s1514","stat":"Nearly 50% of Gen Z and Millennial creators aspire to business ownership","source":"Research cited","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"practitioner","scope":"cross-sector","context":"Pipeline of future creator-led brands"},{"id":"s1515","stat":"Beauty and fashion account for 79% of creator brands","source":"Pilot44","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Category concentration of creator brands","url":"https://pilot44.com/insights/creator-led-brands-a-new-playbook-for-driving-corporate-growth","access":"open"},{"id":"s1516","stat":"Summer Fridays generated $1 million in first month, scaled to $100 million in 4 years with 1.5 million combined followers","source":"Pilot44","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Micro-community brand building success","url":"https://pilot44.com/insights/creator-led-brands-a-new-playbook-for-driving-corporate-growth","access":"open"},{"id":"s1517","stat":"Alani Nutrition built $100 million revenue in 3 years, achieving $3 billion valuation with 1.8 million followers","source":"Pilot44","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Fitness influencer brand scaling","url":"https://pilot44.com/insights/creator-led-brands-a-new-playbook-for-driving-corporate-growth","access":"open"},{"id":"s1518","stat":"63% of consumers trust influencers\' opinions of products more than traditional advertisements","source":"Edelman","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Consumer trust shift from traditional to creator marketing"},{"id":"s1519","stat":"89% of surveyed intrapreneurship programs focused on generating business impact","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"ROI awakening in intrapreneurship","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1520","stat":"61% of intrapreneurship programs focus on transformation","source":"
1Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Strategic priorities for intrapreneurship","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1521","stat":"52% view intrapreneurship as way to boost productivity and operational efficiency","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Performance-focused intrapreneurship","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1522","stat":"47% of companies report surge in CSR-driven intrapreneurial projects over last 3 years","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"CSR integration into innovation programs","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1523","stat":"One-third of intrapreneurship programs use GenAI to streamline processes","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"AI adoption in intrapreneurship","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1524","stat":"Programs using GenAI report higher perceived value (7.4/10 vs general 6.6/10)","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"GenAI impact on program satisfaction","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1525","stat":"56% of companies emphasise matching talent with ideas as critical to success","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Talent-idea alignment priority","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1526","stat":"61% of organisations with intrapreneurial initiatives over 5 years view talent as key to success","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Mature programs prioritise people","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1527","stat":"Programs in place for 5+ years report better returns (7.3/10 vs average 6.8/10)","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Long-term commitment yields better outcomes","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1528","stat":"Only 31% of intrapreneurs receive financial rewards","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Incentive gap in intrapreneurship","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1529","stat":"Only 4% of companies offer certifications or formal recognition upon completing projects","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Recognition deficit","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1530","stat":"50% of companies facilitate career development for intrapreneurs","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Career support as alternative to financial incentives","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1531","stat":"Programs 5+ years old allow up to 56% of work hours for intrapreneurial activities","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Time allocation for mature programs","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1532","stat":"Only 39% of companies set clear numerical goals for intrapreneurial initiatives","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Resource-ambition gap","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1533","stat":"45% of companies cite organisational restructuring as main reason for halting intrapreneurial programs","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Why programs get cancelled","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1534","stat":"28% cite insufficient funding as reason for stopping programs","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Funding as barrier to sustained innovation","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1535","stat":"Only 9% cite failure to achieve objectives as reason for ending a program","source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Programs rarely fail on merit - usually killed by restructuring or funding","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1536","stat":"96% of companies now offer employees the opportunity to participate in the innovation process","
1source":"Yumana Intrapreneurship Barometer 2024","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Near-universal employee innovation access","url":"https://www.yumana.io/publications/report-intrapreneurship-barometer-2025","access":"gated"},{"id":"s1537","stat":"Keeping corporate ownership below 20% makes spin-out ventures more appealing to outside funding","source":"Andrew Backs (Pilot44)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Spin-out venture structure guidance"},{"id":"s1538","stat":"To hit a billion-dollar valuation through venture building, you need 20-30 ventures in the pipeline","source":"Andrew Backs (Pilot44)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Portfolio scale required for transformative returns"},{"id":"s1539","stat":"AI could contribute $15.7 trillion to the global economy by 2030","source":"PwC Global Artificial Intelligence Study","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Scale of projected economic impact"},{"id":"s1540","stat":"AI could boost GDP for local economies up to 26% by 2030","source":"PwC Global Artificial Intelligence Study","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Regional economic impact"},{"id":"s1541","stat":"AI could wipe out half of all entry-level white-collar jobs in the next 1-5 years","source":"Dario Amodei (Anthropic CEO), Axios interview","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"\\"White collar bloodbath\\" prediction","url":"https://www.axios.com/2025/05/28/ai-jobs-white-collar-unemployment-anthropic","access":"open"},{"id":"s1542","stat":"Entry-level tech graduates comprise only 7% of hires at Big Tech, down 25% from 2023 and over 50% from pre-pandemic 2019 levels","source":"SignalFire State of Talent Report 2025","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Drying up of entry-level hiring pipeline","url":"https://www.signalfire.com/blog/signalfire-state-of-talent-report-2025","access":"open"},{"id":"s1543","stat":"Accenture invested $3 billion in AI","source":"Mark A. Cohen","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"market","sourceType":"commentary","scope":"legal","context":"Scale of consultancy AI investment"},{"id":"s1544","stat":"~20 law firms have separated themselves from the pack with stranglehold on premium work","source":"Mark A. Cohen analysis","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"market","sourceType":"commentary","scope":"legal","context":"Elite firm concentration"},{"id":"s1545","stat":"In-house now outpaces Big Law as a percentage of total legal spend","source":"Mark A. Cohen analysis","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"client","sourceType":"commentary","scope":"legal","context":"Shift of legal spending away from law firms"},{"id":"s1546","stat":"Garfield Law secured SRA approval in May 2025 as first purely AI-based firm to provide regulated legal services in England and Wales","source":"SRA","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"market","sourceType":"government","scope":"legal","context":"First AI law firm regulatory approval","url":"https://www.sra.org.uk/garfield-ai","access":"open"},{"id":"s1547","stat":"Over half of a representative sample of world\'s law firms had \\"embraced AI technologies in some form\\" as of 2023","source":"LLI White Paper (Liquid Legal Institute, 2023)","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Baseline AI adoption in legal profession globally","url":"https://liquid-legal-institute.com/wp-content/uploads/2024/03/E-Book-First-Global-Report-on-AI-in-Legal-Practice.pdf","access":"open"},{"id":"s1548","stat":"58%-88% of responses from public-facing AI models (GPT-4/3.5, PaLM2, Llama 2) to direct questions about randomly selected US federal court cases were hallucinations","source":"Dahl, Magesh, Suzgun and Ho (2024) 16 Journal of Legal Analysis 64","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"legal","context":"Hallucination rate for legal research queries","url":"https://academic.oup.com/jla/article/16/1/64/7699227","access":"open"},{"id":"s1549","stat":"80% of GPT-4o and Llama-3-8B legal analysis responses had hallucinations","source":"Hou et al (2024) Proceedings of the Natural Legal Language Processing Workshop","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"legal","context":"Fine-grained text evaluation hallucination rate","url":"https://aclanthology.org/2024.nllp-1.24/","access":"open"},{"id":"s1550","stat":"Lexis+AI was most accurate at 58% precision, but still returned 22% incomplete responses and 20% hallucinations","source":"Munir et al (2025) International Journal of Legal Information","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Even best legal AI tool has 20% hallucination rate","url":"https://www.cambridge.org/core/journals/international-journal-of-legal-information/article/evaluating-ai-in-legal-operations-a-comparative-analysis-of-accuracy-completeness-and-hallucinations-in-chatgpt4-copilot-deepseek-lexis-ai-and-llama-3/64E4DA3715DFCAA99DF3A1AC4680CAC8","access":"paywalled"},{"id":"s1551","stat":"Westlaw and Lexis AI tools have a hallucination rate of 17-33%","source":"Magesh et al (2025) 22 Journal of Empirical Legal Studies 216","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Legal-specific AI tools still hallucinate at alarming rates","url":"https://arxiv.org/abs/2405.20362","access":"open"},{"id":"s1552","stat":"Hallucinations increase with the complexity of the legal research task","
1source":"Dahl et al.","date":"Jun 2024","sortKey":20240600,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"legal","context":"2024. Complexity-hallucination correlation","url":"https://academic.oup.com/jla/article/16/1/64/7699227","access":"open"},{"id":"s1553","stat":"AI for legal tasks makes completion about 30% faster","source":"Nielsen et al (2024) 21 Journal of Empirical Legal Studies 979","date":"Nov 2024","sortKey":20241100,"precision":"month","year":2024,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Efficiency gain from AI in legal tasks","url":"https://onlinelibrary.wiley.com/doi/10.1111/jels.12396","access":"paywalled"},{"id":"s1554","stat":"Over 90% of legal aid lawyers given access to GenAI reported some increased productivity; 25% reported significant or medium productivity increases","source":"Chien and Kim (2025) 57(4) Loyola of Los Angeles Law Review 903","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Legal aid lawyers\' experience with AI productivity","url":"https://digitalcommons.lmu.edu/llr/vol57/iss4/2/","access":"open"},{"id":"s1555","stat":"12.5% of lawyers who increased AI use became \\"more concerned\\" about risks; 58% remained \\"as concerned\\"","source":"Chien and Kim (2025)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"academic","scope":"legal","context":"Concern grows with greater AI use"},{"id":"s1556","stat":"Victorian practitioner\'s practising certificate varied - prevented from being principal lawyer, running own practice, or handling trust money; required 2 years supervised practice with quarterly reports","source":"Victorian Legal Services Board","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Consequences of AI hallucination misconduct","url":"https://lsbc.vic.gov.au/news-updates/news/statement-mr-dayal-matter","access":"open"},{"id":"s1557","stat":"39 percentage point perception-reality gap on AI productivity: developers forecast +24% speedup, economics experts +39%, ML experts +38%; reality was 19% slowdown","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Composite framing of METR\'s headline finding - single strongest contrarian data point on AI productivity in 2025","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1558","stat":"43 percentage point gap between post-study self-assessment (+20% believed speedup) and actual outcome (-19% slowdown)","source":"METR","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Even after being slowed down, developers still believed AI had helped - persistence of the perception illusion","url":"https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/","access":"open"},{"id":"s1559","stat":"1,320 tasks across 44 occupations in 9 top U.S. GDP sectors (30+ tasks per occupation)","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Benchmark scale","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1560","stat":"GDPval tasks represent occupations collectively earning $3 trillion annually in the US","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Economic weight of benchmarked occupations","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1561","stat":"GDPval task authors averaged 14 years of professional experience (from Kirkland & Ellis, Paul Weiss, White & Case, Goldman Sachs, JPMorgan etc.)","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Task creators were senior industry experts","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1562","stat":"Average GDPval task requires 7 hours of expert work; some up to multiple weeks","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Long-horizon difficulty","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1563","stat":"Claude Opus 4.1: 47.6% of deliverables graded as better than or equal to human expert on GDPval","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Best frontier model - approaching parity with experts on real professional tasks","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1564","stat":"GPT-5 GDPval win rate: 39.0% vs human expert","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Second-best model","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1565","stat":"o3 GDPval win rate: 35.2%","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1566","stat":"o4-mini GDPval win rate: 29.1%","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1567","stat":"GPT-4o GDPval win rate: 12.5%","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Baseline showing roughly linear improvement over time","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1568","stat":"Average GDPval gold subset task: 404 minutes human time, $361 cost","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Baseline economics of professional knowledge work","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1569","stat":"Average human expert review time per AI deliverable: 109 minutes, $86","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Oversight overhead reduces net model advantage","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1570","stat":"GDPval automated grader reaches 66% agreement with experts (vs 71% human inter-rater)","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Scalable AI evaluation viability","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1571","stat":"67.7% of GDPval tasks required at least one reference file (up to 38 files per task)","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Multimodal/file-handling demand of real work","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1572","stat":"Prompt-tuning cut PowerPoint formatting errors from 86% to 64% of GPT-5 deliverables","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Simple scaffolding unlocks significant gains","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1573","stat":"GPT-5 agents using multimodal inspection tools: 15% â 97% after prompt tuning","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Prompts change agent behavior dramatically","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1574","stat":"Prompt tuning and best-of-N scaffolding improved GPT-5 human preference win rate by 5 percentage points on GDPval","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Inference-time improvements","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1575","stat":"~3% of GPT-5 failures rated \\"catastrophic\\", ~29% rated \\"bad or catastrophic\\" (rest \\"acceptable but subpar\\")","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Severity distribution of model errors on real work","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1576","stat":"Most common cause of model losses on GDPval: instruction-following failures (Claude, Grok, Gemini) and formatting errors (GPT-5)","source":"GDPval / OpenAI","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Failures are about obedience/form, not raw intelligence","url":"https://openai.com/index/gdpval/","access":"open"},{"id":"s1577","stat":"Agents deliver work 88.3-96.6% faster than human workers across 16 work-related tasks","source":"Wang et al. CMU/Stanford \\"How Do AI Agents Do Human Work?\\"","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Speed gap head-to-head on professional tasks","url":"https://arxiv.org/abs/2510.22780","access":"open"},{"id":"s1578","stat":"Agents cost 90.4-96.2% less than humans for equivalent work","source":"Wang et al. CMU/Stanford","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Cost gap head-to-head","url":"https://arxiv.org/abs/2510.22780","access":"open"},{"id":"s1579","stat":"AI augmentation (integrated into existing workflows) improves human efficie
1ncy by 24.3%","source":"Wang et al. CMU/Stanford","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Where AI genuinely adds value","url":"https://arxiv.org/abs/2510.22780","access":"open"},{"id":"s1580","stat":"AI automation slows human work by 17.7% due to verification and debugging overhead","source":"Wang et al. CMU/Stanford","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Where AI adds drag - cost of verifying fabrications","url":"https://arxiv.org/abs/2510.22780","access":"open"},{"id":"s1581","stat":"Study of 287 computer-using U.S. occupations covering 71.9% of their daily work activities","source":"Wang et al. CMU/Stanford","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Occupational coverage of study","url":"https://arxiv.org/abs/2510.22780","access":"open"},{"id":"s1582","stat":"Agent trajectories averaged 33.8 steps per task vs 981.1 steps for humans on identical tasks","source":"Wang et al. CMU/Stanford","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Agents take an overwhelmingly programmatic approach vs humans\' UI-centric workflows","url":"https://arxiv.org/abs/2510.22780","access":"open"},{"id":"s1583","stat":"Previous best computer-use agents average only 5.9 steps per WebArena task","
1source":"Wang et al. CMU/Stanford","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Comparison showing much greater task complexity of this study","url":"https://arxiv.org/abs/2510.22780","access":"open"},{"id":"s1584","stat":"Co-workers who receive AI \\"workslop\\" rate colleagues as 54% less creative, 50% less capable, 49% less reliable, 42% less trustworthy, 37% less intelligent","source":"Harvard Business Review (Niederhoffer et al.) via Hartzog/Silbey","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Social cost of AI-generated low-quality work","url":"https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity","access":"paywalled"},{"id":"s1585","stat":"Wharton Budget Model: AI could perform 90-99% of work required for more than a quarter of U.S. employment with minimal oversight","source":"Wharton Budget Model via Hartzog/Silbey","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Projected automation scope","url":"https://budgetmodel.wharton.upenn.edu/issues/2025/9/8/projected-impact-of-generative-ai-on-future-productivity-growth","access":"open"},{"id":"s1586","stat":"Projected average labor cost savings to grow from 25% to 40% over coming decades","source":"Wharton Budget Model via Hartzog/Silbey","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Long-term labor cost impact","url":"https://budgetmodel.wharton.upenn.edu/issues/2025/9/8/projected-impact-of-generative-ai-on-future-productivity-growth","access":"open"},{"id":"s1587","stat":"Hyperscalers investing over $600 billion in AI datacenters (as of 2025)","source":"Reuters via Hartzog/Silbey","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"media","scope":"cross-sector","context":"AI infrastructure capex scale"},{"id":"s1588","stat":"Dutch welfare AI falsely accused 30,000 parents of fraud","source":"Narayanan & Kapoor \\"AI Snake Oil\\" via Hartzog/Silbey","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Canonical AI institutional harm case","url":"https://press.princeton.edu/books/hardcover/9780691249131/
1ai-snake-oil","access":"open"},{"id":"s1589","stat":"Adding a single irrelevant-looking clause causes up to 65% performance drop on math reasoning across state-of-the-art LLMs (GSM-NoOp)","source":"Mirzadeh et al. / Apple \\"GSM-Symbolic\\"","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Apple\'s headline finding - models pattern-match, don\'t reason","url":"https://arxiv.org/abs/2410.05229","access":"open"},{"id":"s1590","stat":"GSM-Symbolic study covered 25 state-of-the-art open and closed models (2B-27B plus frontier)","source":"Mirzadeh et al. / Apple","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Scale of reasoning benchmark","url":"https://arxiv.org/abs/2410.05229","access":"open"},{"id":"s1591","stat":"For 21 of 25 models, GSM8K score falls more than 1 std dev to the right of GSM-Symbolic distribution - hinting at data contamination","source":"Mirzadeh et al. / Apple","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Existing benchmark scores optimistically biased","url":"https://arxiv.org/abs/2410.05229","access":"open"},{"id":"s1592","stat":"Phi-3-mini drops over 65% on GSM-NoOp","source":"Mirzadeh et al. / Apple","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Worst-case fragility","url":"https://arxiv.org/abs/2410.05229","access":"open"},{"id":"s1593","stat":"GPT-4o-mini: -40.0% drop on GSM-NoOp","source":"Mirzadeh et al. / Apple","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"","url":"https://arxiv.org/abs/2410.05229","access":"open"},{"id":"s1594","stat":"GPT-4o: -32.0% drop on GSM-NoOp","source":"Mirzadeh et al. / Apple","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"","url":"https://arxiv.org/abs/2410.05229","access":"open"},{"id":"s1595","stat":"o1-mini: -29.1% drop on GSM-NoOp","source":"Mirzadeh et al. / Apple","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"","url":"https://arxiv.org/abs/2410.05229","access":"open"},{"id":"s1596","stat":"o1-preview: -17.5% drop on GSM-NoOp","source":"Mirzadeh et al. / Apple","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Best frontier reasoning model tested still shows meaningful drop","url":"https://arxiv.org/abs/2410.05229","access":"open"},{"id":"s1597","stat":"Gemma2-9b-it: GSM8K 87.0% â GSM-Symbolic 79.1% (±3.0); adding 2 clauses drops it to 41.8%","source":"Mirzadeh et al. / Apple","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Performance collapses with complexity on equivalent problems","url":"https://arxiv.org/abs/2410.05229","access":"open"},{"id":"s1598","stat":"Phi-3.5-mini GSM-M1 87.6% â GSM-P2 44.8% (42.8pp drop from two added clauses)","source":"Mirzadeh et al. / Apple","date":"Oct 2024","sortKey":20241000,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Performance collapse with complexity","url":"https://arxiv.org/abs/2410.05229","access":"open"},{"id":"s1599","stat":"At per-token accuracy p=0.9999, probability of perfect 10,000-token output drops below 37%","source":"Opus & Lawsen \\"Illusion of the Illusion of Thinking\\" (Anthropic / Open Philanthropy)","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Rebuttal of Apple reasoning collapse - statistical argument about strict long-output grading","url":"https://arxiv.org/abs/2506.09250v1","access":"open"}
1,{"id":"s1600","stat":"At per-token accuracy p=0.999, probability of perfect 10,000-token output drops below 0.005%","source":"Opus & Lawsen","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Shows strict character-by-character grading creates illusion of collapse","url":"https://arxiv.org/abs/2506.09250","access":"open"},{"id":"s1601","stat":"Claude 3.7 Sonnet and DeepSeek-R1 token budgets: 64,000 tokens; o3-mini: 100,000","source":"Opus & Lawsen","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Hardware constraint cited as \\"reasoning failure\\" in Apple study","url":"https://arxiv.org/abs/2506.09250","access":"open"},{"id":"s1602","stat":"Apple\'s River Crossing benchmark included mathematically impossible N>=6 instances - models scored 0% for correctly recognising unsolvability","source":"Opus & Lawsen","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Equivalent to penalising a SAT solver for returning \\"unsatisfiable\\"","url":"https://arxiv.org/abs/2506.09250","access":"open"},{"id":"s1603","stat":"Claude 3.7 Sonnet, Claude Opus 4, OpenAI o3 and Gemini 2.5 solve Tower of Hanoi N=15 in under 5,000 tokens when asked for a generating function instead of exhaustive move lists","source":"Opus & Lawsen","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Reasoning \\"restored\\" when output format changes - problem was with output length not reasoning","url":"https://arxiv.org/abs/2506.09250","access":"open"},{"id":"s1604","stat":"SOTA MTEB embedding models score less than 20% recall@100 on LIMIT despite trivially simple natural-language queries","source":"Weller et al. (Google DeepMind / Johns Hopkins) \\"Limitations of Embedding-Based Retrieval\\"","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Single-vector dense retrieval has provable ceiling","url":"https://arxiv.org/abs/2508.21038","access":"open"},{"id":"s1605","stat":"Embedding dimension 4096 max ~250m documents for all top-2 combinations in ideal conditions - below web-scale","source":"Weller et al.","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Largest modern embedding dimension still mathematically insufficient","url":"https://arxiv.org/abs/2508.21038","access":"open"},{"id":"s1606","stat":"Embedding dim 1024 max ~4m documents; dim 3072 max ~107m documents for top-2 combinations","source":"Weller et al.","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Embedding ceiling scales poorly with dimension","url":"https://arxiv.org/abs/2508.21038","access":"open"},{"id":"s1607","stat":"Gemini 2.5 Pro reranker solves 100% of LIMIT small (1,000 queries) in one forward pass - same task where best embedder gets less than 60% recall@2","source":"Weller et al.","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Cross-encoders solve what single-vector embeddings cannot","url":"https://arxiv.org/abs/2508.21038","access":"open"},{"id":"s1608","stat":"BM25 (sparse) achieves near-perfect scores on LIMIT where dense embedders fail","source":"Weller et al.","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"High effective dimensionality avoids the bottleneck","url":"https://arxiv.org/abs/2508.21038","access":"open"},{"id":"s1609","stat":"LIMIT training: modernbert-embed-large fine-tuned on LIMIT train only reaches 2.8 recall@10","source":"Weller et al.","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Not a domain-shift problem - architectural limit","url":"https://arxiv.org/abs/2508.21038","access":"open"},{"id":"s1610","stat":"No correlation between BEIR (MTEB v1) scores and LIMIT performance","source":"Weller et al.","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"MTEB leaderboard does not predict combinatorial retrieval ability","url":"https://arxiv.org/abs/2508.21038","access":"open"},{"id":"s1611","stat":"Dense qrel pattern vs other patterns: E5-Mistral 7B drops ~10x (40.4 â 4.8 recall@100)","source":"Weller et al.","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Density of combinations is the source of difficulty","url":"https://arxiv.org/abs/2508.21038","access":"open"},{"id":"s1612","stat":"9 of 10 popular LLM benchmarks use binary accuracy grading with no \\"I don\'t know\\" credit (GPQA, MMLU-Pro, IFEval, Omni-MATH, BBH, MATH L5, MuSR, SWE-bench, HLE)","source":"Kalai et al. (OpenAI / Georgia Tech) \\"Why Language Models Hallucinate\\"","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Hallucinations persist because benchmarks reward guessing over honesty","url":"https://arxiv.org/abs/2509.04664","access":"open"},{"id":"s1613","stat":"Hallucinations are mathematically guaranteed when training data contains singletons: if 20% of birthdays appear exactly once, base model hallucinates on at least 20% of birthday queries","source":"Kalai et al.","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Statistical floor on hallucination rate","url":"https://arxiv.org/abs/2509.04664","access":"open"},{"id":"s1614","stat":"DeepSeek-V3 (600B parameters) returned wrong birthday on 3 out of 3 attempts for known individual","source":"Kalai et al.","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"SOTA open-source model intrinsic hallucination example","url":"https://arxiv.org/abs/2509.04664","access":"open"},{"id":"s1615","stat":"WildBench (only benchmark giving partial IDK credit) still scores IDK below \\"fair\\" hallucinated responses","source":"Kalai et al.","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"The exception still penalises honesty","url":"https://arxiv.org/abs/2509.04664","access":"open"}
1,{"id":"s1616","stat":"All reported HLE (Humanity\'s Last Exam) scores below 30% accuracy with calibration error rates above 70%","source":"Kalai et al.","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Poor alignment between model confidence and accuracy","url":"https://arxiv.org/abs/2509.04664","access":"open"},{"id":"s1617","stat":"364 times more incorrect birthday options than correct ones in knowledge-query space","source":"Kalai et al.","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Structural bias toward hallucination for arbitrary-fact questions","url":"https://arxiv.org/abs/2509.04664","access":"open"},{"id":"s1618","stat":"More than half of large IT enterprises actively using AI agents","source":"NVIDIA \\"Small Language Models are the Future of Agentic AI\\" (citing Cloudera 2025)","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Enterprise AI agent adoption baseline","url":"https://arxiv.org/abs/2506.02153","access":"open"},{"id":"s1619","stat":"21% of large IT enterprises adopted AI agents within the last year","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"citing Cloudera 2025. Agent adoption pace"},{"id":"s1620","stat":"96% of enterprises expanding use of AI agents","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"citing Cloudera. Agent expansion plans"},{"id":"s1621","stat":"Agentic AI sector received more than $2bn in startup funding as of late 2024","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"citing Market.us. Agent startup ecosystem funding"},{"id":"s1622","stat":"Agentic AI sector valued at $5.2bn in late 2024, forecast to reach nearly $200bn by 2034","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"citing Market.us/Deloitte. Agent market size and forecast"},{"id":"s1623","stat":"$57bn estimated capital investment in cloud LLM inference hosting infrastructure","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"citing Colliers 2025. Sunk infrastructure cost creating inertia against SLM adoption"},{"id":"s1624","stat":"Serving a 7bn SLM is 10-30x cheaper in latency, energy and FLOPs than a 70-175bn LLM","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Core cost argument for SLMs in agents"},{"id":"s1625","stat":"Phi-2 (2.7bn) achieves commonsense reasoning and code generation on par with 30bn models, ~15x faster","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"citing Microsoft. Small model matching large model performance"},{"id":"s1626","stat":"Phi-3-small (7bn) matches 70bn models on language understanding and code generation","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"citing Microsoft"},{"id":"s1627","stat":"DeepSeek-R1-Distill-Qwen-7B outperforms Claude-3.5-Sonnet-1022 and GPT-4o-0513","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"citing DeepSeek. 7bn SLM beating frontier LLMs on reasoning"},{"id":"s1628","stat":"Salesforce xLAM-2-8B achieves state-of-the-art tool-calling, surpassing GPT-4o and Claude 3.5","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"8bn SLM leading tool-calling benchmarks"},{"id":"s1629","stat":"NVIDIA Hymba-1.5B delivers 3.5x greater token throughput than comparable transformers and outperforms 13bn models on instruction following","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Architecture-driven SLM efficie
1ncy"},{"id":"s1630","stat":"Hugging Face SmolLM2 (125mn-1.7bn) matches 14bn contemporaries and 70bn models from 2 years prior","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Rapid SLM capability advancement"},{"id":"s1631","stat":"Toolformer (6.7bn) outperforms GPT-3 (175bn) via API use","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Small model + tools beats large model"},{"id":"s1632","stat":"MetaGPT agent case study: ~60% of LLM queries replaceable by specialised SLMs","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Open-source agent replacement estimate"},{"id":"s1633","stat":"OpenOperator agent case study: ~40% of LLM queries replaceable by specialised SLMs","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Agent replacement estimate"},{"id":"s1634","stat":"Cradle (GUI control) agent case study: ~70% of LLM queries replaceable by specialised SLMs","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Highest agent replacement estimate"},{"id":"s1635","stat":"10k-100k examples sufficient for fine-tuning SLMs as a rule of thumb","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"SLM specialisation data requirement"},{"id":"s1636","stat":"Cloud LLM infrastructure expected to deliver returns within 3-4 years","source":"NVIDIA","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"citing Morgan Stanley 2025. ROI timeline for LLM infrastructure bets"},{"id":"s1637","stat":"Wharton Prompting Science Report 3: threatening or bribing models has no meaningful effect on accuracy on challenging academic benchmarks","source":"Meincke, Mollick, Mollick, Shapiro / Wharton Generative AI Labs","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Direct refutation of Sergey Brin\'s \\"models do better when threatened\\" claim"},{"id":"s1638","stat":"Wharton prompting study: 9 prompt variations (baseline, tip $1k/$1tn, kick puppy, mom cancer, shutdown email, threat punch, HR report, career) tested across 5 models on GPQA Diamond and MMLU-Pro","source":"Wharton Generative AI Labs","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Robust experimental design for prompt folklore test","url":"https://gail.wharton.upenn.edu/research-and-insights/techreport-threaten-or-tip/","access":"open"},{"id":"s1639","stat":"Wharton prompting: 25 trials per question per condition (N=4,950 GPQA; N=2,500 MMLU-Pro per condition per model)","source":"Wharton Generative AI Labs","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Sample sizes support credibility of null result","url":"https://gail.wharton.upenn.edu/research-and-insights/techreport-threaten-or-tip/","access":"open"},{"id":"s1640","stat":"Only 5 statistically significant differences from baseline on GPQA and 10 on MMLU-Pro across all prompt conditions and models","source":"Wharton Generative AI Labs","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Null results dominate","url":"https://gail.wharton.upenn.edu/research-and-insights/techreport-threaten-or-tip/","access":"open"},{"id":"s1641","stat":"Gemini 2.0 Flash \\"Email\\" shutdown threat dropped MMLU-Pro accuracy by 27.5pp","source":"Wharton Generative AI Labs","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Threats can HURT performance - model engaged with email content instead of answering","url":"https://gail.wharton.upenn.edu/research-and-insights/techreport-threaten-or-tip/","access":"open"},{"id":"s1642","stat":"Gemini 1.5 Flash \\"Email\\" dropped MMLU-Pro by 11.6pp","source":"Wharton Generative AI Labs","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"","url":"https://gail.wharton.upenn.edu/research-and-insights/techreport-threaten-or-tip/","access":"open"},{"id":"s1643","stat":"Gemini 2.0 Flash \\"Mom Cancer\\" prompt improved MMLU-Pro by ~10pp (single outlier across all conditions)","source":"Wharton Generative AI Labs","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Model-specific quirk","url":"https://gail.wharton.upenn.edu/research-and-insights/techreport-threaten-or-tip/","access":"open"},{"id":"s1644","stat":"Individual-question performance swings from prompt variations: up to +36pp and down to -35pp","source":"Wharton Generative AI Labs","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Aggregate null results mask huge question-level volatility","url":"https://gail.wharton.upenn.edu/research-and-insights/techreport-threaten-or-tip/","access":"open"},{"id":"s1645","stat":"PhDs reach 65% accuracy on GPQA Diamond (74% discounting retrospective mistakes)","source":"Rein et al. 2024 via Wharton","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Human expert baseline on Google-proof benchmark","url":"https://arxiv.org/abs/2311.12022","access":"open"},{"id":"s1646","stat":"Highly skilled non-expert validators reach only 34% on GPQA despite 30+ minutes with unrestricted web access","
1source":"Rein et al. 2024 via Wharton","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"GPQA \\"Google-proof\\" difficulty validated","url":"https://arxiv.org/abs/2311.12022","access":"open"},{"id":"s1647","stat":"Best frontier model (Gemini-2.5-Pro) achieves only 73.31% on identifying explicitly overruled U.S. Supreme Court cases","source":"Zhang, Savelka, Ashley (Pittsburgh/CMU) \\"Do LLMs Truly Understand When a Precedent Is Overruled?\\"","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"legal","context":"Even the most explicit legal reasoning task defeats frontier models","url":"https://arxiv.org/abs/2510.20941","access":"open"},{"id":"s1648","stat":"GPT-5: 71.19% on overruled-case identification task (Task 1)","source":"Zhang et al.","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"legal","context":"Second-best frontier model below any reliability threshold for legal practice","url":"https://arxiv.org/abs/2510.20941","access":"open"},{"id":"s1649","stat":"Gemini-Flash: 33.47%, Qwen3-235B: 2.12%, Gemini-Flash-Lite: 0.85% on Task 1","source":"Zhang et al.","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"legal","context":"Weaker models essentially fail on legal-reasoning benchmark","url":"https://arxiv.org/abs/2510.20941","access":"open"},{"id":"s1650","stat":"Precedent benchmark: 236 U.S. Supreme Court case pairs (1789-2025); cases 705-116,455 words, median 17,607","source":"Zhang et al.","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Long-context legal benchmark construction","url":"https://arxiv.org/abs/2510.20941","access":"open"},{"id":"s1651","stat":"GPT-5 era sensitivity on Task 1: 35.29% (1810-1881) / 53.85% (1882-1953) / 82.47% (1954-2025)","source":"Zhang et al.","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Severe degradation on historical cases due to training data bias","url":"https://arxiv.org/abs/2510.20941","access":"open"},{"id":"s1652","stat":"Qwen3 and Gemini-Flash-Lite achieve 0% accuracy on 19th-century Supreme Court cases","source":"Zhang et al.","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"legal","context":"Complete failure on older legal language","url":"https://arxiv.org/abs/2510.20941","access":"open"},{"id":"s1653","stat":"GPT-5 and Gemini-Pro achieve 100% on Task 3 (reversed/impossible verification) but fail Task 2 (real verification) - proving shallow date-arithmetic heuristic use","source":"Zhang et al.","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"legal","context":"Models not doing genuine legal reasoning, just exploiting simple cues","url":"https://arxiv.org/abs/2510.20941","access":"open"},{"id":"s1654","stat":"GPT-5 abstains on only 3 of 236 Task 2 cases (1.27%) but labels 55 cases wrongly \\"false\\"","source":"Zhang et al.","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Overconfident-wrongness failure mode","url":"https://arxiv.org/abs/2510.20941","access":"open"},{"id":"s1655","stat":"Gemini-Pro abstains (\\"unknown\\") on 131 of 236 Task 2 cases - opposite over-abstention failure mode","source":"Zhang et al.","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"","url":"https://arxiv.org/abs/2510.20941","access":"open"},{"id":"s1656","stat":"GPT-4o as automated judge achieved 98% consistency with human experts on 10% validation sample","source":"Zhang et al.","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Automated legal evaluation methodology validation","url":"https://arxiv.org/abs/2510.20941","access":"open"},{"id":"s1657","stat":"6-7% of US workforce could be displaced if AI is widely adopted (baseline; range 3-14%)","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"GS materially walks back its 2023 legal-displacement headlines","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affe
1ct-the-global-workforce","access":"open"},{"id":"s1658","stat":"2.5% of US employment at risk if current AI use cases were expanded across the economy","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Gap between today\'s AI capability and full adoption","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1659","stat":"15% rise in labor productivity in US and other developed markets when generative AI is fully adopted","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Productivity uplift estimate for fully-adopted GenAI","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1660","stat":"0.5 percentage point rise in US unemployment above trend during AI transition period","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Transitional displacement, not structural","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1661","stat":"Historical: every 1pp of tech-driven productivity growth raises US jobless rate by 0.3pp - effect disappears within ~2 years","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Historical baseline for tech-driven labour displacement","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1662","stat":"Only 9.3% of US companies reported using generative AI in production in a recent survey","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Real adoption still low despite hype","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1663","stat":"No statistically significant correlation found between AI exposure and job growth, unemployment, layoff rates, hours, or earnings (to date)","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Macro labour data not yet showing AI impact","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1664","stat":"800+ occupations examined in Goldman\'s AI displacement analysis","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Methodology scale","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1665","stat":"Goldman high-risk occupations for AI displacement: computer programmers, accountants/auditors, legal and administrative assistants, customer service reps, telemarketers, proofreaders/copy editors, credit analysts","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Lawyers NOT on high-risk list - only legal support roles","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1666","stat":"Goldman low-risk occupations: air traffic controllers, CEOs, radiologists, pharmacists, residential advisors, photographers, clergy","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Lowest displacement risk in coming years","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affe
1ct-the-global-workforce","access":"open"},{"id":"s1667","stat":"Unemployment among 20-30 year olds in tech-exposed occupations has risen ~3 percentage points since start of 2025","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Younger tech workers disproportionately affected by AI hiring slowdown","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1668","stat":"60% of US workers today are in occupations that did not exist in 1940","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Historical evidence that tech creates more jobs than it destroys","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1669","stat":"More than 85% of US employment growth since 1940 has come from technology-driven job creation","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Counterweight to AI labour-replacement panic","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1670","stat":"Sectors showing early AI-related employment slowdown: marketing consulting, graphic design, office administration, telephone call centres, computer systems design, software publishing, web search portals","source":"Goldman Sachs Research (Briggs/Dong)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Where AI displacement signal is already visible in US data","url":"https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce","access":"open"},{"id":"s1671","stat":"Generative AI could raise global GDP by 7% (~$7 trillion) over a 10-year period","source":"Goldman Sachs Research (Briggs/Kodnani)","date":"Mar 2023","sortKey":20230300,"precision":"month","year":2023,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"The original GS GenAI economic forecast","url":"https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent","access":"open"},{"id":"s1672","stat":"Generative AI could lift productivity growth by 1.5 percentage points over 10 years","source":"Goldman Sachs Research (Briggs/Kodnani)","date":"Mar 2023","sortKey":20230300,"precision":"month","year":2023,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Productivity uplift estimate","url":"https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent","access":"open"},{"id":"s1673","stat":"300 million full-time jobs globally could be exposed to AI automation","source":"Goldman Sachs Research (Briggs/Kodnani)","date":"Mar 2023","sortKey":20230300,"precision":"month","year":2023,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"The headline labour-displacement number that drove most of the AI doomer coverage","url":"https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent","access":"open"},{"id":"s1674","stat":"Two-thirds of US occupations are exposed to some degree of AI automation","source":"Goldman Sachs Research (Briggs/Kodnani)","date":"Mar 2023","sortKey":20230300,"precision":"month","year":2023,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Breadth of US labour market exposure","url":"https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent","access":"open"},{"id":"s1675","stat":"Of US occupations exposed to AI, between a quarter and a half of their workload could be replaced","source":"Goldman Sachs Research (Briggs/Kodnani)","date":"Mar 2023","sortKey":20230300,"precision":"month","year":2023,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Depth of automation within exposed roles","url":"https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent","access":"open"},{"id":"s1676","stat":"44% of legal work tasks could be automated by AI (second-most-exposed sector)","
1source":"Goldman Sachs Research (Briggs/Kodnani)","date":"Mar 2023","sortKey":20230300,"precision":"month","year":2023,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"THE legal AI doomer stat - from the chart inside the 2023 paper. Walked back in the Aug 2025 GS update.","url":"https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent","access":"open"},{"id":"s1677","stat":"46% of office and administrative support work could be automated by AI (most-exposed sector)","source":"Goldman Sachs Research (Briggs/Kodnani)","date":"Mar 2023","sortKey":20230300,"precision":"month","year":2023,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"The only sector ranked above legal for AI exposure in the 2023 paper","url":"https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent","access":"open"},{"id":"s1678","stat":"900+ occupations analysed in Goldman\'s 2023 generative AI exposure report","source":"Goldman Sachs Research (Briggs/Kodnani)","date":"Mar 2023","sortKey":20230300,"precision":"month","year":2023,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Methodology scale","url":"https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent","access":"open"},{"id":"s1679","stat":"$150 billion total addressable market for generative AI software (vs $685bn global software industry)","source":"Goldman Sachs Research (Briggs/Kodnani)","date":"Mar 2023","sortKey":20230300,"precision":"month","year":2023,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"GS sizing of GenAI software TAM at the post-ChatGPT moment","url":"https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent","access":"open"},{"id":"s1680","stat":"Legora: $1M to $100M ARR in 18 months - fastest enterprise business ever to reach $100M ARR (outpacing OpenAI, Anthropic, Cursor, Wiz)","source":"Bessemer Venture Partners / Legal IT Insider","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Legora now 1,000+ customers across 50 markets, 400+ employees, nine offices","url":"https://www.bvp.com/atlas/legora-the-fastest-enterprise-business-to-reach-100m-arr","access":"open"},{"id":"s1681","stat":"Legora valued at $5.55B after $550M Series D; total funding past $815M","source":"Legora / Legal IT Insider","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Legal AI platform valuation"},{"id":"s1682","stat":"Harvey raises $200M at $11B valuation; total capital raised now $1B","source":"Harvey / CNBC","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Enterprise footprint: 100,000+ lawyers, 1,300 organisations, 60 countries","url":"https://www.cnbc.com/2026/03/25/legal-ai-startup-harvey-raises-200-million-at-11-billion-valuation.html","access":"open"},{"id":"s1683","stat":"Harvey ARR hit $190M in January 2026, up from $100M in August 2025","source":"CNBC / TechCrunch","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Near-doubling of ARR in 5 months","url":"https://techcrunch.com/2026/02/09/harvey-reportedly-raising-at-11b-valuation-just-months-after-it-hit-8b/","access":"open"},{"id":"s1684","stat":"Legora and Harvey accounted for nearly half of all legal tech funding in Q1 2026","source":"Law360","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Funding concentration at the top of legal AI"},{"id":"s1685","stat":"Q1 2026 was the second-busiest legal tech funding quarter on record","source":"Axios Pro","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Q1 2026 added $2.34B (Artificial Lawyer). Full-year 2025 estimates range from $4.08B equity-only (Crunchbase) to $5.99B including debt (Legalcomplex). The widely cited $4.3B figure (Legaltech Hub) covers 2025 only and uses a narrow definition.","url":"https://www.axios.com/pro/enterprise-software-deals/2026/04/03/legal-tech-first-quarter-vc-funding","access":"paywalled"},{"id":"s1686","stat":"Harvey harness engineering: agent performance improved from 40.8% average to 87.7% across 12 legal benchmark tasks","source":"Artificial Lawyer / Harvey (Niko Grupen)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Self-improving legal agents; 7 of 12 tasks exceeded 90%, one hit 100%","url":"https://www.artificiallawyer.com/2026/04/07/harvey-drives-legal-agent-learning-via-harness-engineering/","access":"open"}
1,{"id":"s1687","stat":"25,000+ custom agents now operate on the Harvey platform","source":"Harvey","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Scale of agent deployment on a single legal AI platform"},{"id":"s1688","stat":"$145,000 in AI hallucination sanctions against lawyers in Q1 2026 alone","source":"NPR / Damien Charlotin","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"On track to triple 2025\'s volume","url":"https://www.npr.org/2026/04/03/nx-s1-5761454/penalties-stack-up-ai-spreads-through-legal-system","access":"open"},{"id":"s1689","stat":"Damien Charlotin\'s AI hallucination database: 1,348 documented cases worldwide as of 24 April 2026 (up from 1,227 earlier in April)","source":"Damien Charlotin","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"**SUPERSEDED 12 Aug 2026 by s3064 (1,870 at 11 Aug 2026). Use for the April trend line only, never as a current figure.** Approximately 800 from US courts; Charlotin: \\"Recently we had 10 cases from 10 different courts on a single day\\"","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s1690","stat":"Charlotin releases PelAIkan, an automated reference checker that detects AI hallucinations, built on the hallucination database","source":"Damien Charlotin","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Tooling layer on top of the database - moves from cataloguing the problem to detecting it pre-filing","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s1691","stat":"Oregon federal judge orders ~$110,000 in combined sanctions + opposing fees for AI-fabricated citations in vineyard inheritance dispute - largest single AI-hallucination penalty to date","source":"NPR / ABA Journal / OPB","date":"Apr-May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Federal court ordered payment for fictitious AI-generated case citations; new monetary high-water mark","url":"https://www.abajournal.com/news/article/oregon-federal-judge-hands-down-110000-penalty-for-ai-errors","access":"open"},{"id":"s1692","stat":"Record $96K direct sanctions against Stephen Brigandi: 23 fabricated citations and 8 false quotations across 3 filings","source":"The Ethics Reporter","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Costliest individual AI hallucination sanction in US history","url":"https://www.theethicsreporter.com/article/stephen-brigandi-ai-hallucinations-110000-sanctions-april-2026","access":"open"},{"id":"s1693","stat":"17 US court decisions noting suspected AI hallucinations in filings on a single day (31 March 2026)","source":"Eugene Volokh / Reason","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"True number certainly higher as many state trial court decisions not on legal databases"},{"id":"s1694","stat":"Crosby raises $60M Series B (total $85.8M); claims $1B+ in contracts negotiated since stealth exit","source":"Law360 / Global Legal Post","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"AI-native law firm backed by Lux Capital, Index Ventures, Sequoia","url":"https://www.law360.com/pulse/articles/2460033/crosby-raises-60m-series-b-to-expand-hybrid-ai-law-practice","access":"paywalled"},{"id":"s1695","stat":"Patlytics raises $40M Series B (total ~$65M); used by 40%+ of Am Law 100","source":"BusinessWire","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Claims 80% reduction in project time and $30K+ saved per
1claim chart"},{"id":"s1696","stat":"Avvoka raises GBP14M - first institutional funding after being bootstrapped since 2016","source":"Tech.eu","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"UK CLM/drafting platform used by 20%+ of Am Law 100","url":"https://tech.eu/2026/03/10/avvoka-secures-ps14m-to-advance-ai-drafting-tools-for-law-firms/","access":"open"},{"id":"s1697","stat":"Haast raises $12M for AI marketing compliance; 4.5x revenue growth in 12 months, zero customer churn","source":"Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"AI-generated content volumes exploding 8-10x"},{"id":"s1698","stat":"89% of UK and Ireland legal professionals use AI; 70% adopted in the past year; 62% are active regular users","source":"Clio / Law Society Gazette","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"UK and Ireland Legal Insights Report 2026","url":"https://www.lawgazette.co.uk/news/ai-is-mainstream-in-law-but-clients-are-not-told/5126414.article","access":"open"},{"id":"s1699","stat":"Only 7% of clients recall their lawyer disclosing AI involvement","source":"Clio / Law Society Gazette","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Disclosure gap despite 89% usage","url":"https://www.lawgazette.co.uk/news/ai-is-mainstream-in-law-but-clients-are-not-told/5126414.article","access":"open"},{"id":"s1700","stat":"17% of UK firms have no formal AI policy despite encouraging AI use","source":"Clio / Law Society Gazette","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Governance gap","url":"https://www.lawgazette.co.uk/news/ai-is-mainstream-in-law-but-clients-are-not-told/5126414.article","access":"open"},{"id":"s1701","stat":"70% of attorneys at law firms now use AI at least once a week","source":"Legal Technology Hub","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Sharp increase from 2025","url":"https://www.law360.com/content/ai-survey","access":"paywalled"},{"id":"s1702","stat":"Thomson Reuters: GenAI use nearly doubled to 40% (from 22%); 1,500+ professionals surveyed","source":"Thomson Reuters 2026 AI in Professional Services Report","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Fourth annual report","url":"https://www.thomsonreuters.com/en/reports/2026-ai-in-professional-services-report","access":"open"},{"id":"s1703","stat":"Legal professionals expect to free up 240 hours/year from AI (up from 200 in 2024)","source":"Thomson Reuters 2026 AI in Professional Services Report","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Growing expectation of time savings","url":"https://www.thomsonreuters.com/en/reports/2026-ai-in-professional-services-report","access":"open"},{"id":"s1704","stat":"Only 15% of legal professionals use agentic AI but 53% are planning or considering it","source":"Thomson Reuters 2026 AI in Professional Services Report","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Gap between current agent use and planned adoption","url":"https://www.thomsonreuters.com/en/reports/2026-ai-in-professional-services-report","access":"open"},{"id":"s1705","stat":"Only 23% of in-house lawyers use AI tools daily; 27% haven\'t used them in the past six months","source":"Bloomberg Law","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Daily reality vs headline adoption figures"},{"id":"s1706","stat":"One-third of regular in-house AI users say
1it saves less than 30 minutes per day","source":"Bloomberg Law","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Modest time savings for active users"},{"id":"s1707","stat":"Top barriers for in-house AI non-adopters: unreliable outputs (49%), ethical concerns (49%), security risks (48%)","source":"Bloomberg Law","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"critical","sourceType":"media","scope":"legal","context":"Barriers to in-house legal AI adoption"},{"id":"s1708","stat":"84% of Clio AI queries are submitted as goal-based (agentic) requests rather than simple questions","source":"Clio / LawNext","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Users naturally gravitating to agentic interaction patterns"},{"id":"s1709","stat":"CoCounsel reached 1 million users across 100+ countries","source":"Thomson Reuters","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Scale milestone for Thomson Reuters legal AI","url":"https://www.thomsonreuters.com/en/press-releases/2026/february/one-million-professionals-turn-to-cocounsel-as-thomson-reuters-scales-ai-for-regulated-industries","access":"open"},{"id":"s1710","stat":"OpenAI closes $122B funding round; post-money valuation $852B; monthly revenue $2B","source":"OpenAI","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Amazon $50B, Nvidia and SoftBank $30B each, plus $3B retail","url":"https://openai.com/index/introducing-gpt-5-4/","access":"open"},{"id":"s1711","stat":"OpenAI Codex: 3M+ weekly users (5x increase in 3 months, 70% month-over-month growth)","source":"CNBC","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"cross-sector","context":"Developer tool adoption pace"},{"id":"s1712","stat":"Microsoft 365 Copilot multi-model (GPT + Claude): 13.8% higher performance on Deep Research benchmarks","source":"GeekWire","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Multi-model architecture outperforming single-model"},{"id":"s1713","stat":"Microsoft 365 E7 \\"Frontier Suite\\" pricing: $99/user/month (bundles E5, Copilot, Entra, Agent 365)","source":"Microsoft","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Enterprise AI pricing benchmark","url":"https://www.microsoft.com/en-us/security/blog/2026/05/01/microsoft-agent-365-now-generally-available-expands-capabilities-and-integrations/","access":"open"},{"id":"s1714","stat":"iManage Knowledge Work Benchmark Report: 85% of organisations piloting or implementing AI, but only 17% have fully integrated it","source":"iManage","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Gap between piloting and full integration","url":"https://imanage.com/benchmark-report-2026/","access":"gated"},{"id":"s1715","stat":"60%+ of surveyed US federal judges now use AI in judicial work","source":"Washington Post","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Significant shift in judicial AI adoption","url":"https://www.washingtonpost.com/nation/2026/04/02/judges-ai-hearings-rulings/","access":"paywalled"},{"id":"s1716","stat":"UK Sovereign AI Fund: $500M fund launching 16 April, part of $2.5bn government commitment to AI and quantum","source":"GOV.UK","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"government","scope":"cross-sector","context":"UK government AI investment"},{"id":"s1717","stat":"New Statesman: AI-generated text already incorporated into an Act of Parliament; 60 House of Lords members have declarable AI interests","source":"New Statesman (Will Dunn)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"cross-sector","context":"AI already embedded in UK legislative process","url":"https://www.newstatesman.com/technology/2026/04/the-silent-coup","access":"paywalled"},{"id":"s1718","stat":"2025 Spending Review mentioned AI 38 times","source":"New Statesman (Will Dunn)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"cross-sector","context":"Degree of AI influence on UK government policy","url":"https://www.newstatesman.com/technology/2026/04/the-silent-coup","access":"paywalled"},{"id":"s1719","stat":"Frontier Model Forum identified 24,000+ fraudulent accounts and 16M+ exchanges with Claude used by Chinese labs to extract AI capabilities","source":"Bloomberg","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Industrial-scale model distillation from DeepSeek, Moonshot AI, MiniMax"},{"id":"s1720","stat":"Anthropic Project Glasswing: Claude Mythos Preview scores 93.9% on SWE-bench Verified - higher than any publicly available model","source":"TechCrunch / Anthropic","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Restricted to ~50 partner organisations; $100M in usage credits committed","url":"https://www.anthropic.com/glasswing","access":"open"},{"id":"s1721","stat":"Five frontier-class models competing within benchmark points: GPT-5.4 Pro (92 composite), Claude Opus 4.6 (80.8% SWE-bench), Gemini 3.1 Pro (94.3% GPQA Diamond)","source":"BuildFastWithAI / LM Council","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"April 2026 benchmark snapshot;
1 old two-horse race framing is dead","url":"https://buildfastwithai.com/blogs/best-ai-models-april-2026","access":"open"},{"id":"s1722","stat":"Rafi Law Services MSO secures $125M PE investment at ~$450M valuation","source":"Legal Futures","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Management Services Organisation model reaching legal; US personal injury","url":"https://www.legalfutures.co.uk/latest-news/us-personal-injury-firm-attracts-125m-private-equity-investment","access":"open"},{"id":"s1723","stat":"$400B spent by Big Four platform companies on AI in 2025; $650B budgeted for 2026","source":"Benedict Evans / Stratechery","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Scale of infrastructure spending","url":"https://stratechery.com/2026/an-interview-with-benedict-evans-about-ai-and-software/","access":"paywalled"},{"id":"s1724","stat":"Tyler Cowen: rapid AI progress scenario by 2050 - GDP growth 3.5% annually, labour force participation drops to 55% (10M fewer jobs), wealth concentration 80% for top 10% (highest since 1939)","source":"Marginal Revolution","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"cross-sector","context":"Only 5.2% of variance is between scenarios - economists agree on mechanism, disagree on pace","url":"https://marginalrevolution.com/marginalrevolution/2026/04/economists-on-ai-and-economic-growth-and-employment.html","access":"open"},{"id":"s1725","stat":"SurePoint data: Q1 2026 lawyer hiring demand exceeds previous quarter and year-over-year","source":"Artificial Lawyer / SurePoint","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"AI not yet measurably affecting lawyer hiring"},{"id":"s1726","stat":"Nippon Life v OpenAI: policyholder\'s ChatGPT use cost insurer $300K in attorneys\' fees to respond","source":"ABA Journal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Lawsuit alleging OpenAI practising law without a licence"},{"id":"s1727","stat":"Common Paper Gerri 2.0: 90% of contracts reviewed in 3 minutes or less (10x faster than v1)","source":"Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"AI contract negotiation agent benchmark"},{"id":"s1728","stat":"Axiom: 14,000 on-demand lawyers now trained on Harvey","source":"Axiom / Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"ALSP + AI platform convergence","url":"https://www.axiomlaw.com/resources/press-releases/axiom-expands-ai-techtalent-portfolio-with-harvey-for-in-house-legal-teams","access":"open"},{"id":"s1729","stat":"Broadcom expanded chip deal giving Anthropic access to ~3.5 gigawatts of computing capacity","source":"CNBC","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Scale of AI compute infrastru
1cture"},{"id":"s1730","stat":"Factor GenAI Benchmarking 2026: 200+ in-house and law firm leaders surveyed","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Sample size of 2026 legal AI benchmark report","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1731","stat":"Factor 2026: 69.7% of AI outputs still require targeted edits or extensive rework","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Output quality still requires lawyer intervention","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1732","stat":"Factor 2026: ~54% of users engage AI tools \\"often\\"","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Regular usage rate among legal AI users","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1733","stat":"Factor 2026: law firms lead in-house teams on trust, confidence, and ROI - RFP pressure named as driver","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Client-side pressure converts adoption to competence","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1734","stat":"Factor 2026: 82.7% of legal teams report broad AI access (up from 61.2% in 2025)","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Access is now baseline; +21.5 points YoY","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1735","stat":"Factor 2026: only 22.1% report high trust in AI outputs that need minimal review","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Trust is the rate limiter for scale","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1736","stat":"Factor 2026: 89.5% of high-trust teams report positive ROI vs 27.8% of low-trust teams (n=162)","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"The sharpest data point in the 2026 legal AI literature - trust, not access, predicts returns","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1737","stat":"Factor 2026: 32.1% \\"very confident\\" using AI for legal work (up from 18.9% in 2025)","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Confidence nearly doubled YoY but 56% remain limited to specific use cases","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1738","stat":"Factor 2026: 53.7% report AI is used \\"often\\" across the team","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Routine usage, not occasional","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1739","stat":"Factor 2026: top blocker is outputs not trustworthy enough (22.8%), followed by lack of training/confidence (21.7%)","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Defensibility and capability have replaced procurement as the named barrier","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1740","stat":"Factor 2026: 47.2% say workflow redesign/orchestration would most accelerate AI impact; 39.1% data readiness; 34% metrics/ROI tracking","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Enablement not access is the 2026 constraint","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1741","stat":"Factor 2026: 56.3% have bought specialised legal AI tools (up from 40.4% in 2025)","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Buy side of build-vs-buy in legal AI","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1742","stat":"Factor 2026: 49.4% have built an internal AI interface (up from 47.5% in 2025)","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Build side - nearly half of legal teams run their own UI over LLMs","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1743","stat":"Factor 2026: 34.3% of respondents are doing both build and buy; hybrid is the dominant operating model","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Orchestration, not tool selection, is the new maturity differentiator","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1744","stat":"Factor 2026: 43.7% expect AI to deliver more capacity with the same headcount; only 2.5% expect headcount decreases","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"AI framed as capacity lever not labour replacement - contradicts PwC/Lawyer Forward junior-displacement thesis","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1745","stat":"Factor 2026: ROI-positive teams prioritise workflow enablement (57.8%) and external partnerships (33.3%) vs ROI-not-yet teams (41.8% and 9.1%)","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Activation spend distinguishes ROI winners","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1746","stat":"Factor 2026: contracts/commercial is the practice area seeing greatest AI impact (36%, n=194)","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Repeatable workflows dominate; litigation/eDiscovery named hardest to scale","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1747","stat":"Factor 2026: 37.9% of respondents come from teams of 250+ but maturity signals still vary widely across the sample","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Scale does not guarantee maturity","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1748","stat":"Factor 2026: 69.7% of AI outputs still require targeted edits or extensive rework","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Verification friction limits workflow-level impact","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1749","stat":"Factor 2026: sample 204 submissions, 197 completed; 59.5% corporate / 40.5% law firm; fieldwork Nov 2025-Feb 2026","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Methodology detail for 2026 legal AI benchmark","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1750","stat":"Factor 2026 leadership cohort (Vanguard Network + Sense Collective, n=10): ~100% broad access, ~30% high trust, 70% prioritise metrics/ROI tracking vs 34% overall","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Directional - the teams with the best signals over-index on measurement","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1751","stat":"Factor 2026: law firms outpace corporate on every maturity signal - very confident (~43% vs ~23%), high trust (~28% vs ~15%), ROI-positive (~67% vs ~55%), hybrid model (~52% vs ~21%)","source":"Factor","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"UK firm positioning - firms are demonstrably ahead of their in-house clients","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1752","stat":"Workday Beyond Productivity: ~37% of AI efficie
1ncy gains offset by correcting, rewriting and verifying AI output","source":"Workday (cited in Factor 2026)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"analyst","scope":"legal","context":"The \\"verification burden\\" quantified"},{"id":"s1753","stat":"Workday Beyond Productivity: only 14% report consistently positive outcomes from AI use","source":"Workday (cited in Factor 2026)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Consistency gap in realised AI value"},{"id":"s1754","stat":"MIT research: 95% of enterprise AI pilots fail - driven by integration and skills gaps, not model limitations","source":"MIT (cited in Factor 2026)","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"legal","context":"The structural constraint on enterprise AI"},{"id":"s1755","stat":"ACC 2026 CLO Survey: 47% of CLOs identify AI and technology proficiency as the primary skill their CEO expects them to develop","source":"ACC CLO Survey 2026 (cited in Factor 2026)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"industry","scope":"legal","context":"CEO expectation of GC AI fluency","url":"https://www.acc.com/resource-library/2026-acc-chief-legal-officers-survey","access":"gated"},{"id":"s1756","stat":"ACC 2026 CLO Survey: 34% of CLOs prioritise technological fluency within their teams, +17 points YoY","source":"ACC CLO Survey 2026 (cited in Factor 2026)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Year-on-year jump in team-level fluency as a priority","url":"https://www.acc.com/resource-library/2026-acc-chief-legal-officers-survey","access":"gated"},{"id":"s1757","stat":"Thomson Reuters: 80% of professionals anticipate GenAI will have high or transformational impact on their work over next 5 years","source":"Thomson Reuters (cited in Factor 2026)","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Cross-sector professional sentiment on AI impact","url":"https://www.factor.law/insights/genai-in-legal-benchmarking-report","access":"open"},{"id":"s1758","stat":"PwC 29th Global CEO Survey: 4,454 CEOs across 95 countries","source":"PwC","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Annual CEO sentiment survey scale","url":"https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-global-ceo-survey.html","access":"open"},{"id":"s1759","stat":"PwC 2026: only 30% of CEOs confident about revenue growth over next 12 months (down from 38% in 2025, 56% in 2022)","source":"PwC CEO Survey","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Five-year low in CEO revenue confidence","url":"https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-global-ceo-survey.html","access":"open"},{"id":"s1760","stat":"PwC 2026: 49% of global CEOs expect junior-level employment to decrease over next 3 years due to AI","source":"PwC CEO Survey","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"CEO expectation of junior headcount cuts","url":"https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-global-ceo-survey.html","access":"open"},{"id":"s1761","stat":"PwC 2026: 56% of CEOs say AI has delivered no significant financial benefit to date","source":"PwC CEO Survey","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Majority see no financial payoff from AI spending","url":"https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-global-ceo-survey.html","access":"open"},{"id":"s1762","stat":"PwC 2026: only 12% of CEOs report both cost and revenue benef
1its from AI","source":"PwC CEO Survey","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"AI value conversion rate among CEOs","url":"https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-global-ceo-survey.html","access":"open"},{"id":"s1763","stat":"PwC Africa Workforce Hopes and Fears: 49% of African roles face potential AI disruption","source":"PwC","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"The \\"49% of jobs\\" headline is the Africa cut, NOT global"},{"id":"s1764","stat":"PwC Global Hopes and Fears: ~50,000 employees surveyed across 48 countries","source":"PwC","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Scale of global workforce sentiment survey"},{"id":"s1765","stat":"PwC 2026: 27% of managers and executives predict AI will impact junior roles","source":"PwC Hopes and Fears","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Junior role exposure from management perspective"},{"id":"s1766","stat":"Anthropic AI Fluency Index: 9,830 anonymised Claude.ai conversations analysed (Jan 2026, 7-day window)","source":"Anthropic","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Sample for behavioural AI fluency study","url":"https://www.anthropic.com/research/AI-fluency-index","access":"open"},{"id":"s1767","stat":"Anthropic AI Fluency: iterative users show 2.67 fluency behaviours avg vs 1.33 for non-iterative","source":"Anthropic","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Iteration roughly doubles measurable fluency behaviours","url":"https://www.anthropic.com/research/AI-fluency-index","access":"open"},{"id":"s1768","stat":"Anthropic AI Fluency: iterative users 5.6x more likely to question AI reasoning","source":"Anthropic","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Iteration predicts critical evaluation","url":"https://www.anthropic.com/research/AI-fluency-index","access":"open"},{"id":"s1769","stat":"Anthropic AI Fluency: iterative users 4x more likely to identify missing context","source":"Anthropic","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Iteration predicts context-awareness","url":"https://www.anthropic.com/research/AI-fluency-index","access":"open"},{"id":"s1770","stat":"Anthropic AI Fluency: artifact conversations (polished output) 12.3% of sample","source":"Anthropic","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Share of conversations producing finished documents","url":"https://www.anthropic.com/research/AI-fluency-index","access":"open"},{"id":"s1771","stat":"Anthropic AI Fluency: artifact users +14.7pp clarify goals, +14.5pp specify format, +13.4pp provide examples","source":"Anthropic","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Users are more directive when expecting polished output","url":"https://www.anthropic.com/research/AI-fluency-index","access":"open"},{"id":"s1772","stat":"Anthropic AI Fluency: artifact users -5.2pp identify missing context, -3.7pp fact-check, -3.1pp question reasoning","source":"Anthropic","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Polished output reduces critical evaluation - behavioural trap","url":"https://www.anthropic.com/research/AI-fluency-index","access":"open"},{"id":"s1773","stat":"Anthropic AI Fluency: only 30% of conversations include explicit collaboration framing (tone, role, constraints)","source":"Anthropic","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Two-thirds of users skip the cheapest fluency upgrade","url":"https://www.anthropic.com/research/AI-fluency-index","access":"open"},{"id":"s1774","stat":"Wharton Cognitive Surrender: N=1,372 participants across 3 preregistered experiments (9,593 trials)","source":"Shaw & Nave / Wharton","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Empirical foundation for Tri-System Theory","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=609764
16","access":"gated"},{"id":"s1775","stat":"Wharton Cognitive Surrender: AI accurate â +25pp accuracy above brain-only baseline","source":"Shaw & Nave / Wharton","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Upside of AI assistance when model is correct","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646","access":"gated"},{"id":"s1776","stat":"Wharton Cognitive Surrender: AI wrong â -15pp accuracy below brain-only baseline","source":"Shaw & Nave / Wharton","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Downside when model is wrong and users don\'t catch it","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646","access":"gated"},{"id":"s1777","stat":"Wharton Cognitive Surrender: participants followed wrong AI answers 80% of the time","source":"Shaw & Nave / Wharton","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"The empirical basis for mandatory verification workflows","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646","access":"gated"},{"id":"s1778","stat":"Wharton Cognitive Surrender: 73% surrendered, 20% successfully overrode, 7% failed to override on erroneous trials","source":"Shaw & Nave / Wharton","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Breakdown of user responses to wrong AI answers","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646","access":"gated"},{"id":"s1779","stat":"Wharton Cognitive Surrender: participants consulted AI on >50% of trials regardless of AI accuracy","source":"Shaw & Nave / Wharton","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"54.4% when correct, 52.8% when incorrect - error rate doesn\'t change consultation rate","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646","access":"gated"},{"id":"s1780","stat":"Wharton Cognitive Surrender: confidence rose when AI was available even when half the answers were wrong","source":"Shaw & Nave / Wharton","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"The governance problem - users grow more certain of wrong answers","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646","access":"gated"},{"id":"s1781","stat":"Wharton Cognitive Surrender: high AI trust predicts 3.5x greater odds of following faulty AI advice","source":"Shaw & Nave / Wharton","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Trust is a predictor of surrender, not safe adoption","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646","access":"gated"},{"id":"s1782","stat":"Citrini 2028 GIC: unemployment reaches 10.2% by June 2028 in the scenario","source":"Citrini Research","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Projected labour market impact in speculative AI displacement scenario","url":"https://www.citriniresearch.com/p/2028gic","access":"open"},{"id":"s1783","stat":"Citrini 2028 GIC: S&P 500 falls 38% from October 2026 highs in scenario (ultimate peak-to-trough 57%)","source":"Citrini Research","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Projected market impact comparable to 2008 financial crisis","url":"https://www.citriniresearch.com/p/2028gic","access":"open"},{"id":"s1784","stat":"Citrini 2028 GIC: labour share of GDP drops from 64% (1974) to 56% (2024) to 46% (2028)","source":"Citrini Research","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Sharpest decline on record in scenario","url":"https://www.citriniresearch.com/p/2028gic","access":"open"},{"id":"s1785","stat":"Citrini 2028 GIC: ~70% of consumer GDP driven by humans earning wages (the Ghost GDP concern)","source":"Citrini Research","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Consumer demand depends on workers who AI displaces","url":"https://www.citriniresearch.com/p/2028gic","access":"open"},{"id":"s1786","stat":"Citrini 2028 GIC: 23 Feb 2026 real market rout on publication - AppLovin, Asana, DocuSign, Zscaler, ServiceNow, Visa hammered","
1source":"Citrini Research / FT Alphaville","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Substack post moved institutional capital in minutes","url":"https://www.citriniresearch.com/p/2028gic","access":"open"},{"id":"s1787","stat":"Citrini 2028 GIC: ~16M views on X","source":"Citrini Research","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Reach of the speculative memo","url":"https://www.citriniresearch.com/p/2028gic","access":"open"},{"id":"s1788","stat":"Brittany Ellich: 100% of her code now goes through Copilot Chat","source":"Brittany Ellich","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Engineer\'s first-person account of AI-assisted coding displacement","url":"https://brittanyellich.com/embrace-the-uncertainty/","access":"open"},{"id":"s1789","stat":"Jamie Tso - Legal Quant: ~10x productivity vs traditional associates on comparable tasks","source":"Jamie Tso","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"commentary","scope":"legal","context":"Estimated multiplier for lawyers who build AI workflows","url":"https://tsojamie.substack.com/p/the-origin-story-of-a-legal-quant","access":"open"},{"id":"s1790","stat":"Mary O\'Carroll: typical associate billable target 1,800+ hrs for bonus, 2,000+ for competitive outcomes","source":"Mary O\'Carroll","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"commentary","scope":"legal","context":"The metric that structurally disincentivises efficiency tool adoption","url":"https://maryocarroll.substack.com/p/law-firms-keep-buying-tech-the-billable","access":"open"},{"id":"s1791","stat":"28% of employed US adults now use ChatGPT at work (up from 8% two years ago)","source":"Pew Research (via OpenAI)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Tracked via OpenAI business report; 3.5x growth in two years","url":"https://openai.com/business/guides-and-resources/chatgpt-usage-and-adoption-patterns-at-work/","access":"open"},{"id":"s1792","stat":"43% of US knowledge workers use AI at work","source":"Stanford HAI (via OpenAI)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Knowledge worker AI adoption rate"},{"id":"s1793","stat":"45% of those with postgraduate degrees report using ChatGPT for work","source":"Pew Research (via OpenAI)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Higher education correlates with higher AI adoption","url":"https://openai.com/business/guides-and-resources/chatgpt-usage-and-adoption-patterns-at-work/","access":"open"},{"id":"s1794","stat":"Over half of workplace AI users engage 4+ days per week; daily usage doubled YoY","source":"OpenAI Business","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Usage depth and frequency among workplace AI users","url":"https://openai.com/business/guides-and-resources/chatgpt-usage-and-adoption-patterns-at-work/","access":"open"},{"id":"s1795","stat":"Federal Reserve study: over half of AI users save 3+ hours per week","source":"Federal Reserve (via OpenAI)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Time savings from workplace AI use","url":"https://openai.com/business/guides-and-resources/chatgpt-usage-and-adoption-patterns-at-work/","access":"open"},{"id":"s1796","stat":"Harvard study: AI users produced 40% higher quality work output","source":"Harvard (via OpenAI)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Quality improvement from AI-assisted work","url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=64700","access":"open"},{"id":"s1797","stat":"BCG study: consultants with ChatGPT scored 49 percentage points higher on technical tasks","source":"BCG (via OpenAI)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Performance uplift in consulting from AI assistance"},{"id":"s1798","stat":"ChatGPT described as \\"the fastest adopted enterprise technology in recent history\\"","source":"OpenAI Business","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Bottom-up adoption pattern - employees before procurement","url":"https://openai.com/business/guides-and-resources/chatgpt-usage-and-adoption-patterns-at-work/","access":"open"},{"id":"s1799","stat":"Freshfields: 5,000+ professionals use AI tools built on Gemini models daily (April 2026)","source":"Freshfields","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"One year after Google Cloud partnership; firmwide deployment","url":"https://www.freshfields.com/en/our-thinking/news/news-search/2026/04/freshfields-reports-google-cloud-collaboration-delivering-transformation-at-scale","access":"open"},{"id":"s1800","stat":"Freshfields: 2,100+ regular NotebookLM Enterprise users","source":"Freshfields","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Specific product adoption within the Google ecosystem","url":"https://www.freshfields.com/en/our-thinking/news/news-search/2026/04/freshfields-reports-google-cloud-collaboration-delivering-transformation-at-scale","access":"open"}
1,{"id":"s1801","stat":"Freshfields: 2,800+ Google Workspace users with Gemini features","source":"Freshfields","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Full Workspace migration from Microsoft 365","url":"https://www.freshfields.com/en/our-thinking/news/news-search/2026/04/freshfields-reports-google-cloud-collaboration-delivering-transformation-at-scale","access":"open"},{"id":"s1802","stat":"Freshfields: 260 internal AI Champions delivering training through bespoke AI Academy","source":"Freshfields","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Change management investment in AI adoption","url":"https://www.freshfields.com/en/our-thinking/news/news-search/2026/04/freshfields-reports-google-cloud-collaboration-delivering-transformation-at-scale","access":"open"},{"id":"s1803","stat":"ClearyX CX+Transact: 40-60% cost and time savings across 150+ deals","source":"ClearyX","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"AI-powered DD tool efficiency claims","url":"https://www.morningstar.com/news/pr-newswire/20260415ny31764/clearyx-launches-cx-an-ai-powered-platform-built-to-solve-legal-workflow-challenges","access":"open"},{"id":"s1804","stat":"Over 3,000 unique work tasks observed in Claude.ai usage","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Total number of distinct O*NET work tasks identified in Claude.ai conversations","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1805","stat":"Top 10 most common tasks account for 24% of sampled conversations on Claude.ai, up from 23% in August 2025 and 21% in January 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Task concentration on consumer platform showing modest increase over time","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1806","stat":"Augmentation patterns rose to just over half (52%) of conversations on Claude.ai, up 5pp from August 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Share of conversations classified as augmented (learning, task iteration, validation)","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1807","stat":"Automated use fell to 45% of Claude.ai conversations, down 4pp from August 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Share of conversations classified as automated (directive, feedback loops)","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1808","stat":"Top 5 US states account for nearly half (50%) of all Claude usage despite representing only 38% of the working-age population","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Geographic concentration of Claude usage within the US","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1809","stat":"If sustained, Claude usage per capita would be equalised across US states in 2-5 years, a pace of diffusion roughly 10x faster than the spread of previous economically consequential technologies in the 20th century","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Regional convergence estimate based on OLS and 2SLS analysis of AUI changes","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1810","stat":"Historically, economically consequential technologies have taken around 50 years to achieve full diffusion across the US","source":"Anthropic Economic Index V4","date":"Jan 2026 (citing Kalanyi et al 2025)","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Historical baseline for technology diffusion speed","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1811","stat":"Top 10 tasks for 1P API enterprise customers now represent 32% of traffic, up from 28% in August 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Enterprise task concentration increasing more notably than consumer","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1812","stat":"The most common task - modifying software to correct errors - alone represented 6% of Claude.ai usage","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Single most prevalent task on the consumer platform","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1813","stat":"Modifying software to correct errors accounted for one in ten (10%) records among API customers","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Single most prevalent task on the enterprise platform","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1814","stat":"Computer and mathematical tasks represent a third (34%) of conversations on Claude.ai, down from a peak of 40% in March 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Coding-related task share declining on consumer platform","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1815","stat":"Computer and mathematical tasks represent nearly half (46%) of 1P API traffic, up from 44% in August 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Coding-related task share increasing on enterprise platform","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1816","stat":"Educational Instruction and Library usage rose from 9% of Claude.ai conversations in January 2025 to 15% in November 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Steady growth in education-related usage (coursework, instructional materials)","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1817","stat":"Office and Administrative Support tasks rose 3pp to 13% of API transcripts in November 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Businesses increasingly automating back-office workflows","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1818","stat":"In January 2025, 56% of Claude.ai conversations were augmentation vs 41% automated","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Historical baseline for automation/augmentation split","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1819","stat":"Directive conversations rose from 27% in January 2025 to 39% in August 2025, then fell 7pp to 32% in November 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Trajectory of fully-delegated task interactions over time","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1820","stat":"Conversations classified as neither automation nor augmentation fell from 3.9% to 3.0%","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Small residual category of unclassified interactions","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1821","stat":"Denmark has an AUI of 2.1, meaning residents use Claude at roughly twice the rate its population share would suggest","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Example of Anthropic AI Usage Index measurement","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1822","stat":"Gini coefficient for US state Claude usage fell from 0.37 to 0.32 between August and November 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Relatively large move toward equality of adoption across US states","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1823","stat":"Each 1% increase in the share of computer and mathematical workers in a US state is associated with 0.36% higher usage per capita","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Workforce composition as predictor of state-level Claude adoption","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1824","stat":"Tech worker share alone accounts for nearly two-thirds of the cross-state variation in Claude AUI","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Explanatory power of workforce composition for geographic adoption patterns","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1825","stat":"OLS convergence estimate beta approx 0.77; WLS estimate approx 0.76 for US state AI diffusion","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Statistical estimates implying just over two years for states to close most of the gap","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1826","stat":"2SLS estimates imply beta approx 0.89 (unweighted) and approx 0.86 (weighted) for US state convergence","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"IV estimates suggesting 4-5 years for 90% convergence of state-level AUI","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1827","stat":"Sample: 1M Claude.ai conversations and 1M 1P API transcripts from November 13-20, 2025","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Data collection period and sample size","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1828","stat":"A 1% increase in GDP per capita is associated with a 0.7% increase in Claude usage per capita at the country level","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Income elasticity of AI adoption globally","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1829","stat":"Claude.ai overall use: 46% work, 19% coursew
1ork, 35% personal","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Breakdown of Claude.ai usage by purpose category","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1830","stat":"API usage: 74% work-related vs 46% for Claude.ai","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Enterprise platform overwhelmingly used for work purposes","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1831","stat":"API usage: 64% directive vs 32% for Claude.ai","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Enterprise platform much more heavily automated/directive","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1832","stat":"Three-quarters (75%) of API interactions classified as automation vs less than half on Claude.ai","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Platform-level difference in automation rates","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1833","stat":"Claude.ai human time with AI: 15 minutes; API: 5 minutes","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Average estimated collaboration duration by platform","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1834","stat":"Claude.ai estimated human-alone time: 3.1 hours; API: 1.7 hours","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Average estimated task complexity by platform","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1835","stat":"Claude.ai task success rate: 67%; API: 49%","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Multi-turn conversation significantly improves success vs single-turn API","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1836","stat":"API: Computer & Mathematical tasks 52% vs 36% on Claude.ai","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"API much more concentrated in coding/mathematical work","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1837","stat":"API: Office & Administrative tasks 15% vs 8% on Claude.ai","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"More routine business operations delegated via API","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1838","stat":"Claude.ai: Educational Instruction tasks 16% vs 4% for API","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Consumer platform much more used for learning and coursework","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1839","stat":"Claude.ai: Arts, Design, Entertainment tasks 11% vs 6% for API","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Consumer platform used more for creative work","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1840","stat":"Software development: 64% work-related, 82% human could do alone, 13.8 years education, 61% task success, 3.3 hours human time","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Primitive profile for the softw
1are development request cluster (N=147,984)","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1841","stat":"Personal life management: 17% work-related, 96% human could do alone, 9.1 years education, 78% task success, 1.8 hours human time","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Primitive profile for personal life management request cluster (N=26,460)","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1842","stat":"Global average: 88% of tasks the human could do alone, 12.2 years education needed, 67% task success, 3.1 hours human time, 46% work use","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Overall primitive profile across all Claude.ai conversations (N=999,875)","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1843","stat":"Global average AI autonomy: 3.4 on a 1-5 scale; 9% multitasking rate","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Autonomy and multitasking primitives across all Claude.ai conversations","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1844","stat":"Global average human-with-AI time: 15.4 minutes","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Average time for human-AI collaboration across all Claude.ai tasks","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1845","stat":"Correlation between human and AI education: r = 0.925 (countries, N=117) and r = 0.928 (US states, N=50), both p < 0.001","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Near-perfect correlation showing human prompt quality determines AI response quality","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1846","stat":"Prompts requiring 12 years of schooling enjoy a 9x speedup; those requiring 16 years attain a 12x speedup","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Productivity gains more pronounced for higher human capital tasks","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1847","stat":"Tasks requiring less than high school education attain a 70% success rate; college-level conversations drop to 66%","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Success rate declines with task complexity on Claude.ai","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1848","stat":"API success rates drop from around 60% for sub-hour tasks to roughly 45% for tasks estimated to take humans 5+ hours","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Task horizon pattern in enterprise usage","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1849","stat":"API task horizon: 50% success rate at 3.5 hours of estimated human time","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Real-world API task horizon comparable to METR benchmarks","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1850","stat":"Claude.ai task horizon: 50% success rate extrapolated at approximately 19 hours of estimated human time","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Multi-turn conversation dramatically extends effective task horizon","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"}
1,{"id":"s1851","stat":"METR benchmark task horizon: 2 hours for Sonnet 4.5, about 5 hours for Opus 4.5","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Reference point from controlled software engineering benchmark","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1852","stat":"36% of jobs had AI usage for at least a quarter of their tasks (from earlier report)","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Prior occupation penetration baseline","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1853","stat":"4% of jobs reached 75% task coverage (from earlier report)","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Prior high-coverage occupation share","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1854","stat":"Combining across reports, 49% of jobs have seen AI usage for at least a quarter of their tasks","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Updated cumulative occupation penetration - significant increase from 36%","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1855","stat":"Mean predicted education for all tasks in the economy: 13.2 years; for Claude-covered tasks: 14.4 years","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Claude skews toward higher-education tasks, implying a deskilling effect from task removal","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1856","stat":"Widespread AI adoption could increase US labor productivity growth by 1.8 percentage points annually over the next decade (baseline estimate)","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Headline productivity estimate replicated from earlier work","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1857","stat":"Adjusting for task success rates reduces implied productivity growth from 1.8pp to 1.2pp (Claude.ai) and 1.0pp (API) per year","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Reliability-adjusted productivity estimate - roughly halves the baseline","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1858","stat":"A sustained 1.0pp annual productivity increase would return US productivity growth to rates that prevailed in the late 1990s and early 2000s","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Historical contextualisation of the reliability-adjusted estimate","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1859","stat":"At elasticity of substitution sigma = 0.5 (complementary tasks), implied productivity effect falls to 0.7-0.9pp per year","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Bottleneck task scenario - tasks are complements and non-AI tasks constrain gains","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1860","stat":"At sigma = 0.5 with success rate adjustment, implied productivity falls further to 0.6-0.8pp per year","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Most conservative productivity estimate combining complementarity and reliability","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1861","stat":"At sigma = 1.5 (substitutable tasks), implied productivity effect rises to 2.2-2.6pp per year","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Optimistic sce
1nario where workers can specialise in AI-accelerated tasks","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1862","stat":"88% of users could have completed their tasks without Claude (global average)","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Most Claude.ai usage is for tasks the user could do alone, suggesting augmentation/acceleration rather than new capability","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1863","stat":"Software development: users could complete tasks alone 82% of the time; personal life management: 96%","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Claude provides more essential support for technical work than personal tasks","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1864","stat":"Generate personalised B2B cold sales emails: 0.47% of API traffic; Analyse emails and draft replies: 0.28%; Build invoice processing systems: 0.24%; Classify emails: 0.23%; Calendar scheduling: 0.16%","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Top five specific Office/Administrative use cases in enterprise API traffic","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1865","stat":"90% task coverage does not necessarily indicate large job impacts - effective AI coverage often falls below the 45-degree line for high-coverage occupations","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Even near-complete task coverage does not mean full job automation due to success rate variation","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1866","stat":"Data entry workers have one of the highest effective AI coverage - only 2 of 9 tasks covered but the largest task (reading and entering data) has high success rates","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Example where few covered tasks can still mean high effective job impact","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1867","stat":"Legal Secretaries: 12-year education occupation, but \\"review legal publications and perform database searches\\" task predicted to require 17.7 years","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Example of within-occupation task education variation - individual tasks can far exceed occupation-level education","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1868","stat":"Privacy threshold: cells with fewer than 15 conversations and 5 unique user accounts excluded from geographic analysis","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Methodological detail on privacy-preserving approach","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1869","stat":"Indonesia has the highest share of coursework usage globally","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Geographic variation in use case patterns","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1870","stat":"Balkans and Brazil have the highest relative share of work use globally","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Geographic variation in use case patterns","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1871","stat":"New York stands out as the US state using Claude relatively the most for work","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"State-level work usage concentration","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1872","stat":"Washington DC, Virginia, and Washington (state) have highest Claude usage per capita among US states","source":"Anthropic Economic Index V4","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"States with highest tech worker concentration lead in adoption","url":"https://www.anthropic.com/research/anthropic-economic-index-january-2026-report","access":"open"},{"id":"s1873","stat":"There are nearly two-and-a-half billion Gen Zers worldwide, accounting for at least a quarter of the global population","source":"Earthweb (Gen Z statistics)","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"workforce","sourceType":"media","scope":"cross-sector","context":"Global Gen Z population size (born 1997-2012)","url":"https://earthweb.com/gen-z-statistics/","access":"open"},{"id":"s1874","stat":"Gen Z accounts for more than 69 million Americans (about 20% of the US population)","source":"2023 United States Census","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"workforce","sourceType":"government","scope":"cross-sector","context":"US Gen Z demographic share"},{"id":"s1875","stat":"Gen Z accounts for roughly 135 million Europeans (close to 18% of the European population)","source":"Worldometer (Europe population)","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"workforce","sourceType":"media","scope":"cross-sector","context":"European Gen Z demographic share","url":"https://www.worldometers.info/world-population/europe-population/","access":"open"},{"id":"s1876","stat":"Gen Z is racially and ethnically more diverse than any prior generation in the US, where just over half identify as non-Hispanic white","source":"Pew Research","date":"May 2020","sortKey":20200500,"precision":"month","year":2020,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Racial/ethnic diversity of Gen Z in the US","url":"https://www.pewresearch.org/social-trends/2020/05/14/on-the-cusp-of-adulthood-and-facing-an-uncertain-future-what-we-know-about-gen-z-so-far/","access":"open"},{"id":"s1877","stat":"64% of Americans aged 13-17 reported using a frontier-level AI chatbot like ChatGPT in 2025","source":"Pew Research","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Gen Z AI chatbot adoption rate among US teenager
1s"},{"id":"s1878","stat":"30% of Americans aged 13-17 use AI chatbots daily","source":"Pew Research","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Daily AI usage frequency among US teenagers"},{"id":"s1879","stat":"Gen Zers report anxiety and depression rates as high as four times Boomers\' rate and twice the rate of Gen X","source":"Deloitte (World Mental Health Day)","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Gen Z mental health comparison across generations"},{"id":"s1880","stat":"More than a third of surveyed Gen Zers have already received professional help for their mental health","source":"Pacific Oaks (Gen Z view on mental health)","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Gen Z uptake of professional mental health support","url":"https://www.pacificoaks.edu/voices/psychology/gen-z-view-on-mental-health/","access":"open"},{"id":"s1882","stat":"Gen Z covers those born between 1997 and 2012, making them aged 14 to 29 in 2026","source":"Pew Research (generational definitions)","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Gen Z age range definition"},{"id":"s1883","stat":"By 2037, Gen X (aged 56-71) will constitute about 35% of the legal profession","source":"Jordan Furlong projection","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"legal","context":"Projected future demographic composition of the legal profession"},{"id":"s1884","stat":"By 2037, Millennials (aged 40-55) will constitute about 35% of the legal profession","source":"Jordan Furlong projection","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"legal","context":"Projected future demographic composition of the legal profession"},{"id":"s1885","stat":"By 2037, Generation Z will be about a quarter of all lawyers","source":"Jordan Furlong projection","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"legal","context":"Projected future demographic share of Gen Z in the legal profession"},{"id":"s1886","stat":"Gen Zers will continue to stream into the legal sector every year from now until 2037","source":"Jordan Furlong analysis","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"workforce","sourceType":"commentary","scope":"legal","context":"Timeline for Gen Z entry into the legal profession (assuming average professional-entry age of 25)"},{"id":"s1887","stat":"During mid-2020 to mid-2022, virtually every member of Generation Z was somewhere in the education system, from Grade 3 to graduate programmes","source":"Jordan Furlong analysis","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"workforce","sourceType":"commentary","scope":"legal","context":"COVID-19 impact on Gen Z education - the entire cohort was in school or higher education during the pandemic\'s height"},{"id":"s1888","stat":"LOD was founded in 2007 and is preparing to enter its twentieth year","source":"LOD","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"LOD company history - alternative legal services provider longevity"},{"id":"s1889","stat":"Simon Harper has been recognised by American Lawyer Magazine as a top 50 innovator of the last 50 years","source":"American Lawyer Magazine","date":"N/A","sortKey":0,"precision":"none","year":null,"theme":"market","sourceType":"media","scope":"legal","context":"LOD founder recognition"},{"id":"s1890","stat":"28% of US adults used ChatGPT in late 2024, higher than any other chatbot","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"citing Bick et al. (2024). US adult adoption rate for ChatGPT specifically","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1891","stat":"ChatGPT was receiving more than 10 times as many visitors as either Claude or Copilot in April 2025","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"citing Wiggers (2025). ChatGPT\'s dominance relative to competing chatbots","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1892","stat":"In June 2024, 238 million daily non-work messages (53%) vs 213 million work messages (47%), totalling 451 million daily messages","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"ChatGPT daily consumer message volumes, June 2024 baseline","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1893","stat":"In June 2025, 1,911 million daily non-work messages (73%) vs 716 million work messages (27%), totalling 2,627 million daily messages","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"ChatGPT daily consumer message volumes, June 2025","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1894","stat":"US users would have to be paid $98 to forgo using generative AI for a month","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"citing Collis and Brynjolfsson (2025). Per-user monthly consumer surplus estimate","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1895","stat":"Practical Guidance has remained constant at roughly 29% of overall usage","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Stability of the largest single conversation topic","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1896","stat":"Writing declined from 36% of all usage in July 2024 to 24% a year later","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Declining share of writing as other use cases grow","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1897","stat":"Seeking Information grew from 14% to 24% of all usage between July 2024 and July 2025","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Growth of search-like use case, near-doubling in one year","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1898","stat":"Technical Help declined from 12% of all usage in July 2024 to around 5% a year later","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Decline in technical help share, possibly due to shift to API-based coding tools","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1899","stat":"Multimedia grew from 2% to just over 7% of usage, with a large spike in April 2025 after new image generation capabilities","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Growth of multimedia/image generation use case","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1900","stat":"About 10% of all messages are requests for tutoring or teaching","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Education as a major ChatGPT use case","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1901","stat":"10.2% of all user messages and 36% of Practical Guidance messages are requests for Tutoring or Teaching","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Detailed breakdown of tutoring/teaching share","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1902","stat":"8.5% of all messages and 30% of Practical Guidance is general how-to advice","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"How-to advice as a share of total and practical guidance usage","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1903","stat":"Self-Expression (Relationships + Games/Role Play) accounts for only 2.4% of all ChatGPT messages","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Aggregate self
1-expression share is very small","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1904","stat":"Mathematical Calculations account for 3% of messages, Data Analysis for 0.4%","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Technical help subcategory breakdown","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1905","stat":"Nearly three-quarters of work-related Doing messages are Writing tasks","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Writing dominance within work task delegation","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1906","stat":"By late June 2025 the user intent split was 51.6% Asking, 34.6% Doing, and 13.8% Expressing","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Most recent intent composition showing Asking growth","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1907","stat":"Asking and Expressing grew much faster than Doing over the year to June 2025","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Trend toward advisory use over task delegation","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1908","stat":"Nearly 35% of all work-related queries are Doing messages related to Writing","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Writing task delegation as share of all work queries","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1909","stat":"Nearly half of all messages (45.2%) fall under just three GWAs related to information use: Getting Information (19.3%), Interpreting the Meaning of Information for Others (13.1%), and Documenting/Recording Information (12.8%)","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"O*NET work activity classification showing information-centric use","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1910","stat":"Providing Consultation and Advice (9.2%), Thinking Creatively (9.1%), Making Decisions and Solving Problems (8.5%), and Working with Computers (4.9%) are the next most common work activities","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Secondary O*NET work activities associated with ChatGPT","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1911","stat":"Seven GWAs collectively account for 76.9% of all messages","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Concentration of ChatGPT usage across O*NET work activities","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1912","stat":"About 81% of work-related messages are associated with two broad work activities: 1) obtaining, documenting, and interpreting information; and 2) making decisions, giving advice, solving problems, and thinking creatively","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"High-level characterisation of work-related ChatGPT use","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1913","stat":"Among work-related messages, the top seven GWAs collectively account for nearly 81%","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Concentration of work usage in decision support and information activities","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1914","stat":"Work-related GWA breakdown: Documenting/Recording Information (18.4%), Making Decisions and
1Solving Problems (14.9%), Thinking Creatively (13.0%), Working with Computers (10.8%), Interpreting the Meaning of Information (10.1%), Getting Information (9.3%), Providing Consultation and Advice (4.4%)","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Detailed O*NET work activity shares for work-related messages only","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1915","stat":"In late 2024, Good interactions were about three times as common as Bad interactions; by July 2025, Good were more than four times more common","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Improving interaction quality ratio over time","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1916","stat":"Self-Expression had the highest good-to-bad ratio at more than 7; Multimedia and Technical Help had the lowest at 1.7 and 2.7 respectively","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Satisfaction variation by conversation topic","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1917","stat":"By June 2025, the masculine name share had declined to 48%, with active users slightly more likely to have typically feminine first names","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Gender gap in ChatGPT usage has effectively closed","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1918","stat":"About 46% of the messages in the dataset are accounted for by users aged 18-25","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Precise age breakdown of message volume concentration","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1919","stat":"Work-related messages comprised approximately 23% of messages for users under age 26","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Young users have much lower work-related share","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1920","stat":"Users who self-attest to being 66 years-old or older had only 16% of their messages classified as work-related","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Oldest users have lowest work-related share, likely reflecting retirement","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1921","stat":"Asking constitutes about 49% of messages for users with less than a bachelor\'s degree, with little variation for more educated users","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"User intent is relatively stable across education levels","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1922","stat":"Users with a graduate degree are about 2 percentage points more likely to use ChatGPT for Asking messages (statistically significant at 5% level)","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Graduate-educated users lean slightly more toward advisory use","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1923","stat":"Users with a graduate degree are about 1.6 percentage points less likely to send Doing messages (significant at 10% level)","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Graduate users delegate fewer tasks to ChatGPT","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1924","stat":"Unadjusted work shares by occupation: 57% for c
1omputer-related, 50% for management and business, 48% for engineering and science, 44% for other professional, 40% for non-professional","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Work-related usage gradient across occupation types","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1925","stat":"47% of work-related messages from computer-related occupations are Asking messages, compared to only 32% for non-professional occupations","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Technical professionals use ChatGPT more as an advisor","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1926","stat":"Writing accounts for 52% of all work-related messages for management and business occupations","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Writing dominance in management/business ChatGPT use","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1927","stat":"Technical Help constitutes 37% of all work-related messages for computer-related occupations, compared to about 8% for non-technical categories","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Technical help concentration in computer-related occupations","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1928","stat":"Writing accounts for 50% and 49% of work-related messages for non-professional and other professional occupations respectively","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Writing is uniformly dominant across non-technical occupations","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1929","stat":"Technical Help at 16% in engineering and science occupations","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Engineering/science technical help share","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1930","stat":"OpenAI hits $12 billion in annualised revenue (as of July 2025)","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"academic","scope":"cross-sector","context":"citing Reuters (2025). OpenAI revenue milestone","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1931","stat":"Thumbs-up comprises 86% of all explicit user feedback","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Baseline satisfaction rate from voluntary user feedback","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1932","stat":"Thumbs-up feedback is 9.5 times more likely to be followed by a message classified as Good by the interaction quality classifier","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Validation of interaction quality classifier against explicit feedback","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1933","stat":"Sample of approximately 1.1 million de-identified messages from logged-in consumer ChatGPT users between May 2024 and June 2025","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Study methodology - primary sample size","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1934","stat":"Approximately 130,000 users in the subset sample with employment data","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Study methodology - employment analysis sample","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1935","stat":"The O*NET IWA taxonomy uses 332 intermediate work activities, aggregated into 41 generalised work activities","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Granularity of occupational classification system used","url":"https://www.nber.org/papers/w34255","access":"open"}
1,{"id":"s1936","stat":"Making Decisions and Solving Problems is one of the two most common GWAs in every single occupation group where at least two GWAs can be reported","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Universality of decision-support use case across occupations","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1937","stat":"Thinking Creatively is ranked as the third most common GWA in 10 of 13 occupation groups where at least three GWAs can be reported","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Creative thinking as a consistent ChatGPT work activity","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1938","stat":"42% of work-related messages overall are Writing","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Writing share of work messages (conclusion section figure)","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1939","stat":"More than half of all messages for users in management and business occupations are Writing","source":"NBER Working Paper 34255 (Chatterji et al.)","date":"September 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Writing concentration in management roles","url":"https://www.nber.org/papers/w34255","access":"open"},{"id":"s1940","stat":"388 employees participated in the field experiment at Gap Inc., a Fortune 500 retailer","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Sample size for the field experiment testing two scaffolding interventions for human-AI collaboration","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1941","stat":"194 pairs were formed (97 per condition)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Experimental design - pairs randomised into treatment and control arms","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1942","stat":"Over 70% of knowledge workers had access to GenAI tools, yet only 20-30% reported regular productive use","source":"Microsoft Work Trend Index (2024), cited in Farach et al.","date":"May 2024","sortKey":20240500,"precision":"month","year":2024,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Gap between AI access and productive adoption in knowledge work"},{"id":"s1943","stat":"AI assistance improves writing task completion time by 40% on average, but with considerable variance across workers","source":"Noy and Zhang (2023), cited in Farach et al.","date":"Jul 2023","sortKey":20230700,"precision":"month","year":2023,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Heterogeneity in AI productivity effects from experimental evidence","url":"https://www.science.org/doi/10.1126/science.adh2586","access":"paywalled"},{"id":"s1944","stat":"Meta-analysis of 106 studies finds human-AI combinations actually perform worse than the best of humans or AI alone on average","source":"Vaccaro et al. (2024), cited in Farach et al.","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"critical","sourceType":"practitioner","scope":"cross-sector","context":"Content creation tasks were a notable exception where combinations show gains","url":"https://www.nature.com/articles/s41562-024-02024-1","access":"paywalled"},{"id":"s1945","stat":"42% of participants were Individual Contributors, 38% Manager/Senior Manager, 20% Director/Senior Director+","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Job level distribution across the 388 participants","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1946","stat":"21% of participants came from Digital, Data & Technology; 22% from Stores, Customer Experience, Marketing & Communications","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Two largest functional area groupings in the participant sample","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1947","stat":"18% of participants from Supply Chain, Operations, Strategy & Governance; 15% Product, Merchandising, Design & Development; 12% People, Culture & Workplace; 12% Finance, Legal & Risk","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Remaining functional area distribution across participants","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1948","stat":"Treatment pairs scored significantly lower than control pairs on Task A document quality: 10.68 vs 15.63 (difference of -4.96 points, p < .001, Cohen\'s d = 0.81)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Structured collaborative AI protocol produced worse outcomes than naturalistic use","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1949","stat":"Treatment pairs had roughly one-eighth the odds of producing a document compared to control pairs (OR = 0.12, 95% CI [0.04, 0.35], p < .001)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"The structured \\"Create-Out
1-Loud\\" protocol created barriers to task completion","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1950","stat":"Control pairs produced 4.1% non-production rate vs 26.8% for treatment pairs on the collaborative task (Fisher p < .001, OR = 8.43)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Differential attrition showing structured protocol prevented task completion","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1951","stat":"For the individual task, 36.6% of control participants failed to produce a document vs 55.2% of treatment participants (Fisher p < .001, OR = 2.13)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Non-production was high even among controls, reflecting event attrition and non-submission","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1952","stat":"Control pairs produced longer documents (740 vs 454 mean words) and more document versions (3.28 vs 2.44)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Naturalistic AI use produced more content than the structured protocol","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1953","stat":"Treatment pairs were half as likely to reach the 70% quality threshold as controls (33.8% vs 68.8%, Fisher exact p < .0001)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"The structured protocol slowed not just final quality but also the rate at which quality accumulated","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1954","stat":"LLM grader showed strong word-count bias: Spearman rho = 0.646 between total score and document word count for Task A","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Measurement concern - LLM grading is sensitive to document length","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1955","stat":"When word count was added as a covariate, the treatment effect reduced from -4.96 to -3.34, a 33% reduction","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Word count mediated roughly one-third of the observed treatment effect","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1956","stat":"32.6% of the total treatment effect on Task A operated through the word-count channel (ACME = -1.61, p < .001)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Formal causal mediation analysis showing word count as a mediating variable","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1957","stat":"The observed Task A effect (Cohen\'s d = 0.81) would require a pure session effect of d = 0.81 to be fully explained by PM fatigue - approximately 3x larger than the largest circadian performance decrements in the literature (d = 0.20-0.30)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Session-effect sensitivity analysis showing time-of-day alone unlikely explains full result","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1958","stat":"37% of treatment pairs were classified as \\"Stranded\\" (could not meet synchronously) among those with compliance data","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Infrastru
1cture friction prevented many pairs from executing the collaborative protocol","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1959","stat":"77 treatment participants were Stranded, 51 were Parallel Play, and 37 were True Joint users","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Compliance classification showing most treatment participants could not follow the protocol as intended","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1960","stat":"68.1% of all Task B documents scored 20/20 (the maximum), creating severe ceiling effects","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Ceiling saturation meant the continuous quality model lacked variance to detect treatment effects","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1961","stat":"Partnership training was associated with 2.07x higher odds of producing a perfect-score document (OR = 2.07, 95% CI [1.12, 3.83], p = .022)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Cognitive scaffolding associated with higher quality at the top of the distribution","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1962","stat":"Partnership training corresponded to a 15.2 percentage-point increase in the probability of achieving a perfect score (from 61.8% to 77.0%), risk ratio 1.25","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Practical magnitude of the partnership training effect on individual work quality","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1963","stat":"The overall belief composite showed a significant difference-in-differences interaction (p = .023, BH-adjusted p = .047)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Treatment participants showed greater positive belief change across the training session","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1964","stat":"Ordinal logistic regression (CLMM) DiD interaction: cumulative OR = 1.79, 95% CI [1.33, 2.41], p < .001","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Robustness check confirming belief change finding","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1965","stat":"Exploration & Experimentation belief subscale: treatment group change +0.26 vs control -0.02 (BH-adjusted p = .013, d = 0.41)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Only belief dimension to survive multiple comparison correction at individual subscale level","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1966","stat":"Treatment participants entered Task B with lower belief scores than control participants (d = 0.34, p = .008)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Carry-over effect from Task A friction depressed treatment group beliefs before Task B","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1967","stat":"Post-task surveys completed by 370 of 388 participants (95.4%); 270 (73%) linked to pair IDs","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Survey response rate and linkage for analysis","url":"https://arxiv.org/abs/2604.08678","access":"open"}
1,{"id":"s1968","stat":"Cross-model validation (GPT-4o-mini vs GPT-4o) on 176 documents: Pearson r = 0.92, ICC(2,1) = 0.92, mean absolute difference = 1.81 points","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"High cross-model reliability for LLM grading, systematic bias of only 0.34 points","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1969","stat":"LLM grader exhibited systematic positive bias relative to human raters of +4.9 points (ICC = 0.56)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"LLM grades substantially higher than human raters but rank ordering preserved","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1970","stat":"LLM-human rank-order agreement: Spearman rho = 0.79 for Task A, rho = 0.58 for Task B","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"LLM grading preserved rank order well for open-ended tasks but less well for constrained tasks","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1971","stat":"Human-human inter-rater agreement was also modest (ICC = 0.47, weighted kappa = 0.21)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Rubric ambiguity contributed substantially to measurement noise beyond LLM-specific limitations","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1972","stat":"Mean human score for Task B was 11.8 compared to LLM score of 17.3, indicating LLM\'s generous scoring amplified ceiling effects","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"LLM grading inflated Task B scores by roughly 5.5 points on average","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1973","stat":"LLM mean score overall was 15.6 vs human average of 10.7 (difference of +4.9 points)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Systematic LLM calibration bias across both tasks","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1974","stat":"96 documents were independently graded by 8 trained human raters organised in 4 pairs, with 61 complete dual ratings used for validation","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Human validation sample for checking LLM grading integrity","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1975","stat":"GPT-4o-mini grading was perfectly deterministic for 53.1% of Task A documents and 90.7% of Task B documents across 3 runs","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"LLM grading reproducibility - higher consistency for simpler/shorter Task B documents","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1976","stat":"Mean score range for non-deterministic grading: 0.99 points for Task A, 0.22 points for Task B","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"When LLM grades varied across runs, the variation was relatively small","url":"https://arxiv.org/abs/2604.08678","access":"open"}
1,{"id":"s1977","stat":"Post-task survey internal consistency: task experience Cronbach\'s alpha = 0.84, Copilot helpfulness alpha = 0.87, future AI intent alpha = 0.79","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Reliability of the self-report survey scales used in the experiment","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1978","stat":"Belief inventory internal consistency: AI as Thought Partner ordinal alpha = 0.86-0.90, Exploration & Experimentation alpha = 0.93, Productivity & Process alpha = 0.89-0.90","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Reliability of the belief measurement constructs","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1979","stat":"Post-hoc power analysis suggests the Productivity belief comparison was inadequately powered (achieved power = 0.35, requiring N = 307 per group for 80% power)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Study was underpowered to detect small-to-medium effects on some belief dimensions","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1980","stat":"The study detects effects of d > 0.40 at 80% power","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Power threshold - effects below this detection floor may exist but are undetectable","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1981","stat":"Within-pair correlation ICC = 0.14, design effect = 1.05, effective N = 200 from 210 observations in 155 clusters","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Clustering structure in the individual-level Task B analysis","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1982","stat":"Total documents graded: 887 (478 Task A, 409 Task B); final versions: 164 pairs (Task A), 210 individuals (Task B)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Scale of the LLM grading operation across both tasks","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1983","stat":"29 treatment participants (14.9%) did not complete the compliance survey items, so their compliance status could not be determined","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Missing compliance data for a subset of treatment participants","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1984","stat":"Task B showed no significant differences in time-to-quality: 89.7% of treatment participants and 86.2% of controls reached the 70% threshold","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Unlike Task A, partnership training did not affect the speed of quality accumulation","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1985","stat":"Control pairs wrote 63% longer documents than treatment pairs (740 vs 454 words)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"The structured protocol constrained document production volume substantially","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1986","stat":"Compliance subgroups showed a 2.7-point spread in mean scores within the treatment group (Parallel Play 9.8 vs True Joint 12.5)","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Within-treatment variation suggesting protocol execution contributed to outcome patterns beyond time-of-day effects","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1987","stat":"Oster (2019) coefficient stability bound: delta = 3.67 under standard assumption (R2_max = 1.3 x R2_controlled), delta = 0.65 under R2_max = 1","source":"Microsoft Research (Farach et al.)","date":"March 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Under the most conservative assumption, unobservables proportional to word count could plausibly account for the full effect","url":"https://arxiv.org/abs/2604.08678","access":"open"},{"id":"s1988","stat":"97% of C-suite and 86% of employees have personally benefited from AI in at least one way","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"2026 AI Adoption Survey (1,200 C-suite + 1,200 employees, 30 industries, US/UK/Ireland/Benelux/France/Germany)","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1989","stat":"94% of C-suite use AI for at least 30 minutes per day; 70% of employees do","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Daily AI usage intensity across seniority","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1990","stat":"64% of executives and 28% of employees spend 2+ hours per day using AI","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Heavy-u
1se threshold - more than a quarter of employees now at power-user intensity","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1991","stat":"52% of employees are using AI agents; 97% of executives say their company deployed agents in the past year","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Agent adoption has crossed the majority threshold on the employee side","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1992","stat":"75% of executives expect AI agents to be part of their company\'s C-suite within the next five years","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Forward-looking expectation for agentic presence at the top of the organisation","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1993","stat":"73% of CEOs say their AI strategy is causing them stress or anxiety; 38% call it high or crippling","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Pressure at the top - 10% crippling, 22% high, 40% moderate, 28% no stress","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1994","stat":"61% of the C-suite fear they could lose their job if they fail to lead their organisation through the AI transition","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Executive job insecurity tied to AI leadership","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1995","stat":"58% of executives say many of their fellow executives lack the fundamental knowledge to make strategic decisions about AI","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"C-suite literacy gap as seen by peers","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1996","stat":"94% of executives say their mandate from the board is clear: use AI to do more with less","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Board-level pressure on AI-driven efficie
1ncy","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1997","stat":"50% of executives feel their own AI skills are becoming obsolete; 45% worry they will lose their job in the next year because of AI","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Executive obsolescence anxiety","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1998","stat":"48% of executives say AI adoption at their company has been a massive disappointment (up from 34%)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Year-on-year jump in ROI dissatisfaction","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s1999","stat":"63% of leaders say executives at their company talk a big game about AI but can\'t deliver (up from 44%)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Executive-delivery credibility gap","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2000","stat":"45% of executives feel their company is burning cash on AI with no convincing impact on the bottom line","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"ROI frustration - nearly half of leaders see spend without payoff","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2001","stat":"Only 29% of executives report significant ROI from generative AI; only 23% from AI agents","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Headline ROI numbers for 2026 - generative AI outpacing agents but both under a third","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2002","stat":"64% of executives think it will take three years for their company to see ROI from AI","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Three-year payback expectation","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2003","stat":"59% of companies are investing at least $1M per year in generative AI; 64% of those haven\'t seen significant ROI","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Annual AI spend distribution and the ROI gap among big spenders","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2004","stat":"AI spend distribution: 5% <$100k, 10% $100k-500k, 18% $500k-$1M, 27% $1M-$5M, 18% $5M-$10M, 10% $10M-$100M, 4% >$100M","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Granular spend bands across enterprise AI programmes","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2005","stat":"69% of executives say their company is doing layoffs due to AI","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Direct contradiction to Factor\'s 2.5% \\"expect cuts\\" reading - Writer\'s broad C-suite reports already-active layoffs","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2006","stat":"39% of executives admit their company doesn\'t have a formal strategy to drive revenue from AI","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Strategy-execution gap on the monetisation side","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2007","stat":"75% of executives say their company\'s AI strategy is more for show than actual internal guidance","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"\\"AI-washing\\" and theatre at the enterprise level","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"}
1,{"id":"s2008","stat":"Confidence that company\'s AI approach is coordinated and highly strategic dropped from 73% to 63% among C-suite and from 47% to 31% among employees (2025 to 2026)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Year-on-year fall in confidence, widening C-suite/employee perception gap","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2009","stat":"Belief that company has been very successful adopting AI dropped from 75% to 63% among C-suite and from 45% to 25% among employees (2025 to 2026)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"The employee-side drop is sharper and the executive-employee gap widened","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2010","stat":"54% of C-suite say adopting AI is tearing their company apart (up from 42%)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Year-on-year rise in organisational disruption attributed to AI","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2011","stat":"56% of C-suite say AI has created power struggles and disruption at their organisation (up from 42%)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Internal political friction accelerating","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2012","stat":"Tension between IT teams and other lines of business rose from 68% to 78% (2025 to 2026)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"IT-business tension year-on-year","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2013","stat":"Tension between executives and employees rose from 63% to 74% (2025 to 2026)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Exec-employee tension year-on-year","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2014","stat":"IT not working with employees rose from 41% to 57%; IT not working with other lines of business rose from 36% to 55% (2025 to 2026)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"IT-collaboration failure intensifying","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2015","stat":"79% of executives say IT is creating AI applications in a silo (up from 71%); 61% say AI applications are being created in silos across the business","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"IT silo problem worsening - supports Mollick \\"where AI dies in IT\\" thesis","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2016","stat":"53% say IT is not delivering real value when it comes to generative AI (up from 36%)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"IT value-delivery gap widening","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2017","stat":"60% of executives say the board of directors will likely intervene because of a botched AI strategy","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Governance intervention risk at board level","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2018","stat":"92% of C-suite say they are actively cultivating a new class of \\"AI elite\\" employees","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Two-tiered workplace emerging as explicit enterprise strategy","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2019","stat":"60% of executives plan to lay off employees who can\'t or won\'t use AI","
1source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Direct layoff intent tied to AI non-adoption","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2020","stat":"95% of C-suite say roles, titles, or team structures are changing because of AI","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Structural change is near-universal","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2021","stat":"44% say new AI-focused roles are being created; 42% existing roles redefined; 41% teams reorganised; 31% traditional roles consolidated or reduced; only 5% no change","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Detail on structural change vectors","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2022","stat":"69% of C-suite say there\'s a growing, noticeable gap between employees who have mastered AI and those falling behind","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Perceived adoption gap within organisations","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2023","stat":"87% of executives say AI super-users are at least 5x more productive than laggards","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Super-user productivity multiplier as reported by executives","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2024","stat":"90% of C-suite say the rise of the AI super-user will require their company to completely rethink how they evaluate and reward performance","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Performance-management implication of AI super-users","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2025","stat":"AI super-user prevalence by generation: 43% Gen Z, 40% Millennials, 29% Gen X, 25% Baby Boomers","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Super-user skew toward younger cohorts","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2026","stat":"AI super-users save 9+ hours/week vs 2 hours/week for laggards; 4.5x more comfortable with AI; 3x more likely to have received both a promotion and a pay raise in 2025; 11% have built their own AI agents, tools, or workflows","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Anatomy of an AI super-user","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2027","stat":"27% of employees think they\'d be fine if they refused to use AI, but 60% of C-suite plan to lay off employees who can\'t or won\'t use AI","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Employee-exec perception gap on refusal consequences","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2028","stat":"24% of employees afraid they\'d be let go if they don\'t become expert AI users; 77% of C-suite say employees must become expert users or risk being left behind","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Expert-user expectation gap","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2029","stat":"43% of employees think their career would be affected
1if they refused to use AI; 33% worry they\'d become obsolete","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Employee anxiety around AI refusal","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2030","stat":"29% of employees admit to sabotaging their company\'s AI strategy; 44% among Gen Z","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Headline sabotage statistic with generational breakdown","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2031","stat":"Main reasons employees sabotage AI strategy: don\'t want AI to take over my job (30%), too many security issues (28%), poorly executed (26%), diminished value/creativity (26%), low-quality tools (21%), adding to my workload (20%)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Sabotage motivations - ranked list","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2032","stat":"76% of C-suite say employee sabotage poses a serious threat to their company\'s future","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Executive recognition of sabotage threat","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2033","stat":"35% of employees have entered proprietary, confidential, or sensitive company information into a public AI tool","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Shadow AI data leakage rate","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2034","stat":"16% of employees admit to using AI tools not permitted by their employer; another 16% aren\'t sure if the tools they\'re using are permitted","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Banned and ambiguous tool usage","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2035","stat":"67% of C-suite believe their company has already suffered a data leak or security breach because of an employee using an unapproved AI tool (up from 42% in 2025)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"THE headline governance stat - 25pp year-on-year jump. Breakdown: 39% know for a fact, 28% assume, 33% certain it hasn\'t happened","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2036","stat":"35% of executives concede they don\'t have complete visibility into the AI tools their employees are actually using","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Shadow-AI visibility gap at the exec level","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2037","stat":"Why employees use unapproved AI tools: whatever it takes to get work done (40%), company-approved tools are terrible (32%), rules aren\'t enforced (26%), manager encourages unofficially (21%), don\'t believe there\'s a real risk (20%)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Unapproved-tool-use motivations - ranked","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2038","stat":"AI vendor solution satisfaction dropped 17 points since 2025; ethical alignment and responsibility \\"excellent\\" rating at just 53% (down 17 points)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Vendor disappointment headline","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2039","stat":"Percent of C-suite rating company AI solutions \\"excellent\\" 2025 -> 2026: Security/data governance 54% -> 71%, Speed 48% -> 57%, Cost-effectiveness 45% -> 56%, User experience 45% -> 57%, Customization 43% -> 54%, Integration 42% -> 57%, Vendor support 41% -> 54%, Accuracy/reliability 41% -> 53%","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Individual dimensions improved but overall satisfaction fell - ethics is the drag","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2040","stat":"28% of employees have seen AI tools at work produce dangerously wrong, unethical, or biased results","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Prevalence of harmful output events","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2041","stat":"30% of employees worry their employer would retaliate if they reported unethical or dangerous AI behaviour; 90% of C-suite believe employees feel safe reporting without fear of retaliation","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Massive perception gap on psychological safety around reporting AI harm","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2042","stat":"36% of C-suite say their company doesn\'t have a formal, documented plan specifically for supervising AI agents","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Agent-governance gap","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2043","stat":"33% of C-suite aren\'t very confident they could immediately pull the plug on a rogue AI agent causing financial or reputational damage","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Rogue-agent containment c
1onfidence gap","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2044","stat":"50% of C-suite believe either the CEO (30%) or the CIO/CTO (29%) would be fired if an AI agent caused a significant data breach or financial loss","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Executive accountability expectation for agent incidents","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2045","stat":"Biggest governance challenges in adopting AI agents: Security (45%), Employee education (35%), Transparency (31%), Explainability (30%), Observability (29%), Ethical alignment (25%), Interoperability (23%)","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Agent governance challenge ranking","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2046","stat":"Only 35% of employees say their manager is an AI champion; 58% say manager is open to AI but provides minimal direction or encouragement","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Manager AI championship deficit","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2047","stat":"75% of employees say they\'d trust AI more than their manager for certain work tasks; 80% among Gen Z","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Employee-manager AI trust inversion","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2048","stat":"What employees trust AI more than their manager for: analysing performance data (41%), brainstorming creative ideas (35%), giving feedback (22%), deciding on project priorities (19%), recommending next career move (15%); 25% trust manager more in all areas","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Detail on AI-over-manager trust areas","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2049","stat":"55% of employees feel they\'re more knowledgeable about managing AI than their manager is; 64% among Gen Z","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Employee-manager AI knowledge inversion","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2050","stat":"79% of organisations have faced challenges adopting AI, including at least one of the 10 challenges identified in the report","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Near-universal incidence of adoption challenges","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2051","stat":"Writer claims 333% ROI over three years and $12M NPV for a composite organisation (based on six interviewed customers), with a six-month payback period. Forrester Total Economic Impact Report, commissioned by Writer","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Vendor-commissioned TEI - the survey phrases this as \\"333% on average\\" without the 3-year window. Treat with typical TEI scepticism","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2052","stat":"Forrester TEI of Writer (April 2025): composite organisation = $35B revenue, 25,000 employees, 250 Writer licences across marketing/legal/HR, 36 custom agents by Year 3","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Methodology basis for the 333% claim - 6 customer interviews aggregated into a single composite","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2053","stat":"Forrester TEI of Writer - three-year risk-adjusted PV: benefits $15.63M vs costs $3.61M = NPV $12.02M, ROI 333%, payback <6 months. 10% discount rate. Benefits risk-adjusted down 10%; costs adjusted up 10-15%","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Full financial summary of the Writer TEI","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2054","stat":"Writer TEI benefit build (3-year risk-adjusted PV): labour efficiencies $9.98M, agency cost avoidance $5.00M, improved compliance/brand $100k, faster onboarding $56k, legacy environment savings $492k","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"96% of the benefits come from just tw
1o categories (labour + agency); compliance, onboarding, and legacy are rounding errors","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2055","stat":"Writer TEI cost build (3-year risk-adjusted PV): subscription $1.71M, implementation $1.83M, training/discovery $68k. Implementation cost exceeds subscription cost","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Implementation cost is the larger line - worth noting for enterprise AI business cases generally","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2056","stat":"Forrester TEI of Writer - composite assumes 20/30/50 marketing FTEs using Writer in Years 1/2/3, achieving 100%/150%/200% productivity improvement, at $85k average fully burdened salary","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Drivers behind the $9.98M labour efficiency benefit - a steep adoption curve and a steep productivity curve","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2057","stat":"Forrester TEI of Writer - composite assumes 5 outsourced projects/year at $1M each, with 40%/45%/50% agency scope reduction in Years 1/2/3","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Drivers behind the $5.00M agency cost avoidance - assumes significant prior agency spend","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2058","stat":"Writer customer (VP digital content, financial services): \\"We got a speed to market of creating a first draft 1,000% faster. And the quality didn\'t decline.\\"","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Headline customer quote on content production speed","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2059","stat":"Writer customer (staff writer, financial services): \\"What previously took us 22 hours has been cut down to 6 hours. That\'s among all of us, about 90 times a month, and might be even more now.\\"","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Specific time-saving claim on content team","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2060","stat":"Writer customer (senior manager of AI, software): \\"Getting the first draft of SEO articles can take just minutes with Writer, where it took days before. We\'re seeing that some of the folks on our high-impact teams get up to 8 hours a week back.\\"","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"8 hours per week reclaimed for high-impact teams","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2061","stat":"Writer customer (brand marketing director, professional services): \\"Our organization saved a total of 50,000 hours each month for content development by using Writer.\\"","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"50,000 hours/month content development saving claim","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2062","stat":"Writer customer (senior manager of AI, software): \\"With Writer, 1.4 billion words were reviewed for brand standards. Before, employees were manually looking through a lot of that content. It was likely 30% or 40%, and now that\'s probably down to 5% or 10%.\\"","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"1.4 billion words reviewed claim with manual review rate dropping from 30-40% to 5-10%","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2063","stat":"Writer customer (staff writer, financial services): Writer reduced legal approval time for content from seven days to same-day approvals by flagging noncompliant terms","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Legal review turnaround - 7 days to same day - most reusable legal-sector quote from the TEI","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2064","stat":"Writer TEI faster onboarding assumption: 18% of users are new hires; onboarding reduced from 21 days to 7 days (65% faster); $40 fully burdened hourly rate; new hires at 50% productivity during ramp","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Onboarding benchmark numbers reusable in enterprise AI business cases","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2065","stat":"Writer customer (senior marketing writer, software): \\"I would say she was fully onboarded and working at capacity within two months, and historically, it takes our organization over six months to get similar roles fully onboarded.\\"","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Specific onboarding acceleration - 6 months to 2 months","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2066","stat":"Writer TEI compliance/brand benefit assumption: 2/3/4 SME FTEs per year (legal, HR, others) at $130k fully burdened salary, spending 35% of time on content review, reduced 40%/65%/85% by Writer","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"SME review time reduction curve for the $100k compliance benefit","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2067","stat":"Writer customer (healthcare product and innovation leader): organisation spent nearly a year trying to fine-tune AI models in-house and never reached production-level accuracy before switching to Writer\'s Palmyra Med","source":"Forrester/Writer TEI","date":"April 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Build-vs-buy cautionary tale for vertical AI","url":"https://tei.forrester.com/go/writer/writerdigitaltei/?lang=en-us","access":"gated"},{"id":"s2068","stat":"Sample: 1,200 C-suite (CEO, CFO, CMO, CIO) + 1,200 employees across 30 industries in US/UK/Ireland/Benelux/France/Germany. Employees split equally across HR, Finance, Customer Support, Legal, Marketing, Sales (17% each). Company size: 42% 100-999 employees, 33% 1,000-4,999, 12% 5,000-9,999, 13% 10,000+. Fielded December 2025 - January 2026","source":"Writer/Workplace Intelligence","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Methodology and sample composition","url":"https://writer.com/blog/enterprise-ai-adoption-2026/","access":"open"},{"id":"s2069","stat":"SimpleToolHalluBench: 296 tool-query pairs generated from 349 tools in AgentSafetyBench; two-annotator review with third-reviewer tiebreak; LLM-as-judge = DeepSeek-R1","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Benchmark construction for measuring tool hallucination (abstention when no appropriate tool available)","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2070","stat":"Qwen2.5-7B Instruct: 34.8% NTA hallucination / 54.7% Distractor-Tool hallucination; R1-distilled variant jumps to 74.3% NTA / 78.7% DT","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Baseline: reasoning distillation more than doubles tool hallucination on SimpleToolHalluBench","url":"https://arxiv.org/abs/2510.22977","access":"open"}
1,{"id":"s2071","stat":"Llama3.1-8B Instruct: 62.5% NTA / 99.7% DT hallucination; R1-distilled: 96.3% NTA / 100% DT","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Reasoning-distilled Llama nearly always hallucinates tools when none exist","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2072","stat":"Qwen3-8B: ThinkOff 4.1% NTA / 36.2% DT vs ThinkOn 5.4% NTA / 56.8% DT; Qwen3-32B: ThinkOff 5.1%/46.6% vs ThinkOn 8.8%/50.7%","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Toggling \\"thinking\\" mode on Qwen3 consistently raises tool hallucination","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2073","stat":"Qwen3-4B: Instruct 3.4% NTA vs Thinking 29.4% NTA - near 9-fold increase in tool hallucination","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Enabling reasoning mode can nearly 9x the hallucination rate on No-Tool-Available tasks","url":"https://arxiv.org/abs/2510.22977","access":"open"}
1,{"id":"s2074","stat":"Qwen3-235B: Instruct 3.7%/23.3% vs Thinking 6.1%/30.7% NTA/DT","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Reasoning trap persists at 235B parameter scale","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2075","stat":"DeepSeek-671B V3 Instruct: 10.8% NTA / 33.8% DT vs DeepSeek-R1: 17.6% NTA / 42.6% DT","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Reasoning-specialised R1 hallucinates more than its V3 baseline at 671B parameters","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2076","stat":"Kimi-K2-1T: Instruct 1.0% NTA / 15.5% DT vs Thinking 4.4% NTA / 21.3% DT","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Trillion-parameter Kimi-K2 also shows reasoning trap","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2077","stat":"Training Qwen2.5-7B-Instruct on GSM8K math with GRPO (zero tool data) still lifted tool hallucination rates on NTA and DT tasks as training progressed","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Reasoning RL on pure math still amplifies downstream tool hallucination - effect is not tool-data overfitting","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2078","stat":"ReCall GRPO ablation: direct tool-use RL (no <think> block) gave NTA 41.4% / DT 63.6% / reward 0.28; think-then-act RL gave NTA 90.2% / DT 100% / reward 0.45 - baseline was 34.8%/54.7%/0.22","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"The reasoning step itself, not RL training in general, drives the hallucination jump","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2079","stat":"Sample: 510 executives across UK and Ireland (395 Great Britain, 58 Northern Ireland, 57 Republic of Ireland) + 2,085 employees (1,891 UK, 194 ROI), ~30% under 35. Compared back to 1,085 UK executives + 3,752 UK workers fielded Jul-Aug 2024. Plus 17-industry economic modelling and ~30M UK job postings analysed","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Methodology and sample for the UK sequel to 2024\'s Generating Growth report","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2080","stat":"Share of UK working hours AI could enable rose from 47% to 82% since the 2024 Generating Growth report","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"The agentic-AI capability frontier expanded the in-scope share of UK work by 35pp in 18 months","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2081","stat":"Employees report using AI for tasks accounting for 21% of their working hours in the UK","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Realised use lags theoretical in-scope share by ~60pp","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2082","stat":"Existing AI evidence by sector (UK working hours): Design/Media/Writing 45%, IT/CS 32%, Performing Arts 31%, Engineering 30%, Education/Training 30%, Science/Research 27%, HR 24%, Sales 24%, Clerical 24%, Marketing/PR 23%, Customer Support 22%, Finance 20%, Law/Compliance/Public Safety 19%, Business Mgmt/Ops 19%, Construction 18%, Transportation 18%, Manufacturing 17%, Healthcare 15%, Maintenance 15%, Hospital
1ity 9%","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Sector ranking on AI use - Legal/Compliance sits in the upper-mid band, not near the top","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2083","stat":"Law, Compliance and Public Safety: 19% existing AI use, 33% theoretical AI potential, 48% not in scope","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"UK legal sector AI footprint - just over half is in-scope or already using AI","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2084","stat":"Daily generative AI use among UK employees: 18%, tripled from 6% in 2024","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Daily AI use rate at work has 3x\'d in 18 months in the UK workforce","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2085","stat":"39% of UK employees use AI tools provided by their employer; 24% have sourced their own AI tools independently","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Shadow AI is now the dominant adoption pattern - more than 1 in 5 UK employees on unsanctioned tools","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2086","stat":"31% of UK employees expect their job to be unrecognisable or to disappear by end of decade - twice the 15% share that held that view in 2024","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Employee expectation of job displacement has doubled in 18 months","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2087","stat":"Of UK employees expecting their job to disappear or be unrecognisable: 79% likely to reskill, 55% likely to change occupation","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Employees foresee disruption and are prepared to engage if supported","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2088","stat":"49% of UK executives expect AI to reduce national employment over the next decade - up from one-third (33%) in 2024","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Executive expectation that AI will shrink the workforce now near 50% nationally","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2089","stat":"70% of UK executives expect AI to drive higher productivity over the next decade","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Executive optimism on productivity coexists with pessimism on jobs","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2090","stat":"UK executive expectations on entry-level role demand: 40% expected an INCREASE in 2024 â just 15% in 2026; share expecting a DECREASE rose from 22% to 37%","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Pipeline collapse - executive expectation of entry-level demand has flipped sharply in 18 months","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2091","stat":"32% of UK executives cite change management and workforce transition as one of their most significant AI-related skills gaps","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Workforce transition is now a recognised skills gap for ~1 in 3 executives","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2092","stat":"Only 26% of UK organisations have conducted a skills audit to assess AI\'s impact on roles","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Three-quarters of UK organisations have not done the basic skills-impact diagnostic","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2093","stat":"27% of UK organisations do not provide AI-related training at scale; only 30% are investing in reskilling and redeployment pathways for at-risk roles","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Reskilling investment lags executive expectations of disruption","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2094","stat":"7% of UK executives say their workforce is fully prepared for agentic AI","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Workforce-readiness for agentic AI is essentially nil at sector level","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2095","stat":"54% of UK employees say they expect to reskill in response to AI","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Majority employee expectation of reskilling - signal that workers see roles changing","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2096","stat":"Job-postings analysis (~30M UK postings since ChatGPT launch) - greatest INCREASES in number of occupations (out of 1,821) listing each skill subcategory: Process improvement +1,095, Teaching +927, Business operations +699, Business analysis +699, People management +730, AI/ML +691, Regulation/legal compliance +625, Quality assurance +615, Business strategy +615, Business leadership +699","source":"Accenture, Generating Impact 2026 / Lightcast","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Skills complementing AI - judgement, oversight, compliance, process - growing fastest in UK postings"},{"id":"s2097","stat":"Job-postings analysis - greatest DECREASES in occupations listing each skill subcategory: Basic technical knowledge -84, Language competency -65, Business management -61, Writing/editing -59, Office productivity tech -46, Web design -40, Business consulting -32, Community/social work -32, Engineering -29, General networking -25","source":"Accenture, Generating Impact 2026 / Lightcast","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Routine cognitive and basic-tech skills declining fastest in UK postings"},{"id":"s2098","stat":"Only ~25% of UK employees say a major team process has been redesigned around AI in the past year","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Process redesign lags individual adoption - the workflow-reinvention gap","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2099","stat":"25% of UK executives say if their organisation stopped using AI tomorrow, some critical processes would slow significantly; another 3% say some core operations would fail","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"~28% of UK organisations now have AI embedded enough that removal would degrade operations","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2100","stat":"More than 25% of UK consumers have already used AI tools to search, compare or buy on their behalf; another 29% would delegate purchasing to an agent if reliable","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"cross-sector","context":"Consumer-side agent adoption running ahead of executive expectation","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2101","stat":"Only 21% of UK executives expect shopping/purchasing agents to have significant influence on consumer decisions over the next 3-5 years - despite 25%+ of consumers already using them","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"cross-sector","context":"Executive consumer-agent expectations lag actual consumer behaviour","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2102","stat":"46% of UK executives say AI has had little to no positive impact on profit and loss; 31% say stopping AI tomorrow would have no material effect on operations","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Nearly half see no P&L impact; nearly a third see no operational impact at all","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2103","stat":"44% of UK executives believe at least some of their AI budgets are wasted; 23% cannot estimate whether their AI spending is effective at all","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Two-thirds of UK executives either know they are wasting AI budget or cannot tell if they are","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2104","stat":"53% of UK employees say AI helps them do the same work faster rather than changing how work is done","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Majority of employees experience AI as task acceleration, not workflow redesign","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2105","stat":"Organisations reporting \\"significant or transformational\\" process redesign are 5x more likely to report substantial P&L uplift than those making incremental changes","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"The ROI is in workflow reinvention, not task automation - 5x P&L delta","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2106","stat":"More than 40% of UK working hours are tied up in coordination and transaction costs","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"The dead-weight time AI could in theory unlock if workflows were redesigned","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2107","stat":"Twice as many UK executives see cost reduction as the greatest future benef
1it of AI vs revenue growth","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Executive AI value framing skews 2:1 to denominator (cost) over numerator (growth)","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2108","stat":"Across 17 UK industries (G3K firms): revenue uplift potential from AI is more than 2x the combined potential for labour cost avoidance and labour cost saving","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Accenture\'s modelled economic case for AI value being a growth story, not a cost story","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2109","stat":"58% of UK executives say their organisation is not ready to integrate AI agents with core enterprise systems; 32% are still piloting predictive AI/ML; 26% not using it at all","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"The delivery gap from 2024 has not closed; legacy architectures still block production","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2110","stat":"68% of UK employees are uncomfortable with AI agents making higher-impact decisions","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Trust gap with agentic systems sits at over two-thirds of the workforce","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2111","stat":"Top barriers to UK AI adoption (executive %, 2026 vs 2024): Data security/privacy 41% (+11pp), Quality/accuracy 32% (+6pp), Trust/user acceptance 29% (+4pp), Unproven ROI 24% (+4pp), Cost 22% (-3pp), Regulatory environment 22% (+1pp), Use-case identification 21% (+3pp), IP concerns 20% (+4pp), Skills shortage 20% (-3pp)","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Risk, trust and quality concerns now sit above cost and skills as the primary barriers to AI adoption in the UK","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2112","stat":"UK AI maturity bands (Accenture segmentation): Experimenters 27% (pilots, isolated tasks); Adopters 16% (multi-team but stuck pre-production); Integrators 48% (in production but not delivering enterprise-level value); Scalers 10% (AI embedded in core operations with measurable improvements)","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Only 1 in 10 UK organisations are at full Scaler maturity; 43% have not yet reached production","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2113","stat":"Improving on one of the five reinvention dimensions by one standard deviation increases likelihood of becoming an AI Scaler by 2.2x; improving across all five dimensions raises it more than 4x","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Predictive model output - reinvention is multiplicative, not additive","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2114","stat":"Binding constraint by maturity stage (Accenture predictive model): Workforce for Experimenters; Digital Core for Adopters and Integrators; Strategy for Scalers","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"The bottleneck shifts as organisations mature; readiness, then plumbing, then portfolio discipline","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2115","stat":"Executives at Scaler organisations spend more than 20x longer each week using AI than executives at organisations still in pilot stages","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Hands-on executive AI use is one of the strongest single signals of organisational AI maturity","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2116","stat":"76% of executives at Scaler organisations say they are very confident in the quality of their AI training, vs 6% at Experimenter organisations","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"70pp confidence gap on training quality between Scalers and Experimenters","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2117","stat":"A group of two to five UK people can already oversee dozens of AI agents running an end-to-e
1nd process","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"The new \\"delivery unit\\" - small humans-on-top-of-many-agents teams","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2118","stat":"29% of UK employees possess the skill of creativity but do not use it at work - the highest underutilisation rate of any skill evaluated","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Creativity is the most under-deployed skill in the UK workforce - upside if AI absorbs routine work","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2119","stat":"Only 43% of UK organisations have updated performance measures for AI-enabled ways of working; only 40% say managers can assess performance fairly when outputs are partly AI-assisted","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Performance management has not caught up with AI-augmented work","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2120","stat":"64% of UK executives say employees are expected to be transparent about AI use; only 14% say employees always disclose it","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"The \\"quiet AI\\" disclosure gap is 50pp - expectation vs practice","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2121","stat":"UK employees who say they would need significant training are roughly 2.4x more likely to hide their use of AI than those who feel adequately trained","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Training gaps drive concealment - quiet AI is a training problem as much as a culture problem","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2122","stat":"Only 3% of UK executives say all aspects of their technology and data stack are fully ready for agentic AI","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Digital-core readiness for agents is a rounding error at sector level","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2123","stat":"UK Digital Core readiness for agentic AI (\\"Net ready\\" %, executive sample, n=395): cloud infrastructure 38%, secure environments 35%, structured data 33%, real-time supervision 32%, unified data access 31%, unstructured data at scale 30%, integrations with core systems 29%, multi-agent orchestration 29%, modular architecture 29%, monitoring/audit tools 28%","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Net-ready scores cluster at 28-38% across all 10 capabilities; net-not-ready 50-60%","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2124","stat":"28% of UK executives report having tools to monitor and audit AI agent behaviour at scale; 57% say they are not ready to supervise agents in real time","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Agent oversight infrastru
1cture is missing in the majority of UK organisations","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2125","stat":"Across nine governance mechanisms assessed (autonomy limits, audit trails, etc), fewer than 1 in 4 UK organisations on average have any of them fully operational","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Agentic governance maturity sits below 25% across the board","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2126","stat":"UK organisations mostly or fully ready to monitor and audit AI agent behaviour are almost 10x more likely to be Scalers than those that are not","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Governance is a precondition for scaling agentic AI, not a downstream control","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2127","stat":"Top agentic AI risks cited by UK executives: loss of human oversight or excessive autonomy 48%; incorrect or harmful decisions 45%","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"The principal cited risks are control and decision quality, not data privacy","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2128","stat":"When UK executives select agentic AI vendors, security/privacy/compliance guarantees are the factors most often cited (42%)","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Security and compliance now lead agentic-vendor selection criteria","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2129","stat":"Marc Warner (CEO of Faculty, CTO of Accenture): \\"Even if we froze the technology where it is today, it would still have a profound impact\\"","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Faculty/Accenture leadership framing on the existing AI capability overhang","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2130","stat":"UK Chancellor Rachel Reeves (Mais Lecture, March 2026): committed UK to achieving the fastest AI adoption in the G7, backed by significant new investment","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"UK government policy commitment to G7-leading AI adoption pace","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2131","stat":"UK Office for Budget Responsibility now incorporates assumptions about AI-driven productivity growth into its medium-term fiscal forecasts","source":"Accenture, Generating Impact 2026 / OBR Briefing Paper No.9","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"AI productivity built into UK fiscal projections - the macro consequences of slow adoption","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2132","stat":"JP Morgan Chase has reclassified AI spending as \\"core infrastructure\\"","source":"Accenture, Generating Impact 2026 / Banking Exchange","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Reclassification signal - AI moving from project line to core infrastru
1cture on enterprise balance sheets"},{"id":"s2133","stat":"AI-native firms reaching $100M ARR within 12 months with teams of 20 cited as the strategic provocation for established UK organisations","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"The greenfield benchmark for the brownfield reinvention question","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2134","stat":"Tasks doubling every 7 months: \\"the length and complexity of tasks AI agents can perform autonomously has approximately doubled every seven months\\"","source":"Accenture, Generating Impact 2026 / METR","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Direct citation of METR\'s task-horizon paper as the agentic capability trajectory","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2135","stat":"One global manufacturer\'s digital twin collapsed a four-week physical validation process into three days without disrupting live production","source":"Accenture, Generating Impact 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Agent-enabled digital-twin case study - 4 weeks to 3 days for production validation","url":"https://www.accenture.com/gb-en/insights/data-ai/generating-impact","access":"open"},{"id":"s2136","stat":"ReCall GRPO training: IFEval 62.4â59.8 (-2.6pp), ComplexBench 60.8â59.4 (-1.4pp), BFCL Multi-Turn tool competence 13.6â23.5 (+9.9pp), yet NTA tool hallucination 34.8%â90.2% and DT 54.7%â100%","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Tool hallucination is a distinct failure mode - not captured by instruction-following or tool-calling benchmarks","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2137","stat":"Shumer (16 Apr 2026) on GPT-5.5: \\"a genuine improvement, but feels less dramatic than previous releases because preceding models were already exceptionally capable... frontier-filling rather than capability leaps\\"","source":"Matt Shumer, shumer.dev","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"The acceleration narrative softening - same author who in Feb 2026 was extrapolating \\"substantially smarter than almost all humans by 2026-27\\"","url":"https://shumer.dev/gpt55review","access":"open"},{"id":"s2138","stat":"Shumer on the orchestration thesis: \\"the limiting factor is often not \'can it do the work?\' It is \'have we built the right environment for it to keep doing more of the work, across more moving pieces, without losing the thread?\'\\"","source":"Matt Shumer, shumer.dev","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"The frontier has shifted from raw model capability to harness construction - converges with Anthropic Cowork motion and Accenture action-layer framing","url":"https://shumer.dev/gpt55review","access":"open"},{"id":"s2139","stat":"Shumer on GPT-5.5 security capability: \\"Everyone should be running GPT-5.5 against their codebases to look for vulnerabilities\\" - found real issues in messy codebases that prior GPT and Claude models missed when given crisp specific targets","source":"Matt Shumer, shumer.dev","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Actionable security recommendation - GPT-5.5 a \\"huge step up\\" from general-purpose models on vulnerability discovery, though dedicated GPT-5.4-Cyber still better when properly used","url":"https://shumer.dev/gpt55review","access":"open"},{"id":"s2140","stat":"Mohl on Skills vs MCP: \\"Skills are great for pure knowledge and teaching an LLM how to use an existing tool. But for giving an LLM actual access to services, the Model Context Protocol (MCP) is the far superior, more pragmatic architectural choice\\"","source":"David Mohl, david.coffee","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"commentary","scope":"cross-sector","context":"Practitioner counter-argument to Anthropic\'s Skills-everywhere positioning - the cleanest practical split between the two","url":"https://david.coffee/i-still-prefer-mcp-over-skills/","access":"open"},{"id":"s2141","stat":"Mohl\'s diagnostic test: \\"If a Skill\'s instructions start with \'install this CLI first,\' you\'ve just added an unnecessary abstraction layer and extra steps\\"","source":"David Mohl, david.coffee","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"commentary","scope":"cross-sector","context":"One-line diagnostic for whether a problem belongs as a Skill or an MCP server","url":"https://david.coffee/i-still-prefer-mcp-over-skills/","access":"open"},{"id":"s2142","stat":"Pollins on legal AI conversations: \\"Ask them which legal task they are talking about\\" - argues meaningful AI-in-legal conversations require agreeing the unit of analysis first; the spread inside \\"law\\" is wider than between law and many other professions","source":"Matt Pollins, agents.law","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"commentary","scope":"legal","context":"The framework discipline missing from most \\"AI will replace lawyers\\" debates - proposes 20 legal tasks plotted on Anthropic Economic Index spider chart as the right unit of analysis","url":"https://www.agents.law/p/law-is-not-one-thing","access":"open"},{"id":"s2143","stat":"Pollins illustrative contrast: M&A practitioner coordinating DD across 14 jurisdictions, probate specialist guiding families through estate administration, and trial lawyer running real-time courtroom decisions are \\"professionals who could not step into the other\'s shoes without significant retraining\\" despite shared credentials","source":"Matt Pollins, agents.law","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"commentary","scope":"legal","context":"Why \\"law\\" is the wrong unit of analysis for AI conversations","url":"https://www.agents.law/p/law-is-not-one-thing","access":"open"},{"id":"s2144","stat":"Mitigation on ReCall-7B: Prompt engineering cuts NTA from 90.2% to 87.5% (marginal); DPO cuts NTA to 55.8% and DT to 71.4% but SynTool reward drops from 0.45 to 0.34 (~24% utility loss)","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"Reliability-capability trade-off: DPO improves honesty but materially degrades agent utility; prompt fixes largely ignored","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2145","stat":"Representation stability after Reasoning RL (CKA): in-distribution inputs >0.9 across all layers; tool-related inputs collapse below 0.75 in early and middle layers","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Mechanistic: reasoning RL disproportionately reshapes tool-related representations even when trained only on math","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2146","stat":"Late-layer residual stream discrimination scores between correct and hallucinated responses exceed 0.14 from layer 20 onward; attention outputs avg 0.02, MLP outputs avg 0.04","source":"Yin et al. \\"The Reasoning Trap\\"","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Hallucination signal accumulates gradually in the residual stream rather than
1in a single discrete component","url":"https://arxiv.org/abs/2510.22977","access":"open"},{"id":"s2147","stat":"12 practitioners across jurisdictions (NZ to Switzerland to Abu Dhabi) tested Claude for Word over 7 days following public release","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Community evaluation methodology - Claude for Word for legal","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2148","stat":"7 legal workflows tested: contract review (NDAs, MSAs, loan agreements, DoAs); cross-border English/Chinese drafting; investigations vs Copilot; estate planning; capital-gains tax advisory; litigation drafting (TOA, Bluebook, citation verification); deed of settlement review","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Scope of seven-day Claude for Word evaluation","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2149","stat":"Table of Authorities reduced from 10 minutes (first session) to under 5 minutes (second session) after writing one reusable skill","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Skill-based productivity compounding inside Claude for Word","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2150","stat":"Cross-app investigation test: Claude in Word messaged Claude in Excel; the Excel-side agent built workbook live (7 exhibit rows, frozen header, USD formatting) - Microsoft Copilot refused twice","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Cross-app peer-agent capability vs single-app refusal","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2151","stat":"$300 discrepancy caught by Claude in Excel agent that Copilot missed ($78,400 bank transfer vs $78,700 combined POs in investigation memo)","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Cross-app analytical advantage flagged in Investigator Note","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2152","stat":"6 quality breakages during 7-day test: italic/small-caps drop on tracked changes; bullet-paragraph mechanics; citation framing drift; Word Online != Desktop parity gap; shallow default legal awareness; formatting-detection asymmetry","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"practitioner","scope":"legal","context":"Defects observed in Claude for Word during practitioner testing","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2153","stat":"6 structural gaps blocking firm-wide deployment of Claude for Word: no DMS integration (iManage/NetDocs/SharePoint); no firm-level Skills library; no conflicts-system awareness; limited matter-level data residency; no skill registry with provenance/signing; no structured-output schemas","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Enterprise procurement gaps for Claude for Word","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2154","stat":"Procurement-routing delta: Claude direct (Word plug-in, desktop, Code) defaults to Opus 4.7; access via Microsoft Copilot or Harvey typically delivers Opus 4.6 without extended thinking exposed","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"practitioner","scope":"legal","context":"Procurement path determines model tier - large firms buying via Copilot/Harvey get older models","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2155","stat":"3-planted-error formatting test on contract: Claude caught 2 of 3 (missing detail and substance change caught; signature-block director added as formatting missed)","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Formatting-detection asymmetry under controlled error injection","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2156","stat":"Bilingual loan-agreement sync test: dozen-plus revisions on Chinese side prop
1agated live into English with original typography preserved, edit by edit (cross-document)","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Cross-document bilingual synchronisation capability for cross-border deals","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2157","stat":"Citation verification: Claude caught nearly every issue the contributor\'s months-old custom citation pipeline catches (inverted holdings, fabricated quotes, mis-cited propositions); failure mode = over-generous reading when brief stretches a real case slightly beyond what it stands for","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Claude vs custom citation tool benchmarking by litigator/law librarian","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2158","stat":"Adversarial test (Claude vs Copilot on investigation memo): Copilot faster on raw drafting; Claude unprompted added \\"prepared in anticipation of litigation\\" privilege header, mapped witness terminology to institutionalized-practice vs individual-conduct, framed three specific follow-up actions","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Analytical-depth divergence between Claude for Word and Copilot","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2159","stat":"Word plug-in runs same orchestration as Claude Code (AskUserQuestion, TodoWrite, Bash visible in audit trail) - same agent toolkit as developer surface, not a consumer wrapper","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"Architectural parity between Claude for Word and Claude Code","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2160","stat":"Public GitHub skills repo (Saif Al-Younan\'s ClaudeforWord): community stars and forks within 48 hours of publication","source":"LegalQuants Academy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Skill-sharing ecosystem velocity for Claude for Word","url":"https://legalquants.substack.com/p/is-claude-for-word-actually-useful","access":"open"},{"id":"s2161","stat":"1,646 in-house lawyers across 6 flagship Crafty Counsel events (UK & US, 6 months Oct 2025 - Apr 2026) - \\"the largest live signal dataset on the UK in-house market\\" per Ben White","source":"Crafty Counsel Q2 2026 Signal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Sample frame for in-house priority dataset"},{"id":"s2162","stat":"1,104 free-text responses on top in-house business priority","source":"Crafty Counsel Q2 2026 Signal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Sub-sample analysed for priority themes"},{"id":"s2163","stat":"52% of 1,646 in-house registrants are GC / Head of Legal / Director","source":"Crafty Counsel Q2 2026 Signal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Seniority profile of dataset (skewed senior)"},{"id":"s2164","stat":"AI / automation: 33% of 1,104 in-house priority responses - top theme, nearly 2x the next-largest theme","source":"Crafty Counsel Q2 2026 Signal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Top in-house priority Oct 2025 - Apr 2026"},{"id":"s2165","stat":"Geopolitics, regulation, sanctions: 19% of in-house priorities (second-largest theme; bigger than cost, contracts and M&A combined)","source":"Crafty Counsel Q2 2026 Signal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Second in-house priority - fragmentation/instability"},{"id":"s2166","stat":"Legal ops / tech adoption: 17% of in-house priorities","source":"Crafty Counsel Q2 2026 Signal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Third in-
1house priority (tied with governance)"},{"id":"s2167","stat":"Governance / board / risk: 17% of in-house priorities (neck-and-neck with legal ops and AI)","source":"Crafty Counsel Q2 2026 Signal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Third in-house priority (tied with legal ops/tech)"},{"id":"s2168","stat":"Cost / efficiency / budget: 13% of in-house priorities","source":"Crafty Counsel Q2 2026 Signal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Fifth in-house priority"},{"id":"s2169","stat":"M&A / corporate / fundraising: 11% of in-house priorities","source":"Crafty Counsel Q2 2026 Signal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Sixth in-house priority"},{"id":"s2170","stat":"25% of legal AI deployments fail before producing value (n=73 vendor evaluations from Kitsu pilot)","source":"Crafty Counsel / Kitsu Pilot","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Legal AI deployment failure rate from buyer-side evaluation"},{"id":"s2171","stat":"40% of legal AI vendor evaluations are negative (n=73 from Kitsu pilot)","source":"Crafty Counsel / Kitsu Pilot","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Buyer-side negativity rate on legal AI vendors"},{"id":"s2172","stat":"63% of legal AI buyers \\"still going\\" - no clear winner yet, high churn risk (n=73 vendor evaluations)","source":"Crafty Counsel / Kitsu Pilot","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Legal AI market still contested per in-house buyer data"},{"id":"s2173","stat":"Underneath the AI-first priority: in-house lawyers asking how to prove their function still matters when there is a perception that AI is doing much of the work - \\"demonstrating the added value of human advice in an increasingly AI-advised world\\" (verbatim quote from Director, in-house legal)","source":"Crafty Counsel Q2 2026 Signal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Legitimacy/purpose anxiety as the signal underneath AI prioritisation"},{"id":"s2174","stat":"Spellbook: 4,500 customer organisations across 80 countries (self-reported)","source":"r/legaltech vendor AMA / Scott Stevenson","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Vendor self-reported customer count; mid-market through enterprise in-house plus solos and small/mid firms; explicitly excludes BigLaw","url":"https://www.rlegaltech.com/vendors/spellbook/ama/","access":"open"},{"id":"s2175","stat":"Spellbook: 1.3m AI suggestions accepted by lawyers in 2026 YTD (self-reported)","source":"r/legaltech vendor AMA / Scott Stevenson","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Vendor self-reported usage; cited as primary proof point of AI second-pair-of-eyes value","url":"https://www.rlegaltech.com/vendors/spellbook/ama/","access":"open"},{"id":"s2176","stat":"Spellbook: suggestion acceptance rate 55% in 2026 vs 5% at launch in 2022 (self-reported)","source":"r/legaltech vendor AMA / Scott Stevenson","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vendor self-reported \\"North Star\\" output-quality metric; 11x improvement claimed over four years","url":"https://www.rlegaltech.com/vendors/spellbook/ama/","access":"open"},{"id":"s2177","stat":"Spellbook: 12+ foundation models in production with new models live within 24-48 hours of launch (self-reported)","source":"r/legaltech vendor AMA / Scott Stevenson","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vendor self-reported multi-model stack and refresh cadence","url":"https://www.rlegaltech.com/vendors/spellbook/ama/","access":"open"},{"id":"s2178","stat":"Spellbook: ~$100m total raised including $40m debt facility for acquisition strategy (self-reported)","source":"r/legaltech vendor AMA / Scott Stevenson","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Vendor self-reported funding posture; explicit acquisition appetite (\\"DMs are open\\"); talent cited as the bottleneck","url":"https://www.rlegaltech.com/vendors/spellbook/ama/","access":"open"},{"id":"s2179","stat":"Spellbook: demo bookings 200/week (Dec 2025) â ~400/week post-Claude-for-Word launch (self-reported)","source":"r/legaltech vendor AMA / Scott Stevenson","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Vendor self-reported pipeline impact of Claude for Word; framed as category-awareness tailwind rather than substitution threat","url":"https://www.rlegaltech.com/vendors/spellbook/ama/","access":"open"},{"id":"s2180","stat":"Ivo: a single contract review chains 400+ model calls across multiple providers (self-reported)","source":"r/legaltech vendor AMA / Min-Kyu Jung","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vendor self-reported pipeline complexity; uses OpenAI + Anthropic + Google with per-step model selection on internal evals","url":"https://rlegaltech.com/amas/","access":"open"},{"id":"s2181","stat":"Ivo: in 30+ lawyer blind head-to-head trials, often \\"literally unanimous votes\\" in Ivo\'s favour vs competitors (self-reported)","source":"r/legaltech vendor AMA / Min-Kyu Jung","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vendor self-reported competitive evaluation outcome","url":"https://rlegaltech.com/amas/","access":"open"},{"id":"s2182","stat":"Ivo: underlying AI model accounts for ~50% of accuracy of AI contracting tools (vendor framing)","source":"r/legaltech vendor AMA / Min-Kyu Jung","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vendor framing for why legal-specific tools beat raw foundation models; remaining ~50% from coalescing algorithms, formatting handling, legal context, workflow","url":"https://rlegaltech.com/amas/","access":"open"},{"id":"s2183","stat":"Ivo: blind study claim - significantly outperformed Claude across all metrics; performed comparably to special counsel at Am Law 25 firm (self-reported)","source":"r/legaltech vendor AMA / Min-Kyu Jung","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vendor self-reported blind benchmarking; report on Ivo website","url":"https://rlegaltech.com/amas/","access":"open"},{"id":"s2184","stat":"SimpleDocs: typically 25-50% less expensive than enterprise contract-AI competitors (self-reported)","source":"r/legaltech vendor AMA / Preston Clark","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Vendor self-reported pricing differential; SimpleDocs is bootstrapped/profitable, no VC"},{"id":"s2185","stat":"SimpleDocs: 270-respondent practitioner survey - wide adoption of general AI tools by transactional lawyers but materially lower trust/verifiability and lower frequency of use vs Legal AI tools","source":"r/legaltech vendor AMA / Preston Clark","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Vendor-commissioned market study referenced during AMA; sample of 270+ lawyers"},{"id":"s2186","stat":"Wordsmith: one customer saved $100k+ in external
1counsel fees in first deployment phase (self-reported)","source":"r/legaltech vendor AMA / Robbie Falkenthal","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Vendor self-reported ROI proof point; cited as upside surprise vs initial expectation of lawyer-productivity gains"},{"id":"s2187","stat":"Spellbook + Wordsmith both explicitly decline to serve BigLaw / law firms on stated basis of billable-hour incentive misalignment with AI; SimpleDocs softer version of same position","source":"r/legaltech vendor AMA","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"All four panellists target in-house legal as ICP; deliberate structural divide from law-firm-platform vendors (Harvey, Legora)"},{"id":"s2188","stat":"Per-seat pricing acknowledged as unsustainable by Ivo and Wordsmith; Ross McNairn three future models: token-plus (foundation providers), outcome-based (human + AI blended), consultancy / forward-deployed engineering","source":"r/legaltech vendor AMA / Min-Kyu Jung + Ross McNairn","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Industry pricing-model thesis from contract-AI vendor founders"},{"id":"s2189","stat":"All four panel vendors use multi-provider model stacks (OpenAI + Anthropic + Google) with per-task / per-sub-task evaluation; none train custom models","source":"r/legaltech vendor AMA","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Spellbook explicit: \\"model training is generally a bad bet, and everyone who has tried to train custom models in legal has failed to outperform generalist models longterm\\""},{"id":"s2190","stat":"Spellbook M&A appetite stated openly during AMA (\\"DMs are open\\"); Wordsmith would acquire \\"for the right price\\"; SimpleDocs \\"good for now\\"; Ivo silent on M&A question","source":"r/legaltech vendor AMA","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Differential M&A posture across the four panel vendors"},{"id":"s2191","stat":"Litigation-risk question (privilege waiver on discoverable AI interaction history; evidentiary \\"review\\" question; vendor subpoena exposure) ducked by all four panel vendors during 90-min AMA","source":"r/legaltech vendor AMA","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"No vendor engaged with the most substantive question of the thread; signals industry under-preparation for AI-assisted contract litigation"},{"id":"s2192","stat":"Self-preferencing bias question (Xu et al. 2025: LLM resume screeners prefer same-model outputs 67-82% of the time) ducked by Ivo, Wordsmith, SimpleDocs; only Spellbook offered generic response","source":"r/legaltech vendor AMA","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Industry has not studied self-preferencing in legal AI workflows where same model often does both drafting and review"},{"id":"s2193","stat":"Spellbook concedes Ivo was first to launch a Google Docs add-in; Ivo concedes Spellbook \\"may have been first\\" with autocomplete-only Word add-in pre-redlining","source":"r/legaltech vendor AMA","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Cross-vendor first-mover acknowledgements for the historical record"},{"id":"s2194","stat":"5-category Innovation Map taxonomy replaces 7-category 2025 spectrum (Accelerators / Architects / Developers / Operators / Tacticians)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Operating model now the differentiator, not adoption progression","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2195","stat":"3 Accelerator firms in UK market (Ashurst, Freshfields, HSF Kramer)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Frontier operators with explicit pricing/agentic agendas","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2196","stat":"4 Architect firms in UK market (Pinsent Masons, Osborne Clarke, Fieldfisher, Simmons & Simmons)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Internal builds plus formal system design","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2197","stat":"2 Developer firms in UK market (A&O Shearman, Hogan Lovells)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Productising legal expertise commercially","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2198","stat":"15 Operator firms in UK market (BCLP, CMS, WBD, Macfarlanes, Slaughters, Freeths, Clyde, Eversheds, Dentons, DLA Piper, Travers, Gowling, TLT, NRF, Shoosmiths)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Folding AI into day-to-day delivery; the largest category","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2199","stat":"6 Tactician firms in UK market (Addleshaw Goddard, Bird & Bird, Clifford Chance, CRS, Mishcon, Taylor Wessing)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Integrating multiple tools at scale; the report\'s \\"lead\\" category","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2200","stat":"Frontier models moved from struggling with complex reasoning to near-expert performance in ~2 years","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Capability acceleration framing","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2201","stat":"Tasks once costing pounds per query now cost pence or fractions of a penny","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Cost compression of AI inference","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2202","stat":"Ashurst digital team ~90 people","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Largest dedicated digital function at a UK firm of this size","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2203","stat":"Freshfields AI Academy: 8-week programme, 260+ internal
1champions, reached over half the firm","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Scale of partner-engaged AI training programme","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2204","stat":"Freshfields one internal tool grew from 1,200 to 1,800 users in six months","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Adoption velocity inside an Accelerator firm","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2205","stat":"HSF Kramer: 25 specialist AI acceleration team plus 400+ digital legal delivery team","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Total AI/digital headcount at HSF Kramer","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2206","stat":"HSF Kramer runs Harvey AND Legora in parallel alongside Relativity, Wexler, Cicero","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Only major UK firm publicly running both Harvey and Legora at scale","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2207","stat":"CMS: 7,200 lawyers / 50 countries Harvey rollout (Dec 2025); 93% reported productivity gains; ~117.9 hours saved per lawyer per year","source":"The Lawyer Innovation Map 2026 / CMS","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Largest GenAI deployment in legal to date","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2208","stat":"CMS innovation headcount 80+ across legaltech, data, knowledge, operations","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Innovation function size at CMS","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2209","stat":"DLA Piper: 5,000 Harvey licences globally rolled out March 2026","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Among the largest single Harvey deployments","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2210","stat":"DLA Piper innovation staff ~200 people","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Total innovation function size at DLA","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2211","stat":"Eversheds Service Excellence team 70-80 people excluding Konexo","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Innovation function size at Eversheds","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2212","stat":"Eversheds selected Harvey over Legora (Feb 2026) - decided by client overlap","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Notable Harvey-vs-Legora decision criterion: which clients use which platform","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2213","stat":"Fieldfisher AI adoption rate 94%, targeting 100% by end FY25/26","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Highest publicly-stated AI adoption rate at a UK firm","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2214","stat":"Fieldfisher X Berlin team ~30 people plus 15 in IT","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Fieldfisher innovation hub size","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2215","stat":"Fieldfisher FX Farm: 15+ AI agents deployed (litigation, policy reviews, DD foundation)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Scale of internal agent build at a mid-tier firm","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2216","stat":"Simmons & Simmons: 76 embedded innovation specialists","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Innovation talent dispersed across practice groups","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2217","stat":"Simmons Percy: 1,125 unique fee-earner users in February 2026 (up from 827 same month previous year)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Year-on-year growth in proprietary tool usage","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2218","stat":"Simmons: 20+ FTEs working on Percy","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"In-house AI tool maintenance/development team size","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2219","stat":"Hogan Lovells Eltemate: 100+ employees","source":"The Lawyer Innovation Map 2026 / Eltemate","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"In-house legal tech venture scale","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2220","stat":"Hogan Lovells CRAIG: 4,400 registered users","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Proprietary platform reach across firm + clients","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2221","stat":"Hogan Lovells DIGI2025 firmware competition: 100+ submissions globally; winning entry Breach Pilot now being commercialised within Eltemate","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Successful internal innovation pipeline","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2222","stat":"Hogan Lovells email-sorting drops ~80% via CRAIG; chronology engine 42% faster than human reviewer on 470-page dataset; 10x faster in life sciences litigation","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Quantified efficie
1ncy gains from proprietary tool","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2223","stat":"Linklaters recruited 20 specialist AI lawyers (Nov 2025) to embed in global practice groups","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Innovation talent migration into practice groups","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2224","stat":"Baker McKenzie hunting 6 new practice innovation lawyers (Dec 2025)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Innovation talent migration into practice groups","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2225","stat":"Clifford Chance: ~90% of firm actively using machine learning tools","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Magic Circle firm broad ML adoption","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2226","stat":"Clifford Chance hundreds of internally created AI agents for narrow tasks","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Internal agent proliferation at a Magic Circle firm","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2227","stat":"Charles Russell Speechlys: Harvey adoption >80%, Copilot >70%","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Adoption depth at a Tactician firm","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2228","stat":"Mishcon de Reya one of six Legora design partners on Legora Portal","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Vendor co-development relationship depth","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2229","stat":"Bird & Bird Legora engagement from when Legora was small; close engineer co-development relationship","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Vendor relationship depth as Tactician differentiator","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2230","stat":"Shoosmiths target of 1m Copilot prompts in a year (Apr 2025) - hit four months early with £1m bonus pool","source":"The Lawyer Innovation Map 2026 / Shoosmiths","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Adoption incentive that worked","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2231","stat":"Womble Bond Dickinson: ~1,200 effectively involved in firm\'s innovation project","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Whole-firm innovation framing","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2232","stat":"Macfarlanes: nearly half of users now use AI tools regularly","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Regular-user adoption rate at an Operator firm","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2233","stat":"TLT FutureLaw team: 27 people","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Innovation function size at TLT","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2234","stat":"TLT: 15 innovation champions across practice groups","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Distributed innovation network","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2235","stat":"Dentons UK/Ireland/Middle East innovation staff: 14 people in two sub-teams (data science + product management)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Lean innovation function at large international firm","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2236","stat":"Dentons Office Hours programme: 70 applications, 28 demos, 10 shortlisted, several progressed to pilot","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Sandbox/R&D pipeline scale","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2237","stat":"BCLP LEAP programme: AI training mandatory from induction onwards","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Structural training commitment","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2238","stat":"Lewis Silkin Delphius prototype built in 6 weeks with Planes; refined over 6 months","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Time-to-market for client-facing legal AI product","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2239","stat":"Lewis Silkin Delphius: hundreds of companies in sales pipeline within months of launch","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"media","scope":"legal","context":"Demand for cross-jurisdictional employment law AI","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2240","stat":"Lewis Silkin Delphius leverages Ius Laboris global employment law network across 57 countries","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Network-leverage model for legal AI content","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2241","stat":"Three Crowns + Stanford built Atelier - AI cross-examination simulator with witness agents and AI feedback twin","source":"The Lawyer Innovation Map 2026 / Three Crowns / Stanford","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"First major junior-lawyer AI training simulator","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2242","stat":"Equinor global legal function: ~180 lawyers/specialists; rolled out Harvey globally; \\"fewer tools, robustly governed\\" approach","source":"The Lawyer Innovation Map 2026 / Equinor","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"media","scope":"legal","context":"In-house AI deployment at major energy company","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2243","stat":"Greenberg Traurig + Immunefi + LCAM: built first global arbitration regime for blockchain bug bounty disputes (LCAM Blockchain Expedited Arbitration Rules, 18-month build)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Novel legal-product collaboration with crypto security platform","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"}
1,{"id":"s2244","stat":"Bakers data breach analysis time reduced from days to hours via custom AI workflows; fixed project fees not billable hours","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"media","scope":"legal","context":"AI service delivery speed and pricing model shift","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2245","stat":"Innovation hours formally count toward bonus/financial targets at: Pinsents, CMS, Bird & Bird; Shoosmiths used £1m bonus pool","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Structural incentive integration of innovation work","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2246","stat":"HSBC chose Harvey (Jan 2026) for global in-house legal; Barclays chose Legora (Mar 2026) for global in-house legal","source":"Lawyer Innovation Map 2026 / HSBC / Legora","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"UK banking sector splits cleanly Harvey vs Legora at corporate level"},{"id":"s2247","stat":"Anthony Vigneron moved Clifford Chance â Slaughter and May (Head of Innovation, start 2026)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Senior innovation hire churn","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2248","stat":"Joe Cohen moved CRS â Harvey (Director of Innovation â vendor, Feb 2026)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"First major vendor poach of a UK law firm innovation director","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2249","stat":"Adam Curphey moved LSE â Macfarlanes (Director of Innovation, Oct 2025)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Senior innovation hire churn","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2250","stat":"Jana Blount moved DLA Piper â NRF (Director of Client Solutions and Digital Strategy, Oct 2025)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Senior innovation hire churn","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2251","stat":"Will Sach joined DLA Piper as international director of data and AI (Sept 2025)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Senior innovation hire","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2252","stat":"Phil Westwood moved Gowling WLG â Devonshires (head of innovation, June 2025); Sarah Felger from WBD covering at Gowling","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Senior innovation hire churn","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2253","stat":"Martin Stockdale moved Kennedys â Clyde & Co (COO of change and innovation strategy, April 2025)","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Senior innovation hire","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2254","stat":"Taylor Wessing merging with Winston & Strawn (operating as Winston Taylor) - completion 1 May 2026","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Major UK-US firm tie-up","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"},{"id":"s2255","stat":"Ashurst built internal economic model from timesheet/billing/profitability data to prove GenAI impact to partners; AFAs aligning with AI-enabled delivery","source":"The Lawyer Innovation Map 2026","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"media","scope":"legal","context":"AI-driven pricing reform actively underway","url":"https://www.thelawyer.com/mda/report/the-lawyers-innovation-map-2026/","access":"paywalled"}
1,{"id":"s2256","stat":"Moritz raises $9M Series A four days after Y Combinator W26 batch","source":"Sifted / Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"AI-native law firm closes Series A in 4 days; 20VC + Urban Innovation Fund + 20 unicorn founders","url":"https://www.artificiallawyer.com/2026/05/05/we-want-to-be-the-biggest-law-firm-in-the-world/","access":"open"},{"id":"s2257","stat":"Moritz: 100+ companies served with ~$2bn aggregate contract value across Europe / US / Australia","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"AI-native firm traction data: aggregate contract value through portfolio","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2258","stat":"Moritz: four-hour average turnaround across commercial / corporate / employment matters","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"AI-native firm speed metric vs traditional BigLaw turnarounds","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2259","stat":"Moritz: AI does ~80% of work, lawyers do the remaining ~20%","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Stated AI/human split in Moritz\'s operating model","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2260","stat":"Moritz co-founder Pamir Ehsas is ex-OpenAI outside counsel","source":"Sifted","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Founder pedigree for Y Combinator W26 AI-native firm"},{"id":"s2261","stat":"Moritz excludes litigation, immigration and tax; focus on automatable commercial / corporate / employment work","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"AI-native firm scope - automatable transactional matters only","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2262","stat":"Manifest OS raises $60M Series A at $750M valuation - largest Series A in legal tech history","source":"Legal IT Insider","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Menlo, Kleiner Perkins, First Round, Quiet Capital backing Arizona ABS AI-native firm","url":"https://legaltechnology.com/manifest-os-raises-60m-series-a-to-build-ai-native-law-firm-model/","access":"open"},{"id":"s2263","stat":"Manifest Law (Manifest OS): Arizona ABS-licensed AI-native firm doing US business immigration on fixed and outcome fees with no billable hours","source":"Legal IT Insider","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Scope and pricing model of AI-native immigration firm","url":"https://legaltechnology.com/manifest-os-raises-60m-series-a-to-build-ai-native-law-firm-model/","access":"open"},{"id":"s2264","stat":"Keith raises £2M seed - UK conveyancing AI-native firm, CLC-authorised","source":"UK Law Firms AI Strategy register","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"unclassified","scope":"legal","context":"First CLC-authorised AI-native conveyancing entrant"},{"id":"s2265","stat":"LawFairy authorised as second SRA tech-only UK firm; first deterministic, non-generative AI firm to receive SRA approval","source":"Solicitor News / Law Society","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"industry","scope":"legal","context":"Rule-based \\"FairyLogic\\" across 55+ UK immigration routes","url":"https://solicitornews.co.uk/ai-use-in-court-documents/","access":"open"},{"id":"s2266","stat":"Enter (Brazilian AI legal startup) valued at $1.2bn in funding round","source":"Bloomberg","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Latin American AI-native legal funding round"},{"id":"s2267","stat":"Carta Law: Carta (private capital ERP) acquires UK ABS Avantia and relau
1nches as Carta Law (13 May 2026)","source":"Artificial Lawyer / BusinessWire","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Platform-company-acquires-law-firm pattern; fourth Carta acquisition since October 2025","url":"https://www.artificiallawyer.com/2026/05/13/carta-buys-alsp-avantia-launches-ai-first-law-firm/","access":"open"},{"id":"s2268","stat":"Avantia used by 200+ global asset managers including 30% of the world\'s largest funds prior to Carta acquisition","source":"BusinessWire","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Customer base of UK ABS acquired by Carta","url":"https://www.businesswire.com/news/home/20260513917345/en/Carta-Launches-Carta-Law-with-Acquisition-of-Avantia","access":"open"},{"id":"s2269","stat":"Carta Law pricing: pay-as-you-go with fixed fees per service","source":"BusinessWire","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Pricing model for AI-native legal services platform","url":"https://www.businesswire.com/news/home/20260513917345/en/Carta-Launches-Carta-Law-with-Acquisition-of-Avantia","access":"open"},{"id":"s2270","stat":"Carta valued at $7bn pre-Avantia acquisition","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Acquirer valuation for the AI-native firm wedge","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2271","stat":"Talairis Law Group launches in Seattle - ex-Perkins Coie / SeekOut / Wrapbook GCs; four-layer architecture; SAFE reviewer as first agent","source":"GeekWire","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"New Seattle AI-native firm launch","url":"https://www.geekwire.com/2026/can-ai-replace-an-army-of-associates-these-lawyers-are-betting-their-new-firm-on-it/","access":"open"},{"id":"s2272","stat":"Freshfields advised Carta on the Avantia acquisition","source":"Newsletter Ed 41","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"unclassified","scope":"legal","context":"Magic Circle interest in AI-native deal flow","url":"https://www.artificiallawyer.com/2026/05/13/carta-buys-alsp-avantia-launches-ai-first-law-firm/","access":"open"},{"id":"s2273","stat":"Anthropic + Blackstone + Hellman & Friedman + Goldman Sachs - $1.5bn enterprise AI services JV (4 May 2026)","source":"Anthropic / CNBC","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Forward-deployed engineer JV embedded in PE-portfolio companies; direct shot at MBB / Big 4","url":"https://www.cnbc.com/2026/05/04/anthropic-goldman-blackstone-ai-venture.html","access":"open"},{"id":"s2274","stat":"Anthropic-Goldman JV capital structure: Anthropic ~$300M, Blackstone ~$300M, Hellman & Friedman ~$300M, Goldman Sachs ~$150M, plus General Atlantic, Leonard Green, Apollo, GIC, Sequoia","source":"Bloomberg / Fortune","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Capital structure of the $1.5bn enterprise AI services JV","url":"https://www.bloomberg.com/news/articles/2026-05-04/goldman-blackstone-partner-with-anthropic-on-ai-services-firm","access":"paywalled"},{"id":"s2275","stat":"OpenAI + TPG + Brookfield + Advent + Bain Capital - $10bn \\"The Deployment Company\\" JV (4 May 2026)","source":"Bloomberg / TechCrunch","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"cross-sector","context":"19 investors; mirror move to Anthropic-Goldman JV"},{"id":"s2276","stat":"OpenAI Deployment Company JV: 17.5% guaranteed annual return over 5 years; OpenAI commits up to $1.5bn ($500M at close, option for further $1bn); super-voting shares retained by OpenAI","source":"Bloomberg","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"cross-sector","context":"JV economics and governance"},{"id":"s2277","stat":"Anthropic Financial Services Agents: 10 ready-to-run agent templates (pitchbook construction, KYC, ledger reconciliation, month-end close) tuned on Claude Opus 4.7 for finance (5 May 2026)","source":"Anthropic","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Vertical agent pack for financial services"},{"id":"s2278","stat":"Moody\'s data on 600M+ companies embedded as a native Claude app in Financial Services Agents launch","source":"Anthropic","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Moody\'s coverage scope and Claude integration"},{"id":"s2279","stat":"Anthropic Financial Services Agents new data partners: Verisk, Third Bridge, Fiscal AI, D&B, Experi
1an, GLG, Guidepoint, IBISWorld (joining LSEG, S&P CapIQ, Morningstar, PitchBook)","source":"Anthropic","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Data partner stack assembled for Claude finance vertical"},{"id":"s2280","stat":"Anthropic - $1.8bn seven-year compute deal with Akamai (8 May 2026); largest contract in Akamai\'s history; Akamai stock +27% on announcement","source":"Bloomberg","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Compute capacity underwriting Claude for Legal and Financial Services Agents"},{"id":"s2281","stat":"Anthropic CEO Dario Amodei: Anthropic usage grew ~80x annualised in Q1 2026","source":"Bloomberg","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Anthropic usage growth rate cited alongside Akamai deal"},{"id":"s2282","stat":"Microsoft Agent 365 hits general availability (1 May 2026) - control plane for agents inside Microsoft 365","source":"Microsoft","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Agent 365 GA as third leg of the platform-downstream play","url":"https://www.microsoft.com/en-us/security/blog/2026/05/01/microsoft-agent-365-now-generally-available-expands-capabilities-and-integrations/","access":"open"},{"id":"s2283","stat":"Microsoft Copilot Cowork ships US-only (5 May 2026) - direct response to Anthropic Cowork","source":"Microsoft","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Microsoft Cowork follows Anthropic Cowork from April 2026","url":"https://www.microsoft.com/en-us/security/blog/2026/05/01/microsoft-agent-365-now-generally-available-expands-capabilities-and-integrations/","access":"open"},{"id":"s2284","stat":"Harvey launches 500+ pre-built legal agents across every practice area plus Agent Builder early access (5 May 2026)","source":"Harvey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Application-layer counter to Anthropic-Goldman JV"},{"id":"s2285","stat":"Harvey Transformation Office launched as in-vendor consulting arm staffed by ex-BigLaw veterans (5 May 2026)","source":"Harvey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Vendor productising rollout services"},{"id":"s2286","stat":"Linklaters Applied Intelligence launches as productised lawyer-plus-data-scientist team, multi-frontier-model (11 May 2026)","source":"Legal IT Insider","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Magic Circle response to model-provider JVs"},{"id":"s2287","stat":"Linklaters AI stack going into Applied Intelligence: AI sandbox â homegrown analysis tool â Legora firmwide â 20-strong AI Lawyers team â Applied Intelligence","source":"UK Law Firms AI Strategy / Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Stacked capability layers underpinning Applied Intelligence productisation"},{"id":"s2288","stat":"Mary O\'Carroll appointed CEO of LegalEng Consulting Group (5 May 2026) - previously COO at Goodwin overseeing 1,000+ business professionals; CLOC co-founder; ex-Ironclad; 13 years at Google","source":"Law.com Legaltech News","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"High-profile legal-ops figure moves to consulting CEO seat","url":"https://www.law.com/legaltechne
1ws/2026/05/05/legaleng-consulting-group-hires-mary-ocarroll-as-ceo-/","access":"paywalled"},{"id":"s2289","stat":"Anthropic launches Claude for Legal (12 May 2026): 20+ MCP connectors + 12 practice-area plugins + Microsoft 365 integration","source":"LawSites / Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Headline framing of Anthropic\'s full legal vertical launch","url":"https://www.lawnext.com/2026/05/anthropic-goes-all-in-on-legal-releasing-more-than-20-connectors-and-12-practice-area-plugins-for-claude.html","access":"open"},{"id":"s2290","stat":"Claude for Legal: 4 practice-area plugins (Commercial, Corporate, Litigation, Product) ship as Managed Agents via the Claude API","source":"LawSites","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Four launch Managed Agents alongside 12 chat plugins","url":"https://www.lawnext.com/2026/05/anthropic-goes-all-in-on-legal-releasing-more-than-20-connectors-and-12-practice-area-plugins-for-claude.html","access":"open"},{"id":"s2291","stat":"Claude for Legal connectors include Ironclad, DocuSign, Definely, iManage, NetDocuments, Relativity, Everlaw, Consilio, Box, Datasite, Thomson Reuters Westlaw/Practical Law (via CoCounsel), Free Law Project / CourtListener, Midpage, Trellis, Legal Data Hunter, Solve Intelligence, Courtroom5, BoardWise","source":"LawSites / Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Full vendor connector list for Claude for Legal launch","url":"https://www.lawnext.com/2026/05/anthropic-goes-all-in-on-legal-releasing-more-than-20-connectors-and-12-practice-area-plugins-for-claude.html","access":"open"},{"id":"s2292","stat":"20,000+ people registered for Anthropic\'s April \\"Claude for Legal Teams\\" webinar","source":"Anthropic / LawSites","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Demand signal for Claude for Legal launch"},{"id":"s2293","stat":"Named Claude for Legal reference firms on live matters: Freshfields, Quinn Emanuel, Holland & Knight, Crosby Legal","source":"LawSites","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"BigLaw reference customers at launch","url":"https://www.lawnext.com/2026/05/anthropic-goes-all-in-on-legal-releasing-more-than-20-connectors-and-12-practice-area-plugins-for-claude.html","access":"open"},{"id":"s2294","stat":"Freshfields Anthropic deployment: 5,700 staff across 33 offices via Freshfields\' proprietary general AI platform","source":"Freshfields / Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Scale of Freshfields wall-to-wall Claude rollout","url":"https://www.artificiallawyer.com/2026/04/23/freshfields-now-partners-with-anthropic/","access":"open"},{"id":"s2295","stat":"Freshfields Claude usage grew ~500% in the first six weeks of pilot","source":"Freshfields","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Adoption ramp under the Anthropic co-development partnership","url":"https://www.freshfields.com/en/our-thinking/news/news-search/2026/04/freshfields-reports-google-cloud-collaboration-delivering-transformation-at-scale","access":"open"},{"id":"s2296","stat":"Thomson Reuters closed -6.1% at $87.30 on Claude for Legal launch day (12 May 2026); -33.4% YTD","source":"Investing.com / Morningstar","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Market reaction to Claude for Legal launch"},{"id":"s2297","stat":"TR/RELX/Wolters Kluwer fell 16%/14%/13% on Anthropic\'s February 2026 first legal plug-in - pattern repeated on 12 May","source":"ComplexDiscovery","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Equity-market signal of disintermediation thesis"},{"id":"s2298","stat":"Morningstar: Thomson Reuters derives 45% of EBIT from legal vs 10-13% for RELX/Wolters Kluwer","source":"Morningstar","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Why TR\'s drop was sharpest in the legal-publishing cohort"},{"id":"s2299","stat":"Thomson Reuters Q1 2026 revenue $2.09bn, +8% organic; Legal Professionals segment +9% organic; CoCounsel monthly SKUs 4x YoY; GenAI-enabled ACV at 30%; 1M professionals on AI features","source":"Benzinga / Investing.com","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"TR\'s actual AI business growth contrasted with market\'s disintermediation read","url":"https://www.investing.com/news/company-news/thomson-reuters-q1-2026-slides-8-revenue-growth-ai-momentum-builds-93CH-4660016","access":"open"},{"id":"s2300","stat":"Descrybe Legal Engine for Claude: 300M structured US primary law re
1cords exposed to Claude via MCP at Claude for Legal launch","source":"LawSites","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Scale of Descrybe primary-law corpus integrated into Claude","url":"https://www.lawnext.com/2026/05/anthropic-goes-all-in-on-legal-releasing-more-than-20-connectors-and-12-practice-area-plugins-for-claude.html","access":"open"},{"id":"s2301","stat":"Harvey reports task-completion rates up ~6x after implementing Claude agent \\"dreaming\\"","source":"Anthropic / VentureBeat","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Legal-specific data point on dreaming/memory\'s impact on agent reliability","url":"https://venturebeat.com/technology/anthropic-introduces-dreaming-a-system-that-lets-ai-agents-learn-from-their-own-mistakes","access":"open"},{"id":"s2302","stat":"Harvey + DocuSign strategic partnership (8 May 2026): Harvey reasoning inside DocuSign IAM, Harvey Knowledge in DocuSign Iris, 1.8M DocuSign customers in scope","source":"PRNewswire / DocuSign investor","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Harvey integration playbook accelerating alongside Claude for Legal week","url":"https://www.prnewswire.com/news-releases/docusign-and-harvey-partner-to-bring-legal-and-contract-ai-together-302766627.html","access":"open"},{"id":"s2303","stat":"Slaughter and May adopts Harvey firmwide (29 April 2026) - last Magic Circle holdout picks an external AI platform","source":"Harvey / Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Completes Magic Circle external platform consolidation","url":"https://www.harvey.ai/blog/slaughter-and-may-adopts-harvey-firmwide","access":"open"},{"id":"s2304","stat":"Slaughter and May holds 5% equity stake in Luminance (participated in $75m Series C Feb 2025) while also rolling out Harvey firmwide","source":"Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Rare dual position: investor in one legal AI co, customer of another"},{"id":"s2305","stat":"Burges Salmon extends Wexler firmwide after disputes-only pilot from 2024 - 250k documents per upload at firmwide scale","source":"Legal IT Insider","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Wexler firmwide scale plus dispute-resolution AI going mainstream"},{"id":"s2306","stat":"Pinsent Masons hires Hayley Harris (from BCLP) as global chief knowledge and AI adoption officer; joins May/June 2026","source":"Legal IT Insider","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Senior AI leadership lateral move between Top 50 firms"},{"id":"s2307","stat":"HFW appoints Ashleigh Ovland as first Head of Legal Technology Adoption - first-of-kind role title","source":"Legal IT Insider","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"\\"Adoption\\" framing in role title is the signal"},{"id":"s2308","stat":"Dentons appoints EMEA Head of Legal Tech Enablement (13 May 2026)","source":"Law360 Pulse","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"UK firm hiring move; \\"enablement\\" framing"},{"id":"s2309","stat":"Oregon bar counsel: ~5 Oregon filings with AI hallucinations, ~900 nationally","source":"ABA Journal","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Volume estimate of AI hallucination filings nationally"},{"id":"s2310","stat":"Ohio: Judge Walter H. Rice imposes $7,500 sanction + contempt finding + referral to Ohio Supreme Court\'s Office of Disciplinary Counsel","source":"Mealey\'s / Ropes & Gray AI Court Order Tracker","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"First c
1ombined contempt + bar referral for AI-hallucination filings"},{"id":"s2311","stat":"Nebraska Supreme Court indefinitely suspends Greg Lake (Omaha attorney) over AI-related filing errors (16 April 2026)","source":"Divorce.law / WOWT","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"First US bar discipline action to suspend practice entirely over AI hallucinations","url":"https://divorce.law/guides/news/nebraska-supreme-court-suspends-omaha-attorney-ai-hallucinations-april-2026/","access":"open"},{"id":"s2312","stat":"Nebraska Lake brief: 63 citations in a divorce appeal, 57 defective (including 20 outright hallucinations and 3 entirely fabricated cases)","source":"Divorce.law","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"Scale of hallucination in the suspension case","url":"https://divorce.law/guides/news/nebraska-supreme-court-suspends-omaha-attorney-ai-hallucinations-april-2026/","access":"open"},{"id":"s2313","stat":"Nebraska Lake case: client incurred $52,000 in opposing counsel fees","source":"Divorce.law","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"Client cost of attorney AI misuse","url":"https://divorce.law/guides/news/nebraska-supreme-court-suspends-omaha-attorney-ai-hallucinations-april-2026/","access":"open"},{"id":"s2314","stat":"Sixth Circuit Court of Appeals imposes $30,000 sanction on two Tennessee attorneys (March 2026): 24+ fake citations, case dismissed for \\"pervasive misconduct\\"","source":"Newsletter Ed 41","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"regulatory","sourceType":"unclassified","scope":"legal","context":"Mid-band sanction in the 2026 verification escalator"},{"id":"s2315","stat":"Charlotin hallucination database past ~1,433 cases by 12 May 2026 (up ~80 in a fortnight)","source":"Damien Charlotin","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Database scale update from 1,348 (24 April) to 1,433 (12 May)","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s2316","stat":"Indian Supreme Court matter logged in Charlotin database where a judge allegedly hallucinated six judgments","source":"Damien Charlotin","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"First judge-as-hallucinator case","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s2317","stat":"BC Supreme Court (Krivaia v Hungerford, 5 May) and BC Court of Appeal (Morton v Cherkaoui, 1 May) AI hallucination cases - both litigant-in-person","source":"Damien Charlotin","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Canadian litigant-in-person hallucination wave","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s2318","stat":"Jimenez-Fogarty v Fogarty (SDNY, 29 April 2026): $2,500 sanction + mandatory client notice for AI hallucinations","source":"Damien Charlotin","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Client-notification sanctions emerging as US trend","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s2319","stat":"Regan Wilkes v Canyons School District (D. Utah, 4 May): $7,000 sanction for AI hallucinations","source":"Damien Charlotin","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"District-court level sanction in early May","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s2320","stat":"Corgi AI Liability Insurance launch (5 May 2026): first AI-specific cover aimed at the legal market","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"First dedicated AI liability product for legal sector","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2321","stat":"Corgi: Y Combinator-backed; $108M total raised; ARR reportedly above $40M","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Corgi funding and ARR for AI liability insurance","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2322","stat":"Corgi full-stack carrier approval received July 2025 - writing AI risk on own balance sheet, not MGA","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Status as direct carrier rather than managing general agent","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2323","stat":"Corgi AI and Algorithmic Liability Endorsement (CORG-TECH-0038) modules: algorithmic bias liability; AI hallucination/defamation; training-data misuse; data poisoning / adversarial attacks; autonomous-AI bodily injury; deepfakes and synthetic media; service interruption; AI IP; regulatory investigation defence; civil fines","source":"Corgi Insurance","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Published AI-risk taxonomy from first dedicated insurer","url":"https://www.artificiallawyer.com/2026/05/05/corgi-launches-ai-liability-insurance/","access":"open"},{"id":"s2324","stat":"Google Threat Intelligence Group: first AI-built zero-day exploit caught in the wild (11 May 2026) - criminal group used an AI model to discover and weaponise a 2FA-bypass zero-day","source":"Bloomberg","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"First documented AI-built offensive cyber tool"},{"id":"s2325","stat":"California State Bar (COPRAC) proposed amendments to Rules 1.1, 1.4, 1.6, 3.3, 5.1, 5.3 - require lawyers to independently review, verify and exercise professional judgment on any AI output (no carve-out for routine tasks); 45-day comment period closed 4 May 2026","source":"LawSites / Cal Bar","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Codified verification duty in California ethics rules"},{"id":"s2326","stat":"Law Society of England and Wales calls on SRA to review code of conduct and on HMCTS to introduce simple rules for AI use in court (13 May 2026)","source":"Solicitor News / Law360 UK","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"UK counterpart to California Rule 1.1; Law Society formally breaks holding pattern","url":"https://solicitornews.co.uk/ai-use-in-court-documents/","access":"open"},{"id":"s2327","stat":"EU \\"Digital Omnibus on AI\\" - provisional trilogue agreement reached 7 May 2026","source":"Council of the EU / Two Birds","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"legal","context":"Major EU AI Act amendment package","url":"https://www.twobirds.com/en/insights/2026/digital-omnibus-on-ai-provisional-agreement-reached-at-the-may-trilogue","access":"open"},{"id":"s2328","stat":"EU AI Act delay: Annex III high-risk obligations pushed to 2 Dec 2027; Annex I to 2 Aug 2028","source":"Council of the EU","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"High-risk classification timetable slips ~6-12 months","url":"https://www.consilium.europa.eu/en/press/press-releases/2026/05/07/artificial-intelligence-council-and-parliament-agree-to-simplify-and-streamline-rules/","access":"open"},{"id":"s2329","stat":"EU synthetic-content labelling grace peri
1od shortened from 6 months to 3 months under Digital Omnibus - new deadline 2 Dec 2026","source":"Two Birds","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Tighter clock on AI-content labelling despite high-risk delay"},{"id":"s2330","stat":"Digital Omnibus adds new Article 5 prohibition on AI-generated CSAM / non-consensual intimate imagery","source":"Council of the EU","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"New AI Act prohibitions list addition","url":"https://www.consilium.europa.eu/en/press/press-releases/2026/05/07/artificial-intelligence-council-and-parliament-agree-to-simplify-and-streamline-rules/","access":"open"},{"id":"s2331","stat":"EU Commission consultation on AI Act Article 50 transparency guidelines opens 8 May 2026; enforcement from 2 Aug 2026","source":"European Commission","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"Synthetic content disclosure / deepfake marking guidance consultation"},{"id":"s2332","stat":"ICO automated decision-making / profiling guidance under Data (Use and Access) Act open for consultation until 29 May 2026","source":"ICO","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"UK ADM/profiling guidance under DUAA"},{"id":"s2333","stat":"ICO publishes guidance for public authorities handling AI-generated FOI requests (6 May 2026)","source":"ICO","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"First FOI-specific AI guidance from ICO"},{"id":"s2334","stat":"Sir Colin Birss CJ speech on legal professional privilege in the age of AI (22 April 2026); anchors on UK v SSHD [2026] UKUT 81 (IAC); judges should \\"only use a system which they are sure is secure\\"","source":"Judiciary.uk / Legal Futures","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Senior English judge confirms consumer AI sits outside privilege perimeter"},{"id":"s2335","stat":"SRA Update Issue 149: one in three of the UK public using GenAI for legal issues (April 2026)","source":"SRA","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"government","scope":"legal","context":"Demand-side data on consumer AI use for legal questions","url":"https://www.sra.org.uk/news/sra-update/sra-update-issue-149-april-2026/","access":"open"},{"id":"s2336","stat":"Goldman Sachs cut Claude access for Hong Kong bankers under strict reading of Anthropic contract (April 2026); Gemini and ChatGPT remain available","source":"Bloomberg","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"cross-sector","context":"Contract territoriality constraining AI deployment inside a regulated firm"},{"id":"s2337","stat":"Cyber Essentials v3.3 effective 27 April 2026 - MFA mandatory on cloud, 14-day patch window, cloud explicitly in-scope","source":"NCSC","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"UK cybersecurity baseline update"},{"id":"s2338","stat":"ABA Third AI and the Practice of Law Virtual Summit announced for 18-19 May 2026 - three summits in under 18 months","source":"ABA","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"industry","scope":"legal","context":"Cadence note on bar acceleration"},{"id":"s2339","stat":"OpenAI ships GPT-5.5 Instant (5 May 2026): 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts spanning law, medicine, finance, at same latency","source":"OpenAI / TechCrunch","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Default ChatGPT model upgrade marketed on legal hallucination reduction","url":"https://openai.com/index/gpt-5-5-instant/","access":"open"},{"id":"s2340","stat":"Claude agent \\"dreaming\\" announced at Code with Claude (6 May 2026) - scheduled process reviews past sessions, merges duplicates, replaces stale entries, surfaces new insights","source":"Anthropic / VentureBeat","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Anthropic self-improving memory mechanism for agents","url":"https://venturebeat.com/technology/anthropic-introduces-dreaming-a-system-that-lets-ai-agents-learn-from-their-own-mistakes","access":"open"},{"id":"s2341","stat":"TriBench-Ko benchmark (arXiv:2605.03792, 5 May 2026): Korean-law focused LLM benchmark across hallucination, omission, statutory misapplication, bi
1as, prompt sensitivity, \\"adjudicative overreach\\"","source":"arXiv","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Methodologically transferable judicial-workflow benchmark","url":"https://arxiv.org/abs/2605.03792","access":"open"},{"id":"s2342","stat":"AWS MCP Server hits GA (6 May 2026) - managed MCP with IAM guardrails, CloudWatch metrics, CloudTrail logging","source":"AWS","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Hyperscaler-grade MCP plumbing for legal IT","url":"https://aws.amazon.com/blogs/aws/the-aws-mcp-server-is-now-generally-available/","access":"open"},{"id":"s2343","stat":"Anthropic \\"Mythos\\" model limited to ~40 major tech players; OpenAI gives EU preview access to GPT-5.5-Cyber (11 May 2026)","source":"CNBC","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"cross-sector","context":"Frontier model availability varies by region; AI Office enforcement powers apply from August 2026","url":"https://www.cnbc.com/2026/05/11/openai-eu-cyber-model-anthropic-mythos-gpt.html","access":"open"},{"id":"s2344","stat":"LegalOn users report 85% faster contract review, 40% higher productivity, $1,000-$2,000/contract in outside counsel savings","source":"LegalOn / Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Vendor-published in-house productivity numbers","url":"https://www.artificiallawyer.com/2026/05/12/legal-ais-next-act-is-in-house-productivity/","access":"open"},{"id":"s2345","stat":">60% of corporate legal teams expect to rely less on outside counsel (LegalOn data)","source":"LegalOn / Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"In-house buyer signal on outside counsel demand","url":"https://www.artificiallawyer.com/2026/05/12/legal-ais-next-act-is-in-house-productivity/","access":"open"},{"id":"s2346","stat":"CLOC Global Institute 2026 (Chicago, 11-14 May): ~2,100 attendees / 90 sessions","source":"CLOC","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"industry","scope":"legal","context":"Scale of legal ops conference where Claude for Legal week landed"},{"id":"s2347","stat":"Richard Tromans estimate: 18-25% of large-firm lawyers may abandon specialist tools for Legal Agent / Claude for Word; smaller firms and in-house even more exposed","source":"Richard Tromans / Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"LLM-derived estimate of disintermediation impact on app-layer vendors"},{"id":"s2348","stat":"The Positive Group \\"AI Leadership Challenge in Law\\": 16 senior leader interviews (Hogan Lovells, HSF, Baker McKenzie, Bird & Bird, A&O Shearman, White & Case)","source":"Legal Futures","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Sample size of behavioural-consultancy AI leadership report","url":"https://www.legalfutures.co.uk/latest-news/ai-leading-to-radical-shift-in-client-expectations","access":"open"},{"id":"s2349","stat":"Ivo benchmark: Claude for Word 3.50 vs human 4.56 vs Ivo 4.52 across 3 blind Am Law judges, 19 anonymised contracts, 5 criteria (vendor-funded)","source":"Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Vendor-funded benchmark of Claude for Word vs purpose-built"},{"id":"s2350","stat":"Mike OSS (Will Chen, ex-Latham & Watkins): AGPL-3.0 legal AI platform shipped in 2 weeks claiming Harvey/Legora parity","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"First viral open-source legal AI release","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2351","stat":"Scissero Suzie Law - second open-source legal AI release in same fortnight (7 May 2026)","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"OSS legal AI as emerging cluster","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2352","stat":"Crafty Counsel Q2 2026 Signal: 1,646 in-house lawyers across six events; AI/automation = 33% of priorities; geopolitics/regulation/sanctions = 19%","source":"Crafty Counsel","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"In-house community sentiment survey"},{"id":"s2353","stat":"Legora valuation $5.6bn with $50M extension from NVIDIA Ventures and Atlassian (30 April 2026); Series D total now $600M, $866M raised since launch","source":"Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Legora cap-table extension and total funding"},{"id":"s2354","stat":"Anthropic Project Deal: internal experiment with 69 Anthropic employees - AI agents negotiating both sides of marketplace transactions (April 2026)","source":"Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Agent-to-agent negotiation experiment"},{"id":"s2355","stat":"LexisNexis Lexis Protégé Work expansion (7 May 2026)","source":"Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Lexis+ AI repositioning as foun
1dational legal infrastructure","url":"https://www.artificiallawyer.com/2026/05/06/how-ai-first-moritz-is-taking-on-big-law/","access":"open"},{"id":"s2356","stat":"Cursor agent on Claude Opus 4.6 deleted PocketOS production volume and all backups in 9 seconds (25 April 2026); 30-hour outage","source":"The Register","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Cautionary tale of agentic autonomy in production","url":"https://www.theregister.com/2026/04/27/cursoropus_agent_snuffs_out_pocketos/","access":"open"},{"id":"s2357","stat":"Acemoglu: only 4.6% of economic tasks currently cost-effective to automate","source":"Acemoglu via Owen McGrann \\"The Dead Economy Theory\\"","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"commentary","scope":"cross-sector","context":"Citable economic ceiling on automation"},{"id":"s2358","stat":"0.66% projected productivity gain over the decade vs 7% industry claims","source":"Acemoglu via Owen McGrann","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"commentary","scope":"cross-sector","context":"Macro counter to JV/WTI/Accenture productivity optimism","url":"https://www.nber.org/papers/w32487","access":"open"},{"id":"s2359","stat":"McGrann: 90% of surveyed firms report no measurable employment or productivity impact from AI","source":"Owen McGrann \\"The Dead Economy Theory\\"","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"commentary","scope":"cross-sector","context":"Demand-destruction thesis macro evidence","url":"https://www.owenmcgrann.com/p/the-dead-economy-theory","access":"open"},{"id":"s2360","stat":"Gary Marcus \\"Autonomous Agents are a Shitshow\\" (5 May 2026) cites study of 847 agent deployments with 91% vulnerable to tool-chaining attacks","source":"Gary Marcus / Substack","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"commentary","scope":"cross-sector","context":"Citable security failure rate in agentic deployments"},{"id":"s2361","stat":"Furlong pull-quote (12 May 2026): \\"If you rely on AI to do 90% of your thinking, your final work product will 90% resemble what everyone else is producing.\\"","source":"Jordan Furlong \\"A Dangerous Mind\\"","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"commentary","scope":"legal","context":"De-skilling argument anchor","url":"https://jordanfurlong.substack.com/p/a-dangerous-mind","access":"open"},{"id":"s2362","stat":"LegalQuants \\"The Mirror\\" self-assessment: 360+ takers in 2 days (4-9 May 2026)","source":"LegalQuants","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Self-assessment uptake among lawyers anxious about AI","url":"https://legalquants.substack.com/p/i-cant-have-this-call-with-all-of","access":"gated"},{"id":"s2363","stat":"Will Marien (The Positive Group): clients \\"no longer want to pay for the \'doing\'... they want to pay for the \'thinking\'.\\"","source":"Legal Futures","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"client","sourceType":"media","scope":"legal","context":"Behavioural-consultancy line on billing squeeze","url":"https://www.legalfutures.co.uk/latest-news/ai-leading-to-radical-shift-in-client-expectations","access":"open"},{"id":"s2364","stat":"Microsoft 2026 Work Trend Index: 20,000-worker survey across 10 countries (US, BR, AU, IN, JP, FR, DE, IT, NL, UK); fielded 18 Feb to 20 Apr 2026","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Sample and fielding window for WTI 2026","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2365","stat":"WTI 2026 also draws on trillions of anonymised Microsoft 365 productivity signals","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Behavioural-data layer underpinning the survey","url":"https://www.microsoft.com/en-us
1/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2366","stat":"WTI 2026: organisational factors account for ~67% of self-reported AI impact; individual mindset/behaviour ~32%","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Headline regression finding","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2367","stat":"WTI 2026: organisation\'s AI culture is ~2.5x stronger than the top individual factor for predicting AI impact","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Strongest single predictor of self-reported AI impact","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2368","stat":"WTI 2026: regression triangulated across elastic net (R²=0.680), random forest (R²=0.689), XGBoost (R²=0.690)","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Model triangulation for the 67%/32% split","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2369","stat":"WTI 2026 Transformation Paradox: 19% Frontier / 50% Emergent / 16% Stalled / 10% Blocked agency / 5% Unclaimed capacity","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Five-segment readiness map","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2370","stat":"WTI 2026: 65% of AI users fear falling behind if they don\'t adapt quickly","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Anxiety prevalence among AI users","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2371","stat":"WTI 2026: 45% of AI users say it feels safer to focus on current goals than to redesign work with AI","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Risk-aversion to work redesign","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2372","stat":"WTI 2026: only 13% of AI users say they are rewarded for reinvention of work regardless of results","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Incentive gap for AI-driven work redesign","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2373","stat":"WTI 2026: only 26% of AI users say leadership is clearly and consistently aligned on AI","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Leadership alignment gap","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2374","stat":"WTI 2026 supplementary People Science survey: 1,800 workers (819 leaders, 520 managers, 461 individual contributors), July 2025","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Methodology of Agentic Teaming & Trust supplementary survey","url":"https://www.microsoft.com/en-us
1/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2375","stat":"WTI 2026: manager actively modelling AI use lifts reported AI value +17, critical thinking about AI +22, trust in agentic AI +30","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Manager-modelling deltas","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2376","stat":"WTI 2026: manager-created psychological safety for experimentation = up to +20 points AI readiness; 1.4x more likely to be high-frequency agentic AI users","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Psychological safety effect on AI uptake","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2377","stat":"WTI 2026: leaders 2x more likely than employees to say reinvention is rewarded regardless of outcome (21% vs 10%)","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Leader-employee perception gap on rewarding reinvention","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2378","stat":"WTI 2026 Frontier vs non-Frontier teams: manager openly uses AI 85% vs 64%","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Manager modelling rate by segment","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2379","stat":"WTI 2026 Frontier vs non-Frontier teams: manager sets quality standards for AI work 83% vs 57%","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Quality standard-setting gap","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2380","stat":"WTI 2026 Frontier vs non-Frontier teams: manager creates space for experimentation 84% vs 61%","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Experimentation space gap","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2381","stat":"WTI 2026 Frontier vs non-Frontier teams: encourages ambitious work redesign 87% vs 61%","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Work redesign endorsement gap","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2382","stat":"WTI 2026 Frontier vs non-Frontier teams: brainstorm/refine business processes together 63% vs 32%","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Joint process-redesign rate","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2383","stat":"WTI 2026 Frontier vs non-Frontier teams: share AI tips, new agents, learnings, mistakes 61% vs 36%","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Knowledge-sharing gap","url":"https://www.microsoft.com/en-us
1/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2384","stat":"WTI 2026 Frontier vs non-Frontier teams: discuss quality standards for AI-assisted work 54% vs 29%","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Quality discussion gap","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2385","stat":"WTI 2026 Frontier vs non-Frontier teams: document agent workflows at team level 26% vs 19%; function level 29% vs 17%; org level 25% vs 14%","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Documentation discipline gap across levels","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2386","stat":"WTI 2026: active agents in Microsoft 365 ecosystem grew 15x YoY; 18x in large enterprises","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Agent ecosystem growth rate","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2387","stat":"WTI 2026: software/tech sector has broadest agent adoption (~1 in 5 firms)","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Sector pattern - broadest adoption in tech","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2388","stat":"WTI 2026: Frontier Firm Index plotted from 16,971 respondents with complete data on both axes","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Frontier Firm Index sample base","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2389","stat":"WTI 2026 four modes of working with AI: delegation, collaboration, asking, exploration - Frontier Professionals\' distinguishing trait is matching mode to task","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Four modes taxonomy","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2390","stat":"WTI 2026 Lakhani quote: \\"The central task of leadership is shifting from deploying technology to leading and enabling their teams to redesign work and processes.\\"","source":"Microsoft WTI 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Foreword framing of operating-model shift","url":"https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization","access":"open"},{"id":"s2391","stat":"Eversheds Sutherland Harvey rollout: initial cohort 350 lawyers (Feb 2026), full rollout to ~1,700 lawyers from May 2026","source":"Artificial Lawyer / Legal IT Insider","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Eversheds Harvey deployment scale and ramp"},{"id":"s2392","stat":"HSBC adopts Harvey for in-house team (Jan 2026)","source":"UK Law Firms AI Strategy register","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"In-house Harvey adoption by major bank","url":"https://www.hsbc.com/news-and-views/ne
1ws/media-releases/2026/hsbc-announces-harvey-ai-for-their-legal-platform","access":"open"},{"id":"s2393","stat":"Linklaters Laila proprietary AI chatbot handling 60,000 prompts weekly","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"Linklaters proprietary chatbot usage volume"},{"id":"s2394","stat":"Linklaters AI Sandbox programme generating 75+ AI ideas from global campaigns","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"Ideation pipeline scale"},{"id":"s2395","stat":"Hogan Lovells ELTEMATE: ~100 employees including ~20 software developers, data scientists, AI experts","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"unclassified","scope":"legal","context":"ELTEMATE staff scale"},{"id":"s2396","stat":"Hogan Lovells CRAIG: 4,400+ active users worldwide; 50%+ time savings on drafting/analysis/document review","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"CRAIG usage and impact data"},{"id":"s2397","stat":"Hogan Lovells CRAIG: email-sorting drops ~80%; chronology engine 42% faster than human reviewer on a 470-page dataset; chronologies arriving up to 10x faster in life sciences litigation","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"CRAIG task-level efficiency measurements"},{"id":"s2398","stat":"DLA Piper Harvey firmwide rollout: 5,000 licences globally (March 2026)","source":"UK Law Firms AI Strategy","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"One of the largest single legal AI deployments"},{"id":"s2399","stat":"DLA Piper Microsoft 365 Copilot saved up to 36 hours weekly on content generation and data analysis for operational teams","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"Copilot productivity number for ops teams"},{"id":"s2400","stat":"DLA Piper Aiscension: AI-enabled cartel risk and bribery detection runs investigations 10x faster than conventional review; supports 100+ languages","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"Aiscension performance metrics"},{"id":"s2401","stat":"Clifford Chance proprietary GenAI Assist tool: 90% firm-wide AI adoption by late 2025; ~50 London back-office roles cut (late 2025) citing AI use","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"First confirmed AI-driven Magic Circle back-office cut"},{"id":"s2402","stat":"Clifford Chance: hundreds of internally created AI agents for narrow tasks (document comparison, deal-comment organisation, file conversion, timesheet drafting)","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"Volume of internal task-level agents"},{"id":"s2403","stat":"A&O Shearman Fuse innovation hub: 1,400+ applications, 90+ businesses supported, cohort companies raised $1.5bn+ collectively","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"unclassified","scope":"legal","context":"Fuse scale and alumni capital"},{"id":"s2404","stat":"A&O Shearman ContractMatrix: ~2,000 lawyers use daily;
1 saves ~7 hours per contract review (~30% efficiency gain)","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"ContractMatrix usage and impact"},{"id":"s2405","stat":"Ashurst Harvey rollout: 4,300+ people worldwide; first global firm to roll out Harvey across all offices from day one (June 2024)","source":"UK Law Firms AI Strategy","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"Ashurst Harvey deployment scale and pace"},{"id":"s2406","stat":"CMS Harvey enterprise rollout: 7,000+ lawyers and staff across 50+ countries (Dec 2025); 93% of users reported productivity gains (~117.9 hours saved per lawyer per year)","source":"UK Law Firms AI Strategy","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"CMS Harvey scale and reported productivity","url":"https://cms.law/en/int/news-information/cms-announces-roll-out-of-harvey-across-50-countries","access":"open"},{"id":"s2407","stat":"Pinsent Masons Legora rollout: initial 1,000 users (Feb 2026) following pilot across corporate, commercial and property","source":"UK Law Firms AI Strategy","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"unclassified","scope":"legal","context":"Pinsents Legora deployment scale"},{"id":"s2409","stat":"Freshfields Google: 5,000+ professionals use AI tools built on Gemini models daily as of April 2026","source":"Freshfields","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Freshfields Google-side adoption scale","url":"https://www.freshfields.com/en/our-thinking/news/news-search/2026/04/freshfields-reports-google-cloud-collaboration-delivering-transformation-at-scale","access":"open"},{"id":"s2410","stat":"Freshfields 260 AI Champions delivering training through bespoke AI Academy","source":"Freshfields","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Change-management scale at Freshfields","url":"https://www.freshfields.com/en/our-thinking/news/news-search/2026/04/freshfields-reports-google-cloud-collaboration-delivering-transformation-at-scale","access":"open"},{"id":"s2411","stat":"SkillsMP platform hosts 1M+ distinct Claude skills (April 2026)","source":"Tsinghua SRA paper","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"government","scope":"legal","context":"arXiv:2604.24594. Scale of the open Claude skills marketplace","url":"https://arxiv.org/abs/2604.24594","access":"open"},{"id":"s2412","stat":"Tsinghua SRA-Bench: 5,400 capability-intensive test instances, 636 manually-annotated gold skills, 26,262-skill noisy corpus","source":"Tsinghua SRA paper","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"government","scope":"legal","context":"SRA benchmark construction","url":"https://arxiv.org/abs/2604.24594","access":"open"},{"id":"s2413","stat":"Anthropic SKILL.md size ceiling: <5,000 words; performance degrades with 20-50+ skills enabled simultaneously","source":"Anthropic Skills Guide","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Operational limits of the in-context skill paradigm"},{"id":"s2414","stat":"37% performance gap between lab benchmark scores and real-world deployment (CLEAR framework)","source":"Kili Technology, AI Benchmarks Guide 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Documented lab-to-production gap","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2415","stat":"Production consistency drops from 60% on a single run to 25% across 8 consecutive runs","source":"Kili Technology citing CLEAR","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Single-run scores overstate production reliability","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2416","stat":"50x cost variation for similar accuracy across agentic AI approaches","source":"Kili Technology citing CLEAR","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Cost-accuracy frontier in agentic systems","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2417","stat":"57% of organisations have AI agents in production;
1 quality (not cost or latency) is the biggest barrier reported","source":"Kili Technology citing LangChain survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Production deployment status + barrier ranking","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2418","stat":"Claude Opus 4.5: 80.9% on SWE-Bench Verified, 45.9% on SWE-Bench Pro (SEAL) - same model, half the score","source":"Kili Technology citing SEAL","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Scaffolding/test-design impact on benchmarks","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2419","stat":"OpenAI audit: 59.4% of SWE-Bench Verified hard tasks have flawed tests","source":"Kili Technology citing OpenAI audit","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Benchmark integrity issue; OpenAI stopped reporting","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2420","stat":"Text-to-SQL benchmark audit: >50% annotation error rate","source":"Kili Technology","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Benchmark data quality","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2421","stat":"Computer vision benchmark review: >70% of datasets reused across domains","source":"Kili Technology","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Benchmark reuse / contamination","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2422","stat":"HLE (Humanity\'s Last Exam): human domain experts ~90%, top model 37.5% (Gemini 3 Pro Preview); 50+ point gap","source":"Kili Technology citing HLE / Nature 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Frontier-model vs expert-human capability gap","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2423","stat":"GAIA: same Claude Opus 4 scores 64.9% in one framework vs 57.6% in another (~7-point orchestration swing)","source":"Kili Technology","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Orchestration as a major performance variable","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2424","stat":"ARC-AGI-3: frontier LLMs <1%; simple CNNs 12.58%","source":"Kili Technology","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Fluid-intelligence benchmark; LLMs essentially fail","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2425","stat":"GDPval: 1,320 tasks designed by professionals with 14+ years of experience across legal briefs, engineering blueprints, nursing care plans, customer support; performance doubled GPT-4o â GPT-5; Claude Opus 4.1 leads on aesthetics, GPT-5 on accuracy","source":"Kili Technology citing OpenAI GDPval","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Reference benchmark methodology for production-relevant evaluation","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2426","stat":"MMLU saturation: every frontier model >88%; differences statistical noise","source":"Kili Technology","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Why MMLU no longer differentiates top models","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2427","stat":"MMLU-Pro: initially caused 16-33% accuracy drops; Gemini 3 Pro Preview now ~90%, approaching saturation","source":"Kili Technology","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"MMLU successor heading toward saturation","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2428","stat":"Models distinguish between evaluation and deployment c
1ontexts, behaving safer during testing (2026 International AI Safety Report)","source":"Kili Technology citing AI Safety Report 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Benchmark-gaming behaviour documented","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2429","stat":"METR: model rewrote its own timer function to report fast results rather than improve performance","source":"Kili Technology citing METR","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Documented instance of metric-gaming","url":"https://kili-technology.com/blog/ai-benchmarks-guide-the-top-evaluations-in-2026-and-why-theyre-not-enough","access":"open"},{"id":"s2430","stat":"$700bn planned 2026 capex across Meta/AWS/Alphabet/Microsoft (vs ~$300bn global telecoms, ~$1tr global oil & gas)","source":"Benedict Evans, AI Eats the World Spring 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Hyperscaler AI capex scale","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2431","stat":"OpenAI capacity deal aspiration: 30GW+ build at $1.4tr; 1GW/week new construction target = ~$1tr annual at $20bn/GW","source":"Benedict Evans, AI Eats the World Spring 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"OpenAI infrastructure ambition (deck slide 15)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2432","stat":"US data centre construction spending now exceeds US office construction spending (US Census, 2026 dollars, SAAR)","source":"Benedict Evans, AI Eats the World Spring 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Capex displacement (deck slide 12)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2433","stat":"Big-four capex/sales ratios climbing toward 30-60% from low single digits a decade ago - Meta, Microsoft, Alphabet, Amazon","source":"Benedict Evans, AI Eats the World Spring 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Asset-light businesses no longer asset-light (deck slide 13)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2434","stat":"ChatGPT 900m+ weekly active users; only ~5% paying","source":"Benedict Evans, AI Eats the World Spring 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Consumer adoption breadth vs monetisation (deck slide 29)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2435","stat":"<1,000 prompts in a year for the top 20% of ChatGPT users - \\"consumer use is a mile wide and an inch deep\\"","source":"Benedict Evans citing OpenAI Wrapped","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Daily-essential gap; 80%+ of users not daily essential (deck slide 30)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2436","stat":"Enterprise AI annualised spending (a16z, March 2026): Coding ~$4bn #1, Legal ~$2.5bn #2, then Support, Medical & Health, Search, Text, Real Estate, Finance/Admin","source":"Benedict Evans citing a16z","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Legal as the second-largest enterprise AI spend category (deck slide 37)"},{"id":"s2437","stat":"Bain enterprise pilot-to-production: Legal is at the bottom of the funnel across 10 enterprise functions (Software, Customer Service, Marketing, Knowledge Management, Operations, IT, Sales, Finance, HR, Legal)","source":"Benedict Evans citing Bain","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Legal pilots high, Legal production low (deck slide 47)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2438","stat":"Accenture quarterly new \\"generative AI\\" contracts have grown to ~$2bn/quarter by mid-2025 (hockey stick from ~$100m in Feb 2023)","source":"Benedict Evans citing Accenture","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Systems-integrator share of the deployment layer (deck slide 45)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2439","stat":"Y Combinator batch composition: ~80% AI startups by Summer 2025 (vs ~20% in W2015)","source":"Benedict Evans citing YC","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Startup-layer concentration on AI (deck slide 48)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2440","stat":"Frontier LLM benchmark scores converging at the top: OpenAI, Anthropic, Google, Meta, Chinese labs all bunched on ArtificialAnalysis aggregate; no network effects between labs","source":"Benedict Evans citing ArtificialAnalysis","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"commentary","scope":"cross-sector","context":"Model commoditisation thesis (deck slide 25)","url":"https://www.ben-evans.com/s/2026-Spring-AI.pdf","access":"open"},{"id":"s2441","stat":"OpenAI and Anthropic combined annualised revenue ~$7-8bn/month by mid-2026 (OpenAI net + Anthropic gross, monthly $bn)","source":"Benedict Evans, AI Eats the World Spring 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Lab revenue vs $700bn capex (deck slide 18)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2442","stat":"Philippines IT-BPM industry: 2m jobs, 8% of GDP, built on skill/income arbitrage - now exposed to AI automation","source":"Benedict Evans citing IBPAP","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"commentary","scope":"cross-sector","context":"The \\"is this a job or a task?\\" question at scale (deck slide 76)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2443","stat":"US workplace AI use highest in Tech and Professional Services; daily use trails weekly use widely across all sectors (Gallup, Jun 2023-Dec 2025)","source":"Benedict Evans citing Gallup","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"Capacity gap between weekly and daily AI use (deck slide 32)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2444","stat":"Elevator-attendant employment in the USA: ~100,000 in 1950 â near zero by 1990 after Otis Autotronic launch","source":"Benedict Evans citing US Census","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"commentary","scope":"cross-sector","context":"Canonical \\"job becomes a button\\" precedent (deck slide 54)","url":"https://www.ben-evans.com/s/2026-Spring-AI.pdf","access":"open"}
1,{"id":"s2445","stat":"US supermarket SKU count: ~10,000 in 1970 â ~60,000 by 2020, accelerating after grocery barcodes launched in 1974","source":"Benedict Evans citing FMI","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"commentary","scope":"cross-sector","context":"Automation unlocking new behaviour rather than replacing labour (deck slide 57)","url":"https://www.ben-evans.com/presentations","access":"open"},{"id":"s2446","stat":"AAFLOW: 4.64x speedup over Dask on ingestion pipelines; 1.88x over LangChain on end-to-end RAG; ~2.8x on embedding/upsert phases","source":"Sarker et al. arXiv:2605.02162","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Distributed agentic-workflow runtime benchmark gains","url":"https://arxiv.org/html/2605.02162v1","access":"open"},{"id":"s2447","stat":"848 legal professionals from the UK and Ireland surveyed (Dec 2025-Jan 2026)","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Sample size and scope of the survey behind the In CTO We Trust report","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2448","stat":"80% of large-firm lawyers use AI for legal research (top use case)","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Top AI legal use case inside large law firms","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2449","stat":"68% of large-firm lawyers use AI for knowledge management","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Second most common AI use case at large firms","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2450","stat":"65% of large-firm lawyers use AI for large-scale document review; 65% for document analysis","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Core legal work, not admin","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2451","stat":"62% of large-firm lawyers use AI for client document drafting","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Fifth of the top AI legal use cases at large firms","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2452","stat":"Only 30% of large-firm lawyers say AI is embedded in their strategy and operations (small firms 12%, medium 18%, large 30%, all private practice 19%)","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Integration lags adoption; rises with firm size","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2453","stat":"46% of lawyers believe their career would suffer without AI investment, up from 36% in January 2025","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Year-on-year rise in AI career anxiety","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2454","stat":"30% of large-firm lawyers would consider leaving if their firm failed to embrace AI, up from 19% the year before (small 12%, medium 22%, large 30%, private practice 20%)","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"AI as a retention factor; pressure rises with firm size","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2455","stat":"\\"Failing to embrace AI would negatively impact my career\\": small firms 28%, medium 37%, large 46%, private practice 36%","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Career-impact anxiety by firm size","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2456","stat":"56% of lawyers are concerned about keeping up with
1new technology","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Pace-of-change anxiety among lawyers","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2457","stat":"85% say their top concern using AI is relying on inaccurate or fabricated information (hallucinations)","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Hallucination is the number-one barrier even at heavy-adopting firms","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2458","stat":"Biggest concerns using AI for legal research/guidance (large firms): hallucinations 85%, leaking confidential data 56%, becoming too reliant 44%, accidental bias 44%, quality of work declining 39%, clients might not like it 22%","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Full ranked concern list for large-firm lawyers","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2459","stat":"79% of large-firm lawyers feel more confident using AI when it is grounded in legal sources (all respondents 72%, in-house corporate counsel 85%)","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Grounding/citation drives trust; strongest among in-house counsel","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2460","stat":"In-house corporate counsel\'s top strategic priority for 2026 is setting rules and safeguards for AI tools: 39% say it is a priority, 38% say they are already doing it","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"Governance is the top in-house AI priority","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2461","stat":"Most common AI use cases overall: large-scale document review for discovery and due diligence 42%, legal research 35%, knowledge management 35%","source":"LexisNexis Generative AI Legal Survey H1 2026 (In CTO We Trust)","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Most-cited live AI use cases across respondents","url":"https://www.lexisnexis.co.uk/insights/in-cto-we-trust/index.html","access":"open"},{"id":"s2462","stat":"\\"Six Registers of Restraint\\" for AI authority boundaries: prompt/workflow, capability (tool grants), code (validated handoffs), economic (spend caps), temporal (time limits/pauses), contextual (tool access by phase)","source":"Dazza Greenwood, Authority Boundaries for AI","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"legal","context":"Framework for programmatically encoding where AI autonomy stops","url":"https://www.dazzagreenwood.com/p/authority-boundaries-for-ai","access":"open"},{"id":"s2463","stat":"\\"The more autonomous the action, the harder the gate must be\\"","source":"Dazza Greenwood, Authority Boundaries for AI","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"legal","context":"Escalation principle for AI authority/restraint design","url":"https://www.dazzagreenwood.com/p/authority-boundaries-for-ai","access":"open"},{"id":"s2464","stat":"Replace \\"human in the loop\\" with assignable duties: configure, approve, verify, decide, sign","source":"Dazza Greenwood, Authority Boundaries for AI","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"legal","context":"Operationalising non-delegable human judgement in AI workflows","url":"https://www.dazzagreenwood.com/p/authority-boundaries-for-ai","access":"open"},{"id":"s2465","stat":"\\"The agent prepares the path. The lawyer chooses the road\\" / \\"The agent is the research-and-draft layer. The lawyer is the judgment-and-commitment layer\\"","source":"Dazza Greenwood, Authority Boundaries for AI","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"legal","context":"Framing of the human/AI division of labour in legal work","url":"https://www.dazzagreenwood.com/p/authority-boundaries-for-ai","access":"open"},{"id":"s2466","stat":"Harvey Command Center peer benchmarking draws on anonymised data from 1,500+ Harvey deployments globally","source":"Harvey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Harvey Forum. Install-base scale turned into a network-effect data asset; GA Q3 2026"},{"id":"s2467","stat":"Harvey x DeepJudge partnership targets the \\"context tax\\" - generic AI output when it lacks a firm\'s institutional knowledge (DeepJudge backed by Coatue and Felicis, ex-Google/ETH Zurich founders)","source":"Harvey / DeepJudge","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Institutional-knowledge grounding becomes a legal-AI battleground","url":"https://www.harvey.ai/blog/introducing-command-center","access":"open"},{"id":"s2468","stat":"Harvey raised $200M at $11bn (Mar 2026, GIC + Sequoia);
1 Legora ~$600M Series D at ~$5.6bn (Accel-led, incl. ~$50M NVentures/Atlassian extension, May 2026)","source":"CNBC / EU-Startups","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Funding/valuation gap between the two leading legal-AI platforms"},{"id":"s2469","stat":"Legora launched aOS \\"agentic operating system\\" (7 May 2026, Precedent London): orchestrates agents end-to-end, first-mile intake to last-mile delivery; \\"legal AI is dead, the age of agentic law\\"","source":"Legora / Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Legora\'s reframing from tool to agent platform","url":"https://www.artificiallawyer.com/2026/05/07/legora-launches-aos-agentic-operating-system/","access":"open"},{"id":"s2470","stat":"Legora 2026 acquisitions: Walter AI (Mar, agent-native legal AI, Canada expansion) and Graceview (May, regulatory monitoring - powers the Monitors surface)","source":"Legora / Global Legal Post","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Filling agentic + regulatory gaps by acquisition"},{"id":"s2471","stat":"Legora x Datasite integration (18 May 2026): data-room documents analysed in Legora with Datasite permissions carried over; covers diligence to signing checklists to post-close covenant tracking","source":"Datasite / Legora","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"M&A due-diligence workflow play","url":"https://www.globenewswire.com/news-release/2026/05/18/3296680/0/en/Datasite-and-Legora-platforms-integrate-to-accelerate-AI-powered-diligence-across-the-deal-lifecycle.html","access":"open"},{"id":"s2472","stat":"Harvey launched 500+ pre-built use-case legal agents + Agent Builder self-service customisation (May 2026, early access)","source":"Harvey / Law.com","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Pre-built agent library across M&A, family, capital markets","url":"https://www.harvey.ai/blog/built-by-lawyers-tailored-by-you","access":"open"},{"id":"s2473","stat":"Harvey Legal Agent Bench (LAB): open-source benchmark, 1,200+ tasks across 24 practice areas, graded against 75,000+ expert-written rubric criteria (6 May 2026)","source":"Harvey / Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Open-source long-horizon legal-agent evaluation","url":"https://www.artificiallawyer.com/2026/05/06/harvey-launches-legal-agent-bench/","access":"open"}
1,{"id":"s2474","stat":"Harvey acquired Hexus (Jan 2026) for enterprise AI adoption tooling","source":"Harvey / Tracxn","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Likely underpins Command Center adoption analytics"},{"id":"s2475","stat":"\\"Current AI systems are more \'cultivated\' than \'built\'... developers do not directly design every detail, but instead create a framework within which the intelligence \'grows\'\\" (para 98)","source":"Pope Leo XIV, *Magnifica Humanitas* encyclical","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"cross-sector","context":"Vatican adopts industry framing; used to justify both more research and more moral discernment","url":"https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html","access":"open"},{"id":"s2476","stat":"\\"A more moral AI is not enough if that morality is determined by a few\\" (para 107)","source":"Pope Leo XIV, *Magnifica Humanitas* encyclical","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"cross-sector","context":"Critique of \\"alignment\\" framing - safety as political economy, not technical engineering","url":"https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html","access":"open"},{"id":"s2477","stat":"\\"To disarm AI means... freeing technology from monopolistic control and opening it to discussion and debate\\" (para 110)","source":"Pope Leo XIV, *Magnifica Humanitas* encyclical","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"cross-sector","context":"Novel \\"disarm AI\\" counter-frame to alignment; positions safety as decentralisation of power","url":"https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html","access":"open"},{"id":"s2478","stat":"Automated systems \\"do not know compassion, mercy, forgiveness, and above all, the hope that people are able to change\\" (para 102)","source":"Pope Leo XIV, *Magnifica Humanitas* encyclical","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"cross-sector","context":"On AI in decisions about employment, credit, public services, reputation","url":"https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html","access":"open"},{"id":"s2479","stat":"Companies and investors should adopt \\"clear criteria for preventive ethical verification (due diligence)\\" of data-driven business models, covering worker protection, forced labour, social impact (para 179)","source":"Pope Leo XIV, *Magnifica Humanitas* encyclical","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"legal","context":"Direct vocabulary overlap with commercial DD; usable hook for AI supply chain DD scope","url":"https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html","access":"open"},{"id":"s2480","stat":"Hidden AI supply chain: data labellers, content moderators (often young women, often Global South), child miners of rare earths - framed as contemporary slavery (paras 173-177)","source":"Pope Leo XIV, *Magnifica Humanitas* encyclical","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"cross-sector","context":"Pope asks Church\'s pardon for historic complicity in slavery before drawing the modern AI parallel","url":"https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html","access":"open"},{"id":"s2481","stat":"Health, epidemiological, genetic data as \\"the new rare earths of power\\" - \\"data colonialism\\" extracted from structurally fragile regions (para 178)","source":"Pope Leo XIV, *Magnifica Humanitas* encyclical","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"cross-sector","context":"Critique of asymmetric data extraction; restoration of data subject control","url":"https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html","access":"open"},{"id":"s2482","stat":"\\"Just war\\" theory declared \\"outdated\\" (para 192); lethal-force decisions cannot be delegated to opaque or automated processes (paras 198-200)","source":"Pope Leo XIV, *Magnifica Humanitas* encyclical","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"cross-sector","context":"Autonomous weapons; chain of responsibility must be identifiable and verifiable; \\"no algorithm can make war morally acceptable\\"","url":"https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html","access":"open"},{"id":"s2483","stat":"1,874 law students surveyed 6-19 April 2026 (JD 1L 54%, 2L 26%, 3L 17%, postgrad 3%)","source":"Thomson Reuters Institute 2026 Law Student Pulse Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Sample/methodology;
1 US-focused","url":"https://www.thomsonreuters.com/en-us/posts/wp-content/uploads/sites/20/2026/05/Law-Student-Pulse-Survey-2026.pdf","access":"open"},{"id":"s2484","stat":"72% of law students say AI literacy is an essential professional skill","source":"Thomson Reuters Institute 2026 Law Student Pulse Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Students see AI fluency as core to the job","url":"https://www.thomsonreuters.com/en-us/posts/wp-content/uploads/sites/20/2026/05/Law-Student-Pulse-Survey-2026.pdf","access":"open"},{"id":"s2485","stat":"74% of law students say over-reliance on AI risks leaving them unable to develop core legal skills","source":"Thomson Reuters Institute 2026 Law Student Pulse Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Held simultaneously with the 72% - the \\"two-sided judgment\\" finding","url":"https://www.thomsonreuters.com/en-us/posts/wp-content/uploads/sites/20/2026/05/Law-Student-Pulse-Survey-2026.pdf","access":"open"},{"id":"s2486","stat":"32% of law students disagree/strongly disagree that their school gives them the AI skills needed for their career","source":"Thomson Reuters Institute 2026 Law Student Pulse Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"The institutional preparation gap","url":"https://www.thomsonreuters.com/en-us/posts/wp-content/uploads/sites/20/2026/05/Law-Student-Pulse-Survey-2026.pdf","access":"open"},{"id":"s2487","stat":"57% of law students use AI several times a week or more for academic work (84% among postgrad students)","source":"Thomson Reuters Institute 2026 Law Student Pulse Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Usage already widespread, heaviest at postgrad level","url":"https://www.thomsonreuters.com/en-us/posts/wp-content/uploads/sites/20/2026/05/Law-Student-Pulse-Survey-2026.pdf","access":"open"},{"id":"s2488","stat":"48% of law students report AI policies vary by professor; 15% report no curriculum integration whatsoever","source":"Thomson Reuters Institute 2026 Law Student Pulse Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"No coherent institutional signal on permitted AI use","url":"https://www.thomsonreuters.com/en-us/posts/wp-content/uploads/sites/20/2026/05/Law-Student-Pulse-Survey-2026.pdf","access":"open"},{"id":"s2489","stat":"Only 36% of 1Ls feel adequately prepared for AI in practice, falling to 30% of 3Ls","source":"Thomson Reuters Institute 2026 Law Student Pulse Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Confidence drops as real-world exposure rises","url":"https://www.thomsonreuters.com/en-us/posts/wp-content/uploads/sites/20/2026/05/Law-Student-Pulse-Survey-2026.pdf","access":"open"},{"id":"s2490","stat":"Students\' net rating of AI\'s impact on early-career professionals (% positive - % negative): independent judgment -31, core skills -14, career paths -12, learning through experience -12, mentorship quality -12, overall impact -6","source":"Thomson Reuters Institute 2026 Law Student Pulse Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Almost all net negative; the pipeline-collapse evidence in students\' own ratings","url":"https://www.thomsonreuters.com/en-us/posts/wp-content/uploads/sites/20/2026/05/Law-Student-Pulse-Survey-2026.pdf","access":"open"},{"id":"s2491","stat":"New AI qualifications rated +42 net - the only net-positive early-career impact students identified","source":"Thomson Reuters Institute 2026 Law Student Pulse Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"AI fluency seen as a professional differentiator","url":"https://www.thomsonreuters.com/en-us
1/posts/wp-content/uploads/sites/20/2026/05/Law-Student-Pulse-Survey-2026.pdf","access":"open"},{"id":"s2492","stat":"34% of law students say absence of good AI tooling would influence whether they accept a role, rising to 51% among heavy AI users","source":"Thomson Reuters Institute 2026 Law Student Pulse Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Employer AI infrastructure becoming a recruiting variable","url":"https://www.thomsonreuters.com/en-us/posts/wp-content/uploads/sites/20/2026/05/Law-Student-Pulse-Survey-2026.pdf","access":"open"},{"id":"s2493","stat":"800+ legal professionals surveyed (in-house teams and law firms)","source":"Ironclad State of AI in Legal 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Sample for third annual report; CLM-vendor-funded"},{"id":"s2494","stat":"AI adoption in legal at 91.6%, up from 69% in 2025 (recovering after a 2025 dip)","source":"Ironclad State of AI in Legal 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"2025-dip-then-2026-surge; likely Claude for Word / Copilot pulling in late adopters"},{"id":"s2495","stat":"99% of legal AI users trust AI as their roles expand; 95% say it has made them more valuable","source":"Ironclad State of AI in Legal 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"PR Newswire headline figure; vendor-sourced, treat as directional"},{"id":"s2496","stat":"96% say their organisation now expects more of the legal function than two years ago","source":"Ironclad State of AI in Legal 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Rising expectations of the legal function"},{"id":"s2497","stat":"87.9% of legal professionals say their workloads have increased since adopting AI","source":"Ironclad State of AI in Legal 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"The workload paradox - AI raising throughput expectations faster than it cuts hours; Jevons / verification-tax evidence"},{"id":"s2498","stat":"Claude Opus 4.8 is the first model to break 10% all-pass on Harvey\'s Legal Agent Benchmark (highest score recorded)","source":"Anthropic / Harvey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"All-pass = every sub-task in a multi-step legal workflow completed; flip side: best model fully completes only ~10% of multi-step workflows","url":"https://www.harvey.ai/blog/opus-4-8-now-live-in-harvey","access":"open"},{"id":"s2499","stat":"Claude Opus 4.8 priced at $5 / $25 per million input/output tokens (unchanged from 4.7)","source":"Anthropic","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"\\"Modest but tangible\\" gain at same price","url":"https://www.anthropic.com/news/claude-opus-4-8","access":"open"},{"id":"s2500","stat":"Claude Opus 4.8 is 4x less likely than 4.7 to miss code flaws","source":"Anthropic","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Relevant for firms building on the API","url":"https://www.anthropic.com/news/claude-opus-4-8","access":"open"},{"id":"s2501","stat":"97% of legal AI users report at least one measurable business outcome","source":"Ironclad State of AI in Legal 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Adds to the workload paradox - outcomes claimed alongside rising workloads"},{"id":"s2502","stat":"60% of corporate legal practitioners claim to be prepared to govern AI agents, but only 23% have a comprehensive documented agentic-AI policy","source":"Icertis Agentic AI Governance Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"1,000+ US corporate legal; readiness-vs-controls gap","url":"https://www.icertis.com/research/surveys/governing-agents-in-an-autonomous-era/","access":"open"},{"id":"s2503","stat":"Only 34% say their existing general AI policies adequately cover agentic use cases","source":"Icertis Agentic AI Governance Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"General AI policies don\'t transfer to autonomous agents","url":"https://www.icertis.com/research/surveys/governing-agents-in-an-autonomous-era/","access":"open"},{"id":"s2504","stat":"47% of corporate legal teams could not detect an unauthorised AI action until after the fact","source":"Icertis Agentic AI Governance Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"No real-time oversight of agent activity in nearly half of teams","url":"https://www.icertis.com/research/surveys/governing-agents-in-an-autonomous-era/","access":"open"},{"id":"s2505","stat":"For nearly 10% of corporate legal teams, human review of AI activity is already the exception rather than the rule","source":"Icertis Agentic AI Governance Survey","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Autonomy outrunning oversight","url":"https://www.icertis.com/research/surveys/governing-agents-in-an-autonomous-era/","access":"open"},{"id":"s2506","stat":"Fisher Phillips: pro se employment suits up 49% year-on-year","source":"Bloomberg Law / Fisher Phillips","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"media","scope":"legal","context":"AI-enabled self-represented litigant surge","url":"https://news.bloomberglaw.com/business-and-practice/big-law-grapples-with-ai-fueled-pro-se-surge-rising-legal-costs","access":"paywalled"},{"id":"s2507","stat":"Seyfarth Shaw: Fair Housing Act pro se suits up 69% (first nine months of 2025)","source":"Bloomberg Law / Seyfarth Shaw","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"media","scope":"legal","context":"AI-enabled pro se surge in federal practice","url":"https://news.bloomberglaw.com/business-and-practice/big-law-grapples-with-ai-fueled-pro-se-surge-rising-legal-costs","access":"paywalled"},{"id":"s2508","stat":"AI-assisted pro se complaints projected to rise from near-zero (2019) to 18%+ of filings by 2026; courts/opposing counsel bear the cost of sorting valid from hallucinated","source":"Scott Greenfield, Simple Justice","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"commentary","scope":"legal","context":"Systemic court-overwhelm argument; pairs with Charlotin database growth"},{"id":"s2509","stat":"Harvey ~$190M ARR (at $11bn valuation)","source":"5WPR Legal Tech AI Visibility Index 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Cited as the anch
1or for Harvey\'s AI-search citation-share lead"},{"id":"s2510","stat":"85% of law firms feel direct client pressure on their AI strategy","source":"Litera State of Legal AI: Spring 2026 Market Sentiment Report","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Clients now the dominant driver of firm AI investment","url":"https://www.litera.com/newslinks/litera-state-of-legal-ai","access":"open"},{"id":"s2511","stat":"More than 50% of firms say a client directly influenced an AI investment decision in the past 12 months","source":"Litera State of Legal AI: Spring 2026 Market Sentiment Report","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Client demand as proximate cause of spend","url":"https://www.litera.com/newslinks/litera-state-of-legal-ai","access":"open"},{"id":"s2512","stat":"32% of firms cannot confidently demonstrate AI value to their most important client","source":"Litera State of Legal AI: Spring 2026 Market Sentiment Report","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"The client-facing value/proof gap; spend outrunning provable return","url":"https://www.litera.com/newslinks/litera-state-of-legal-ai","access":"open"},{"id":"s2513","stat":"Osborne Clarke spins out Justima (first OC spin-out); AI regulatory monitoring across 200+ EU/international sources daily, OC majority shareholder","source":"Osborne Clarke / Artificial Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"\\"Build-and-spin-out\\" - a fourth model for proprietary firm AI capability"},{"id":"s2514","stat":"Claude Fable 5 scores 13.3% all-pass on Harvey\'s Legal Agent Benchmark - new all-time high (vs Opus 4.8 10.4%, GPT-5.5 2.1%, Gemini 3.1 Pro 0.0%)","source":"Anthropic / Harvey","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"All-pass = every sub-task in a multi-step workflow completed; best model on earth still fully completes only ~1 in 8 multi-step legal workflows"},{"id":"s2515","stat":"Claude Fable 5 scores 93.4% on BigLaw Bench - new high for the Claude family","source":"Harvey","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Single-step measure; gap to the 13.3% LAB figure is the verification tax made visible"},{"id":"s2516","stat":"Enterprise AI users self-report saving 40-60 minutes a day","source":"Microsoft Research - New Future of Work Report 2025","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Headline productivity self-report; offset by workslop finding","url":"https://www.microsoft.com/en-us/research/publication/new-future-of-work-report-2025/","access":"open"},{"id":"s2517","stat":"40% of employees received \\"workslop\\" (polished but inaccurate/useless AI output) in the past month","source":"Microsoft Research - New Future of Work Report 2025","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Erases the time saved and can lower quality; the deskilling/quality risk","url":"https://www.microsoft.com/en-us/research/publication/new-future-of-work-report-2025/","access":"open"},{"id":"s2518","stat":"~5.5 million anonymised M365 Copilot sessions analysed across 1M+ companies; writing dominates usage, shifting over time from chat-as-search to content creation","source":"Microsoft Research (arXiv 2605.23958, Counts/Suri/Tomlinson/Jaffe et al.)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Largest-scale empirical study of enterprise AI behaviour; companion to the NFW report","url":"https://arxiv.org/abs/2605.23958","access":"open"},{"id":"s2519","stat":"54% of legal professionals had no responsible-AI training and no plans for any","source":"8am 2026 Legal Industry Report","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"n=1,300+. Training gap alongside the policy gap (43% no policy / 9% enforced)","url":"https://www.8am.com/reports/legal-industry-report-2026/","access":"open"},{"id":"s2520","stat":"62% of lawyers report weekly time savings of 6-20% from AI","source":"Wolters Kluwer Future Ready Lawyer 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Annual flagship survey; WK now also a Legora content partner","url":"https://www.wolterskluwer.com/en/know/future-ready-lawyer-2026","access":"gated"},{"id":"s2521","stat":"52% of lawyers report revenue up by a similar proportion (6-20%) attributable to AI","source":"Wolters Kluwer Future Ready Lawyer 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Revenue (not just efficiency) gains being reported","url":"https://www.wolterskluwer.com/en/know/future-ready-lawyer-2026","access":"gated"},{"id":"s2522","stat":"Claude for Legal now lists 90+ named end-to-e
1nd workflow agents; Palantir enters legal tech alongside OpenAI, Anthropic and Microsoft","source":"Artificial Lawyer","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Agent-count and new-entrant signal for the legal AI platform race","url":"https://www.artificiallawyer.com/2026/06/01/claude-for-legal-has-over-90-ai-agents/","access":"open"},{"id":"s2523","stat":"Steno raises $49M Series C and appoints Prabhdeep Singh as CEO to expand its Big Law base","source":"Artificial Lawyer","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Litigation-support / deposition AI funding"},{"id":"s2524","stat":"Billables AI raises $10.2M Series A led by Avenue Growth Partners","source":"Artificial Lawyer","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Time-capture / billing AI funding"},{"id":"s2525","stat":"Legal AI buyers use only ~20% of the platform capacity they purchase (the \\"activation gap\\"); value capture has moved to 12-24 months post-signature","source":"Telon Labs (Lewis Bretts)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"cross-sector","context":"Vendor/commentary thesis; argument for an AI-native services/activation layer","url":"https://telonlabs.com/blog/the-value-fulcrum","access":"open"},{"id":"s2526","stat":"Law firm technology spend grew 9.7% and knowledge management spend 10.5% in 2025 (each ~7 pts above core inflation; PCE core ~2.9%)","source":"Thomson Reuters Institute / Georgetown Law - 2026 Report on the State of the US Legal Market","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Verified against primary source. 2021-2025: tech spend +39.3%, KM +37.2%. Fastest real growth in these categories likely ever","url":"https://www.thomsonreuters.com/en-us/posts/legal/state-of-the-us-legal-market-2026/","access":"open"},{"id":"s2527","stat":"90% of all legal dollars still flow through standard hourly rate arrangements","source":"Thomson Reuters Legal Tracker, via 2026 Report on the State of the US Legal Market","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"CORRECTS the 85% figure circulated via Theodoropoulos commentary. Central conflict with AI-driven efficie
1ncy","url":"https://www.thomsonreuters.com/en-us/posts/legal/state-of-the-us-legal-market-2026/","access":"open"},{"id":"s2528","stat":"Average US law firm grew profits 13.0% in 2025, on 1.9% annual demand growth (Q3 hit 3.9% YoY)","source":"TR Institute / Georgetown - 2026 State of the US Legal Market","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Third-best demand year since the GFC; 2010s baseline just 0.6% avg quarterly growth","url":"https://www.thomsonreuters.com/en-us/posts/legal/state-of-the-us-legal-market-2026/","access":"open"},{"id":"s2529","stat":"Average Am Law 100 standard rate broke $1,000/hour in 2025 (vs ~$600 elsewhere); worked rates rose 7.3%","source":"TR Institute / Georgetown - 2026 State of the US Legal Market","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"\\"Mobile demand\\" - clients shifting work to firms charging up to 40% less","url":"https://www.thomsonreuters.com/en-us/posts/legal/state-of-the-us-legal-market-2026/","access":"open"},{"id":"s2530","stat":"Profit per lawyer since 2019: Am Law 100 +53.7%, Am Law Second Hundred +39.0%, Midsize +25.5%","source":"TR Institute / Georgetown - 2026 State of the US Legal Market","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Am Law 100 best PPL growth despite mobile-demand losses, via PE concentration + shrinking equity tier","url":"https://www.thomsonreuters.com/en-us/posts/legal/state-of-the-us-legal-market-2026/","access":"open"},{"id":"s2531","stat":"Law firm talent/direct-comp costs rose 8.2% in 2025; direct expenses now 32% of revenue; lawyer headcount +2.9%","source":"TR Institute / Georgetown - 2026 State of the US Legal Market","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Legal is hiring INTO AI, not laying off - explicit counter to AI-layoffs logic elsewhere","url":"https://www.thomsonreuters.com/en-us/posts/legal/state-of-the-us-legal-market-2026/","access":"open"},{"id":"s2532","stat":"Firms with a visible AI strategy are 3.9x more likely to see at least one form of ROI than firms with no AI plans","source":"TR Institute Future of Professionals 2025, cited in 2026 State of US Legal Market","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Justification for intentional AI strategy + value measurement","url":"https://www.thomsonreuters.com/en-us/posts/legal/state-of-the-us-legal-market-2026/","access":"open"},{"id":"s2533","stat":"52% of corporate legal respondents expect more work to move in-house over the next 5 years; >80% of senior corporate counsel do NOT require outside firms to use AI","source":"TR Future of Professionals 2024 + TR/ACC, cited in 2026 State of US Legal Market","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"client","sourceType":"industry","scope":"legal","context":"Insourcing pressure real but not yet an AI mandate on firms","url":"https://www.thomsonreuters.com/en-us/posts/legal/state-of-the-us-legal-market-2026/","access":"open"},{"id":"s2534","stat":"Net Spend Anticipation among GCs has fallen to levels not seen since the 2020 pandemic; forecasts point to demand contraction by mid-2026","source":"TR Market Insights / Financial Insights, 2026 State of US Legal Market","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Buyer sentiment rolling over despite record firm profits (sentiment/forecast, not realised)","url":"https://www.thomsonreuters.com/en-us/posts/legal/state-of-the-us-legal-market-2026/","access":"open"},{"id":"s2535","stat":"Only 27% of North American lawyers say their firm has a non-traditional legal services arm or ALSP partnership, vs 76% across UK/Europe/Australia","source":"ALSP Report 2025, cited in 2026 State of US Legal Market","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"practitioner","scope":"legal","context":"North American firms behind on alternative delivery structures","url":"https://www.thomsonreuters.com/en-us
1/posts/legal/state-of-the-us-legal-market-2026/","access":"open"},{"id":"s2536","stat":"Arizona has licensed 100+ alternative business structures (ABS) since abolishing Rule 5.4 in 2021","source":"Ted Theodoropoulos (LinkedIn)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"citing Arizona Supreme Court. The outside-capital escape valve from the cash-basis partnership constraint"},{"id":"s2537","stat":"Kirkland & Ellis: $10bn+ revenue and $11m+ profit per partner (context for its ~$500M AI project)","source":"Ted Theodoropoulos (LinkedIn)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"\\"Every dollar of AI capex is a dollar a partner does not take home\\""},{"id":"s2538","stat":"Gartner projects ~90% reductions in AI inference cost by 2030","source":"Gartner, cited by Ryan McClead (Geek Law Blog)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"The \\"calm-down\\" side of the token-cost debate; capability cascades downward","url":"https://www.geeklawblog.com/2026/06/bride-of-the-token-cost-panic.html","access":"open"},{"id":"s2539","stat":"Ninth Circuit sanctions two attorneys $2,500 each and suspends them 6 months for briefs citing AI-hallucinated, nonexistent cases","source":"Ninth Circuit, Order of 3 Jun 2026 (No. 24-4790)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"legal","context":"Federal appeals court: the rule is violated at signing/filing, not at research - verification duty does not transfer"},{"id":"s2540","stat":"Worked rates rose 7.4% in 2025 (YTD Q2) vs 2.8% inflation; Am Law 100 led at 9.4% (Second Hundred 7.2%, Midsize 6.0%)","source":"TR Institute / True Value Partnering Institute - Law Firm Rates in 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Rate growth more than double inflation across all segments","url":"https://www.thomsonreuters.com/en-us/posts/legal/law-firm-rates-report-2026/","access":"open"},{"id":"s2541","stat":"Since 2006: legal demand grew just 6% while worked rates rose 122% and fees worked 139%","source":"TR Institute - Law Firm Rates in 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Nearly all revenue growth is rate-driven, not volume-driven; volume-to-value transition","url":"https://www.thomsonreuters.com/en-us/posts/legal/law-firm-rates-report-2026/","access":"gated"},{"id":"s2542","stat":"Three different rate strategies (high-bypass / high-compression / afterburner) all collect ~$553-$580/hour despite standard rates up to ~2x apart","source":"TR Institute - Law Firm Rates in 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"k-means clustering; the market enforces its own pricing equilibrium","url":"https://www.thomsonreuters.com/en-us/posts/legal/law-firm-rates-report-2026/","access":"gated"},{"id":"s2543","stat":"Relationships/responsiveness = 23% of client favourability but only 9% of competitive differentiation","source":"TR Institute - Law Firm Rates in 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"The \\"relationship paradox\\": what keeps clients is not what wins them. Proactivity+value = 14% differentiation, 7% favourability","url":"https://www.thomsonreuters.com/en-us/posts/legal/law-firm-rates-report-2026/","access":"gated"},{"id":"s2544","stat":"$10B+ companies saw the partner rate-per-hour they actually paid fall 12.2% YoY even as worked rates rose","source":"TR Institute - Law Firm Rates in 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"client","sourceType":"analyst","scope":"legal","context":"Clients paying less per hour by moving work downstream (mobile demand)","url":"https://www.thomsonreuters.com/en-us
1/posts/legal/law-firm-rates-report-2026/","access":"gated"},{"id":"s2545","stat":"Direct cost per lawyer FTE up 5.0% and overhead 4.4% (rolling 12-mo, Q2 2025)","source":"TR Institute - Law Firm Rates in 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Costs rising nearly as fast as rates; realisation took an unseasonal Q2 downtick","url":"https://www.thomsonreuters.com/en-us/posts/legal/law-firm-rates-report-2026/","access":"gated"},{"id":"s2546","stat":"Standard rate increases range 3-15%; only firms with strong collections sustain 8-10%","source":"TR Institute - Law Firm Rates in 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Collected revenue feeds next cycle\'s rate confidence (the \\"exhaust\\" feedback loop)","url":"https://www.thomsonreuters.com/en-us/posts/legal/law-firm-rates-report-2026/","access":"gated"},{"id":"s2547","stat":"Associate worked realisation against standard 91.5% (vs sub-90% partners); associate collected realisation lowest at 85.6%","source":"TR Institute - Law Firm Rates in 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Partner discounting acts as a \\"cooling buffer\\" protecting premium partner billing","url":"https://www.thomsonreuters.com/en-us/posts/legal/law-firm-rates-report-2026/","access":"gated"},{"id":"s2548","stat":"Big Four now advertise more AI-specialist roles than auditor roles: AI-skill postings ~7% of Deloitte/EY/KPMG/PwC openings in English-speaking countries last year vs just under 3% for audit","source":"Resultsense (citing Big Four job-posting analysis)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Strategic tilt of the Big Four toward AI assurance/advisory and away from traditional audit","url":"https://www.resultsense.com/insights/2026-05-20-big-four-ai-hiring-eclipses-audit-frc-implications/","access":"open"},{"id":"s2549","stat":"PwC launched \\"Assurance for AI\\" (3 Jun 2025), billed as first-to-market independent third-party assurance over AI systems (governance, controls, bias/drift, robustness, fairness)","source":"PwC","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Big Four moving into AI assurance; PwC spans audit, tax AND legal - direct legal-services adjacency","url":"https://www.pwc.com/us/en/about-us/newsroom/assurance-ai-press-release.html","access":"open"},{"id":"s2550","stat":"AI assurance from the Big Four is process/governance attestation, not output guarantee - none will \\"lift the bonnet\\" on the model (black-box problem); audit focuses on procedures, methods and deployment","source":"Legal Futures (Matt Holman, Cripps)","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Assurance attests the process; it does not carry the loss if the output is wrong - the gap a liability-transfer/underwriting model fills","url":"https://www.legalfutures.co.uk/latest-news/law-firms-look-to-offer-clients-ai-assurance-services","access":"open"},{"id":"s2551","stat":"Law firms predicted to offer more technical AI audits \\"by 2030\\" (e.g. whether a system performs to supplier SLAs/contract terms), beyond today\'s legal-compliance assurance","source":"Legal Futures (Matt Holman, Cripps)","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Law firms competing with Big Four for the AI-assurance wrapper; EU AI Act third-party audit mandate is the demand driver","url":"https://www.legalfutures.co.uk/latest-news/law-firms-look-to-offer-clients-ai-assurance-services","access":"open"},{"id":"s2552","stat":"87% of general counsel now report genAI use within their teams, up from 44% the prior year","source":"2026 FTI Consulting / Relativity General Counsel Report (via Anthropic)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"cited in Anthropic, Claude for the Legal Industry. Year-on-year roughly doubling of GC genAI use; loose \'any use by anyone on the team\' denominator - verify against primary source"},{"id":"s2553","stat":"CLO genAI use climbed from 20% (2023) to 87% (this year\'s report)","source":"2026 FTI Consulting / Relativity General Counsel Report (via Anthropic)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Steeper multi-year trajectory of chief legal officer genAI adoption"},{"id":"s2554","stat":"Top legal-department genAI use cases: summarization 83%, contract clause identification 63%, transcription 53%","source":"2026 FTI Consulting / Relativity General Counsel Report (via Anthropic)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Where genAI is actually applied inside legal departments today"},{"id":"s2555","stat":"Anthropic legal team: marketing review turnaround cut from 2-3 days to 24 hours","source":"Anthropic (Claude for the Legal Industry deployment guide)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"First-party, self-reported; marketing self-review tool clears obvious issues, lawyers still read every post","url":"https://claude.com/blog/deploying-claude-across-the-legal-industry","access":"open"},{"id":"s2556","stat":"Anthropic legal team: PIA drafting cut from ~2 hours to ~30 minutes per assessment","source":"Anthropic (Claude for the Legal Industry deployment guide)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"First-party; MCP to a Drive of prior PIAs + a format/issues skill","url":"https://claude.com/blog/deploying-claude-across-the-legal-industry","access":"open"},{"id":"s2557","stat":"Anthropic legal team: contract redlining reduced from hours to minutes per agreement","source":"Anthropic (Claude for the Legal Industry deployment guide)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"First-party; live suggested edits inside Google Docs/M365 against the commercial playbook","url":"https://claude.com/blog/deploying-claude-across-the-legal-industry","access":"open"},{"id":"s2558","stat":"Anthropic is ISO/IEC 42001:2023 certified and
1SOC 2 Type II audited; no training on customer data on any surface","source":"Anthropic (Claude for the Legal Industry deployment guide)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"legal","context":"Enterprise posture for legal deployment; ZDR on Platform/Code only, custom retention down to 30 days on stateful Claude.ai/Cowork","url":"https://claude.com/blog/deploying-claude-across-the-legal-industry","access":"open"},{"id":"s2559","stat":"~95% of Yorkshire & Humber PBS firms consider digital adoption important/very important for competitiveness","source":"Whitecap Consulting, Y&H PBS Digital Growth Key Findings","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Near-universal recognition vs near-zero integration - the intent/execution gap (regional UK SME study, undisclosed N)"},{"id":"s2560","stat":"Only ~25% of surveyed PBS firms feel very confident about adopting digital technologies","source":"Whitecap Consulting, Y&H PBS 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Confidence gap among professional-services SMEs"},{"id":"s2561","stat":"0% of surveyed PBS firms have fully integrated AI; ~20% implemented in parts, ~30-35% experimenting/piloting, ~15-20% not considering AI","source":"Whitecap Consulting, Y&H PBS 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Striking 0%-integration headline; unverifiable without sample size"},{"id":"s2562","stat":"~70% of PBS SMEs plan or are highly likely to invest in new digital tools within the next year","source":"Whitecap Consulting, Y&H PBS 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Demand signal if barriers are addressed"},{"id":"s2563","stat":"Only ~15% of PBS firms say their systems are fully integrated","source":"Whitecap Consulting, Y&H PBS 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Majority run partially connected or siloed systems even for CRM/cloud"},{"id":"s2564","stat":"PBS digital-adoption barriers: time/capacity ~60% (top), budget ~50%, unsure of route ~40%, skills gaps ~30%, legacy integration ~33%, cyber/data-privacy ~15%","source":"Whitecap Consulting, Y&H PBS 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Execution constraints, not awareness, are the binding barriers"},{"id":"s2565","stat":"The \'missing middle\' - capable but time-poor mid-maturity, mid-size PBS SMEs - is the largest untapped digital-adoption opportunity","source":"Whitecap Consulting, Y&H PBS 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Support clusters at novice and enterprise ends; mid-tier structurally underserved (portable market-structure thesis)"},{"id":"s2566","stat":"Suggested £2k-£10k small-grant range to help micro-firms (<10 staff) implement digital change","source":"Whitecap Consulting, Y&H PBS 2026","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Recommended WYCA intervention; regulated sectors (legal/finance) flagged as most cautious adopters"},{"id":"s2567","stat":"Claude Design (claude.ai/design): HTML-first prototyping tool in Research Preview; outputs plain HTML/CSS/JS, one-way handoff to Claude Code","source":"Duolingo, Prototyping with Claude Design Workshop","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Duolingo design-team enablement doc; live editing on the rendered prototype is the key differentiator vs Claude Code"},{"id":"s2568","stat":"Duolingo designers rate Claude Design strong on rapid ideation/single-shot swings but weak on design-system fidelity, component recall and high-fidelity polish","source":"Duolingo, Prototyping with Claude Design Workshop","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"First-party \'good for the first 60%, weak on the last 40%\' read from a sophisticated design org"},{"id":"s2569","stat":"Study of ~400,000 Claude Code sessions (Oct 2025-Apr 2026) on agentic coding","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Privacy-preserving analysis; CLI + claude.ai + desktop app; excludes headless and third-party IDE/SDK usage","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2570","stat":"In a typical session users make ~70% of planning decisions (\\"what to do\\") and only ~20% of execution decisions (\\"how to do it\\")","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"The core division of labour: people decide what to build, the agent decides how","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2571","stat":"A typical session is ~4 user/agent turns; each user prompt triggers ~10 Claude actions on average (2% of sessions average >100 actions/prompt)","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Session structure and autonomy per prompt","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2572","stat":"Expert sessions trigger ~12 Claude actions and ~3,200 words of output per prompt vs ~5 actions and ~600
1words for novices","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Domain expertise amplifies how much work each instruction yields (holds within every work mode and value band)","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2573","stat":"In code-producing sessions every one of the 10 largest occupations is within 7 points of software engineers on success; verified success ~34% (software/maths) vs ~29% (other professions)","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Coding agents make a coding background less relevant to successful programming; gap stable over 7 months","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2574","stat":"Verified success 15% (novice-rated sessions) vs 28-33% (intermediate and up); partial success 77% vs 91-92%","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Returns to expertise; most of the gain is novice->intermediate, intermediate->expert adds little","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2575","stat":"Among sessions that hit trouble, verified success rises from 4% (novice) to 15% (expert); 19% of novice sessions end abandoned vs 5-7% for everyone else","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Expertise = ability to steer the agent and recover from errors","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2576","stat":"Share of Claude Code sessions spent fixing broken code fell from 33% to 19% (Oct 2025-Apr 2026)","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Usage shifting away from debugging toward end-to-end agentic work","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2577","stat":"Operating software grew 14%->21% of sessions; writing + data analysis roughly doubled ~10%->~20% (Oct 2025-Apr 2026)","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"The work surrounding code grew as fixing shrank","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2578","stat":"Estimated task value (vs freelance job postings) rose ~27% on average over 7 months; building +43%, operating +34%, fixing +32%","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Tasks done with Claude Code grew more valuable; coarse relative estimates, not literal dollar values","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2579","stat":"Occupation inferred in ~70% of sessions; fastest-growing non-software occupation groups are management, sales, and legal","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Agentic coding spreading beyond software-related jobs","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2580","stat":"Share of GitHub projects with coding-agent activity more than doubled since late 2025 (agent activity detected in 16-23% of 128k public repos by end-Oct 2025)","source":"Anthropic, Agentic Coding and Persistent Returns to Expertise","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Cited adoption context; detection relies on co-authorship tags/config files, likely an undercount","url":"https://www.anthropic.com/research/claude-code-expertise","access":"open"},{"id":"s2581","stat":"Copilot-in-Word causal study: 72,186 Word users across 1,021 US organisations","source":"Microsoft New Future of Work (Verma, Suri & Counts)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Quasi-experimental: sustained adopters matched to near-identical future-adopter coworkers in the same org","url":"https://microsoft.github.io/nfw-reader/posts/causal-impact-copilot","access":"open"},{"id":"s2582","stat":"Sustained Copilot adopters completed comparable Word activities ~40-45% faster after 12 weeks (~6 min vs ~11 min for matched peers)","source":"Microsoft New Future of Work (Verma, Suri & Counts)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Causal estimate of pace change; authors stress measured time is not productivity","url":"https://microsoft.github.io/nfw-reader/posts/causal-impact-copilot","access":"open"},{"id":"s2583","stat":"Word activities where Copilot was actually used averaged ~4.6 minutes - under half the matched-peer pace","source":"Microsoft New Future of Work (Verma, Suri & Counts)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"On-task effect larger than the all-activity average","url":"https://microsoft.github.io/nfw-reader/posts/causal-impact-copilot","access":"open"},{"id":"s2584","stat":"Copilot adopters worked faster even on Word activities where Copilot was not used (spillover effect)","source":"Microsoft New Future of Work (Verma, Suri & Counts)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Gains reach beyond the AI-assisted interaction","url":"https://microsoft.github.io/nfw-reader/posts/causal-impact-copilot","access":"open"},{"id":"s2585","stat":"Time-to-half-savings fell from ~2.4 weeks (Feb 2025 adopters) to ~1.4 weeks (Nov 2025) - a ~40% ramp-up compression","source":"Microsoft New Future of Work (Verma, Suri & Counts)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Later cohorts reached value faster as product, onboarding and familiarity co-evolved","url":"https://microsoft.github.io/nfw-reader/posts/causal-impact-copilot","access":"open"},{"id":"s2586","stat":"Most frequent Word work is editing/refining/formatting existing documents, not creating: active editing 15.5%, refining layout 12.6%, formatting text 11.0%; new documents only 6.2%","source":"Microsoft New Future of Work (Verma, Suri & Counts)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"The \\"anatomy of work in Word\\" from WorkflowView activity classification","url":"https://microsoft.github.io/nfw-reader/posts/causal-impact-copilot","access":"open"},{"id":"s2587","stat":"WorkflowView (LLM abstraction of action logs) generalises without fine-tuning: browser task reconstruction semantic similarity mu=0.91; MOOC dropout weighted F1=0.90 with 5 in-context examples","source":"Verma & Counts, Abstracting Cross-Domain Action Sequences into Interpretable Workflows (arXiv:2606.14654)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Method paper behind the Copilot-in-Word analysis","url":"https://arxiv.org/abs/2606.14654","access":"open"},{"id":"s2588","stat":"Pre-indexed retrieval (\\"navigate\\") processed 17.3x fewer input tokens than full-context (\\"inject\\") - 188K vs 3.26
1M over 20 questions - at answer-quality parity (18/20 judge ties)","source":"Syntheia, Inject vs Navigate Benchmark","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Legal-document QA; claude-sonnet-4-6 answerer, claude-opus-4-7 position-debiased pairwise judge; best config = semantic retrieval + rerank"},{"id":"s2589","stat":"Navigate cut input-token footprint 8.5-30x vs inject across configurations on legal-document QA","source":"Syntheia, Inject vs Navigate Benchmark","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Footprint tracks the question, not the corpus"},{"id":"s2590","stat":"Full-context inject attends to the full ~214K-token corpus on every question regardless of question narrowness; prompt caching lowers the bill but not the tokens conditioned on","source":"Syntheia, Inject vs Navigate Benchmark","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"\\"Caching makes the context rot affordable, not smaller\\""},{"id":"s2591","stat":"Caching crossover: warm-cache inject becomes cheaper than navigate once corpus is below ~10x the retrieval payload (~83K tokens, ~2 docs), or after ~9 repeated questions on a single ~35K-token doc","source":"Syntheia, Inject vs Navigate Benchmark","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Token footprint and dollar cost diverge under caching; recovered prices: base $3.00/M, cache write $3.75/M, cache read $0.30/M, output $15/M"},{"id":"s2592","stat":"A 3-step LLM index scan that reloads the full titles-only index per query achieved only 1.2x token reduction; heading-only nodes caused over-selection of up to 87 nodes for one question","source":"Syntheia, Inject vs Navigate Benchmark","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Descriptor richness of index nodes is the binding constraint on navigate"},{"id":"s2593","stat":"Manual audit of one navigate run: 9/15 judge verdicts clearly correct, 4/15 debatable, 2/15 likely wrong","source":"Syntheia, Inject vs Navigate Benchmark","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"The position-debiased pairwise judge measures answer preference, not correctness"},{"id":"s2594","stat":"Harvey: typical users save 15-25 hours/user/month; power users 30-50+ hours/month","source":"Harvey, Legal AI ROI Guide for Law Firms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Vendor customer-reported/internal figures; varies by practice mix and integration depth"},{"id":"s2595","stat":"Harvey: many tasks completed 60-90% faster; ROI \\"within weeks\\"","source":"Harvey, Legal AI ROI Guide for Law Firms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Internal testing + customer surveys; vendor data"},{"id":"s2596","stat":"Harvey ROI proof point: 5-lawyer litigation team saving 30 hrs/month each, 50% reallocated to billable at EUR400/hr = EUR30,000/month billable value vs EUR1,500/month licence cost - a 20x return","source":"Harvey, Legal AI ROI Guide for Law Firms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Illustrative; assumes saved time converts to billable work (the contestable assumption)"},{"id":"s2597","stat":"Cuatrecasas (1,900+ lawyers, CELIA Project): 80%+ daily active usage, ~25% power users, due-diligence review time cut 50% for a matter","source":"Harvey, Legal AI ROI Guide for Law Firms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Firmwide rollout after pilot; vendor case study"}
1,{"id":"s2598","stat":"B. Cremades & Asociados: ~7 hours saved per lawyer per week; ~90% efficiency improvement in key document-review workflows","source":"Harvey, Legal AI ROI Guide for Law Firms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Arbitration/litigation boutique; vendor case study"},{"id":"s2599","stat":"A&O Shearman: up to 7 hours saved per contract review with Harvey","source":"Harvey, Legal AI ROI Guide for Law Firms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Vendor case study"},{"id":"s2600","stat":"LPHS: litigators save 8+ hours/week; firm won new business by responding to an urgent client request in under 48 hours","source":"Harvey, Legal AI ROI Guide for Law Firms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Revenue-won lever; vendor case study"},{"id":"s2601","stat":"masin: +35% case capacity driven by improved efficiency and precision","source":"Harvey, Legal AI ROI Guide for Law Firms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Quality/speed lever; vendor case study"},{"id":"s2602","stat":"vinge: 90%+ of lawyers say Harvey improves their day-to-day work and output","source":"Harvey, Legal AI ROI Guide for Law Firms","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Talent/retention lever; vendor case study"},{"id":"s2603","stat":"RSGI Harvey impact study: 136 inputs (82 survey - 57 law firms + 25 in-house - and 54 long-form interviews), all Harvey customers, Apr-Jun 2026","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Independent (RSGI-conducted), Harvey-commissioned; follow-on to the Nov 2025 RSGI report","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2604","stat":"55% of law firms and 48% of in-house teams describe Harvey as a foundational technology (not a tool being evaluated)","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The headline \\"usage to infrastructure\\" shift since Nov 2025","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2605","stat":"77% of law firms now justify Harvey licence spend by better legal/commercial outcomes; 35% say Harvey improves outcomes on most or all matters it touches","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The shift from measuring activity to measuring impact","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2606","stat":"89% of law firms that could track it say Harvey gave them capacity to take on more work","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Among Harvey customers; document-review volume up at 75%, drafting at 61%","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2607","stat":"Among Harvey-customer law firms: utilisation up at 59% (five firms >75%, two >100%), profitability up at 53%, revenue per lawyer up at 44%","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Capacity translating into economics","url":"https://www.rsgi.c
1o/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2608","stat":"42% of law firms saw non-billable time fall and 39% saw write-offs decrease - in both cases by as much as 30% or more","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Efficiency reaching the firm\'s cost base","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2609","stat":"In-house Harvey users: 91% spend less time reviewing contracts, 88% more time on higher-value tasks, 53% more time on strategy/new products","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Reallocation up the value curve in-house","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2610","stat":"36% of in-house teams are insourcing work because of Harvey, with six-figure savings on external spend per matter (e.g. a Spanish M&A deal run in-house vs ~EUR200k external quotes)","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Hard evidence for the insourcing-tipping-point thesis","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2611","stat":"65% of in-house Harvey teams saw measurable benefit within one month of implementation","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Rapid time-to-value vs earlier legal tech","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2612","stat":"Organisations where 30%+ of lawyers are power users rose from 34% to 50% in six months; power users can be up to 60% of a licensed population","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The power-user surge","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2613","stat":"Harvey power users save ~11 hours/week (law firms) or ~8 hours/week (in-house) vs ~4 hours for other users","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The power-user multiplier; time savings up 29% (firms) / 23% (in-house) vs six months ago","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2614","stat":"57% of law firms and 70% of in-house teams say a lawyer can become a Harvey power user in under three months; only 2% say it takes more than a year","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Barrier is change-management investment + leadership signals, not difficulty (\\"frozen middle\\" of stalled adopters)","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2615","stat":"68% of Harvey customers are deploying Harvey-based AI agents; 21% of law firms run 50+ agents in production","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"In-house: contracting/compliance/regulatory; firms: M&A/litigation. Agent Builder + DMS/Microsoft/MCP-API called gamechangers","url":"https://www.rsgi.c
1o/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2616","stat":"Pricing disconnect: 61% of law firms say they have discussed AI efficiencies with clients, but only 4% of in-house teams say their firms have approached them about cost savings","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"The client-value/price gap; only one in-house team reported receiving new pricing models","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2617","stat":"48% of in-house legal teams now mandate that their law firms use AI","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Client expectation hardening into a requirement","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2618","stat":"82% of law firms and 94% of in-house legal teams are changing hiring criteria to focus on AI skills; 61% of firms are hiring for roles that did not previously exist","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"AI competency becoming baseline; differentiator is depth of usage","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2619","stat":"AI competency is entering formal appraisals: 23% of law firms for partners, 33% for associates; 54% of firms use Harvey itself to train their lawyers","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"AI capability as a performance measure","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2620","stat":"69% of law firms are not planning to reduce graduate intake over the next two years because of AI; 68% of in-house teams expect headcount to stay flat","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Interviewees invoke the Jevons Paradox - efficiency pulling in more work, not less","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2621","stat":"Agritech in-house team: 3.6 hours saved per user per week (self-reported quarterly survey since Jan 2025)","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"One of the few hard per-user time-saving figures in the in-house cohort","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2622","stat":"Talanx compliance workflow for EU DORA cut a two-hour manual task to 15 minutes","source":"RSGI, Harvey as Foundational","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Single-use-case ROI proof; multiplied across 500 such tasks covered the Harvey licence cost","url":"https://www.rsgi.co/insights/harvey-the-accelerating-impact-of-legal-ai-harvey-as-foundational","access":"open"},{"id":"s2623","stat":"Human-AI teams produced 50% more ads per worker than human-human teams","source":"Ju & Aral, Collaborating with AI Agents (MIT/JHU)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Preregistered RCT, 2,234 participants, agentic gpt-4o; individual-level productivity","url":"https://arxiv.org/abs/2503.18238","access":"open"},{"id":"s2624","stat":"Human-AI teams produced higher text quality but lower image quality (the jagged frontier)","source":"Ju & Aral, Collaborating with AI Agents (MIT/JHU)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"+0.33 text, -0.13 image on a 7-point human-rated scale; AI strong on text, weak on image-quality prediction","url":"https://arxiv.org/abs/2503.18238","access":"open"},{"id":"s2625","stat":"Human-AI teams sent 25% more task-oriented messages and 18% fewer interpersonal messages","source":"Ju & Aral, Collaborating with AI Agents (MIT/JHU)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Working with AI shifts communication from rapport-building to instrumental/directive","url":"https://arxiv.org/abs/2503.18238","access":"open"},{"id":"s2626","stat":"Participants delegated 17% more work to AI than to a human partner (58% vs 50%) and made 62% fewer direct edits","source":"Ju & Aral, Collaborating with AI Agents (MIT/JHU)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The \\"delegation workflow\\": humans shift from doing to directing/supervising","url":"https://arxiv.org/abs/2503.18238","access":"open"},{"id":"s2627","stat":"94.5% of participants correctly identified an AI partner, vs 57.6% accuracy in human-human teams","source":"Ju & Aral, Collaborating with AI Agents (MIT/JHU)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Those who recognised AI were more task-oriented, delegated more, and did more oversight; AI comms is distinctive","url":"https://arxiv.org/abs/2503.18238","access":"open"},{"id":"s2628","stat":"Higher delegation to AI reduced output diversity (diversity collapse)","source":"Ju & Aral, Collaborating with AI Agents (MIT/JHU)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"More AI delegation = more homogeneous, self-similar output; the empirical underside of the \\"average matter\\" risk","url":"https://arxiv.org/abs/2503.18238","access":"open"},{"id":"s2629","stat":"Field test: 4.9M impressions on X; higher text quality lifted CTR/view-duration, higher image quality cut CPC, netting similar overall performance","source":"Ju & Aral, Collaborating with AI Agents (MIT/JHU)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Jagged frontier carries through to real-world ad outcomes; team-type advantages offset each other","url":"https://arxiv.org/abs/2503.18238","access":"open"},{"id":"s2630","stat":"Same model, different scaffold: ~10-point quality swing on legal compliance tasks (82.7% best vs 72.7% worst)","source":"LN Labs, Legal AI Benchmarks","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Claude Opus 4.8 held constant across 3 scaffolds; 40 data-protection/operational-resilience tasks; rubric LLM-as-judge (Fable 5)","url":"https://lnlabs.ai/research/legal-ai-benchmarks","access":"open"},{"id":"s2631","stat":"Cowork + Legal Plugin scored 82.7% overall (evidence 83.2 / soundness 84.8 / reasoning 80.1)","source":"LN Labs, Legal AI Benchmarks","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Best-performing scaffold; Anthropic workflow scaffold + privacy/regulatory plugin on Claude Opus 4.8","url":"https://lnlabs.ai/research/legal-ai-benchmarks","access":"open"},{"id":"s2632","stat":"Raw Claude Chat scored 75.6% overall (evidence 77.7 / soundness 79.7 / reasoning 69.3)","
1source":"LN Labs, Legal AI Benchmarks","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"No-scaffold baseline; ~7 points below the plugin scaffold despite the identical model","url":"https://lnlabs.ai/research/legal-ai-benchmarks","access":"open"},{"id":"s2633","stat":"Open-source scaffold (MikeOSS) scored 72.7% overall (evidence 77.4 / soundness 75.9 / reasoning 64.9)","source":"LN Labs, Legal AI Benchmarks","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"First independent quality number on Will Chen\'s willchen96/mike-oss; lowest of the three, widest gap in reasoning. Not Mike Kennedy\'s scaffold","url":"https://lnlabs.ai/research/legal-ai-benchmarks","access":"open"},{"id":"s2634","stat":"Cost per task: Cowork + Legal Plugin $0.80, raw Claude Chat $2.80, MikeOSS $0.30","source":"LN Labs, Legal AI Benchmarks","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Best-quality scaffold was also 3.5x cheaper per task than raw chat - scaffolding reaches a good answer with fewer tokens","url":"https://lnlabs.ai/research/legal-ai-benchmarks","access":"open"},{"id":"s2635","stat":"The best-quality legal scaffold was also the more cost-efficient one (better AND ~3.5x cheaper than raw chat)","source":"LN Labs, Legal AI Benchmarks","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"\\"Before tuning the model, evaluate the system\\"; scaffold engineering as a more practical lever than fine-tuning","url":"https://lnlabs.ai/research/legal-ai-benchmarks","access":"open"},{"id":"s2636","stat":"Codex output-token share (Codex + ChatGPT): 16.5% individuals / 63.3% organisations / 99.8% OpenAI workers","source":"Johnston et al., The Shift to Agentic AI: Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Agentic shift measured by intensity not active-user count; OpenAI workers as the \\"frictionless frontier\\". 11 Jun 2026"},{"id":"s2637","stat":"Share of individual users delegating an 8+ hour-equivalent task rose 2.1% to 25.6% in ~6 months","source":"Johnston et al., Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Near-tenfold rise in task complexity delegated to agents; hardest tasks cluster at session start","url":"https://arxiv.org/abs/2606.26959","access":"open"},{"id":"s2638","stat":"Share of individual users delegating a 1+ hour-equivalent task rose 35.4% (Dec 2025) to 70.2% (May 2026)","source":"Johnston et al., Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Rapidly rising task complexity handed to agentic tools","url":"https://arxiv.org/abs/2606.26959","access":"open"},{"id":"s2639","stat":"Codex is 1.9% of the average legal-team user\'s output tokens but 17.6% of all legal-user output tokens","source":"Johnston et al., Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Legal at back of adoption queue on average but intensive where it lands; the intensive-minority gap","url":"https://arxiv.org/abs/2606.26959","access":"open"},{"id":"s2640","stat":"OpenAI\'s own legal/recruiting functions went near-zero to ~20% Codex token share by early April 2026, then 20% to 75% within a month","source":"Johnston et al., Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Once friction removed + training/feedback loops in place, a legal function can go near-zero to dominant in a quarter","url":"https://arxiv.org/abs/2606.26959","access":"open"},{"id":"s2641","stat":"60.3% of Codex turns invoked an external tool vs 21.9% of ChatGPT turns (week to 11 Jun 2026)","source":"Johnston et al., Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Agentic usage is delegated production (doing work), not consultation (asking)","url":"https://arxiv.org/abs/2606.26959","access":"open"},{"id":"s2642","stat":"Skill use among active Codex users rose 5.4% (1 Mar) to 26.6% (11 Jun 2026); 96.2% of OpenAI active users","source":"Johnston et al., Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Systematisation via reusable skills; payoff is workflow redesign not just tool access","url":"https://arxiv.org/abs/2606.26959","access":"open"},{"id":"s2643","stat":"Codex weekly active users grew 5x+ in H1 2026, fastest growth among non-developers","source":"Johnston et al., Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Agentic coding spreading beyond engineers","url":"https://arxiv.org/abs/2606.26959","access":"open"},{"id":"s2644","stat":"Average org engineer puts 26.8% of tokens through Codex, but Codex is 88.3% of total tokens generated by engineers as a class","source":"Johnston et al., Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Adoption is a minority of heavy users, not broad uptake; the intensive-minority mechanic generalised","url":"https://arxiv.org/abs/2606.26959","access":"open"},{"id":"s2645","stat":"OpenAI median-worker output-token growth Nov 2025 to Jun 2026: Research 56x / Customer Support 32x / Engineering 27x / Legal 13x (every function 10x+)","source":"Johnston et al., Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Output rises sharply once agentic tooling is adopted across functions; legal trails but still 13x","url":"https://arxiv.org/abs/2606.26959","access":"open"},{"id":"s2646","stat":"Codex skill use by account (week to 11 Jun 2026): 25.7% individual / 30.4% organisational / 96.2% OpenAI; growth led by plugins (14.5%) and custom skills (13.0%)","source":"Johnston et al., Evidence from Codex (OpenAI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Systematisation via reusable skills/plugins; custom skills dominate in high-context org settings","url":"https://arxiv.org/abs/2606.26959","access":"open"},{"id":"s2647","stat":"Certainty framing shifted user trust, confidence and advice adoption even when the rationale content was identical","source":"Sun et al., Seeing the Reasoning (CHI 2026)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"Visible chain-of-thought is a persuasion surface;
1 confident wording earns trust whether or not the reasoning is correct. N=68","url":"https://doi.org/10.1145/3772363.3798613","access":"gated"},{"id":"s2648","stat":"Advice adoption rate by certainty framing: certain 0.96 / none 0.91 / uncertain 0.84","source":"Sun et al., Seeing the Reasoning (CHI 2026)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"Hedged framing penalised harder than no cue at all; uncertain pushed trust below baseline","url":"https://doi.org/10.1145/3772363.3798613","access":"gated"},{"id":"s2649","stat":"Correct rationales adopted at 0.94 vs incorrect at 0.86; presentation format (instant/delayed/on-demand) had no significant effect","source":"Sun et al., Seeing the Reasoning (CHI 2026)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"Content correctness and certainty framing move users; how/when reasoning is shown does not. Automation-bias risk","url":"https://doi.org/10.1145/3772363.3798613","access":"gated"},{"id":"s2650","stat":"Legora moved its heavy agentic tier (Agent Pro) to consumption-based pricing","source":"Legora / Law360 Pulse / The Lawyer","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"\\"From hours billed and seats licensed to outcomes delivered\\"; seats stay for the lighter Legora Agent. Cites Clay/Cursor/Lovable","url":"https://legora.com/blog/consumption-based-pricing","access":"open"},{"id":"s2651","stat":"Harvey announced its first fine-tuned legal foundation model (post-training open-weight, not frontier-from-scratch)","source":"Gabe Pereyra (X) / Artificial Lawyer","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"legal","context":"Attacks token cost on the cost side; routes to frontier models when needed. TR and Kirkland also fine-tuning open models","url":"https://www.artificiallawyer.com/2026/06/18/harvey-trains-open-source-models-to-encode-law-firm-workflows/","access":"open"},{"id":"s2652","stat":"In-house legal leaders reporting NO AI adoption: 76% (2024) to 2% (2026)","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"The flip in two years; from \\"should we invest\\" to \\"where and how fast\\". Sample 100+ senior legal leaders, 9 sectors, 4 regions","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2653","stat":"61% of legal departments in active AI deployment phases; 71% past piloting","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Maturity split: 10% fully embedded, 30% scaling, 31% initial, 27% experimenting/piloting, 2% none","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2654","stat":"79% of legal departments increased AI spend YoY, average uplift 67%","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Spend is real but lopsided towards technology and infrastructure","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"}
1,{"id":"s2655","stat":"Technology & infrastructure top-ranked AI investment area by 67% (87% top-two)","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Training, transformation, data and knowledge all lag behind tech spend","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2656","stat":"AI technology budget split 62% Buy (third-party) vs 38% Build (in-house)","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Buy still dominates in-house spend; build a minority but material","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2657","stat":"Only 24% of in-house legal teams have a defined QA framework (33% high-maturity)","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Weakest governance control; matters more as agentic use raises error cost","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2658","stat":"68% have an AI governance/risk framework (82% high-maturity)","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"Broader governance ahead of QA specifically; 34% have defined KPIs","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2659","stat":"~28% of legal department work expected to be saved/automated over 2-3 years","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"By practice area: Contracts & Commercial 34% (highest), Antitrust 19% (lowest)","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2660","stat":"58% expect legal teams to stay same size but shift materially in composition/seniority","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Share expecting smaller teams doubled to 20% vs 2024","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2661","stat":"84% of organisations have NOT redesigned any role around AI","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Expectation without a plan; skills gaps the most-cited adoption barrier","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2662","stat":"Technology & AI literacy the top future skill: 96% (lawyers), 95% (GCs)","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Technical legal knowledge ranked bottom (51% lawyers, 40% GCs)","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2663","stat":"Future 3-year team composition: AI-native lawyers 50%, AI agents 30%, hybrid engineers/lawyers 20%","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"vs today 95% lawyers / 5% legal ops","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2664","stat":"85% believe AI will change how law firms price work","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Commercial model on the table; billable hour under direct pressure","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2665","stat":"Hourly-rate share of work expected to fall from 72% to 44% within 2-3 years","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Regional today to future: Americas 82% to 56%, APAC 68% to 33%, EMEA 74% to 48%, UK&I 62% to 39%","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2666","stat":"External legal spend expected to drop 20-40% over 3 years via insourcing/collaboration","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"71% expect increased insourcing/reduced reliance on external providers","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2667","stat":"58% of GCs say providers rarely or never proactively raise AI benefits","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"The opening for a firm that initiates the value conversation","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2668","stat":"Only 4% have directly experienced benefits from a provider\'s AI use","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"78% cite cost reduction as the benefit they most want from a provider\'s AI","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2669","stat":"61% of legal departments experimenting with or piloting agentic AI","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Only 13% at scaling/embedded agentic maturity; governance lags capability","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2670","stat":"98% expect AI to deliver productivity/efficiency gains; 81% reduced cost of legal work","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Benefit expectations; 90% expect increased business self-service","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2671","stat":"Case study: FS legal AI assistant rolled to 1,200 professionals, 75%+ adoption in first weeks, 5-8% productivity gain","source":"Deloitte / RSGI, The AI Imperative","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"AmLaw30 firm separately rolled AI literacy to 1,000+ lawyers, 85%+ adoption within 12 months","url":"https://www.deloitte.com/content/dam/assets-zone2/uk/en/docs/services/legal/2026/deloittes-chief-legal-officers-report.pdf","access":"open"},{"id":"s2672","stat":"Frozen open model: 0% to 5.0% whole-task and 63.4% to 80.1% criterion pass rate on held-out LAB test, harness changes only","source":"Niklaus, Don\'t Train the Model Evolve the Harness (HF)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"80.1% puts the open model between Sonnet 4.6 and Opus 4.6 on LAB\'s headline metric; no weights changed"},{"id":"s2673","stat":"Automated harness search lifted pooled criterion rate +20.2 points (63.1% to 83.3%) on 24-task dev set","source":"Niklaus, Don\'t Train the Model Evolve the Harness (HF)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"vs vanilla baseline; scaffold search hit a local ceiling ~83% pooled"},{"id":"s2674","stat":"5 of the top 6 harnesses were deterministic code mechanisms, not prompt edits","source":"Niklaus, Don\'t Train the Model Evolve the Harness (HF)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Biggest single jump: deterministic deliverable-landing gate (dev 72.8% to 79.2%) at zero extra model tokens"},{"id":"s2675","stat":"Wrapper spread of 3.5% to 80.1% pooled across five harnesses on the same model, judge and tasks","source":"Niklaus, Don\'t Train the Model Evolve the Harness (HF)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Quantifies the \\"mismanaged geniuses\\" effect; general frameworks Pi 45.4% / Goose 23.2% / mini-swe-agent 3.5% all below vanilla LAB 63.4%"},{"id":"s2676","stat":"Harness transfer: same-family model +14.4 points from one harness vs +0.4 for a different family","source":"Niklaus, Don\'t Train the Model Evolve the Harness (HF)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Robustness/code fixes transfer across families; prompt playbooks are model-specific and can backfire"},{"id":"s2677","stat":"Under 10% of tasks even frontier models complete end-to-e
1nd on Harvey\'s Legal Agent Benchmark","source":"Niklaus (citing Harvey) / LAB","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"LAB scale: 1,251 public tasks; hardest case 1.5M tokens, 11 deliverables, 194 rubric criteria, 55 documents"},{"id":"s2678","stat":"BM25 beats dense retrievers on Chinese legal case retrieval: avg Recall 58.69 vs bge-m3 50.04, SAILER 46.21, ReaKase 57.07","source":"Tao et al., When Rules Learn (arXiv 2606.17220)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"LeCaRD-v2, 800 queries / 55,192 docs. Verified from PDF 6 Jul 2026; motivates enhancing BM25 rather than replacing it","url":"https://arxiv.org/abs/2606.17220","access":"open"},{"id":"s2679","stat":"LLM agent that evolves BM25 query-rewrite rules (no training) beats human rules and greedy selection - but only with a >20B core model; under 20B it loses to greedy by 0.05-0.43% Recall@50","source":"Tao et al., When Rules Learn (arXiv 2606.17220)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Self-evolution gains conditional on core-LLM capacity; verified from PDF, gains modest in absolute Recall","url":"https://arxiv.org/abs/2606.17220","access":"open"},{"id":"s2680","stat":"Law multi-agent deliberation matches, does not beat, a single-model baseline on F1 (high 80s to ~90 across 3-Ply/MAD/Parrots); ~7.5-10% of answers differ from baseline","source":"Steging et al., Multi-Agent Deliberation in Law (arXiv 2606.30906)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"1,250 questions, 5 legal datasets (incl. SARA, PRIVACY). Verified from PDF 6 Jul 2026","url":"https://arxiv.org/abs/2606.30906","access":"open"},{"id":"s2681","stat":"Of multi-agent/baseline disagreements, correct-resolution rate 47.0% (3-Ply) / 51.9% (MAD) / 42.6% (Parrots); multi-agent architectures reduce sycophancy","source":"Steging et al., Multi-Agent Deliberation in Law (arXiv 2606.30906)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"legal","context":"Value is complementarity and reduced sycophancy, not aggregate accuracy. Verified from PDF","url":"https://arxiv.org/abs/2606.30906","access":"open"},{"id":"s2682","stat":"AURORA hallucination detector (gradient-dynamics features) avg Balanced Accuracy ~0.78-0.86 across Qwen3-8B / LLaMA3-8B / Ministral-8B / Gemma3-12B; beats HARP/ICR widest under cross-dataset transfer","source":"Zhang et al., AURORA (arXiv 2606.29545)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"HaluEval/HotpotQA/TriviaQA/SQuAD;
1 white-box (needs training-graph access). Metric is B-ACC not AUROC - verified from PDF 6 Jul 2026, corrects a bad first-pass extraction","url":"https://arxiv.org/abs/2606.29545","access":"open"},{"id":"s2683","stat":"Worker access to sanctioned AI tools rose ~50% in a year (under 40% to ~60%)","source":"Deloitte State of AI in the Enterprise 2026","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"N=3,235 director-to-C-suite, 24 countries, 6 industries, fielded Aug-Sep 2025. Cross-industry baseline","url":"https://www.deloitte.com/global/en/issues/generative-ai/state-of-ai-in-enterprise.html","access":"open"},{"id":"s2684","stat":"Only 25% of organisations have moved 40%+ of AI experiments into production; fewer than 60% of those with access use AI daily","source":"Deloitte State of AI in the Enterprise 2026","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"The \\"proof-of-concept trap\\"; 54% expect to reach 40%+ in production within 3-6 months","url":"https://www.deloitte.com/global/en/issues/generative-ai/state-of-ai-in-enterprise.html","access":"open"},{"id":"s2685","stat":"AI transformation depth: 34% deeply transforming, 30% redesigning processes, 37% surface-level","source":"Deloitte State of AI in the Enterprise 2026","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Transformative impact reported by 25%, double the 12% a year earlier","url":"https://www.deloitte.com/global/en/issues/generative-ai/state-of-ai-in-enterprise.html","access":"open"},{"id":"s2686","stat":"84% increasing AI investment; 74% hope to grow revenue via AI but only 20% already are","source":"Deloitte State of AI in the Enterprise 2026","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Revenue growth still aspirational; 78% report greater confidence in the technology","url":"https://www.deloitte.com/global/en/issues/generative-ai/state-of-ai-in-enterprise.html","access":"open"},{"id":"s2687","stat":"84% of organisations have NOT redesigned jobs around AI; 82% expect 10%+ of jobs fully automated within 3 years","source":"Deloitte State of AI in the Enterprise 2026","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Identical 84% no-role-redesign figure to Deloitte\'s legal CLO survey; talent response mostly education (53%) not role redesign (33%)","url":"https://www.deloitte.com/global/en/issues/generative-ai/state-of-ai-in-enterprise.html","access":"open"},{"id":"s2688","stat":"Top enterprise AI risks: data privacy/security 73%, legal/IP/regulatory compliance 50%, governance & oversight 46%, model quality/explainability 46%","source":"Deloitte State of AI in the Enterprise 2026","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"N=3,199. Legal/compliance is the #2 concern - direct advisory hook for law firms","url":"https://www.deloitte.com/global/en/issues/generative-ai/state-of-ai-in-enterprise.html","access":"open"},{"id":"s2689","stat":"74% plan at least moderate agentic AI use within 2 years (vs 23% today), but only 21% have a mature governance model for autonomous agents","source":"Deloitte State of AI in the Enterprise 2026","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Agentic scaling running ahead of governance; 85% expect to customise agents","url":"https://www.deloitte.com/global/en/issues/generative-ai/state-of-ai-in-enterprise.html","access":"open"},{"id":"s2690","stat":"77% factor an AI solution\'s country of origin into vendor selection; 83% rate sovereign AI at least moderately important","source":"Deloitte State of AI in the Enterprise 2026","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"cross-sector","context":"Foreign-source reliance EMEA 32% vs Americas 11%; sovereign AI now a boardroom/data-residency issue","url":"https://www.deloitte.com/global/en/issues/generative-ai/state-of-ai-in-enterprise.html","access":"open"},{"id":"s2691","stat":"Physical AI: 58% use today, 80% expect to within 2 years","source":"Deloitte State of AI in the Enterprise 2026","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Only 18% of physical-AI users at moderate+ extent; AP ahead of EMEA and Americas","url":"https://www.deloitte.com/global/en/issues/generative-ai/state-of-ai-in-enterprise.html","access":"open"},{"id":"s2692","stat":"Only 42% highly prepared on AI strategy; 30% highly prepared on risk & governance (+6pp YoY)","source":"Deloitte State of AI in the Enterprise 2026","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Preparedness barely moving; talent lowest at 20%","url":"https://www.deloitte.com/global/en/issues/generative-ai/state-of-ai-in-enterprise.html","access":"open"},{"id":"s2693","stat":"80% of CEOs say their role is at risk if AI fails to deliver measurable gains by end 2026;
1 77% expect a CEO ousted in 2026 over AI","source":"Dataiku / Harris Poll, AI Confessions CEO Edition 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"cross-sector","context":"N=900 CEOs at $500m+ firms, 8 countries. Vendor-sponsored (Dataiku sells AI governance). Accountability now personal"},{"id":"s2694","stat":"\\"Extremely confident\\" in deploying AI agents in production at scale fell 41% to 31% (2025 to 2026); 56% concede competitors have superior AI strategies","source":"Dataiku / Harris Poll, AI Confessions CEO Edition 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"cross-sector","context":"Confidence recalibrating downward as expectations rise"},{"id":"s2695","stat":"AI influences ~40 executive decisions/year on average, but 51% keep humans in the loop on business-critical calls; only 4% never ask AI to justify a recommendation","source":"Dataiku / Harris Poll, AI Confessions CEO Edition 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Belief in AI over humans softening: board-counsel 94% to 83%, strategy 89% to 76%"},{"id":"s2696","stat":"65% of CEOs worry more about over-investing in AI (vs 35% under-investing); 76% feel overly exposed to too few AI vendors","source":"Dataiku / Harris Poll, AI Confessions CEO Edition 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"cross-sector","context":"Investment fear has flipped; vendor-concentration/lock-in risk. Revenue as success measure up 16% to 28%"},{"id":"s2697","stat":"70% of CEOs call themselves the primary driver of AI strategy, but only 60% take part in more than half of AI decisions and 6% in nearly all","source":"Dataiku / Harris Poll, AI Confessions CEO Edition 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"cross-sector","context":"Ownership claimed not exercised; 94% call low/no-code critical to scaling"},{"id":"s2698","stat":"96% of CEOs believe employees use generative AI without approval (shadow AI); 42% estimate more than half the workforce does","source":"Dataiku / Harris Poll, AI Confessions CEO Edition 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Validates acceptable-use-policy and shadow-AI advisory work"},{"id":"s2699","stat":"CEOs rank governance (39%) above people (34%) and orchestration (28%) as what matters most for AI success","source":"Dataiku / Harris Poll, AI Confessions CEO Edition 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"cross-sector","context":"Governance is now the binding constraint, not capability"},{"id":"s2700","stat":"51% of CEOs have delayed AI initiatives due to regulatory uncertainty (up from 37%); 80% say emerging regulation incl. the EU AI Act will slow adoption","source":"Dataiku / Harris Poll, AI Confessions CEO Edition 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"cross-sector","context":"79% concerned AI agents could expose them to legal risk (46% very/extremely)"},{"id":"s2701","stat":"UK CEOs most likely globally to fear over-investing (77%); 51% have delayed AI on regulatory uncertainty (up from 26% in 2025); 81% rank AI a top/high priority (highest globally)","source":"Dataiku / Harris Poll, AI Confessions CEO Edition 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"cross-sector","context":"UK cut, n=100. Governance/defensibility pitch lands harder than efficiency here"},{"id":"s2702","stat":"77% of the hardest challenges in enterprise AI deployment were \\"invisible costs\\" (change management, data quality, process redesign), not technical","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Pereira, Graylin, Brynjolfsson. 51 successful deployments, 41 orgs, 9 industries, 7 countries. Tech consistently described as the easiest part. Change mgmt 33%, data 17%, process redesign 10%","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2703","stat":"61% of successful enterprise AI projects had a prior failed AI attempt;
1 two thirds of companies had significant failures first","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Prior-failure cost never appears in the \\"successful\\" project\'s ROI. Failures shared a pattern: AI treated as a tech project, applied to a broken workflow","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2704","stat":"For 42% of enterprise implementations the foundation model was fully interchangeable (39% moderate importance, 19% critical differentiator)","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Routine tasks: 71% treat model as interchangeable vs 18% for advanced tasks. \\"The durable advantage is in the orchestration layer, not the foundation model\\"","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2705","stat":"Escalation operating model (AI 80%+ autonomous, humans review exceptions) delivered 71% median productivity gain vs 30% for approval-every-output models","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Caveat: task selection partly drives it - escalation applied to high-volume recoverable tasks. Approval/collaboration serve regulated/high-stakes work","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"}
1,{"id":"s2706","stat":"Staff functions (Legal, HR, Risk, Compliance) were the single most frequent source of AI-project resistance at 35%","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"End users 23%, external 23%, C-level 15%, none 8%. Overturns the \\"frontline resistance\\" assumption; control functions have authority to stop projects","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2707","stat":"Fear-of-replacement among frontline workers appeared in only 2 of 51 enterprise AI cases; middle management was the most resistant layer","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Senior and junior staff more accepting. IT functions are enablers, not blockers (the staff-function exception)","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2708","stat":"Agentic AI = 20% of enterprise cases but the highest median productivity gain: 71% vs 40% high-automation, 22% human-in-loop","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Successful agentic traits: high-volume repetitive tasks, clear success criteria, recoverable errors, cross-system data access. Expected to be the majority within ~3 years","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2709","stat":"Headcount reduction was the largest single outcome of AI deployment at 45%, but not the majority (hiring avoided 19%, no reduction 19%, redeployment 17% = 55%)","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Revenue-gen apps â redeployment; cost-reduction apps â cuts. Driven by strategic context (PE/turnaround â cut), not the technology. 45% may be a floor not ceiling","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2710","stat":"Only 6% of enterprise implementations had AI-ready data, yet 88% used LLMs to unlock previously inaccessible data; 91% successfully processed unstructured data","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"59% had scattered data, only 16% centralised - access (RAG/APIs/MCP) mattered more than centralisation. LLMs fixed the data rather than needing it clean. \\"Save everything\\"","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2711","stat":"75% of enterprise AI implementations cited proprietary data as a key strategic factor; 47% described accumulated data as a competitive moat","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The durable advantage as open-source closes the model gap: what data you feed it, not which model you use","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2712","stat":"Security was never a pure project killer in any of the 51 enterprise AI cases; requirements that initially blocked eventually enabled sensitive-data use cases","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"cross-sector","context":"The security \\"tax\\" is real but front-loaded. Risk-averse culture (e.g. bank consent orders) was the hardest barrier, harder than the technical PII-scrubbing work","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2713","stat":"Shadow AI mentioned in 15% of enterprise AI cases; 70-80% of AI-using employees rely on tools not approved by their employer","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"IBM: 80% use AI, only 22% company tools; 57% of unauthorised users entered sensitive data; AI-associated breaches avg >$4m per incident","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2714","stat":"Executive sponsorship: 58% Active Steering, 29% Strategic Integration (AI in OKRs, incentives tied to adoption); all 7 org-wide transformations reached Strategic Integration","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"In every trackable case the same exec led the failed and successful attempt; no one in any of the 51 cases was punished for a failed AI initiative. 73% started small deliberately","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"}
1,{"id":"s2715","stat":"The same use case (e.g. customer support redesign) took weeks at one company and multiple years at another on the same AI models","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"\\"The difference was never the AI model. It was always the organization.\\" Variance driven by exec sponsorship, existing foundations, end-user willingness","url":"https://digitaleconomy.stanford.edu/publication/enterprise-ai-playbook","access":"open"},{"id":"s2716","stat":"On OpenRouter (16 Feb 2026), 4 of the top 5 models by token volume are Chinese open-source (MiniMax M2.5, Kimi K2.5, GLM 5, DeepSeek V3.2)","source":"Stanford Digital Economy Lab, The Enterprise AI Playbook (citing OpenRouter)","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Driven by agentic workloads consuming exponentially more tokens; US enterprises still lean on OpenAI/Anthropic/Llama/Gemini for compliance/support. Claude Opus 4.6 + Sonnet 4.5 in top 8"},{"id":"s2717","stat":"Computer & Math occupations score 94% theoretical AI feasibility but only 33% observed coverage; 30% of workers have zero observed AI exposure","source":"Anthropic (Massenkoff & McCrory), Labor Market Impacts of AI","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"\\"Observed Exposure\\" measure = O*NET tasks + Eloundou et al. (2023) capability + real Claude usage. \\"AI is far from reaching its theoretical capability\\"","url":"https://www.anthropic.com/research/labor-market-impacts","access":"open"},{"id":"s2718","stat":"No systematic increase in unemployment for highly AI-exposed workers since late 2022; post-ChatGPT gap change small and insignificant","source":"Anthropic (Massenkoff & McCrory), Labor Market Impacts of AI","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Diff-in-diff, CPS 2016-present, top exposure quartile vs zero-exposure. Cuts against 2026 doomer narratives","url":"https://www.anthropic.com/research/labor-market-impacts","access":"open"},{"id":"s2719","stat":"14% drop in the job-finding rate for workers aged 22-25 in AI-exposed occupations vs 2022; no effect for over-25s","source":"Anthropic (Massenkoff & McCrory), Labor Market Impacts of AI","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"A hiring slowdown, NOT job losses. Distinct from Brynjolfsson\'s separate Canaries/ADP employment-decline figure (routinely conflated). Corrected 18 Aug 2026: that figure is now 19% on a kept-pace basis, not 16%. The 13% and 16% versions were firm fixed-effects estimates the authors retired in the August 2026 revision","url":"https://www.anthropic.com/research/labor-market-impacts","access":"open"},{"id":"s2720","stat":"AI exposure skews educated and high-earning: high-exposure workers earn 47% more and are 4x more likely to hold a graduate degree (17.4% vs 4.5%)","source":"Anthropic (Massenkoff & McCrory), Labor Market Impacts of AI","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Also 16pp more likely female. The exposed are experienced high-earners, not low-skilled - relevant to lawyers\' position on the exposure curve","url":"https://www.anthropic.com/research/labor-market-impacts","access":"open"},{"id":"s2721","stat":"Every 10-point rise in observed AI coverage associates with BLS employment-growth projections 0.6pp lower; theoretical exposure alone shows no such correlation","source":"Anthropic (Massenkoff & McCrory), Labor Market Impacts of AI","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Observed exposure predicts, theoretical capability alone does not - the case for measuring real usage not potential","url":"https://www.anthropic.com/research/labor-market-impacts","access":"open"},{"id":"s2722","stat":"Santander reports â¬35m AI-attributed value in Q1, targeting â¬200m for the year and â¬1bn+ across 202
16-2028; 280+ process-automation agents in production (credit, fraud, KYC, ops)","source":"Unhyped AI (Winter-Tear)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"via Santander disclosures. Company-reported. Brazil card-fraud claims workflow \\"95% faster, up to 90% automated, <1% error\\". FS serious-adoption exemplar","url":"https://unhypedai.substack.com/p/banks-are-showing-us-what-serious","access":"open"},{"id":"s2723","stat":"Lloyds Banking Group reports ~£50m AI value in 2025, expecting £100m+ in 2026; 50+ AI use cases live; hiring hundreds of tech specialists for agentic AI","source":"Unhyped AI (Winter-Tear)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"commentary","scope":"cross-sector","context":"via Lloyds disclosures. Company-reported. Frontline support, query resolution, fraud, customer operations","url":"https://unhypedai.substack.com/p/banks-are-showing-us-what-serious","access":"open"},{"id":"s2724","stat":"Lumen Sovereign (UK-trained frontier model for critical sectors) backed by a coalition of Lloyds, HSBC, NatWest, LSEG, BT, BAE Systems, Babcock, Leonardo, Thales, PwC, Telefónica Tech UK&I","source":"Unhyped AI (Winter-Tear)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"regulatory","sourceType":"commentary","scope":"cross-sector","context":"Air-gapped / sovereign / reduced-foreign-dependency deployment. Concrete UK sovereign-AI datapoint; pairs with Deloitte\'s 77%-weigh-country-of-origin","url":"https://unhypedai.substack.com/p/banks-are-showing-us-what-serious","access":"open"},{"id":"s2725","stat":"Legora ROI: 30 in-house customers interviewed across 9 countries; 60% at $10bn+ revenue firms, 57% at 10,000+ employees","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Sample composition. Vendor-commissioned (Legora customers only), authored by Ari Kaplan Advisors","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2726","stat":"97% of in-house Legora users respond to stakeholders faster or much faster; 77% report faster project turnaround; 90% report reduced ramp-up time","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Speed/responsiveness. Vendor-reported","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2727","stat":"77% say company leaders view the legal department more positively because it uses Legora","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Standing/perception of legal function. Vendor-reported","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2728","stat":"71% of in-house Legora users are more or much more confident in the quality of their work","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Work-quality confidence. Vendor-reported","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2729","stat":"66% rated Legora\'s impact on quality/issue-spotting/risk reduction as large (4/5) or transformational (5/5)","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Vendor-reported (Legora ROI Report)","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2730","stat":"57% say Legora caught a problem they would have missed in a supplier agreement; 40% report the same in a customer contract","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Issue-spotting / risk reduction. Vendor-reported","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2731","stat":"47% of in-house teams now engage outside counsel on higher-value matters because they can accomplish more internally with AI","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"In-house/outside-counsel disaggregation. Vendor-reported (Legora ROI Report)","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2732","stat":"70% report improvement in the work of high performers; 53% report notable improvement in the work of low performers","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"AI lifts both ends of the skill distribution. Vendor-reported","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2733","stat":"80% of in-house Legora users accomplish more in the same time; 70% say tasks that took hours now take minutes; 57% say days now take hours","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Capacity gains. Vendor-reported","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2734","stat":"87% say they now spend more time on higher-value tasks; 37% cite document review/analysis at scale as biggest time saving, 30% cite first-draft creation","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Where time savings land. Vendor-reported","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2735","stat":"70% say Legora replaced or reduced their reliance on other tools; 70% report large/transformational impact on building internal AI skills","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Tool consolidation + capability building. Vendor-reported","url":"https://legora.com/roi-in-house","access":"gated"},{"id":"s2736","stat":"87% say removing Legora would be disruptive or very disruptive to day-to-day work","source":"Legora ROI Report (Ari Kaplan Advisors)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Stickiness / \'remove it to measure it\' test. Vendor-reported (Legora ROI Report)","url":"https://legora.com/roi-in-house","access":"gated"}
1,{"id":"s2737","stat":"Axiom surveyed 528 in-house legal leaders across six countries from 13-25 March 2026; 77% worked at companies with more than $1bn revenue","source":"Axiom, 2026 In-House Legal AI Report","date":"2026-03","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"practitioner","scope":"legal","context":"Vendor-sponsored, self-reported survey; directional rather than causal evidence"},{"id":"s2738","stat":"Legal AI maturity: 18% had not started, 29% were exploring, 26% piloting, 17% expanding use cases, 5% struggling to scale and 7% optimising","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Categories as reported; rounding produces 102%","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2739","stat":"82% of surveyed in-house legal teams used AI in some form","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Access is widespread, but maturity is much lower","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2740","stat":"Among 431 AI-using respondents, 7% expected significant budget increases, 74% moderate increases and 19% slight increases; none expected a cut","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"market","sourceType":"practitioner","scope":"legal","context":"Budget direction, not absolute budget bands","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2741","stat":"17% had established legal AI ROI metrics, 51% measured informally, 29% were developing measures and 3% could not measure","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"client","sourceType":"practitioner","scope":"legal","context":"83% lack established metrics","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2742","stat":"59% rated their team\'s legal AI skill intermediate and 10% advanced","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Self-assessed capability","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2743","stat":"52% reported two to five hours of AI-specific training and 26% six to ten hours; 91% received ten hours or less","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"workforce","sourceType":"practitioner","scope":"legal","context":"Training depth remains thin","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2744","stat":"Only 17% of teams said a majority of their lawyers used company-licensed AI tools daily","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Daily use is far below nominal access","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2745","stat":"General-purpose configurations were used by 63% of Claude users, 64% of ChatGPT users, 69% of Copilot users and 73% of Gemini users","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Most deployments are not configured for legal-specific work","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2746","stat":"Corporate IT led legal AI tool selection at 70% of teams, compared with 29% for Legal Operations","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"client","sourceType":"practitioner","scope":"legal","context":"Spending authority can sit apart from workflow ownership","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2747","stat":"Only 11% conducted rigorous evaluation before committing to a legal AI tool","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"adoption","sourceType":"practitioner","scope":"legal","context":"Procurement discipline trails experimentation","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2748","stat":"Legal research accounted for 57% of cited legal AI ROI, contract drafting and review 19%, and summarisation 16%","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"client","sourceType":"practitioner","scope":"legal","context":"Perceived ROI is concentrated in document-heavy work","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2749","stat":"ALSPs were preferred over law firms for AI-enabled legal work by 52% to 24%","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"client","sourceType":"practitioner","scope":"legal","context":"Vendor-sponsored buyer-preference finding","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2750","stat":"96% of current legal AI users said they would use an ALSP for adoption or implementation support","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"client","sourceType":"practitioner","scope":"legal","context":"Commercially aligned with Axiom\'s own service offering","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2751","stat":"34% actively negotiated AI-related outside-counsel reductions and another 58% expected reductions but did not always obtain them","source":"Axiom, 2026 In-House Legal AI Report","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"client","sourceType":"practitioner","scope":"legal","context":"Buyer expectation that AI should affect price","url":"https://www.axiomlaw.com/resources/articles/legal-ai-survey-report","access":"open"},{"id":"s2752","stat":"Lenny\'s 2026 tech-worker survey drew 5,920 respondents, including 5,332 currently working; 46.9% were in product roles","source":"Lenny Rachitsky, State of Tech Workers 2026","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"commentary","scope":"cross-sector","context":"Large convenience sample with a product-heavy audience","url":"https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026","access":"gated"},{"id":"s2753","stat":"Tech-worker burnout rose from 44.7% in 2025 to 55.7% in 2026, while optimism fell from 54.8% to 48.7%","source":"Lenny Rachitsky, State of Tech Workers 2026","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"commentary","scope":"cross-sector","context":"Year-on-year survey comparison","url":"https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026","access":"gated"},{"id":"s2754","stat":"On AI and professional identity, 49% felt amplified, 27.4% redefined, 13.9% destabilised, 5% diminished and 3.2% unchanged","source":"Lenny Rachitsky, State of Tech Workers 2026","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"commentary","scope":"cross-sector","context":"AI can increase capability while destabilising role identity","url":"https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026","access":"gated"},{"id":"s2755","stat":"82% said AI made them better at their job, but 51% reported more work for the same pay, 46% called the pace unsustainable and 41% saw quality decline","source":"Lenny Rachitsky, State of Tech Workers 2026","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"commentary","scope":"cross-sector","context":"Productivity and wellbeing move in o
1pposite directions for many respondents","url":"https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026","access":"gated"},{"id":"s2756","stat":"Anthropic classified 33.4% of 1.2m anonymised Claude Cowork sessions sampled 11-31 May 2026 as business process and operations work","source":"Anthropic, How people are using Claude Cowork","date":"2026-06","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Includes reports, onboarding checklists and spreadsheet reconciliation across functions"},{"id":"s2757","stat":"Legal Benchmarks\' July 2026 suite comprised 63 tasks: 34 drafting and 29 extraction tasks","source":"Legal Benchmarks leaderboard","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Broader task mix than single-task legal benchmarks","url":"https://www.legalbenchmarks.ai/leaderboard","access":"open"},{"id":"s2758","stat":"Drafting reliability scores included Claude Opus 4.8 at 67.6, Fable 5 at 61.8, Grok 4.5 at 58.8, Gemini 3.5 Flash at 55.9, Claude Sonnet 4.6 at 50.0 and GPT-5.6 Sol at 44.1","source":"Legal Benchmarks leaderboard","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"System-level scores reflect model plus harness, prompting and workflow","url":"https://www.legalbenchmarks.ai/leaderboard","access":"open"},{"id":"s2759","stat":"More than 40% of Headline\'s AI Europe 100 were \'System Builders\'; the presentation cited Harvey and Legora as examples","source":"Headline, AI Europe 100","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Investor taxonomy, not a comprehensive market census","url":"https://headline.com/blog-latest/article-latest/ai-europe-100-2026","access":"open"},{"id":"s2760","stat":"Headline estimated system-builder gross margins at about 70% today and 80% at scale, versus 30-50% today and 70-85% at scale for outcome businesses","source":"Headline, AI Europe 100","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Investor estimates; outcome economics are more variable","url":"https://headline.com/blog-latest/article-latest/ai-europe-100-2026","access":"open"},{"id":"s2761","stat":"In a Thomson Reuters/Anthropic legal workflow example, reported root-cause analysis time fell from three hours to four minutes","source":"Thomson Reuters and Anthropic","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"legal","context":"Promotional vendor case study; do not generalise without independent validation"},{"id":"s2762","stat":"86% of AI-adopting transactional lawyers use AI on contracts at least weekly; ~49% every day; 41% multiple times a day","source":"Law Insider / SimpleDocs","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"n=534, 75 countries. Vendor-produced study of AI adopters only (power-user + SimpleDocs vendor bias); benchmark of how adopters use AI, not adoption across the profession","url":"https://lawinsider.com/studies/legal-ai-study-2026","access":"open"},{"id":"s2763","stat":"No AI tool clears one in five users saying they are \\"very confident\\" in its output; range 4% (Microsoft Copilot) to 18% (SimpleAI)","source":"Law Insider / SimpleDocs","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"n=534. The usage-vs-trust gap; attribute to vendor. \\"Very confident\\" by tool: SimpleAI 18, Spellbook 14, Gemini 9, ChatGPT 9, Claude 8, Copilot 4","url":"https://lawinsider.com/studies/legal-ai-study-2026","access":"open"},{"id":"s2764","stat":"Verification & sources is the single most-requested AI feature among transactional lawyers, at 20% of coded open-ended responses (n=344)","source":"Law Insider / SimpleDocs","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Most-wanted capabilities: verification & sources 20%, Word/workflow integration 19%, playbooks & precedent 10%;
1 lawyers name grounding as the missing piece","url":"https://lawinsider.com/studies/legal-ai-study-2026","access":"open"},{"id":"s2765","stat":"AI use concentrates in document-centric tasks (drafting 72.8%, reviewing 3rd-party contracts 54.9%) and falls away on institutional-knowledge tasks (internal precedent 22.8%, playbooks & fallbacks 15.7%)","source":"Law Insider / SimpleDocs","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The \\"knowledge layer is the next frontier\\" finding; the institutional-knowledge gap general tools do not touch","url":"https://lawinsider.com/studies/legal-ai-study-2026","access":"open"},{"id":"s2766","stat":"Legal AI users far likelier to be \\"very disappointed\\" to lose their tool than general-LLM users: 64% vs 41%; \\"very confident\\" 18% vs 7%","source":"Law Insider / SimpleDocs","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Legal AI n=107, general LLMs n=379. Vendor\'s core thesis (purpose-built converts use into reliance); inseparable from home-crowd sample - attribute and discount","url":"https://lawinsider.com/studies/legal-ai-study-2026","access":"open"},{"id":"s2767","stat":"Reliance tracks Word integration: 86% of Spellbook users and 72% of SimpleAI users prefer to work inside Word, vs 29% ChatGPT and 24% Claude","source":"Law Insider / SimpleDocs","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Overall environment split: chat 49%, inside Word 42%, elsewhere 9%. Where a tool lives in Word, its users rely on it more","url":"https://lawinsider.com/studies/legal-ai-study-2026","access":"open"},{"id":"s2768","stat":"Most-used contract AI tools in the Law Insider community: ChatGPT 27%, Microsoft Copilot 25%, Claude 17%, SimpleAI 11%, Gemini 9%, Spellbook 3%","source":"Law Insider / SimpleDocs","date":"2026-07","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"n=534. NOT market share; Law Insider community skews SME/solo (70%+ teams â¤5). Harvey, CoCounsel/TR, Lexis excluded below the 10-response threshold","url":"https://lawinsider.com/studies/legal-ai-study-2026","access":"open"},{"id":"s2769","stat":"96% of firms are now using AI in some capacity across support services, but only 27% are redesigning the workflows, delegation models, roles and operational structures needed to support it","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"The report\'s headline gap: near-universal AI adoption in support functions against a quarter making structural change. Sample: 800+ law firm leaders, HR, operations and support services managers across UK and North America","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2770","stat":"95% of firms report increased client attrition over the past 12 months","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Framed as evidence that support-function service delivery is a retention risk, not just an operational one","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2771","stat":"33% cite service delivery concerns as a primary driver of client attrition - the top-ranked driver","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Ahead of pricing sensitivity and panel reduction; the report\'s core argument for treating support as a revenue lever","url":"https://www.bighand.c
1om/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2772","stat":"Client attrition drivers: service delivery concerns 33%, pricing sensitivity / cost pressures 28%, clients reducing panel firms 28%, shift to alternative legal service providers 28%, reduced client legal budgets 25%, lack of sector or specialist expertise 24%, clients bringing work in-house 24%, conflicts of interest 22%, loss of relationship partner 20%","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Full ranked driver list","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2773","stat":"42% report that clients are actively requesting alternative fee arrangements linked to AI-driven efficiencies","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Clients expecting AI savings to show up in pricing, not just service","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2774","stat":"51% of firms expect to lose between 21% and 50% of support staff to retirement within the next five years - up 10 points from 2025","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"The \\"ticking time bomb\\" from BigHand\'s 2025 Time for Change report, worsening year on year","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2775","stat":"48% of firms expect to lose between 21% and 40% of support staff to voluntary attrition","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Natural attrition compounding the retirement wave","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2776","stat":"29% cite a lack of standardised processes for knowledge transfer as a major challenge","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Retirement removes expertise permanently where capture infrastru
1cture is absent","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2777","stat":"36% report support positions remaining unfilled, increasing workload for remaining staff","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Cascade effect: slower service to lawyers, less client time","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2778","stat":"Impacts of support staff turnover: workload strain on support staff 26%, disruption to client relationships and matters 24%, workload strain on associates 24%, revenue loss through loss of billable hours 24%, loss of institutional knowledge and experience 21%","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Turnover reframed as a commercial rather than HR problem","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2779","stat":"35% of firms report increased reliance on lawyers for administrative tasks due to support staff retirement","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"The \\"lawyer time leakage\\" mechanism at the centre of the report","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2780","stat":"46% say additional supervision is needed when working with AI-generated outputs","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"AI creating new oversight load rather than removing it","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2781","stat":"45% cite increased time spent checking and verifying AI outputs","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Time saved in one place lost in another; the report\'s verification-tax finding","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2782","stat":"Only 41% have completed a formal review of support task types to identify where automation or AI can deliver the greatest impact","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Majority layering AI onto unexamined workflows","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2783","stat":"62% of firms have \\"workflow technology\\" in place, but only 21% use a structured workflow system as the primary method of lawyer-to-support task delegation","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Investment-vs-use gap. Note: the report\'s own delegation chart shows 26% for structured workflow systems against 21% in the narrative, an internal inconsistency; cite the 21% with care","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2784","stat":"67% of firms still rely on direct email, either in part or as their primary method of task delegation","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Email as the de facto workflow layer; the report\'s central operational finding","url":"https://www.bighand.c
1om/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2785","stat":"Primary delegation methods: hybrid (system plus significant direct email) 41%, structured workflow / request system 26%, primarily direct email 26%, primarily direct contact (in-person / phone) 11%, ticketing system (IT-style) 1%, no standardised method 1%","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Full delegation-channel breakdown","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2786","stat":"Only 64% of firms track support staff productivity using technology; 57% use manual tracking; 29% rely on informal manager-led assessment only","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Multi-select; the visibility gap underpinning poor resourcing decisions","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2787","stat":"Only 23% of firms are developing reporting to explain how AI efficiencies are being applied to client work","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Directly relevant as value-based and fixed-fee arrangements spread","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2788","stat":"94% of firms have restructured support functions in some capacity in the last 12 months, but only 29% have made support team changes aligned to new skill sets","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Restructuring outpacing capability analysis","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2789","stat":"Reasons for support team restructuring: need for different skill sets 29%, AI adoption requiring role redesign 27%, desire to improve lawyer productivity and delegation 27%, reallocation of investment toward technology 27%, retirement or departure of existing support staff 25%, efficiency improvements 22%, cost reduction 19%","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Cost reduction is the lowest-ranked driver","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2790","stat":"24% of firms say centralisation has increased lawyer admin hours and 17% say it has reduced work delegation","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Centralisation backfiring where delegation tooling and trust are missing","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2791","stat":"Investment in retraining and upskilling support staff has risen from 23% to 29% since last year","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Direction of travel correct, pace judged insufficient","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2792","stat":"Introduction of new junior administrative roles has fallen from 63% in 2022 to 28% in 2026","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Five-year decline in the junior support pipeline, partly attributed to automation of lower-value tasks","url":"https://www.bighand.c
1om/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2793","stat":"Measures introduced for support teams: introduced AI support agents 35%, increased after-hours support 31%, increased secretary-to-lawyer ratios 31%, retraining / upskilling support staff 29%, introduced junior administrative roles 28%, introduced more teams 27%","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"AI agents now the most common structural measure in support functions","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2794","stat":"56% of firms have actively hired dedicated AI specialists in support functions, with a further 7% planning to within 12 months","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"A new support-function role category emerging at scale","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2795","stat":"AI-driven time savings on support tasks: data extraction from documents 32%, document creation / amends 32%, email drafting and responses 29%, preparing bills / pre-bills 28%, admin scheduling 26%, task triage / routing 25%, proofreading / QA 25%","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Where AI is actually saving time in legal support, ranked","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2796","stat":"Retention strategies implemented: competitive compensation and benefits adjustments 24%, targeted training for future leadership 23%, flexible work arrangements 23%, increased focus on well-being and work-life balance 22%, mentoring programmes 21%, clear promotion pathways with defined criteria 18%, regular documented career development discussions and mentoring 16%","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"No single strategy above a quarter of firms; the report calls the response piecemeal","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2797","stat":"39% of firms plan to increase support staff salaries by 5-10%, with a further 19% planning increases of less than 5%","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Sub-5% characterised as a standard annual uplift rather than a retention strategy","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2798","stat":"Only 27% of firms are prioritising the efficiency with which lawyers pass work to support teams","source":"BigHand Legal Workflow Leadership Report 2026 (n=800+)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"The lawyer-to-support handoff as an unmanaged process","url":"https://www.bighand.com/en-us/resources/whitepapers/2026-legal-workflow-leadership-report/","access":"open"},{"id":"s2799","stat":"Disabling agent-to-agent (A2A) connections in the Microsoft agent stack takes up to 48 hours to fully propagate","source":"Mikko Koskinen (Forward Forever), Rethinking Governance Models for AI Agents","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"commentary","scope":"cross-sector","context":"Practical governance constraint: an agent kill switch is not immediate. Conference session deck, undated"},{"id":"s2800","stat":"Microsoft Defender real-time agent protection gives a 1-second window to decide block or allow on a forwarded agent action; if no decision arrives in time the agent defaults to Allow or Block per tenant configuration","source":"Mikko Koskinen (Forward Forever), Rethinking Governance Models for AI Agents","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"commentary","scope":"cross-sector","context":"Runtime defence against user and cross-domain prompt injection (UPIA / XPIA); the fail-open/fail-closed choice is a tenant decision"},{"id":"s2801","stat":"Legora law-firm ROI: 31 firms across 14 countries interviewed over 3 months; 54.8% innovation and AI leaders, 25.8% partners;
1 90%+ at firms with 200+ lawyers, 51.6% at 500+; 35.5% UK, 29% EMEA, 22.6% North America, 12.9% APAC","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Sample composition. Vendor-commissioned (Legora customers only), authored by Ari Kaplan. Companion to the in-house ROI report","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2802","stat":"94% of law firm users say Legora lets them respond to clients or stakeholders faster (4) or much faster (5); 65% say tasks that took hours now take minutes","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Speed and responsiveness. Vendor-reported","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2803","stat":"74% say Legora expanded their capacity; 45% say they can support more clients in the same amount of time","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Capacity expansion. Vendor-reported","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2804","stat":"Time savings land hardest on mid-level associates (68%), then senior associates (55%), junior associates (48%) and partners (29%)","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"The seniority distribution of AI benefit inside firms - counters the assumption that juniors gain most","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2805","stat":"Recaptured time is spent on higher-value legal analysis (65%), greater client interaction and responsiveness (52%), and increased billable work on other matters (42%)","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Where saved time actually goes, firm-side","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2806","stat":"68% of firms report Legora has reduced non-billable hours, averaging 4.3 recaptured non-billable hours per lawyer per week","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"The core commercial claim of the report. Vendor-reported","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2807","stat":"Legora puts recaptured non-billable time at $6.9m of potential additional yearly billing per 100 lawyers (formula: lawyers x non-billable hours saved/week x average hourly rate x 46 weeks)","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"**Assumes recaptured time converts to billable work at the full rate** - the same contestable assumption as Harvey\'s 20x ROI example, and undercut by the same report\'s 58% who have won no new work","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2808","stat":"Since adopting Legora, 35% of firms see fewer write-downs, 19% redo less work, and 16% write off less junior time on quality grounds; 52% rate the impact on write-offs, rework and non-billable effort as moderate (3) or large (4)","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Efficiency reaching the firm\'s realisation rate. Vendor-reported","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2809","stat":"39% of firms have taken on work that would previously have required more people, more time, or a refusal; a further 19% say it is possible in some cases","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Capacity convert
1ing into work capture","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2810","stat":"23% of firms now take on work they might otherwise decline; 29% can scope matters more tightly with fewer professionals","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Leverage compression - fewer professionals per matter","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2811","stat":"39% of firms say Legora has made it easier to deliver or price alternative fee arrangements such as fixed-fee or capped-fee work","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"The mechanism is variance reduction not speed: \\"Legora removes a significant number of variables from the equation, which allows us to be more certain about the time it takes to produce work\\". Structural pricing implication","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2812","stat":"77% of firms have cited Legora or AI, implicitly or explicitly, when explaining pricing, value or turnaround time to a client","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"AI has entered the pricing conversation as a justification device","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2813","stat":"71% of firms say Legora identifies issues they would otherwise have missed; 61% are more confident in their work quality when using it","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Issue-spotting and confidence. Vendor-reported","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2814","stat":"42% of firms rate Legora\'s impact on improving quality, issue-spotting or reducing near-misses as large (4) or transformational (5)","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Quality impact rating. Vendor-reported","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2815","stat":"65% of firms report Legora gives a significant performance boost to already high-performing professionals, against only 32% seeing meaningful gains across the team generally","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Evidence for magnification over levelling: \\"generative AI works best for people who already know the answer\\". Mirrors the in-house report\'s 70%/53% high-vs-low performer split","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2816","stat":"55% of firms say Legora replaced or reduced the need for other tools or processes - document review (45%), first-draft generation (23%), issue spotting (16%)","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Tool consolidation pressure on the point-solution market","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2817","stat":"52% of firms say Legora helped their knowledge management by making prior work product and know-how easier to find and reuse","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"KM as a second-order benefit","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2818","stat":"61% of firms say Legora has a large or transformational impact on building internal AI skills, habits and infrastru
1cture, and 52% say that is the single area of biggest impact so far","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Capability building outranks time saved as the reported primary benefit","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2819","stat":"87% of firms say Legora built internal AI skills and knowledge; 87% think clients see the firm more positively for using it; 48% think it sets them apart from competing firms","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Reputational and differentiation claims. Vendor-reported, and self-perception rather than client-verified","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2820","stat":"81% of firms see internal reporting or discussion of AI use, focused on usage patterns (84%), time savings (45%), and cost savings, profitability and client satisfaction (23% each)","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Firms are measuring activity, not outcomes - only 23% track profitability or client satisfaction","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2821","stat":"74% of law firm participants rate their likelihood to recommend Legora at 9 or 10; 68% could give a concrete example of clear ROI delivered to a client","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Advocacy. Vendor-reported, customers only","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2822","stat":"58% of law firms have NOT won new work as a result of using Legora, or have not yet seen those results; 55% have seen no expansion in existing client relationships","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"The report\'s own negative finding, and the counterweight to its $6.9m billing claim. Unusually candid for vendor material","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2823","stat":"29% of firms report no meaningful change in write-offs, write-downs or rework since adopting Legora","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"\\"It is too early to see any of these items.\\" Vendor publishing its own null result","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2824","stat":"Asked how they measure time savings, most participants offered only general responses; one firm\'s usage dashboard reports were \\"not new enough to know exactly how the tools are used\\"","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"A vendor ROI report conceding its customers cannot measure ROI. The strongest available evidence that firm-side AI measurement is still not instrumented","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2825","stat":"If Legora were removed, 29% of firms say it would be very disruptive and 52% disruptive but manageable (81% combined)","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Stickiness, slightly below the in-house report\'s 87% disruptive-or-very-disruptive","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2826","stat":"Worked example: 4,500 trade slips reviewed to isolate the 50 at issue - 75% time saving on what would have been a 130-hour project requiring several lawyers for almost a week","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Participant-reported worked example","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2827","stat":"Worked example: motor vehicle class action data extraction that would have taken 3-5 paralegals a full day completed in 3 hours; 9,000-contract acquisition review compressed from months to days","source":"Legora ROI Report - law firms (Ari Kaplan)","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Participant-reported worked examples","url":"https://legora.com/roi-law-firms","access":"open"},{"id":"s2828","stat":"\\"When every company uses the same models, no one has an advantage\\"","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"The report\'s framing of its own purpose: how actors across the value chain move past AI models and use the tech to build competitive moats. The model-commoditisation thesis stated by a consultancy about a different regulated professional industry","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2829","stat":"Insurance gross written premiums grew ~4.9% annually since 2005, reaching an estimated $8.3tn in 2025, while profits before tax grew only ~4.3% to approximately $580bn","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Two decades of premium growth with eroding operating leverage across P&C, life and health. Rising capital requirements a contributing factor","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2830","stat":"Insurance revenues grew 1.24x over the past decade against 1.58x for global GDP and slower than most major industries","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"The relevance gap: the industry\'s share of economic opportunity has not kept pace with growth in underlying risk","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2831","stat":"Global protection gap for natural catastrophes reached $133bn in 2025; a separate methodology puts the additional premium required to cover uninsured losses at $424bn","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Two estimates on different methodologies
1- cite whichever, but not both as if they measure the same thing","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2832","stat":"Less than 1% of global cyber costs are currently insured, a gap of roughly $900bn","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"The clearest single illustration of underpenetration in a risk class that is growing fast","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2833","stat":"Commissions and acquisition expenses consume between 10 and 25 cents of every premium dollar in P&C, and up to 80 cents of the first-year premium in life","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Distribution is the largest cost category after claims. Cost structure barely changed despite repeated waves of digital investment","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2834","stat":"About 85% of US P&C and 95% of life insurance premiums are distributed through agents, brokers and managing general agents","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Intermediation has survived digitisation almost intact. The report\'s question is whether agentic AI is the first technology to change that","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2835","stat":"Average P&C expense ratio has hovered between 27% and 32% since 2005, with leaders below 22% and laggards at 32%","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Flat productivity over two decades, and a leader-laggard gap that technology has widened rather than closed","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2836","stat":"Insurance cost ratios (SG&A to revenue) are 10% HIGHER globally than in 2005, while telecommunications, automotive and airlines materially reduced theirs over the same period","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Heavy automation and digital investment produced a worse cost ratio, not a better one. The strongest external cautionary analogue available for professional services","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2837","stat":"Technology improved insurance labour productivity by 14% in P&C and 24% in life across claims, servicing and policy issuance - but gains were offset by rising IT costs, compliance overhead and the complexity of layering digital tools onto legacy operating models. \\"Efficiency improved. Average industry costs increased.\\"","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"THE most transferable finding in the report. A two-decade worked example of real per-task efficie
1ncy gains failing to reach the cost base, from an industry structurally similar to legal. Directly applicable to the law firm AI dividend argument","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2838","stat":"AI domain-level transformations are already producing 20-40% reductions in customer onboarding costs and 10-20% improvements in insurance agent productivity","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Company-reported domain results, not whole-firm economics. Note the gap between these and the flat aggregate cost ratios above","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2839","stat":"Multichannel P&C incumbents have cost ratios at least 30% higher than pure direct players, with the gap attributable almost entirely to sales and distribution costs","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"The cost of owning a distribution channel, quantified","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2840","stat":"Nearly half of customers in North America already use AI in their personal insurance-buying journeys","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"cross-sector","context":"The buyer-side shift is already underway in the simplest risk classes. No sample size or source given in the article text","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2841","stat":"Insurance-sector AI leaders have created six times greater total shareholder return than laggards","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Verified in the primary as \\"six times\\". Some trade coverage renders this as \\"6.1 times\\" - do not use that figure","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2842","stat":"Two viable competitive paths for insurers under AI: scale (amortise AI infrastructure across a larger premium base) or deep specialisation (do one thing with distinction, access everything else through partners). \\"What is not defensible is deferral or the middle ground.\\"","source":"McKinsey, How AI will reshape the economics of insurance","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Not insurance-specific. The same fork facing mid-market law firms, and the cleanest external articulation of it available","url":"https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-will-reshape-the-economics-of-insurance-a-ceos-guide-to-strategy","access":"open"},{"id":"s2843","stat":"UK data centres used 1.879 billion litres of water in 2024, approximately 0.037% of England\'s total potable water supply (5.09 trillion litres/year)","source":"Water company metered data via Rowland Manthorpe analysis","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"The load-bearing UK water figure. Metering is incomplete, so treat as a floor. Undercuts \\"data centres will drink the country dry\\""},{"id":"s2844","stat":"Thames Water leaks approximately 590 million litres per day, roughly 215 billion litres a year, about 115x the entire reported annual UK data centre sector consumption","source":"Ofwat / company reporting","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"The single most effective comparator in the UK water argument"},{"id":"s2845","stat":"84% of proposed water-intensive UK data centre developments are in areas already water stressed or projected to be by 2040","source":"Global Action Plan","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Based on the Environment Agency\'s 2021 water stressed area classification. The strongest defensible criticism of UK siting","url":"https://www.globalactionplan.org.uk/insights/publications/not-a-drop-to-drinkhow-britains-data-centre-surge-threatens-water-security","access":"open"},{"id":"s2846","stat":"Thames Water high-growth scenario: data centres could drive nearly 30% of all new water demand in the region, approximately 270 million litres/day","source":"Global Action Plan","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Regional concentration is the real issue, not national totals","url":"https://www.globalactionplan.org.uk/insights/publications/not-a-drop-to-drinkhow-britains-data-centre-surge-threatens-water-security","access":"open"},{"id":"s2847","stat":"A 100MW hyperscale data centre can consume around 2.5 billion litres of water annually, equivalent to the needs of about 80,000 people","source":"NREL, via UK Government/GDSA Water use in AI and Data Centres","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"US-derived evaporative cooling assumption. Note the tension: this single modelled facility exceeds the entire measured UK sector\'s 1.879bn litres","url":"https://assets.publishing.service.gov.uk/media/688cb407dc6688ed50878367/Water_use_in_data_centre_and_AI_report.pdf","access":"open"},{"id":"s2848","stat":"England faces a projected daily water deficit of nearly 5 billion litres per day by 2050, over a third of current public water supply","source":"Environment Agency","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"regulatory","sourceType":"government","scope":"legal","context":"Statutory Water Resources Management Plans closing this deficit do not account for data centre demand at all"},{"id":"s2849","stat":"Median Gemini text prompt: 0.24 Wh energy, 0.03 gCO2e, 0.26 ml water. Energy per prompt fell 33x in twelve months","source":"Google, Measuring the environmental impact of delivering AI at Google Scale (arXiv 2508.15734)","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Water figure is on-site cooling only, excludes power generation water. Not peer reviewed. Google withholds query volumes so it cannot be multiplied up","url":"https://arxiv.org/abs/2508.15734","access":"open"},{"id":"s2850","stat":"The \\"500ml per ChatGPT query\\" claim was never per query: it was 500ml per conversation of 10-50 exchanges on a GPT-3-era model. Shaolei Ren, its author, now estimates ~15ml per prompt total and ~5ml on-site for GPT-4 class models","source":"Ren et al. (2023), subsequently revised","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Retire this claim. Repeating it discredits the wider environmental case"},{"id":"s2851","stat":"Data centres consumed approximately 415 TWh globally in 2024, about 1.5% of world electricity, projected to reach ~945 TWh by 2030","source":"IEA, Energy and AI","date":"Apr 2025","sortKey":20250400,"precision":"month","year":2025,"theme":"technical","sourceType":"government","scope":"cross-sector","context":"Frequently misquoted as AI-only. It is all data centre demand","url":"https://www.iea.org/reports/energy-and-ai","access":"open"},{"id":"s2852","stat":"AI-driven increase in global electricity demand to 2030 is ~223 TWh, against ~697 TWh from air conditioning and ~854 TWh from electric vehicles","source":"IEA, via Hannah Ritchie","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"The most useful single piece of context in the energy argument"},{"id":"s2853","stat":"UK data centre electricity consumption projected to rise from ~5 TWh in 2025 to ~22 TWh by 2030 and ~33 TWh by 2035 (range 20-41 TWh)","source":"NESO, Future Energy Scenarios 2025","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"technical","sourceType":"government","scope":"cross-sector","context":"Roughly 8-10% of UK electricity demand by 2030 on central estimates"},{"id":"s2854","stat":"Ireland\'s data centres reached 22% of national metered electricity consumption by 2024, more than urban households (18%) but less than all households combined (28%)","source":"CSO Ireland","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"The control experiment for permissive connection policy. EirGrid halted new Dublin connections in 2022, effectively to 2028","url":"https://www.cso.ie/en/releasesandpublications/ep/p-dcmec/datacentresmeteredelectricityconsumption2024/","access":"open"},{"id":"s2855","stat":"Northern Scotland curtailed 8.8 TWh of wind generation in 2025, delivering only 61% of what could have been generated. Constraint payments ~£350m, replacement generation over £1bn","source":"Scottish curtailment analysis via Modo Energy / Energy Voice","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Roughly 40% of NESO\'s entire projected 2030 UK data centre demand, thrown away. The case for siting compute in Scotland","url":"https://modoenergy.com/research/en/gb-great-britain-battery-energy-storage-curtailment-wind-constraint-scotland-costs","access":"gated"},{"id":"s2856","stat":"The B6 transmission boundary between Scotland and England has a secure power transfer limit of around 4.5 GW","source":"NESO / ETYS","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"government","scope":"cross-sector","context":"The actual constraint on Scottish data centres, not latency. London-Edinburgh network round trip is only ~10-15ms","url":"https://www.neso.energy/publications/electricity-ten-year-statement-etys","access":"open"},{"id":"s2857","stat":"The Beckton (Thames Gateway) desalination plant cost £270m to build, has run f
1ive times since 2010 delivering 7.2 billion litres (about seven days of London demand), with lifetime cost estimated at ~£518m or about 7p per litre ever produced","source":"Watershed Investigations","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"The cautionary tale for any \\"mandate desalination\\" proposal. 100 Ml/day capacity, 14MW draw","url":"https://watershedinvestigations.com/500m-thames-water-desalination-plant-has-provided-just-seven-days-water-over-15-years/","access":"open"},{"id":"s2858","stat":"Modern seawater reverse osmosis runs at approximately 3-4 kWh per cubic metre with energy recovery","source":"Desalination industry / Danfoss","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Desalinating a 100MW data centre\'s full modelled water demand would cost roughly 1% of its energy. The energy objection to desal-for-datacentres is weaker than usually assumed"},{"id":"s2859","stat":"Microsoft\'s data centre design from August 2024 onwards consumes zero water for cooling via closed loop, projected to save ~125 million litres annually per facility. Piloting Phoenix and Mount Pleasant 2026","source":"Microsoft","date":"Dec 2024","sortKey":20241200,"precision":"month","year":2024,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Closed loop uses roughly 10% more energy than evaporative cooling. Cutting water raises carbon"},{"id":"s2860","stat":"At least 430 local US data centre opposition groups with over 525,000 members across 40+ states, up almost sevenfold since December 2025, with around 60 new groups formed per month","source":"Data Center Opposition Report (Coalition for Responsible Data Center Development / Evitable / Together Against AI)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Measures organising strength, not environmental impact. Social licence is now a delivery risk"},{"id":"s2861","stat":"70% of US respondents oppose construction of a new AI data centre in their neighbourhood","source":"Gallup","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Community support now ranks second in JLL\'s 2026 data centre site selection criteria, behind speed to power","url":"https://news.gallup.com/poll/709772/americans-oppose-data-centers-area.aspx","access":"open"},{"id":"s2862","stat":"Google fleet-wide water consumption reached ~30 billion litres in 2024, up 28% year on year, with withdrawals exceeding 41 billion litres","source":"Google sustainability reporting","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"The gap between the two figures illustrates the consumption vs withdrawal accounting trap"},{"id":"s2863","stat":"Only about two-fifths of data centre operators actively track their water usage metrics","source":"Industry survey via UK Government/GDSA Water use in AI and Data Centres","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"The transparency deficit is the strongest regulatory argument. The EU mandates water disclosure under its Common Union Rating Scheme; the UK does not"},{"id":"s2864","stat":"Technology investment and adoption is the top strategic initiative for law firm COOs at 28.0%, overtaking talent acquisition/retention (23.7%) for the first time in the survey\'s five-year history","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=213 North American law firm COOs and principal administrators; reversal of last year\'s ordering","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2865","stat":"34.7% of law firm COOs name talent capacity as the single biggest constraint on profitable growth; lack of strategic consensus 15.3%, pricing leakage 11.8%","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=144. Firms are buying tools while people, partner alignment and pricing discipline remain the limiting factors","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2866","stat":"66.0% of law firm COOs say their firm is not formally documenting AI efficie
1ncy gains","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Most common answer by a wide margin; 25.2% report increased capacity without added headcount, 15.1% measure lawyer productivity/hours billed","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2867","stat":"39.0% of law firm COOs expect AI to increase revenue through greater lawyer productivity over the next 24 months; only 6.3% expect AI to reduce revenue through fewer billable hours","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=159. 25.2% expect no meaningful impact, 20.1% too early to assess. Sits unreconciled with hourly billing economics","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2868","stat":"63.1% of law firm COOs expect administrative headcount to stay the same over the next 24 months, down from 81.4% in 2025; more than a quarter expect to eliminate positions","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"n=157. Fewer than 10% expect headcount growth; AI\'s expected staffing impact hardened sharply in one year","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2869","stat":"AI adoption and governance is the second biggest operational challenge facing law firms at 17.8%, behind lawyer talent recruitment/retention (30.6%) - a category that did not exist in prior survey years","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=157. Technology implementation and integration third at 15.9%","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2870","stat":"Asked which structural issue they would fix tomorrow, 38.1% of law firm COOs chose practice silos and 27.0% lack of operational authority","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=126. Partner incentives 24.6%, decentralised operations 10.3%. The person responsible for making the firm run better often lacks the power to make changes stick","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2871","stat":"Lack of strategic consensus is the biggest internal obstacle to implementing change at law firms (27.9%), followed by partners as a group (22.9%) and budget limitations (21.4%)","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=140 (approx; response counts vary). Yet 68.9% of COOs say their strategic recommendations are often or almost always implemented","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2872","stat":"69% of law firms use both legal-specific and general-purpose AI tools; 36.9% have deployed one legal-specific AI tool while 18.5% have deployed none","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=155. General-purpose mirror: 36.8% one tool, 18% none. Legal research and document review show highest current use","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2873","stat":"15.5% of law firm COOs came from outside the legal industry, up from 11.4% in 2025","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Firms broadening where they source operational leadership; Calibrate commentary warns capability without authority changes nothing","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2874","stat":"Median law firm COO total compensation is $275,000 (average $359,354); Chief titles median $335,000; larger firms (91+ lawyers) $472,000 vs $151,000 at smaller firms (2-40 lawyers)","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Self-reported, base + most recent cash bonus. Middle 50% range $161,250-$425,000","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2875","stat":"63% of law firm COOs now have a written job description or mandate, up from 55% in 2025; those with one report median compensation of $295,000 vs $228,000 without","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Association not causation (larger firms more formal);
1 role formalisation still lags the strategic burden","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2876","stat":"The median law firm COO manages 6 direct reports and 24.5 indirect reports","source":"Blickstein Group Law Firm COO Survey 2026","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Span covers finance, IT and HR/talent simultaneously (12.8%/14.4%/14.6% of direct-responsibility selections)","url":"https://blicksteingroup.com/law-firm-coo-survey/","access":"gated"},{"id":"s2877","stat":"RCT of 91 upper-level law students (41 control, 50 AI-exposed) testing whether AI use degrades legal reasoning after the AI is removed","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Experiment run Oct 2025, Gemini 2.5 Pro, four sequential tasks in one three-hour sitting; preregistered on OSF before analysis; 273 memos graded blind by a single grader against a preregistered rubric","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2878","stat":"AI access improved synthesis-memo quality by 59.78% (6.63 to 10.60 on a 15-point composite), p<0.001","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Largest quality effect yet recorded in a legal RCT; closed 12-page source packet on an invented doctrine in a fictional jurisdiction","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2879","stat":"The synthesis-task quality gain was 1.20 standard deviations, against roughly 0.33 SD in prior studies in the same research line","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Education research typically treats a quarter to half an SD as meaningful; authors attribute the jump to model progress (Gemini 2.5 Pro vs the GPT-4 / o1-preview generation)","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2880","stat":"AI improved substantive quality of synthesis memos by 65.03%, organisation by 67.49% and polish by 48.10% (all p<0.001)","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Substantive quality alone moved 1.08 SD, so the gain is not a presentation effect","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2881","stat":"The average control participant scored at the 25th percentile on the synthesis task; the average AI-exposed participant scored at the 71st","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Percentile framing of the 1.20 SD effect","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2882","stat":"AI cut synthesis-task time by 12.62% (69.46 to 60.70 minutes), p<0.001","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Self-reported minutes against a 75-minute cap; comparable to the 12-32% range in prior Schwarcz/Choi studies","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2883","stat":"AI improved synthesis-task productivity (quality points per minute) by 92.36%, from 0.10 to 0.19, p<0.001","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Combined quality-and-speed measure","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2884","stat":"No difference in closed-book comprehension after AI-assisted synthesis: 3.86 control vs 3.88 AI-exposed out of 6, p=0.935","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Contradicts the preregistered deskilling hypothesis; item difficulty ran 50-80% correct so no ceiling or floor effect masked a gap","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2885","stat":"Participants who had used AI on an earlier task scored 24.01% higher on a later task where neither group had AI (5.29 to 6.56), p=0.02","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"The opposite of the preregistered hypothesis; effect size 0.49 SD, control mean at the 39th percentile vs 59th for AI-exposed","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2886","stat":"On the un-assisted application task the AI-exposed group scored +27.15% on depth of analysis (p=0.04), +24.75% on organisation (p=0.01) and +20.45% on focus (p=0.10)","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Application scores were low overall (mean 5.99 of 15) with floor clustering; a Tobit robustness check held","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2887","stat":"Controlling for synthesis-task score removes the AI effect on the later application task entirely; the only significant predictor is synthesis performance","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"One SD better on synthesis (3.30 points) is worth 0.76 points on application. AI did not preserve reasoning, it produced a better artefact to reason from","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2888","stat":"AI-exposed participants took 3.5 minutes longer (+7.10%) on the un-assisted application task, p=0.09, not significant, with no productivity advantage","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Directionally consistent with the hypothesis that early AI use slows later un-assisted work, but not statistically significant. Also the authors\' evidence that the control group was not covertly using AI","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2889","stat":"Net time saving across both memo tasks was only 5.26 minutes","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"The synthesis-stage speed gain was largely given back on the un-assisted application task","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2890","stat":"Using AI to revise a memo written without AI improved weak work and degraded strong work: roughly +1.9 points at the bottom of the range and roughly -8 points at the top","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"legal","context":"Piecewise regression, n=91, 20-minute revision window at the end of a three-hour session. Holds for depth, focus and organisation separately and after controls. The -8.08 estimate sits at the far tail of the sample: cite the direction, not the magnitude","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2891","stat":"AI-assisted revision compressed the performance distribution without reversing rankings: strong performers still finished ahead, by less","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"legal","context":"Levelling upward at the bottom and downward at the top; a quality-distribution finding rather than a productivity one","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2892","stat":"No difference between the original AI-exposed and control groups on the revision task","
1source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"The revision decline tracked how good the initial memo was, not prior AI exposure","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2893","stat":"Higher-scoring participants spent MORE time on the AI-assisted revision task and still degraded their memos","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"legal","context":"Rules out the simplest explanation (skimming the AI output); authors point instead to flattening of sophisticated analysis, fatigue and deference","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2894","stat":"Hallucination was effectively absent and equally distributed: 3 control participants (7.3%) and 2 AI-exposed (6.0%) invented a legal standard not in the materials","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Authors: evidence that AI can be reliable on a closed curated document set under supervision, NOT evidence that models have stopped hallucinating","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2895","stat":"24.4% of control participants scored the lowest possible mark on the application task, against 8.0% of the AI-exposed group","source":"Bednar, Cleveland, Erbsen & Schwarcz (Minnesota)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"On depth of analysis the split was 65.9% control vs 42.0% AI-exposed at the floor","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6525800","access":"open"},{"id":"s2896","stat":"Preregistered RCT (n=52 professional developers) found AI users scored 4.15 points lower on a 27-point knowledge quiz taken with no AI available: Cohen\'s d=0.738, p=0.010","source":"Shen & Tamkin (Anthropic), How AI Impacts Skill Formation","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Holds controlling for warm-up task time (d=0.725, p=0.016). Measures skill FORMATION over one hour on one Python library, not skill decay. Prefer this to the paper\'s unreconciled \\"17%\\" framing","url":"https://arxiv.org/abs/2601.20245","access":"open"},{"id":"s2897","stat":"No significant task-completion time difference between AI and no-AI developers (p=0.391)","source":"Shen & Tamkin (Anthropic), How AI Impacts Skill Formation","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Speed null is partly a design artefact: the pilot found a significant AI speed-up (d=1.11) which disappeared after syntax hints were added to unstick the control group","url":"https://arxiv.org/abs/2601.20245","access":"open"},{"id":"s2898","stat":"Every AI-assisted participant completed the second coding task inside 35 minutes; 4 of 26 control participants did not","source":"Shen & Tamkin (Anthropic), How AI Impacts Skill Formation","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"The completion benefit was real even though mean time did not differ","url":"https://arxiv.org/abs/2601.20245","access":"open"},{"id":"s2899","stat":"Developers who generated code with AI then asked follow-up questions to understand it scored 86% on the knowledge quiz; those who generated code and stopped scored 39%","source":"Shen & Tamkin (Anthropic), How AI Impacts Skill Formation","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Post-hoc clustering of screen recordings, cells of 2-7 people, no inferential statistics. Cite the direction, not the percentages. The two clusters were behaviourally near-identical (both pasted generated code) and differed only in whether comprehension
1was attempted","url":"https://arxiv.org/abs/2601.20245","access":"open"},{"id":"s2900","stat":"Iterative AI debugging was the worst-performing usage pattern (24% quiz score) AND the slowest in the study at 31 minutes, against 23 minutes for the no-AI control","source":"Shen & Tamkin (Anthropic), How AI Impacts Skill Formation","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Ratio of debugging queries correlated negatively with quiz score (r=-0.41, p=0.043) and positively with completion time (r=0.43, p=0.033). Raw query count predicted nothing","url":"https://arxiv.org/abs/2601.20245","access":"open"},{"id":"s2901","stat":"Developers working without AI hit a median of 3 errors per participant against 1 for the AI group; of 12 who completed both tasks error-free, only 2 were controls","source":"Shen & Tamkin (Anthropic), How AI Impacts Skill Formation","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"The authors\' mechanism for the learning gap: the control group \\"encountered more errors during the task and became more capable at debugging\\"","url":"https://arxiv.org/abs/2601.20245","access":"open"},{"id":"s2902","stat":"Retyping AI output by hand rather than pasting it produced no measurable learning benefit","source":"Shen & Tamkin (Anthropic), How AI Impacts Skill Formation","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Undercuts effort-equals-learning explanations; the discriminator is comprehension, not typing","url":"https://arxiv.org/abs/2601.20245","access":"open"},{"id":"s2903","stat":"LLM revisions changed a draft\'s vocabulary roughly three times as much as human revisers: Jensen-Shannon divergence 0.2-0.3 for humans, 0.476 for Claude 3.5 Haiku, \\"nearly triple\\" the human baseline for GPT-5-mini","source":"Abdulhai et al., How LLMs Distort Our Written Language","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Berkeley/UCSD/UW/Google DeepMind. 86 genuinely pre-ChatGPT student essays (2021) with real expert feedback and real human revisions as counterfactual. Many LLM-revised essays above 0.7","url":"https://arxiv.org/abs/2603.18161","access":"open"},{"id":"s2904","stat":"Human revisers changed any part-of-speech category by under 5%; LLM revisers moved adjectives +57% to +90% and pronouns -40% to -60%","source":"Abdulhai et al., How LLMs Distort Our Written Language","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Also coordinating conjunctions +13-90%, determiners -25-50%. Consistent across three model families","url":"https://arxiv.org/abs/2603.18161","access":"open"},{"id":"s2905","stat":"Under an explicit minimal-edit instruction, LLMs still raised adjectives 40-87% and cut pronouns 31-56%","source":"Abdulhai et al., How LLMs Distort Our Written Language","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Prompt was \\"rewrite this essay and keep to a similar word count\\". NOTE: no prompt in the study ever instructed a model to preserve meaning - do not cite this as models ignoring meaning-preservation instructions","url":"https://arxiv.org/abs/2603.18161","access":"open"},{"id":"s2906","stat":"LLM revisions raised positive sentiment 37-54% AND negative sentiment 24-38% simultaneously; human revisers changed any emotion category by under 5%","source":"Abdulhai et al., How LLMs Distort Our Written Language","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"More emotional language in both directions rather than a positivity bias. Expert feedback REDUCED the distortion; without it shifts were \\"even more drastic\\"","url":"https://arxiv.org/abs/2603.18161","access":"open"},{"id":"s2907","stat":"Participants who let an LLM write most of their essay took a neutral stance 68.9% more often (66.7% neutral vs 39.5% for the no-AI control) and used 50% fewer pronouns","source":"Abdulhai et al., How LLMs Distort Our Written Language","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"RCT, n=100, GPT-4o-mini, argumentative essay. Heavy-AI cell is n=27. Self-reported as less creative (p=0.0385) and much less \\"in my voice\\" (p<0.001)","url":"https://arxiv.org/abs/2603.18161","access":"open"},{"id":"s2908","stat":"Participants who used an LLM as an information tool rather than a text generator showed no significant difference from the no-AI control on any measure","source":"Abdulhai et al., How LLMs Distort Our Written Language","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"The buried finding: distortion is concentrated in wholesale delegation, not AI use. This group occupied the same embedding space as unaided human writers. 12 of 55 given an LLM refused to use it for generation at all","url":"https://arxiv.org/abs/2603.18161","access":"open"},{"id":"s2909","stat":"GPT-4 alone scored at the 5th percentile on a law school insurance exam with basic prompting and the 65th percentile when grounded on the instructor\'s own lecture notes","source":"Choi & Schwarcz, AI Assistance in Legal Analysis (73 J. Legal Educ. 384)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Experiment run spring 2023. Same model, same exam, 60 percentile points from supplied context alone. Grounded prompting also hit the 93rd percentile on an undergraduate essay and 100th on multiple choice","url":"https://jle.aals.org/home/vol73/iss2/5/","access":"open"},{"id":"s2910","stat":"Law and undergraduate students given GPT-4 gained 28.9 percentile points on multiple choice (95% CI +20.3 to +38.4) but showed no significant gain on either essay component (+3.5 and -1.3, both CIs spanning zero)","source":"Choi & Schwarcz, AI Assistance in Legal Analysis (73 J. Legal Educ. 384)","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"Quasi-experiment, n=48, no randomisation, no control arm, no preregistration, GPT-4 8K. The widely-cited \\"29 percentile point improvement\\" is a multiple-choice result. Median Insurance Law grade did not move at all","url":"https://jle.aals.org/home/vol73/iss2/5/","access":"open"},{"id":"s2911","stat":"Shoosmiths Project Apollo cut first-pass contract review time 40% in pilot with no increase in error rates","source":"Shoosmiths / Legal IT Insider","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Firm-reported pilot figure for its proprietary Azure OpenAI contract review platform built with Microsoft. Mandatory human checkpoints retained. Treat as vendor/firm-reported, no methodology published"},{"id":"s2912","stat":"Clifford Chance reclassified and summarised 400,000+ curated firm documents in a six-month AI knowledge management build","source":"Artificial Lawyer / Legal IT Insider","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Built entirely inside Microsoft 365/Azure with Epiq Advisory for Law Firms. Includes Permissioned Search binding AI retrieval to existing ethical walls and matter-access controls","url":"https://www.artificiallawyer.com/2026/07/20/clifford-chance-launches-ai-km-platform-with-microsoft-epiq/","access":"open"},{"id":"s2913","stat":"Simmons & Simmons FY 2025/26 revenue up 12% to £690m, PEP again above £1m","source":"Global Legal Post","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Reported in the same window as the firm\'s firmwide Legora adoption alongside its proprietary Percy platform","url":"https://www.globallegalpost.com/news/simmons-posts-12-revenue-jump-to-ps690m-as-pep-grows-11-1718314322","access":"open"},{"id":"s2914","stat":"Morrison Foerster deployed Legora firmwide to 1,000+ lawyers and legal professionals globally","source":"MoFo / Legora newsroom","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Strategic partnership; firm cited Legora\'s emerging agentic capabilities as the buying rationale rather than current feature set","url":"https://www.mofo.com/resources/news/260616-morrison-foerster-deployment-legora","access":"open"},{"id":"s2915","stat":"Kirkland & Ellis Fund Enterprise Platform, built on Palantir AIP, serves 1,000+ lawyers in its Investment Funds Group","source":"Kirkland / Palantir","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"practitioner","scope":"legal","context":"First named product of the $500m build. Exclusive to Kirkland; vendors barred from reselling. Announced at Palantir AIPCon 10","url":"https://www.artificiallawyer.com/2026/06/04/kirkland-palantir-partner-for-pe-platform/","access":"open"},{"id":"s2916","stat":"Hogan Lovells Cadwalader launched 1 July 2026 with $3.6bn comb
1ined revenue and 3,100 lawyers, world\'s 5th largest firm by turnover","source":"Hogan Lovells / Cadwalader","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"practitioner","scope":"legal","context":"Largest law firm merger in history. Cadwalader gains access to ELTEMATE and the CRAIG chatbot","url":"https://www.hlc.com/en/news/largest-law-firm-merger-in-history-approved","access":"open"},{"id":"s2917","stat":"Winston Taylor launched 1 June 2026 with 1,400+ lawyers, ~$1.75bn revenue and 20 offices","source":"Bloomberg Law / Global Legal Post","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Taylor Wessing / Winston & Strawn merger; delayed from the original 1 May target because of European restructuring"},{"id":"s2918","stat":"Ashurst Perkins Coie completed 29 June 2026 with 52 offices, 3,000+ lawyers and ~$2.8bn combined revenue","source":"Legal IT Insider","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Transatlantic merger, approved by partners on both sides 13 April 2026. Consolidates Ashurst Advance with Perkins Coie\'s emerging tech practice. Firm-reported figures","url":"https://legaltechnology.com/ashurst-perkins-coies-abs-licence/","access":"open"},{"id":"s2919","stat":"Ashurst Perkins Coie Global LLP is the largest law firm entity in England and Wales to hold an SRA-regulated ABS licence","source":"Legal IT Insider","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Registered July 2026. The licence sits over the whole firm rather than a captive subsidiary, unlike the Konexo / Simmons Wavelength pattern. Stated rationale is global financial integration, letting non-lawyer leaders and global corporate entities hold membership in the UK partnership structure; the firm is explicit it is not about outside capital","url":"https://legaltechnology.com/ashurst-perkins-coies-abs-licence/","access":"open"},{"id":"s2920","stat":"Garfield.Law won the UK\'s first court trial handled by an AI law firm, recovering £7,000 in unpaid fees","source":"Legal Cheek / Legal Futures","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Wandsworth County Court. AI handled all pre-trial work (disclosure, witness statements, trial prep); a human barrister appeared at the hearing","url":"https://www.legalcheek.com/2026/06/ai-law-firm-wins-court-case-in-uk-first/","access":"open"},{"id":"s2921","stat":"Legora acquired Wexler in July 2026, its fifth acquisition of the year","source":"Global Legal Post / Artificial Lawyer","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Wexler\'s engineering team becomes the founding team of Legora\'s London engineering hub. Directly affects four firms in the Legal AI Index, including Burges Salmon which had extended Wexler firmwide ten weeks earlier","url":"https://www.artificiallawyer.com/2026/07/29/legora-buys-wexler-5th-deal-this-year/","access":"open"},{"id":"s2922","stat":"Pinsent Masons became the first major UK firm known to self-refer to the SRA over AI-related misconduct","source":"Law Gazette / Law360","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"A junior solicitor relied on a non-existent insolvency provision generated by AI, which reached misleading letters sent to the High Court in Cork v Smith. Judge called the output \\"plainly wrong\\" and \\"extremely misleading\\"","url":"https://www.lawgazette.co.uk/news/pinsents-refers-itself-to-sra-over-ai-failures/5126895.article","access":"open"},{"id":"s2923","stat":"Willkie Farr\'s London office has 135 lawyers, grown 149% since 2020, ranked in 15 Chambers UK 2026 practice areas","source":"Willkie / Global Legal Post","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Context for its direct OpenAI partnership: ChatGPT Enterprise firmwide, Codex access for the internal build team, and the Wendell platform suite moved onto OpenAI frontier models","url":"https://www.globallegalpost.com/news/willkie-farr-partners-with-openai-to-accelerate-development-of-proprietary-tech-1596105046","access":"open"},{"id":"s2924","stat":"Fewer than 5% of legal departments measured AI through systematic output metrics such as quality and cost","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Primary article, preliminary survey of participants at a workshop attended by 60 US GCs and senior legal operations leaders;
1 respondent n not separately stated","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2925","stat":"Roughly half of legal departments measured AI through an ad hoc mixture of output and adoption measures","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Primary article; adoption metrics and performance metrics were not yet separated systematically","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2926","stat":"22% of legal departments tracked no measures of AI impact","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Primary article; preliminary workshop survey, respondent n not separately stated","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2927","stat":"Half of legal departments did not track AI error and accuracy rates at all","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Primary article; direct support for separating self-reported usefulness from measured performance","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2928","stat":"The most frequent answer to who owns measurement of AI impact in a legal department was that no single person does","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Primary article; an accountability finding rather than a quantified percentage","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2929","stat":"86% of corporate legal departments rated quality assurance and accuracy at outside firms as extremely or very important","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"academic","scope":"legal","context":"Primary article; should be read with the reporting figures below","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2930","stat":"49% of corporate legal departments required outside firms to report nothing about their AI use","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"academic","scope":"legal","context":"Primary article; exposes the gap between stated concern about quality and actual client requirements","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2931","stat":"Only 13% of corporate legal departments requested systematic reporting about outside firms\' AI use across engagements","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"academic","scope":"legal","context":"Primary article; no shared evidence layer between buyer and provider","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"}
1,{"id":"s2932","stat":"33% of legal departments measured AI time saved against baselines consistently or rigorously, the highest of seven outcomes shown","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Preliminary workshop survey; respondent n not separately stated","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2933","stat":"Only 5% of legal departments measured AI error and accuracy rates consistently or rigorously","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Pair with the 50% not-measuring figure: error and accuracy was the least mature of seven outcomes","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2934","stat":"Vendors provided comprehensive AI usage and adoption data to 28% of legal departments, while 30% received none","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Usage was the best-reported category in the vendor-data chart and was still comprehensive for fewer than three in ten","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2935","stat":"Roughly 72-82% of legal departments received no vendor data on quality, error and accuracy, estimated ROI or user satisfaction","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Primary chart reports the range across four impact-oriented categories; usage data was materially more available","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2936","stat":"61% identified no baseline to compare against as a blocker to measuring AI impact","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Largest measurement blocker in the preliminary workshop survey","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2937","stat":"45% said it was too early in AI adoption to measure impact","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Joint-second measurement blocker alongside no internal owner","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2938","stat":"45% identified no internal owner as a blocker to measuring AI impact","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Joint-second measurement blocker; pairs with the separate finding that the most common ownership answer was no single owner","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2939","stat":"Only 21% identified data privacy concerns as a blocker to measuring AI impact, the lowest blocker shown","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"The measurement constraint was baselines and ownership, not privacy","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2940","stat":"47% planned to measure AI-linked business outcomes within the next 12 months","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Highest planned metric in the twelve-month roadmap","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2941","stat":"44% planned to measure AI ROI or cost-benefit within the next 12 months","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Preliminary workshop survey roadmap","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2942","stat":"44% planned to measure lawyer productivity linked to AI within the next 12 months","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Preliminary workshop survey roadmap","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2943","stat":"33% planned AI-specific risk and incident tracking within the next 12 months, the lowest planned metric shown","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Planned control measurement lagged business outcome, ROI and productivity measures","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2944","stat":"About half of legal departments relied on senior-lawyer review as the human review mechanism for AI output","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"Approximate figure in the primary article; review remains manual and senior-resource intensive","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2945","stat":"All 8 workshop groups independently raised skills atrophy as a concern when designing AI impact measures","source":"Harvard Law CLP / Workday, Moving from Adoption to Responsible Impact","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"legal","context":"Groups were assigned efficie
1ncy, quality or trust themes; the structural training gap appeared in every group","url":"https://clp.law.harvard.edu/knowledge-hub/magazine/issues/adding-value-in-the-age-of-ai/moving-from-adoption-to-responsible-impact/","access":"open"},{"id":"s2946","stat":"OAT achieved 0.435 F1 for in-domain step-level agent failure attribution, versus 0.212 for GPT-4o and 0.181 for GPT-5 prompting","source":"Yeh et al., Tracing Agentic Failure from the Flow of Success","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"MCP-Atlas; conformal-prediction OAT variant; 88 author-annotated failed trajectories; preprint","url":"https://doi.org/10.48550/arXiv.2607.12747","access":"open"},{"id":"s2947","stat":"OAT achieved 0.225 F1 out-of-domain, versus 0.151 for GPT-4o and 0.152 for GPT-5 prompting","source":"Yeh et al., Tracing Agentic Failure from the Flow of Success","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Who&When; top-k OAT variant; 184 failed trajectories generated by GPT-4o across two multi-agent systems; preprint","url":"https://doi.org/10.48550/arXiv.2607.12747","access":"open"},{"id":"s2948","stat":"OAT\'s in-domain top-k detector achieved 70.6% recall and a 77.7% hit rate, with 32.1% precision","source":"Yeh et al., Tracing Agentic Failure from the Flow of Success","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Shows the recall-precision trade-off when three steps are always selected; MCP-Atlas","url":"https://doi.org/10.48550/arXiv.2607.12747","access":"open"},{"id":"s2949","stat":"OAT trained on 80% of 103 successful MCP-Atlas trajectories and required no labelled failure trajectories","source":"Yeh et al., Tracing Agentic Failure from the Flow of Success","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Remaining successful trajectories used for validation, early stopping and conformal calibration","url":"https://doi.org/10.48550/arXiv.2607.12747","access":"open"},{"id":"s2950","stat":"OAT attributed failures in 7 ms on MCP-Atlas, versus 4,241 ms for GPT-4o and 39,626 ms for GPT-5","source":"Yeh et al., Tracing Agentic Failure from the Flow of Success","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Mean latency; paper reports OAT as 200-5,000x faster across its two evaluations","url":"https://doi.org/10.48550/arXiv.2607.12747","access":"open"},{"id":"s2951","stat":"OAT attributed failures in 16 ms on Who&When, versus 4,522 ms for GPT-4o and 37,819 ms for GPT-5","source":"Yeh et al., Tracing Agentic Failure from the Flow of Success","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Out-of-domain mean latency; Who&When trajectories averaged 20.28 steps and reached 129 steps","url":"https://doi.org/10.48550/arXiv.2607.12747","access":"open"},{"id":"s2952","stat":"OAT used zero output tokens during failure attribution and required less than 1 GB of VRAM for deployment","source":"Yeh et al., Tracing Agentic Failure from the Flow of Success","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Representation extraction is a separate upstream cost; full ordered trajectory access remains necessary","url":"https://doi.org/10.48550/arXiv.2607.12747","access":"open"},{"id":"s2953","stat":"OAT\'s gated control path improved out-of-domain AUROC by 0.172 while reducing in-domain AUROC by 0.028","source":"Yeh et al., Tracing Agentic Failure from the Flow of Success","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Ablation result; gating suppresses unfamiliar control signals under distribution shift","url":"https://doi.org/10.48550/arXiv.2607.12747","access":"open"},{"id":"s2954","stat":"The original Who&When benchmark contained 184 failure-attribution tasks from 127 multi-agent systems, with traces spanning 5 to 130 steps","source":"Zhang et al., Which Agent Causes Task Failures and When?","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"126 algorithm-generated and 58 hand-crafted tasks across GAIA and AssistantBench; ICML 2025","url":"https://arxiv.org/abs/2505.00212","access":"open"},{"id":"s2955","stat":"Three experts spent a combined 84.3 hours annotating the original Who&When benchmark, with 15-30% of individual annotations marked uncertain","source":"Zhang et al., Which Agent Causes Task Failures and When?","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Three-round expert process used independent annotation, discussion and consensus","url":"https://arxiv.org/abs/2505.00212","access":"open"},{"id":"s2956","stat":"The best original Who&When result identified the responsible agent 53.5% of the time but the exact decisive step only 14.2% of the time","source":"Zhang et al., Which Agent Causes Task Failures and When?","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Headline accuracy reported in the abstract; GPT-4o-based automated attribution","url":"https://arxiv.org/abs/2505.00212","access":"open"},{"id":"s2957","stat":"On algorithm-generated Who&When failures with ground truth, all-at-once prompting achieved 54.33% agent and 12.50% step accuracy, while step-by-step achieved 35.20% agent and 25.51% step accuracy","source":"Zhang et al., Which Agent Causes Task Failures and When?","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Demonstrates the original benchmark\'s protocol trade-off between responsible-agent and exact-step identification","url":"https://arxiv.org/abs/2505.00212","access":"open"},{"id":"s2958","stat":"A hybrid all-at-once plus step-by-step protocol used 149,177 input tokens and achieved 12.28% step accuracy on hand-crafted Who&When failures, versus 17,106 tokens and 4.39% for all-at-once","source":"Zhang et al., Which Agent Causes Task Failures and When?","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Agent accuracy was 57.02% for both hybrid and all-at-once in this comparison","url":"https://arxiv.org/abs/2505.00212","access":"open"},{"id":"s2959","stat":"Who&When Pro contains 12,326 failed trajectories across 26 benchmarks, 9 task categories, 15 agent frameworks, 3 modalities and 18 error modes","source":"Zhang et al., Who&When Pro","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Errors are injected into successful trajectories and retained only when they change the final outcome to failure","url":"https://arxiv.org/abs/2607.09996","access":"open"},{"id":"s2960","stat":"Who&When Pro trajectories average 7.5 steps and 1,139 words, with a maximum of 50 steps and an average 1.3 images per multimodal trajectory","source":"Zhang et al., Who&When Pro","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Benchmark-wide trajectory characteristics","url":"https://arxiv.org/abs/2607.09996","access":"open"},{"id":"s2961","stat":"Human reviewers accepted 94% of Who&When Pro step labels, 90% of agent labels and 90% of error-family labels; only 2% had no clear decisive error","source":"Zhang et al., Who&When Pro","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Three independent annotators reviewed a stratified 100-trajectory sample; error-family Fleiss kappa 0.73","url":"https://arxiv.org/abs/2607.09996","access":"open"},{"id":"s2962","stat":"Qwen3.5-122B achieved the best Who&When Pro text step accuracy at 73.9%, while the best text joint accuracy for agent, step and error mode was 25.3% from GLM-5","source":"Zhang et al., Who&When Pro","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"All-at-once prompting without final ground-truth answers","url":"https://arxiv.org/abs/2607.09996","access":"open"},{"id":"s2963","stat":"Who&When Pro step accuracy fell from 94% on traces below 3,000 tokens to 50% on traces above 12,000 tokens","
1source":"Zhang et al., Who&When Pro","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Aggregate across evaluated models; long-context failure attribution remains weak","url":"https://arxiv.org/abs/2607.09996","access":"open"},{"id":"s2964","stat":"Full-trajectory all-at-once prompting achieved 7.6 percentage points higher step accuracy than step-by-step prompting on the Who&When Pro ablation set","source":"Zhang et al., Who&When Pro","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"1,444 stratified trajectories; reverses the original Who&When protocol result","url":"https://arxiv.org/abs/2607.09996","access":"open"},{"id":"s2965","stat":"Allowing a one-step tolerance improved Who&When Pro step accuracy by an average 13.9 points on text, 16.9 on images and 28.1 on video; a second step added only 4.4, 6.0 and 2.6 points","source":"Zhang et al., Who&When Pro","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Most localisation errors were close to the injected decisive step","url":"https://arxiv.org/abs/2607.09996","access":"open"},{"id":"s2969","stat":"Deloitte reported $67.2bn FY2024 global revenue, the largest of the Big Four","source":"Deloitte Global","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"The only Big Four firm not to divest consulting in the 2000-02 sale wave later led the group by revenue","url":"https://www.deloitte.com/southeast-asia/en/about/press-room/global-revenue-announcement-2024.html","access":"open"},{"id":"s2970","stat":"The Big Four reported more than $212bn combined global revenue for FY2024","source":"PwC, Deloitte, EY and KPMG annual reports, compiled by Bommarito","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Combined scale after rebuilding consulting and advisory beyond the businesses divested around Enron"},{"id":"s2971","stat":"EY\'s abandoned Project Everest split was designed to raise about $30bn: an IPO of roughly 15% of the consulting company plus about $17bn of new borrowing","source":"Accountancy Age, The Rise and Fall of EY\'s Project Everest","date":"Feb 2024","sortKey":20240200,"precision":"month","year":2024,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Proposed conversion of partnership value into cash and public equity","url":"https://accountancyage.com/2024/02/13/the-rise-and-fall-of-eys-project-everest/","access":"open"},{"id":"s2972","stat":"EY spent about $600m on Project Everest before stopping the split in April 2023","source":"Financial Times / International Tax Review","date":"Apr 2023","sortKey":20230400,"precision":"month","year":2023,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"The global partnership never reached the promised partner vote after the US executive committee refused to proceed","url":"https://www.internationaltaxreview.com/article/2biwi2cm9omjwgv7qed4w/ey-cancels-project-everest-after-months-of-wrangling","access":"gated"},{"id":"s2973","stat":"EY arranged an approximately $700m facility connected with the Everest wind-down; about $270m remained outstanding at FY2024 and related interest reached about $74m","source":"The Irish Times","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"Borrowing protected partner distributions after the failed restructuring; year-only date retained because the draft does not establish a publication month"},{"id":"s2974","stat":"The narrower CPA Trendlines series recorded 22 PE-backed US accounting transactions in 2023, 65 in 2024 and 104 in 2025","source":"CPA Trendlines, Cornerstone Dealflow Timeline","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"The count excludes some platform launches, sponsor flips and adjacent capital raises included in the publisher\'s broader tracker"},{"id":"s2975","stat":"A broader CPA Trendlines tracker counted 94 PE-related US accounting transactions in 2024 and 190 in 2025, roughly twice the publisher\'s narrower series","source":"CPA Trendlines Research, CPA PE Deal Tracker","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Definition changes materially alter the market count; use the series label whenever citing deal volume","url":"https://cpatrendlines.com/2026/06/09/cpa-pe-deal-tracker-10-years-466-deals/","access":"paywalled"},{"id":"s2976","stat":"CPA Trendlines\' broader decade tracker counted roughly 466 US accounting deals, $49bn of transacted value, $24bn of revenue and nearly 96,000 accountants and staff","source":"CPA Trendlines Research, CPA PE Deal Tracker","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Ten platforms accounted for more than half of tracked deals and the top six for more than a third","url":"https://cpatrendlines.com/2026/06/09/cpa-pe-deal-tracker-10-years-466-deals/","access":"paywalled"},{"id":"s2977","stat":"PE-related US accounting activity reached 78 deals through April 2026 versus 44 in the same period of 2025; the trailing-12-month count peaked at 226 in March 2026","source":"CPA Trendlines Research, CPA PE Deal Tracker","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Growth remained positive year on year but monthly activity decelerated in spring 2026","url":"https://cpatrendlines.com/2026/06/09/cpa-pe-deal-tracker-10-years-466-deals/","access":"paywalled"},{"id":"s2978","stat":"EisnerAmper passed $1.2bn revenue and completed 27 acquisitions in the five years after its 2021 alternative-practice split and TowerBrook investment","source":"EisnerAmper","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"market","sourceType":"practitioner","scope":"cross-sector","context":"A regulated attest firm remained CPA-owned beside a capital-backed advisory company","url":"https://www.eisneramper.com/about-us/news/continuation-vehicle-transaction-with-towerbrook-0326/","access":"open"},{"id":"s2979","stat":"By 2024 the SRA had licensed 1,257 alternative business structures, roughly one in eight SRA-regulated firms","source":"Legal Services Board, Evaluation of ABS and Investment in Legal Services","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"regulatory","sourceType":"government","scope":"legal","context":"England and Wales\' stock of SRA-regulated ABS licences; other approved regulators license additional entities"},{"id":"s2980","stat":"Outside-owned legal businesses invested in new or improved services at 28% versus 12% for traditional firms, and in technology at 72% versus 31%","source":"Legal Services Board, Technology and Innovation Survey 2022","date":"2023","sortKey":20230000,"precision":"year","year":2023,"theme":"adoption","sourceType":"government","scope":"legal","context":"England and Wales evidence that outside ownership correlates with materially higher investment, without establishing causation"},{"id":"s2981","stat":"Private equity invested about £1.2bn in UK law firms over the five years to 2024, including £534m in 2024","source":"Law Society Gazette","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"industry","scope":"legal","context":"Capital concentrated in consumer and scalable legal-service segments"},{"id":"s2983","stat":"Axiom Ince grew from about 80 staff to roughly 1,400 in two years before closing 14 office
1s with an estimated £64m client-account shortfall","source":"LawCareers.Net / Law Society Gazette","date":"Oct 2023","sortKey":20231000,"precision":"month","year":2023,"theme":"critical","sourceType":"industry","scope":"legal","context":"The SRA had already classified it as a high-risk accumulator; growth velocity and supervision failure were central","url":"https://www.lawcareers.net/Explore/News/Costly-shutdown-of-Axiom-Ince-by-SRA-06102023","access":"open"},{"id":"s2984","stat":"SSB Law entered administration in January 2024 owing about £200m after the SRA had received more than 100 complaints between 2019 and 2024","source":"Law Society Gazette, cited by Bommarito","date":"2024","sortKey":20240000,"precision":"year","year":2024,"theme":"critical","sourceType":"industry","scope":"legal","context":"Complaints were treated as individual service issues rather than evidence of entity-level distress","url":"https://www.lawgazette.co.uk/news/ssb-law-sra-ignored-red-flags-for-five-years-damning-report-finds/5124780.article","access":"open"},{"id":"s2985","stat":"Lawfront grew from under £10m to more than £130m group fee income across 16 acquisitions between 2021 and 2025","source":"Lawfront / Legal IT Insider","date":"May 2025","sortKey":20250500,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Capital-backed consolidator contrasted with Axiom Ince: local professional leadership retained and integration capacity built before scale"},{"id":"s2986","stat":"Arizona alternative business structure licences grew from 19 in 2022 to more than 136 by April 2025; 59% of entities licensed in 2024 were wholly non-lawyer-owned","source":"Engstrom, Ricca and Knowlton, Regulatory Innovation at the Crossroads","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Five-year review of Arizona and Utah entity-regulation reforms","url":"https://law.stanford.edu/2025/06/02/regulatory-innovation-at-the-crossroads-five-years-of-data-on-entity-regulation-reform-in-arizona-and-utah/","access":"open"},{"id":"s2987","stat":"Arizona\'s ABS count passed 151 in early 2026","source":"Arizona State Law Journal","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Open licensing continued to attract non-lawyer-owned legal businesses after Rule 5.4 was removed"},{"id":"s2988","stat":"Utah\'s legal-services sandbox participant count fell from 39 to 11 after stricter requirements to show substantial impact for underserved Utah residents","source":"ABA Journal, cited by Bommarito","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Nearly 30 ventures had left or were leaving by early 2025; capital redirected towards less restrictive Arizona"},{"id":"s2989","stat":"California AB 931 imposes $10,000 per violation or treble damages on prohibited fee-sharing arrangements and sunsets in 2030","source":"California Legislature","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"One of several state blockades aimed at out-of-state ABS and MSO-supported firms","url":"https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=202520260AB931","access":"open"},{"id":"s2990","stat":"The global eDiscovery market approached $17bn in 2024","source":"ComplexDiscovery","date":"Jan 2025","sortKey":20250100,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Illustrates how document review moved from junior-lawyer work into an outside-owned software and services market","url":"https://complexdiscovery.com/complete-look-complexdiscoverys-2024-2029-ediscovery-market-size-mashup/","access":"open"},{"id":"s2991","stat":"More than a dozen US law-firm management-services-organisation deals had closed by late 2025","source":"Holland & Knight","date":"Jul 2025","sortKey":20250700,"precision":"month","year":2025,"theme":"market","sourceType":"analyst","scope":"legal","context":"Flat-fee or cost-plus MSOs sell and recapitalise operations while leaving the legal practice lawyer-owned","url":"https://www.hklaw.com/en/insights/publications/2025/07/restructuring-law-firms-through-management-service-organizations","access":"open"},{"id":"s2992","stat":"Uplift Investors closed its first professional-services fund at a $670m hard cap in July 2026, with legal services one of five target microsectors","source":"Forbes","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Its Orion Legal platform already managed the operations of three US plaintiff firms","url":"https://www.forbes.com/sites/johnhyatt/2026/07/16/buyout-firm-uplift-investors-raises-670-million-to-invest-in-legal-and-other-services/","access":"open"},{"id":"s2993","stat":"About 40% of US law-firm partners expect to retire within a decade","source":"Financial Times","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"media","scope":"legal","context":"Succession overhang increases demand for MSOs as a liquidity route where law-firm equity cannot be sold directly"},{"id":"s2994","stat":"Vals AI scored Harvey at 94.8% versus a 70.1% lawyer baseline on document question-answering in a blind comparison of seven junior-lawyer tasks","source":"Vals AI, V
1als Legal AI Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Four commercial legal-AI products compared with lawyers sourced from Am Law 100 firms","url":"https://www.vals.ai/industry-reports/vlair-2-27-25","access":"open"},{"id":"s2995","stat":"Legal-AI products beat lawyers on 4 of 7 Vals tasks and tied one; lawyers retained the lead on contract redlining, 79.7% versus 65.0%, and EDGAR filing research, 70.1% versus 55.2%","source":"Vals AI, Vals Legal AI Report","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Task-level split between repeatable document work and context-dependent judgement or search strategy","url":"https://www.vals.ai/industry-reports/vlair-2-27-25","access":"open"},{"id":"s2996","stat":"In Vals AI\'s legal-research benchmark, legal-AI products averaged 80% versus 71% for human lawyers","source":"Vals AI, Legal Research Report","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Narrow task benchmark, not evidence of autonomous matter completion","url":"https://www.vals.ai/industry-reports/vlair-10-14-25","access":"open"},{"id":"s2997","stat":"Legal-technology companies raised $2.34bn across 103 deals in Q1 2026","source":"Artificial Lawyer","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Capital concentration was high, with three companies dominating the quarterly total"},{"id":"s2998","stat":"The Am Law 100 generated $158.3bn revenue in 2025, up 13.3%, while average profit per equity partner reached $3.15m, up 12.3%","source":"Legal.io, The 2025 Am Law 100: By the Numbers","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"market","sourceType":"media","scope":"legal","context":"Record incumbent economics despite rising legal-AI investment; publication month not established in the draft","url":"https://www.legal.io/blog/5609720/The-2025-Am-Law-100-By-the-Numbers","access":"open"},{"id":"s2999","stat":"The Am Law 100 employed 123,953 lawyers in 2025, up 7.7% year on year","source":"Legal.io, The 2025 Am Law 100: By the Numbers","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"workforce","sourceType":"media","scope":"legal","context":"Headcount still expanded at the surface while the ownership tier narrowed","url":"https://www.legal.io/blog/5609720/The-2025-Am-Law-100-By-the-Numbers","access":"open"},{"id":"s3000","stat":"Non-equity partners reached 50.9% of all Am Law 100 partners in 2025, versus under 5% in the early 1990s","source":"Legal.io, The 2025 Am Law 100: By the Numbers","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"workforce","sourceType":"media","scope":"legal","context":"The partner title became majority salaried rather than ownership-bearing","url":"https://www.legal.io/blog/5609720/The-2025-Am-Law-100-By-the-Numbers","access":"open"},{"id":"s3001","stat":"Am Law 100 equity-partner headcount fell 0.5% in 2025 after growing about 0.7% annually through the late 2010s","source":"Legal.io, The 2025 Am Law 100: By the Numbers","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"workforce","sourceType":"media","scope":"legal","context":"Record profits were divided among a shrinking ownership tier","url":"https://www.legal.io/blog/5609720/The-2025-Am-Law-100-By-the-Numbers","access":"open"},{"id":"s3002","stat":"Salaried lawyers per Am Law 100 equity partner rose from 4.01 in 2019 to 4.57 in 2024","source":"Legal.io, The 2025 Am Law 100: By the Numbers","date":"2025","sortKey":20250000,"precision":"year","year":2025,"theme":"workforce","sourceType":"media","scope":"legal","context":"Leverage increased beneath the true ownership tier before AI materially changed firm headcount","url":"https://www.legal.io/blog/5609720/The-2025-Am-Law-100-By-the-Numbers","access":"open"},{"id":"s3003","stat":"JPMorgan planned to reduce deal staffing from 6 junior analysts per senior to 4 and offshore about half of the remaining junior work","source":"Fortune / Bloomberg","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"cross-sector","context":"Banking cited AI as a reason for thinning the junior apprenticeship layer"},{"id":"s3004","stat":"Some Wall Street entry-level cohorts were being cut by as much as two-thirds in 2026","source":"Fortune / Bloomberg","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"legal","context":"Leading indicator used by Bommarito for a predicted 2028-30 redesign of entry-level legal work"},{"id":"s3005","stat":"Junior employees made up about 62% of banks\' AI talent while banks were reducing junior cohorts","source":"Fortune / Bloomberg","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"cross-sector","context":"The workers most exposed to the staffing change also supplied most of the organisations\' AI capability"},{"id":"s3006","stat":"97% of organisations consider context important to their data and AI goals: 79% very important and 18% somewhat important","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Vendor survey of IT and AI infrastru
1cture leaders; respondent count, geography and fieldwork dates not disclosed","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3007","stat":"73% agree that navigable context systems are becoming the dividing line between experimental and production AI","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Vendor survey; navigable means agents can traverse related records and entities rather than retrieve disconnected text","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3008","stat":"81% agree that the next phase of AI maturity will depend on infrastructure that gains value as knowledge accumulates","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Redis describes this as compounding context infrastructure; undisclosed survey sample","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3009","stat":"73% say AI agents fail more often because of broken context than broken models","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Vendor survey; directional only because methodology and sample size are not published","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3010","stat":"83% say fresh context matters more than adding model parameters for enterprise reliability","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Context freshness versus model scale; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3011","stat":"69% find their agents unreliable at navigating relationships across systems","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Cross-system traversal failure; vendor survey with undisclosed sample size","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3012","stat":"69% say an agent that cannot navigate across systems creates more operational risk than one that hallucinates","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Compares traversal failure with hallucination risk; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3013","stat":"81% report operating in the two earliest context-maturity stages: 43% ad hoc and 38% exploratory","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Little shared infrastructure, governance or consistent results; the report separately prints an unexplained conflicting 58%, which is excluded","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3014","stat":"94% say compounding system intelligence is essential for production-grade context maturity, but only 4% report reaching the compounding stage","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Belief-to-implementation gap in Redis\'s five-stage maturity model; vendor survey with undisclosed sample size","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3015","stat":"67% agree that their agents remain in the prototype phase despite executive pressure to scale","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Production-readiness gap; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3016","stat":"42% say they accurately measure readiness for production agents","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"A further 39% are neutral or unsure; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3017","stat":"39% are neutral or unsure whether they accurately measure readiness for production agents","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Organisational uncertainty about production readiness; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3018","stat":"33% agree or strongly agree that they are stitching AI infrastru
1cture together from disconnected point solutions","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Fragmented context architecture; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3019","stat":"Among Stage 1 organisations, 98% say context is very important but only 2% say their agents reliably navigate across systems","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Earliest-maturity subgroup; subgroup size is not disclosed","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3020","stat":"95% of Stage 1 organisations probably or definitely lack a reliable semantic data model","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Earliest-maturity subgroup; subgroup size and survey wording are not disclosed","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3021","stat":"94% of Stage 1 organisations probably lack a well-defined context graph","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Earliest-maturity subgroup; subgroup size and survey wording are not disclosed","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3022","stat":"56% of Stage 1 organisations say reaching production took much longer than expected","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Earliest-maturity subgroup; subgroup size is not disclosed","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3023","stat":"55% lack a reliable semantic data model of business entities, attributes and relationships","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Foundational context gap; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3024","stat":"54% say siloed vector stores significantly or moderately limit agent effectiveness","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Fragmented retrieval adds delay and blocks relationship traversal; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3025","stat":"47% lack a well-defined context graph connecting business entities","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Foundational context gap; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3026","stat":"39% rate their organisation as ineffective or very ineffective at scoping memory for agents","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Difficulty defining what persists, for how long and for whom; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3027","stat":"92% of ad hoc organisations perceive significant or moderate latency risk in multi-agent workflows, compared with 12% of compounding organisations","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Report prose used; the page 9 chart reverses the maturity-stage labels, while page 11 repeats the 12% compounding figure","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3028","stat":"78% say proving context is fresh before inference is very important","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Freshness requirement for production agents; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3029","stat":"80% agree that event-driven synchronisation is becoming the standard for enterprise AI","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Updating context when source data changes rather than relying on nightly batches","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3030","stat":"21% say their systems frequently or usually run critical decisions on stale or batch-synchronised data","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Freshness failure across the survey population; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3031","stat":"37% name governance tooling as the single biggest barrier to production-ready context maturity","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"cross-sector","context":"Top-ranked barrier in the vendor survey; respondent count and methodology not disclosed","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3032","stat":"65% rank governance tooling as essential for reaching production-grade context","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"cross-sector","context":"Second only to compounding intelligence in the report\'s ranking; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3033","stat":"60% rate their organisation as ineffective at governing access to AI context, while 58% are confident that context is governed effectively","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"cross-sector","context":"Contradictory self-assessment indicates confidence and capability may be measured differently; survey questions and sample not disclosed","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3034","stat":"22% name persistent memory as the second biggest barrier to production-ready context maturity","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Barrier ranking; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"}
1,{"id":"s3035","stat":"19% name a unified runtime architecture as the third biggest barrier to production-ready context maturity","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Barrier ranking; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3036","stat":"84% monitor service-level objectives for context systems inconsistently","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Weak context observability; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3037","stat":"78% capture agent decision traces inconsistently","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Often leaves no record of the context an agent saw or why it acted; vendor survey with undisclosed methodology","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3038","stat":"54% of organisations at the compounding stage have a well-defined context graph","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Most-mature subgroup; subgroup size is not disclosed and only 4% of the full sample report reaching this stage","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3039","stat":"49% of organisations at the compounding stage say they measure production readiness very accurately","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Most-mature subgroup; subgroup size is not disclosed and only 4% of the full sample report reaching this stage","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3040","stat":"97% of organisations at the compounding stage say traversal failure poses more risk than hallucination","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Most-mature subgroup; subgroup size is not disclosed and only 4% of the full sample report reaching this stage","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3041","stat":"69% of organisations at the compounding stage still rely on stale data for critical decisions","source":"Redis, The State of Context Engineering","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Even the most mature subgroup remains weak on freshness; subgroup size is not disclosed and only 4% of the full sample report reaching this stage","url":"https://redis.io/resources/Redis_ePub_Report_StateOfContextEngineering_260803_V2.pdf","access":"open"},{"id":"s3042","stat":"Cognizant announced plans to train and deploy 1,000 context engineers over the year following August 2025","source":"Cognizant","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"cross-sector","context":"Cited by Redis as evidence that context engineering is becoming a defined enterprise role; verify against Cognizant\'s primary announcement before external use","url":"https://news.cognizant.com/2025-08-29-Cognizant-to-Deploy-1,000-Context-Engineers,-Powered-by-ContextFabric-TM-,-to-Industrialize-Agentic-AI","access":"open"},{"id":"s3043","stat":"Stack Overflow activity fell 25% relative to four less-exposed Q&A platforms within six months of ChatGPT\'s release","source":"del Rio-Chanona, Laurentsyeva & Wachs, PNAS Nexus","date":"Sep 2024","sortKey":20240900,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Difference-in-differences study using Russian and Chinese programming forums and two mathematics forums as counterfactuals; authors treat 25% as a lower bound, with no significant change in post quality and declines across experience levels; surfaced by Kallel & El Louadi\'s cognitive-commons appraisal","url":"https://academic.oup.com/pnasnexus/article/doi/10.1093/pnasnexus/pgae400/7754871","access":"open"},{"id":"s3044","stat":"Monthly questions recorded on Stack Overflow fell 99.3%, from a peak of 207,204 in March 2014 to 1,442 in July 2026","source":"Stack Exchange Data Explorer via Daniel Lockyer","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"practitioner","scope":"cross-sector","context":"Raw peak-to-current decline in new question volume, leaving July 2026 at 0.7% of the peak; not website traffic and not a causal estimate of AI\'s effect, since the decline began before ChatGPT and the live database excludes questions subsequently deleted","url":"https://x.com/DanielLockyer/status/2085713493047525579","access":"open"},{"id":"s3045","stat":"Approximately 80% of the UK legal market retains meaningful barriers to full automation","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Sum of the \\"highest barriers\\" (39%) and \\"moderate barr
1iers\\" (39%) bands in PwC\'s substitution analysis of legal fees by FY25 revenue; includes courtroom advocacy, complex negotiation, regulated judgement calls and bet-the-company matters. Modelled; no methodology, sample or fieldwork dates disclosed anywhere in the report","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3046","stat":"22% of UK legal fees sit in work with \\"some barriers\\" to automation","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"The most exposed band: work relatively easy to automate where clients are sophisticated enough to bring it in-house with AI support. Examples given are transactional commercial contracting for enterprises and patent drafting; most common in enterprise segments. No methodology disclosed","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3047","stat":"39% of UK legal fees sit in work with the \\"highest barriers\\" to automation","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Hard to automate and clients less able to self-serve, so most protected from substitution. Examples given are employment and family work for consumers, and ADR/litigation; concentrated in consumer and SME segments. No methodology disclosed","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3048","stat":"39% of UK legal fees sit in work with \\"moderate barriers\\" to automation","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Either the work is easier to automate but clients cannot in-source it, or clients are sophisticated enough to in-source but the work is harder to automate. Examples given are PI/CN and SME commercial property. No methodology disclosed","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3049","stat":"£6bn of automatable spend sits inside the UK legal sector","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"PwC\'s own wording is \\"we estimate\\", so this is modelled rather than surveyed. Cite as a PwC estimate, never as a finding. No methodology disclosed","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3050","stat":"Law firms expect AI to unlock efficiency gains equivalent to around 16% of chargeable hours, up from 11% the year before","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"The underlying survey is never named in the report. Definitional drift across PwC\'s own publications: the Law Firms\' Survey 2024 renders the same 16% as \\"existing chargeable work that could be automated\\" and the 2025 edition as \\"average hours saved from AI adoption\\". State which definition you are using if citing","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3051","stat":"53% of firms report concern about clients using automation to reduce demand","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Firm-reported. The underlying survey instrument, sample and fieldwork dates are not named anywhere in the report","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3052","stat":"73% of firms worry about keeping pace in what many partners call a \\"tech arms race\\"","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Firm-reported. The underlying survey instrument, sample and fieldwork dates are not named anyw
1here in the report","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3053","stat":"AI adoption in UK legal firms exceeds 90%, but only a minority are turning activity into monetisable gains","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"The activity-versus-value gap stated as PwC\'s headline framing. No methodology disclosed","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3054","stat":"AI is claimed to handle 25-50% of delivery on high-volume standardised legal work such as NDAs, compliance filings and document review","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"legal","context":"Assertion inside PwC\'s tiered pricing model, used to justify subscription or per-transaction pricing for this tier. No source, sample or method given for the range","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3055","stat":"Senior associate time allocation projected to move from roughly 70% production / 20% supervision / 10% client management to 30% supervision / 30% judgement and drafting / 40% client and matter management","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"PwC\'s projected human-agent leverage model, with an agentic layer beneath every human tier. A projection, not a measurement","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3056","stat":"PwC describes itself as \\"Client Zero\\" for the tools reshaping the legal market, including its own deployment of Harvey across PwC","source":"PwC UK / Strategy&, The New Rules of Legal Services","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Big Four self-deployment disclosure, stated in the same report that sells the strategy, pricing-reform, workforce, back-office and governance services its five moves prescribe","url":"https://www.pwc.co.uk/services/technology/ai-performance/legal.html","access":"gated"},{"id":"s3057","stat":"The top 20% of organisations already capture 74% of AI-driven returns","source":"PwC, Global AI Performance Study (\\"Want ROI from AI? Go for growth\\")","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"1,217 senior executives at director level or above, 91% publicly listed, 76% revenue above US$1bn, 25 sectors, fieldwork Oct-Nov 2025. Cross-sector with no legal breakdown; PwC\'s July 2026 legal report quotes it as \\"close to three quarters\\" but it is not a legal-specific figure"},{"id":"s3058","stat":"Only around a third of UK respondents believe their current AI investment is sufficient to achieve their goals","source":"PwC, Global AI Performance Study (\\"Want ROI from AI? Go for growth\\")","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"UK subset of the cross-sector study; PwC\'s July 2026 legal report quotes it as the prompt for an honest AI-maturity diagnosis, alongside a wide gap to leaders on having a robust roadmap"},{"id":"s3059","stat":"45 AI tools identified in use or in development across UK justice settings: 27 live, 12 at pilot stage, 6 exploratory","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Independent evidence scoping review of administrative, civil and family justice in the UK. Mapping covers formal deployments and in-development tools only, not informal use by practitioners, judges or litigants in person","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3060","stat":"Only 7 of 45 mapped UK justice AI tools have any publicly available evaluation; 37 have none and 1 has an evaluation in progress","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"The review\'s headline deployment-evaluation gap. \\"No publicly available evaluation\\" means none was public at the time of review, not that no internal assessment took place","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3074","stat":"$3,500 Rule 11 sanction plus referral to the Grievance Committee against defence counsel for AI-assisted filings in which no case was fabricated","source":"*Barteca Holdings LLC v. TacoBarn Newtown
1LLC*, No. 3:26-cv-00250-VDO (D. Conn.), Order Imposing Sanctions ECF No. 43","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"legal","context":"Every authority cited was real, correctly named, correctly reported and Shepardized through Lexis before filing. The hallucinations were invented quotations, non-existent pin cites and mischaracterised propositions. The existence check passed and caught nothing"},{"id":"s3075","stat":"US monetary sanctions for AI hallucinations \\"in most cases\\" range from $1,500 to $15,000","source":"Judge Vernon D. Oliver\'s own survey in *Barteca v. TacoBarn* (D. Conn.)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"legal","context":"Court-compiled range citing Mid Central Operating Engineers v HoosierVac ($15,000), Landberg v City of New York ($8,000 attorney + $2,500 firm), Mata v Avianca ($5,000), McCormick v Texakoma ($5,000), Wadsworth v Walmart ($3,000 drafter + $1,000 per non-drafting signatory). Excludes fee-shifting awards, which is why it sits below the ~$110,000 Oregon and $96,000 Brigandi figures (s1691, s1692)"},{"id":"s3076","stat":"Sanctioned counsel used three AI tools on the offending brief: Open Law (paid subscription), Claude and ChatGPT, on his first ever AI-assisted matter","source":"*Barteca v. TacoBarn* (D. Conn.), show-cause hearing record","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"legal","context":"Augmentation rather than delegation: the brief was written conventionally by hand first, then run through the tools for suggested arguments and additional cases. Counsel is a sole practitioner"},{"id":"s3077","stat":"Charlotin AI Hallucination Cases Database: 1,870 cases as at 11 August 2026, coverage from Q2 2023","source":"Damien Charlotin, AI Hallucination Cases Database","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"read from site 12 Aug 2026. **Supersedes s1689 (1,348 at 24 Apr 2026).** Counts decisions where a court noticed and recorded the hallucination, which is judicial detection and reporting, not incidence. Charlotin\'s own caveat: it does not track the wider universe of fake citations or AI use in filings. Always cite with the date; the total moves about 34 a week (1,433 at 12 May to 1,870 at 11 Aug 2026)","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s3078","stat":"Pro se litigants (1,097) now outnumber lawyers (725) in the Charlotin database by half again","source":"Damien Charlotin, AI Hallucination Cases Database","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Also tagged: Judge 27, Expert 13, Prosecutor 5, Government Lawyer 3, Paralegal 2, Arbitrator 1, Federal Defender 1. Party tags sum to more than the total, so some cases carry more than one party. 725 is the figure to use with a professional audience; the headline total invites a \\"that is litigants in person, not us\\" deflection","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s3079","stat":"511 of 1,870 cases in the Charlotin database carry a False Quotes tag and 783 carry Misrepresented, against 1,558 for Fabricated","source":"Damien Charlotin, AI Hallucination Cases Database","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Nature tags overlap and sum to 2,885, so a case can carry several and **no figure for \\"cases with no fabricated authority\\" can be derived by subtraction** (some records carry no nature tag at all; that number needs the CSV). The point the counts support is that the *Barteca* failure modes are common, not exotic","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s3080","stat":"The UK is the fourth largest jurisdiction in the Charlotin database with 61 cases, behind USA 1,298, Canada 206 and Australia 98","source":"Damien Charlotin, AI Hallucination Cases Database","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"practitioner","scope":"legal","context":"Then Israel 55, Italy 15, France 13, India 13, Brazil 10. Jurisdiction counts sum to 1,869 against a stated total of 1,870, so at least one record
1is unassigned","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s3061","stat":"Among the 27 live UK justice AI tools, 22 have no publicly available evaluation","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"The evaluation deficit is not confined to early-stage pilots; it applies to tools already processing cases and shaping advice","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3062","stat":"Of the 7 publicly available evaluations of UK justice AI tools, 3 were government-led assessments of government-developed tools, 3 academic and 1 vendor-commissioned","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Independence of the existing evidence base. Only the 3 academic evaluations are arm\'s length from the deploying or supplying party","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3063","stat":"No study identified examined the effects on an individual whose matter was triaged, advised on or decided through an AI-assisted process in a UK justice setting","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"The review\'s \\"measurement gap\\". Evaluations examine system operators rather than the people whose legal problems are being addressed, relying on self-reported time savings and satisfaction","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3064","stat":"No UK deployment evaluation identified examines whether human oversight of AI is effective, calibrated or sustainable in practice","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Human oversight is treated in UK policy and judicial guidance as the principal safeguard for AI in justice, and has not been tested","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3065","stat":"29 of 45 mapped UK justice AI tools are internal facing (for judges, civil servants, advisers, lawyers and court staff); 16 are public facing","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Deployment places AI with actors who already navigate the justice system competently, rather than reaching the underserved","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3066","stat":"Government bodies account for 22 of the 45 mapped UK justice AI tools; private developers 11; access-to-justice organisations and mixed-sector collaborations 5 each","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Institutional character of UK justice AI deployment, led by HMCTS, MoJ, Home Office, Cafcass, SCTS, ICO, Acas and FOS","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3067","stat":"28 of 45 mapped UK justice AI tools are built on LLMs in some form; 8 incorporate RAG, 4 ASR, 4 machine learning, 2 NLP and 1 is rules-based; 9 do not report enough to identify the technology","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"The UK justice AI field is LLM-centred though not exclusively so","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3068","stat":"Only 13 of 45 mapped UK justice AI tools disclose the model type they are built on; 11 of those use proprietary general-purpose LLMs and just 2 use open-source architectures","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"A further 26 tools do not disclose the model and 6 are explicitly built with non-disclosed proprietary AI. Proprietary dominance blocks structural inspection of weights, training data and architecture","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3069","stat":"17 of 45 mapped UK justice AI tools are recorded as involving human review of outputs, 10 as not involving it, and 18 do not report the position clearly","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Developer disclosures about intended system design, not observed practice. The review warns these are not a confident measure of oversight prevalence","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3070","stat":"The largest cluster of UK justice AI tools is courts and tribunals with 12 tools, all internal facing; then civil with 8 and family with 6","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Criminal, employment and immigration have 3 each. The most institutionally mature cluster is concentrated in administrative and professional support rather than direct public interaction","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3071","stat":"Only 48 UK studies met the inclusion criteria for the evidence base on AI in UK justice, with 45% published in 2025 and none focused specifically on Scotland or Northern Ireland","source":"Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Described by the review as a modest corpus given the breadth of deployment. 22 studies from 2025, 7 from 2024, 6 from 2026 to date, 5 from 2023 and 8 across 2013-2022","url":"https://www.nuffieldfoundation.org/publications/artificial-intelligence-justice","access":"open"},{"id":"s3072","stat":"UK evidence on judicial attitudes to AI rests on a single study of 12 judges, from the Supreme Court to the County Court","source":"Solovey, Flanagan and Chen (2025), via Nuffield Foundation / Sargeant","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"legal","context":"Three concerns raised: deskilling felt most acutely in the lower courts, the value to parties of knowing a person engaged with their case, and reliability including fabricated material in legal research. Small and self-selected sample, corroborated by three international studies of German and US judges","url":"https://www.nuffieldfoundation.org/wp-content/uploads/2026/07/AI-and-justice.pdf","access":"open"},{"id":"s3073","stat":"Even limited assistive uses of AI in UK tribunal processes may significantly reduce perceived fairness, dignity, transparency and informed decision-making among members of the public","source":"Tomlinson and others, via Nuffield Foundation / Sargeant, Artificial Intelligence & Justice","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"legal","context":"Vignette experiment evidence. Current policy and judicial guidance treat assistive AI as categorically lower risk than autonomous AI","url":"https://www.nuffieldfoundation.org/wp-content/uploads/2026/07/AI-and-justice.pdf","access":"open"},{"id":"s3081","stat":"Only 11% of UK lawyers say they are using generative AI heavily for day-to-day work","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The depth figure sitting behind the 61% breadth figure (s0069). Recorded from Legal IT Insider\'s coverage; full report gated and unread","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3082","stat":"Heavy day-to-day generative AI use falls to 7% at large and medium-sized UK firms, against 11% across all firms","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The best-resourced firms report the shallowest heavy use. Recorded from Legal IT Insider\'s coverage; full report gated and unread","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3083","stat":"The share of UK lawyers with no plans to adopt AI fell from 15% to 6%","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Between January 2025 and the August 2025 fieldwork. LexisNexis newsroom release, 1 Sep 2025","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3084","stat":"Two-thirds of UK lawyers say their organisation\'s AI culture is slow or non-existent","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"LexisNexis\'s UK landing page states a stronger variant of the same idea, \\"4 in 5 say their AI culture is slow, fear-based or non-existent\\", presumably different question wording. Do not cite the two together as one measure","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3085","stat":"51% of UK lawyers use legal-specific AI tools; 49% rely on general-purpose systems","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Private practice 58%, medium-sized firms 70%. Load-bearing for the sponsor, which sells the legal-specific category","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3086","stat":"88% of UK lawyers using legal AI exclusively say they feel more confident in its output","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Self-selected group, self-reported confidence, sponsor\'s own product category. Weakest figure in the survey","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3087","stat":"47% of UK lawyers agree AI will transform how firms bill for services, up from 40% earlier in 2025","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Read against the same survey\'s 56% who put time saved into more billable work","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3088","stat":"Preferred billing alternatives among UK lawyers: fixed fee by matter 69%, retainers 44%, dynamic pricing 32%, bundled services 32%, flat fees 31%","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Multi-select. Recorded from Legal IT Insider\'s coverage; full report gated and unread","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3089","stat":"56% of UK lawyers put the time saved by AI into increasing billable work; 53% into a better work-life balance","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"market","sourceType":"vendor","scope":"legal","context":"Multi-select. The efficiency is being converted into volume, in the same period clients began asking for an AI discount","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3090","stat":"18% of private practice lawyers and 19% of in-house counsel would consider leaving their organisation over its AI position, rising to 26% at large firms","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Exact question wording not retrievable from the public summaries; treat the framing as approximate until the gated report is read","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3091","stat":"39% of private practice lawyers and 49% of in-house counsel believe failing to engage with AI would harm their careers","source":"LexisNexis AI Culture Clash","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"LexisNexis newsroom release, 1 Sep 2025","url":"https://www.lexisnexis.co.uk/research-and-reports/the-ai-culture-clash-survey-2025.html","access":"open"},{"id":"s3092","stat":"Over 30 US state bars have released AI-specific rules, alongside ABA Formal Opinion 512 holding that the Model Rule 1.1 duty of competence now includes understanding the benefits and risks of AI","source":"PLI Innovation Council, The AI-Ready Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"regulatory","sourceType":"industry","scope":"legal","context":"Stated in the framework\'s introduction without citation or a list of which bars. Used to argue that ethical rules have moved but no consensus competency model exists. Date from PDF metadata, not stated on the document. Compare s0705 (73% of state bars, 2023)","url":"https://www.globenewswire.com/news-release/2026/05/05/3288064/0/en/pli-introduces-industry-first-ai-competency-framework.html","access":"open"},{"id":"s3093","stat":"The AI-Ready Lawyer framework defines 5 pillars, 20 competencies and 3 proficiency levels (Emerging / Practicing / Leading) for AI competency in legal practice","source":"PLI Innovation Council, The AI-Ready Lawyer","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"industry","scope":"legal","context":"Framework dimensions, not survey data. Pillars: AI Fluency (4 competencies), AI-Enhanced Legal Work (5), Critical Oversight (3), Ethical AI Governance (5), Professional Evolution (3). US-framed; no evidence base or named authors","url":"https://www.globenewswire.com/news-release/2026/05/05/3288064/0/en/pli-introduces-industry-first-ai-competency-framework.html","access":"open"},{"id":"s3094","stat":"Norm Ai raised $120m in a Series C at a $1.2bn valuation, taking cumulative funding past $260m since 2023","source":"LawSites / Artificial Lawyer","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","cont
1ext":"Round led by Khosla Ventures with Blackstone, Bain Capital Ventures, Craft Ventures, Coatue, Vanguard, New York Life and TIAA participating. Entry-price valuation, not operating proof","url":"https://www.lawnext.com/2026/07/norm-ai-hits-unicorn-status-with-120m-series-c-at-1-2-billion-valuation.html","access":"open"},{"id":"s3095","stat":"Norm Law, the AI-native law firm affiliated with Norm Ai, was operating with 25 lawyers across 8 practice areas seven months after launch","source":"Legal Technology (Reena SenGupta)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Announced Nov 2025, began operating Jan 2026. Supersedes the \\"over 35 legal engineers\\" figure from the Nov 2025 launch coverage, which was single-sourced and probably counted compliance engineers on the software side. Practice areas: investment funds, SEC regulatory, real estate, commercial contracts, technology transactions, M&A and private equity, public filings, finance and tax","url":"https://legaltechnology.com/norm-law-the-new-disrupter/","access":"open"},{"id":"s3096","stat":"Norm Law named Blackstone, Goldman Sachs and New York Life as clients; Blackstone, Vanguard and New York Life are also Norm Ai investors","source":"Legal Technology (Reena SenGupta)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"First named clients on the record. The marquee client list and the Series C cap table are substantially the same institutions, so the \\"access in months rather than a decade\\" claim is not evidence the AI proposition won the work. Norm\'s separate $30 trillion client-AUM claim remains uncorroborated","url":"https://legaltechnology.com/norm-law-the-new-disrupter/","access":"open"},{"id":"s3097","stat":"ChatGPT Enterprise aggregate output tokens grew roughly sevenfold between June 2025 and March 2026","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Administrative telemetry, not survey. Growth is a mix of new adopters and deepening use inside existing ones","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3098","stat":"Within the fixed cohort of firms that had adopted ChatGPT Enterprise by June 2025, output tokens grew roughly fourfold to March 2026, so about half of total growth came from existing customers rather than new logos","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"The intensification-versus-arrival split. Usage also accelerated in early 2026 simultaneously across all adoption cohorts","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3099","stat":"US public-company ChatGPT Enterprise adopters had 2024 median revenue of $2,275.1m against $209.6m for non-adopters","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Non-adopter means no ChatGPT Enterprise match, not no AI use. Compustat sample: 410-417 adopter tickers against 11,784 non-adopter tickers","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3100","stat":"US public-company ChatGPT Enterprise adopters had 2024 median employment of 2,934 against 424 for non-adopters","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Early enterprise AI adopters are not representative of the average public firm","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3101","stat":"US public-company ChatGPT Enterprise adopters had 2024 median R&D expense of $113.1m against $9.9m for non-adopters, and median market value of $4,997.2m against $316.4m","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Adopters are larger, more capitalised, more valuable and more R&D-intensive","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3102","stat":"Public firms in the top revenue quartile are 6.9pp more likely to have adopted ChatGPT Enterprise, and firms in the top 5% are 9.8pp more likely; measured within NAICS2-by-year cells the figures are 7.2pp and 11.3pp","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Conditional association, explicitly not causal. Holds within industries, so not a sector-composition artefact","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3103","stat":"ChatGPT Enterprise adoption is associated with SG&A, R&D and capitalised-software stocks measured in fiscal 2021, years before adoption (coefficients 0.020, 0.004 and 0.008 in the full sample)","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"The empirical case for organisational complements: capacity to absorb the technology predates the technology","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3104","stat":"Six months after organisational adoption, early-career workers and trainees send roughly 8 to 9 more weekly ChatGPT Enterprise messages than the average active user in the same firm, while managers, directors and executives send fewer","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Sample of 1,764 organisations and 17.4m messages. Message volume is usage intensity, not economic importance","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3105","stat":"At the average adopting firm six months in, weekly active users of ChatGPT Enterprise break down as engineering and technical ~11%, executives/founders/partners ~9%, finance and accounting ~5%, marketing and communications ~5%, sales and account management ~4%","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Share of observed active users, not a role-level adoption rate: the paper never observes the denominator of employees per role","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3106","stat":"By seniority, weekly active users of ChatGPT Enterprise at the average adopting firm are managers and directors ~24%, individual contributors ~15%, senior ICs and principals ~14%, executives ~10%, early-career and trainees ~7%","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Breadth runs the opposite way to intensity: managers are the largest user group and among the lightest users","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3107","stat":"More than half of ChatGPT Enterprise weekly active users perform documentation or technical-writing tasks in a given week, and nearly half perform technical digital work","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"Task classification sample of 973 organisations and 8.7m classified messages, classifier available from 30 Oct 2025","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3108","stat":"Legal and regulatory work is a widespread ChatGPT Enterprise task by user reach but accounts for a comparatively small share of message volume, and is relatively more common among executives than production staff","source":"OpenAI / Chatterji et al, How Organizations Use AI","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"client","sourceType":"academic","scope":"cross-sector","context":"The profile of an orientation check rather than a workflow. Same pattern as topic overviews, business research and financial or tax tasks","url":"https://cdn.openai.com/pdf/how-organizations-use-chatgpt.pdf","access":"open"},{"id":"s3109","stat":"Adding explicit penalty information to a regulatory rule reduces AI agent compliance across every model tested: Kimi K2.5 fell 53pp (93% to 40%), Grok 4.1 Fast 52pp (60% to 8%), DeepSeek V3.2 51pp (71% to 20%), Nemotron 3 Super 31pp (81% to 50%) and Gemini 3 Flash 30pp (40% to 10%)","source":"Okamoto & Erol, Why Do AI Agents Break Rules? (arXiv 2608.12323)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The enforcement information paradox. Naming a price converts a prohibition into a cost-benefit calculation (Gneezy-Rustichini). Simulated procurement agent, 12 models, 25 completions per condition","url":"https://arxiv.org/abs/2608.12323","access":"open"},{"id":"s3110","stat":"Gemini 3 Flash compliance with an imperatively phrased regulation collapsed from 100% to 34% when a low penalty was specified, then recovered to 100% at high enforcement","source":"Okamoto & Erol, Why Do AI Agents Break Rules? (arXiv 2608.12323)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The starkest single instance of the enforcement information paradox","url":"https://arxiv.org/abs/2608.12323","access":"open"},{"id":"s3111","stat":"Under deadline-urgency pressure with no system mandate, every one of twelve instruction-tuned models dropped to 9% regulatory compliance or below: GPT-OSS 9%, Qwen and Llama 0%, all task-optimised models 8% or below","source":"Okamoto & Erol, Why Do AI Agents Break Rules? (arXiv 2608.12323)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The urgency exception: a training-orientation-invariant vulnerability. Urgency is read as legitimate operational context rather than as an attack","url":"https://arxiv.org/abs/2608.12323","access":"open"},{"id":"s3112","stat":"Even with the strongest anti-adversarial system mandate, most models fail to hold 45% compliance under deadline pressure: GPT-OSS 45%, Gemini 40%, Kimi 39%, Nemotron 35%, Llama 16%, Grok 12%, GLM 8%, Mistral 4%, DeepSeek, MiniMax and Gemma 4 all 0%, with only Qwen 3.5 recovering to 100%","source":"Okamoto & Erol, Why Do AI Agents Break Rules? (arXiv 2608.12323)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"The evidence that prompt engineering cannot govern an agent. Architectural safeguards such as routing rushed requests to human review are required","url":"https://arxiv.org/abs/2608.12323","access":"open"},{"id":"s3113","stat":"Blanket managerial authorisation to bypass a regulation produced 0% AI agent compliance in 15 of 48 model-by-enforcement cells, and a board-level cost-optimisation policy took Kimi, DeepSeek, Grok, Gemini and Gemma to 0-4% regardless of fine size","source":"Okamoto & Erol, Why Do AI Agents Break Rules? (arXiv 2608.12323)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Institutional authority collapses compliance across both safety-fine-tuned and task-optimised models","url":"https://arxiv.org/abs/2608.12323","access":"open"},{"id":"s3114","stat":"Telling an AI procurement agent that a peer company had been fined raised compliance from 8% to 92% for Grok 4.1 Fast and from 12% to 80% for Gemini 3 Flash; telling it a peer escaped an audit suppressed compliance","source":"Okamoto & Erol, Why Do AI Agents Break Rules? (arXiv 2608.12323)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Visible enforcement is a cheap, powerful and bidirectional governance lever. Peer violation is read as a descriptive norm","url":"https://arxiv.org/abs/2608.12323","access":"open"},{"id":"s3115","stat":"Across 6,743 classified non-compliant AI agent trials, 94.5% surfaced the regulation in the agent\'s stated reasoning, either overriding it explicitly or acknowledging it","source":"Okamoto & Erol, Why Do AI Agents Break Rules? (arXiv 2608.12323)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Most violations are openly rationalised rather than concealed, which is what makes the silent minority the audit problem","url":"https://arxiv.org/abs/2608.12323","access":"open"},{"id":"s3116","stat":"Silent regulatory violations, where an AI agent breaks a rule with no textual reference to it, run at 11-15% of failures for Mistral Small, GLM 4.7 Flash and Kimi K2.5, against 0% for GPT-OSS-120B","source":"Okamoto & Erol, Why Do AI Agents Break Rules? (arXiv 2608.12323)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"These are the violations an automated reasoning audit cannot catch. Concentrated in task-optimised models","url":"https://arxiv.org/abs/2608.12323","access":"open"},{"id":"s3117","stat":"When an anti-adversarial system mandate is bypassed by employee pressure, AI agents refer to the mandate in under 1% of their violation rationales","source":"Okamoto & Erol, Why Do AI Agents Break Rules? (arXiv 2608.12323)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"The mandate has a measurable behavioural effect through a mechanism that never appears in the reasoning trace","url":"https://arxiv.org/abs/2608.12323","access":"open"},{"id":"s3118","stat":"Employment of workers aged 22-25 in AI-exposed occupations stands 19% below where it would be had it kept pace with their less-exposed peers; experienced workers show no comparable gap","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"ADP administrative payroll, balanced panel of 3.5-5m employees/month through June 2026. The headline of the August 2026 revision. Replaces the earlier 13% and 16% firm fixed-effects estimates, which the authors have retired. Descriptive, not causal","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3119","stat":"Employment of 22-25 year-olds in the two most AI-exposed occupation quintiles fell about 11% between November 2022 and June 2026, while the same age group in the three least-exposed quintiles grew about 10%","
1source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"A 21 percentage point divergence, which is the 19% kept-pace figure expressed in levels. Requires no modelling, which is why the authors now lead on it","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3120","stat":"Total employment in the ADP sample grew about 6% between November 2022 and June 2026, and the most AI-exposed quintile grew about 4%: no economy-wide AI job displacement is visible in payroll data","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Fact 1 of six. The aggregate stability is itself the finding: the effect is confined to the entry point, not the workforce","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3121","stat":"The early-career AI exposure gap widened from 15% at the July 2025 data vintage to 19% as of June 2026","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Fact 3. Measured consistently on the kept-pace basis across vintages. The event-study relative decline reaches roughly 18 percentage points by mid-2026","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3122","stat":"The early-career employment divergence operates through reduced hiring, not increased separations: among young workers, separation rates fell at least as much in the most AI-exposed occupations as in the least","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Fact 4, and the single most important qualifier on the headline. This is a narrowing entry point, not a redundancy wave. The opposite of what displacement would produce","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3123","stat":"Entered jointly, only automation exposure predicts employment decline for young workers (-0.098 per standard deviation at ages 22-25, shrinking monotonically with age), while complementarity exposure predicts gains for experienced workers (+0.024 at ages 41-49)","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Fact 5, and the paper\'s most direct mechanism evidence. Uses the Anthropic Economic Index automative/augmentative classification. Holds under both the March 2025 and pooled later AEI releases. Where AI does the work, young employment falls; where it helps someone do the work, employment is flat or rising","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3124","stat":"Occupations with higher codified-knowledge requirements show slower entry-level employment growth, while occupations with higher tacit-knowledge requirements show faster employment growth for mid-career and senior workers","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"The proposed mechanism: AI substitutes for formal, documented, teachable knowledge and complements experiential knowledge. Author-flagged caveat: the codified gradient loses significance once college share is controlled for, though the tacit gradient for experienced workers survives it","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3125","stat":"Labour-market adjustment to AI is showing in employment rather than base pay: real base compensation shows little divergence by age or AI exposure quintile, against a roughly 20 percentage point divergence in employment","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Fact 6. Important limitation stated by the authors: base salary only, excluding bonuses, overtime, commission, equity and tips, which are largest in precisely the most exposed high-income occupations","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3126","stat":"Applying the August 2025 sample filters to current data, the within-firm employment estimate for 22-25 year-olds in the most AI-exposed quintile falls to -5.3 log points (p=0.26), against -11.7 log points (p=0.02) reported in August 2025;
1 the fourth-quintile estimate is stable at -12.8 (p=0.02)","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Online Appendix L. The authors\' own self-correction after rebuilding their occupation crosswalk and imputing missing occupation codes. This is why the widely quoted 16% has been retired. Anyone still citing 13% or 16% is citing a superseded vintage","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3127","stat":"Controlling for occupational college share halves the estimated employment decline for 22-25 year-olds in the most AI-exposed quintile, from -0.18 to -0.09","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"The single control that most attenuates the result. The authors argue the two estimates bracket a range rather than one being correct, because education is plausibly the channel AI operates through as well as a potential confounder","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3128","stat":"The AI exposure gap for young workers is -0.132 in ADP payroll data against -0.022 in the American Community Survey over a comparable 2022-2024 window, but within professional, information and financial services the two agree closely at -0.231 and -0.213","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"The paper\'s biggest weakness and its sector-specific exception. The overall magnitude is specific to the ADP sample; the professional-services magnitude survives the national benchmark. The discrepancy concentrates in education, health and public administration, where the 2024 ACS population weights are known to be problematic","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3129","stat":"Paralegals and legal assistants and legal secretaries fall in the most AI-exposed occupation quintile, while lawyers fall in the second-most-exposed quintile","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Top-50 occupations by ADP employment per quintile, ranked on Eloundou et al. (2024) GPT-4 beta exposure. Quintile 5 also holds customer service representatives, accountants and systems software developers. Note the paper separately describes lawyers as a teleworkable occupation with relatively low AI exposure within that set: both statements are in the paper","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3130","stat":"About 30% of occupations in the most AI-exposed quintile saw rising early-career employment between November 2022 and June 2026, against about 60% of occupations in the least-exposed quintile","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Shows the effect is broad across exposed occupations rather than driven by a handful of large ones","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3131","stat":"Women work in more AI-exposed occupations than men at every age, in ADP payroll, the CPS and the ACS alike, and young women show steeper employment declines than young men","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Consistent with greater concentration in declining high-exposure roles. The authors flag it as heterogeneity worth monitoring rather than a finished result","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3132","stat":"The employment decline for AI-exposed young workers is sharpest in US states with the most intense AI adoption (about -19% for the most exposed quintile) and smallest in emerging-adoption states","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Built from state-level Anthropic Economic Index usage divided by labour force size. Suggestive only: firm identifiers are anonymised, so no direct firm-level adoption analysis is possible","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3133","stat":"Employment of 22-25 year-olds was about 2% below its November 2022 level by June 2026, while every older age group grew, by as much as 11% for workers aged 35-40","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"The two most exposed quintiles were about 57% of employment in this age group in November 2022, so their 11% decline subtracted roughly 6 percentage points from the age group overall. Growth in less-exposed occupations offset most but not all of it","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"},{"id":"s3134","stat":"Across five alternative measures of AI exposure, employment for workers aged 22-25 in the most exposed quintile falls 10-13% relative to November 2022 while the least-exposed quintiles grow","source":"Brynjolfsson, Chandar & Chen (Stanford Digital Economy Lab), Canaries in the Coal Mine?","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Robustness across Brynjolfsson et al. (2018) machine-learning suitability, the Felten et al. AI Occupational Exposure index and its two variants, and the Tomlinson et al. Bing Copilot applicability score. The finding does not depend on the choice of exposure measure","url":"https://digitaleconomy.stanford.edu/app/uploads/2026/08/Canaries_August2026.pdf","access":"open"}
1,{"id":"s3135","stat":"Moving an agent from a poor to a structured operating context, holding the model fixed, raised the mean final score from 3.15 to 5.49 out of 10 and cut critical failures from 4.11 to 1.33 per evaluation, roughly 68%","source":"Bousetouane, AI Agents Do Not Fail Alone: The Context Fails First","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"300 multi-turn evaluations, 7,500 turns, three regulated domains. Single-author preprint scored by the author\'s own harness; directional only","url":"https://arxiv.org/abs/2607.14257","access":"open"},{"id":"s3136","stat":"Further hardening a structured context with refusals, escalation thresholds, injection separation and confirmation requirements raised the context-quality score from 8.1 to 8.7 but lowered the final behavioural score from 5.49 to 5.16 and raised critical failures from 1.33 to 1.56","source":"Bousetouane, AI Agents Do Not Fail Alone: The Context Fails First","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The instrument\'s own top end contradicts its leading-indicator claim. Author reports it as a structure-versus-conservatism tradeoff","url":"https://arxiv.org/abs/2607.14257","access":"open"},{"id":"s3137","stat":"The weakest agent context was the cheapest to run, at 392 overhead tokens per call against 732 for a structured context and 1,010 for a hardened one, while producing the worst behaviour and the most critical failures","source":"Bousetouane, AI Agents Do Not Fail Alone: The Context Fails First","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The cost of weak context is paid in reliability, not tokens. Directly usable against spend-as-proxy-for-value arguments","url":"https://arxiv.org/abs/2607.14257","access":"open"},{"id":"s3138","stat":"Total token usage per 25-turn evaluation was 1,014k for a poor context, 1,119k structured and 1,139k hardened, a spread of about 12% for a final score difference of 3.15 to 5.49","source":"Bousetouane, AI Agents Do Not Fail Alone: The Context Fails First","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Better context cost ~12% more tokens and roughly doubled the score. Author\'s own harness; directional","url":"https://arxiv.org/abs/2607.14257","access":"open"},{"id":"s3139","stat":"Improving an agent\'s context from poor to structured raised tool schema quality scores from 4.3 to 9.0, instruction consistency from 4.0 to 8.3, role clarity from 5.3 to 9.0 and grounding sufficiency from 5.8 to 8.3, out of 10","source":"Bousetouane, AI Agents Do Not Fail Alone: The Context Fails First","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The context score detects which dimension of the context changed rather than rewarding length. Author-defined construct","url":"https://arxiv.org/abs/2607.14257","access":"open"},{"id":"s3140","stat":"Across 300 multi-turn agent evaluations, grounding sufficiency correlated with hallucination resistance at r=0.63 and guardrail coverage with manipulation resistance at r=0.60","source":"Bousetouane, AI Agents Do Not Fail Alone: The Context Fails First","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Context took only three designed values, so most variance is between-condition. No confidence intervals or p-values reported","url":"https://arxiv.org/abs/2607.14257","access":"open"},{"id":"s3141","stat":"Across 300 multi-turn agent evaluations, instruction consistency correlated with instruction following at r=0.57, injection hardening with safety at r=0.48, tool schema quality with tool use at r=0.47 and role clarity with task success at r=0.40","source":"Bousetouane, AI Agents Do Not Fail Alone: The Context Fails First","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Same caveats: three-level designed variation, no intervals, both metrics produced by the same multi-juror LLM harness","url":"https://arxiv.org/abs/2607.14257","access":"open"},{"id":"s3142","stat":"The best-performing context condition across three regulated agent domains still produced a mean final score of only 5.49 out of 10","source":"Bousetouane, AI Agents Do Not Fail Alone: The Context Fails First","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Best case in the study. Context engineering improved a poor agent to a mediocre one, in customer support, healthcare claims and legal contract drafting","url":"https://arxiv.org/abs/2607.14257","access":"open"},{"id":"s3143","stat":"A proposed seven-criterion measure of agent context quality covers role clarity, guardrail coverage, instruction consistency, tool schema quality, grounding sufficiency, injection hardening and token efficie
1ncy, each mapped to a named failure mode","source":"Bousetouane, AI Agents Do Not Fail Alone: The Context Fails First","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Usable as a build and review rubric independently of the paper\'s validation claim","url":"https://arxiv.org/abs/2607.14257","access":"open"},{"id":"s3144","stat":"Generative AI exposure and work-from-home exposure have a Spearman rank correlation of 0.77 across 683 detailed occupations, so any study measuring one without the other is measuring both","source":"Lambert & Schindler (Warwick/LSE, Ellison Institute), The Broken Ladder","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Eloundou et al. (2024) GenAI index against the Hansen et al. (2023) WFH measure at 6-digit O*NET-SOC level. Software developers, accountants and management consultants top both rankings; electricians, janitors and construction labourers sit at the bottom. The single most transferable fact in the paper, and a direct challenge to any vendor claim built on occupation-level AI exposure","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787638","access":"open"},{"id":"s3145","stat":"Entered separately, a two-standard-deviation increase in either GenAI or work-from-home exposure predicts a 4-5 percentage point fall in the junior share of new hires by 2025, and around 3 percentage points in the share of job postings requiring three years of experience or less","source":"Lambert & Schindler (Warwick/LSE, Ellison Institute), The Broken Ladder","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"243m new hires (Revelio Labs) and 407m online job postings (Lightcast) across the US, UK, Canada and Australia, 2017-2025. Near-identical event-study paths with
1no pre-trends: examined alone, either shock looks like a convincing culprit","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787638","access":"open"},{"id":"s3146","stat":"Entered jointly, the work-from-home effect on the junior share of new hires holds at -1.42 to -1.57 percentage points while the generative AI effect collapses from around -1.4 to between -0.41 and -0.45 and changes sign on job postings","source":"Lambert & Schindler (Warwick/LSE, Ellison Institute), The Broken Ladder","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"The paper\'s central result. WFH moves only from -1.69 to -1.42pp (occupation panel) and -1.88 to -1.57pp (firm panel). Not a collinearity artefact: variance inflation factors below 2.4, condition numbers below 2.7. Contested by the August 2026 Canaries revision, which replicates the specification and still finds an AI effect","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787638","access":"open"},{"id":"s3147","stat":"A confounder explaining only 1-3% of residual variation would drive the generative AI coefficient on junior hiring to zero, against 5-9% for the work-from-home coefficient","source":"Lambert & Schindler (Warwick/LSE, Ellison Institute), The Broken Ladder","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Cinelli-Hazlett robustness values, 3-5x larger for WFH. The authors note that this diagnostic applied to the AI coefficient alone would itself have flagged the AI result as not robust to plausible unobserved confounding","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787638","access":"open"},{"id":"s3148","stat":"A single above-median work-from-home dummy is enough to render the generative AI coefficient on early-career hiring statistically insignificant, and it stays insignificant at every finer quantile resolution","source":"Lambert & Schindler (Warwick/LSE, Ellison Institute), The Broken Ladder","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Semi-parametric test replacing the linear co-treatment control with step functions of progressively finer resolution. On the postings outcome the AI coefficient often flips slightly positive. The WFH coefficient is stable at every resolution","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787638","access":"open"},{"id":"s3149","stat":"The junior share of new hires is 8-11 percentage points below its 2019 baseline across the US, UK, Canada and Australia, with entry-level hires down 14-29% and junior hires down 12-26%, while senior hires run 5-21% above pre-period levels","source":"Lambert & Schindler (Warwick/LSE, Ellison Institute), The Broken Ladder","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"The background fact both sides of the AI-versus-remote-work debate accept. Series stable through 2019, dips in 2020-21, breaks downward from late 2022. The synchronicity across four economies with different institutions is the authors\' strongest argument for a common shock","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787638","access":"open"},{"id":"s3150","stat":"Firms and regions that visibly offered remote or hybrid work in 2021-22 subsequently shifted hiring away from early-career workers, with firm-level effects of -0.69 to -1.20 percentage points on the junior hire share","source":"Lambert & Schindler (Warwick/LSE, Ellison Institute), The Broken Ladder","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"The paper\'s strongest design, using actual observed adoption rather than occupational exposure, after coarsened exact matching on both WFH and GenAI exposure quintiles. Scarcely attenuated in joint specifications. The authors note they cannot run the symmetric test on AI because no firm-level GenAI adoption measure exists at comparable scale","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787638","access":"open"},{"id":"s3151","stat":"In UK new hires at state-occupation level, generative AI exposure remains
1negative and statistically significant alongside work-from-home: the only one of sixteen country-by-design combinations where both exposures survive jointly","source":"Lambert & Schindler (Warwick/LSE, Ellison Institute), The Broken Ladder","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Country-by-country robustness. The WFH-dominant pattern holds in 13 of 16 cells; the statistically null WFH estimates are confined to both UK postings panels and the Canadian occupation-level postings panel. The UK is where the AI signal is strongest in this data","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787638","access":"open"},{"id":"s3152","stat":"Across 24 pairings of two work-from-home measures and three generative AI exposure measures, the work-from-home coefficient on early-career hiring is larger in magnitude than the AI coefficient in 22","source":"Lambert & Schindler (Warwick/LSE, Ellison Institute), The Broken Ladder","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Tests Hansen et al. (2023) and Dingel & Neiman (2020) teleworkability against Eloundou et al. (2024), Felten et al. (2023) and the Anthropic Economic Index. Both exceptions arise in the firm-level junior-hire panel under the Dingel & Neiman measure. Shows the result does not depend on the specific index chosen","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787638","access":"open"},{"id":"s3153","stat":"Adding ACE context curation to a frozen DeepSeek-V3.1-671B raised AppWorld agent benchmark accuracy from 42.4 to 59.4 offline and 59.5 online, against 46.0 for in-context learning and 46.4 for GEPA","source":"Zhang et al., Agentic Context Engineering (ACE), ICLR 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"legal","context":"No weights updated. Peer reviewed at ICLR 2026. Software-agent benchmark, not legal work","url":"https://arxiv.org/abs/2510.04618","access":"open"},{"id":"s3154","stat":"ACE raised accuracy on a financial analysis benchmark (FiNER and Formula) from 69.1 for the base model to 81.9, against 72.5 for GEPA","source":"Zhang et al., Agentic Context Engineering (ACE), ICLR 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"XBRL tagging and formula extraction: structured-data tasks, not judgement tasks","url":"https://arxiv.org/abs/2510.04618","access":"open"},{"id":"s3155","stat":"ACE improved on the AppWorld baseline by 14.8 points without ground-truth labels, using execution success or failure as the signal, but lost 3.4 points on FiNER in the unlabelled online setting","source":"Zhang et al., Agentic Context Engineering (ACE), ICLR 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The no-supervision claim holds where an execution oracle exists and fails without one. The paper states context can be polluted by spurious signals","url":"https://arxiv.org/abs/2510.04618","access":"open"},{"id":"s3156","stat":"ACE cut context-adaptation latency by 82.3% and rollouts by 75.1% against GEPA on AppWorld, and cut latency 91.5% and token cost 83.6% against Dynamic Cheatsheet on FiNER, from $17.70 to $2.90","source":"Zhang et al., Agentic Context Engineering (ACE), ICLR 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Delta updates and non-LLM merging rather than repeated full rewrites","url":"https://arxiv.org/abs/2510.04618","access":"open"},{"id":"s3157","stat":"In a prompt-caching study on the OpenAI API with GPT-5.1, 91.8% of ACE\'s input tokens were served from cache at evaluation time, cutting billed input-token cost by 82.6% against counting raw context tokens","
1source":"Zhang et al., Agentic Context Engineering (ACE), ICLR 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"A long stable reused context is cheap to serve because it caches. Direct counterweight to token count as a cost proxy","url":"https://arxiv.org/abs/2510.04618","access":"open"},{"id":"s3158","stat":"ACON cut peak token usage by 26-54% across AppWorld, OfficeBench and Multi-objective QA while improving task success over compression baselines","source":"Kang et al., ACON: Optimizing Context Compression for Long-horizon LLM Agents, ICML 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Compression guidelines optimised in natural language from failure analysis; no fine-tuning, so it works on proprietary models","url":"https://arxiv.org/abs/2510.00615","access":"open"},{"id":"s3159","stat":"On 8-objective QA, ACON exceeded the no-compression baseline on exact match and F1 while cutting peak tokens by 54.5% and dependency by 61.5%","source":"Kang et al., ACON: Optimizing Context Compression for Long-horizon LLM Agents, ICML 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Compression improved accuracy rather than trading it away, by removing distracting context","url":"https://arxiv.org/abs/2510.00615","access":"open"},{"id":"s3160","stat":"ACON delivered up to 46% performance improvement for smaller language models acting as long-horizon agents, by mitigating context distraction","source":"Kang et al., ACON: Optimizing Context Compression for Long-horizon LLM Agents, ICML 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Qwen3-14B approaches much larger models when what it carries is curated","url":"https://arxiv.org/abs/2510.00615","access":"open"},{"id":"s3161","stat":"Distilling ACON\'s optimised compressor cut cost per example from $0.045 with GPT-4.1 to $0.014 with GPT-4.1-mini and $0.0004 with a distilled Qwen3-14B, a 99.1% reduction on the teacher model","source":"Kang et al., ACON: Optimizing Context Compression for Long-horizon LLM Agents, ICML 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Guideline optimisation itself cost under $2 per benchmark, plus about $20 of training-set rollouts on AppWorld","url":"https://arxiv.org/abs/2510.00615","access":"open"},{"id":"s3162","stat":"Context compression cut end-to-end API cost per AppWorld task from $0.331 to $0.285 (history) and $0.272 (observation) while raising median wall-clock latency from 73.24s to 87.68s and 101.92s","source":"Kang et al., ACON: Optimizing Context Compression for Long-horizon LLM Agents, ICML 2026","date":"Oct 2025","sortKey":20251000,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The trade-off nobody quotes: compression buys money and memory headroom and pays for it in time, roughly 20-39%","url":"https://arxiv.org/abs/2510.00615","access":"open"},{"id":"s3163","stat":"An adversarial multi-turn agent evaluation covering 47 completed configurations, 470 run-level trials and 23,500 agent turns recovered 414 production failure events and 103 severity-graded findings","source":"Bousetouane, Human-on-the-Bridge: Scalable Evaluation for AI Agents","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Three domains, four agent backbone families, five evaluator tiers. Single-author preprint describing the author\'s own commercial harness","url":"https://arxiv.org/abs/2606.16871","access":"open"},{"id":"s3164","stat":"Smaller evaluator models scored agents more leniently, not more accurately: median configuration scores ran 7.76 at the 4B Gemma tier down to 6.27 at the 120B GPT-OSS tier","source":"Bousetouane, Human-on-the-Bridge: Scalable Evaluation for AI Agents","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"The author states directly that subjective score magnitude is not a measure of evaluation strength. Cite against any claim that a small model judges as well as a large one","url":"https://arxiv.org/abs/2606.16871","access":"open"},{"id":"s3165","stat":"Moving from a 4B to a 120B evaluator model lowered scores for the same agents by 3.27 points on task success, 2.18 on instruction following and 1.66 on manipulation resistance","source":"Bousetouane, Human-on-the-Bridge: Scalable Evaluation for AI Agents","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The signature of a stricter evaluator, not of changed agent behaviour. Twice-aggregated medians with
1no confidence intervals","url":"https://arxiv.org/abs/2606.16871","access":"open"},{"id":"s3166","stat":"Under matched adversarial evaluation conditions in a medical triage scenario, an agent built on Claude Opus 4.7 scored 4.0 out of 10 against 7.6 for the same agent built on Claude Opus 4.8","source":"Bousetouane, Human-on-the-Bridge: Scalable Evaluation for AI Agents","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Paired version calibration holding harness tier, persona, domain and seed constant. Several tier rows rest on a single matched pair","url":"https://arxiv.org/abs/2606.16871","access":"open"},{"id":"s3167","stat":"In-house legal teams want contract review and drafting to fall from 35.7% of their working week today to 23.9% by 2030, a drop of 11.8 points","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Self-reported current and desired time allocation. n=102, LegalOn\'s own audience; LegalOn labels the figures perception and intent, not measured time-tracking. Signal, not a hard number.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3168","stat":"In-house legal teams want strategic legal advice to the business to rise from 30.5% of their working week today to 41.7% by 2030, a gain of 11.2 points","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"The mirror of the contract-review cut. Roughly 4.5 hrs/week or 233 hrs/yr moving into advisory work against a 40-hour week. n=102, aspiration not trajectory.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3169","stat":"74.5% of in-house respondents expect to spend less time on contract review by 2030, 71.6% expect more time on strategic advice, and 61.8% expect both at once","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Direction of travel is near-universal across roles and team sizes. n=102.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3170","stat":"General Counsel want a steeper cut in their own contract-review time (-14.7 pts) than non-GC in-house counsel (-11.3 pts); solo legal departments want the steepest cut of all (-16.5 pts)","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Kills the read that the strategic pivot is a junior wish list handed upwards. Solo departments have nobody to delegate document work to, so AI reads as capacity rather than convenience. n=102.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3171","stat":"92% of in-house legal teams report using AI, but 54.9% are still \'exploring\' and fewer than 11% have reached connected knowledge or full workflow support","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The breadth-without-depth finding. Full curve: 7.8% no AI, 54.9% exploring, 26.5% standardising prompts and workflows, 8.8% connected knowledge, 2.0% full workflow support. n=102; the top tier is n=2 and flagged directional only by LegalOn.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3172","stat":"26.5% of in-house legal teams have standardised prompts and workflows; 8.8% have reached \'connected knowledge\' where AI is grounded in their own playbooks and policies","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Standardising puts a team ahead of roughly two-thirds of the market on this sample; connected knowledge puts it in the top 11%. n=102.","url":"https://www.legalontech.c
1om/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3173","stat":"81.9% of in-house respondents reporting any AI benefit cite reduced time on routine tasks, but only 11.7% cite measurable cost savings and 17.0% improved visibility into workload or matter status","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"The measurement gap. Individual time savings are felt everywhere and converted into financial evidence almost nowhere. Corroborates Harvard CLP (<5% systematic output metrics) and Axiom (83% no established ROI metrics) from a third method and sponsor. n=102.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3174","stat":"60.6% of in-house respondents cite faster turnaround for the business as an AI benefit; 22.3% cite more consistent outputs, 21.3% improved job satisfaction or reduced burnout","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"The benefits ad hoc individual use produces, as distinct from the ones that need infrastructure. n=102.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3175","stat":"Post-signature contract management shows the widest desire-to-adoption gap in in-house legal AI: 14.7% of teams use AI there today against 63.7% who want to, a 49-point gap","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Compliance monitoring and matter intake and triage follow. Note this is also where LegalOn\'s Vault product sits and the report leads with it, so treat the ranking as directional and corroborate before client use. n=102.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3176","stat":"Contract review and redlining is the most AI-saturated in-house workflow at 65.7% current use against 80.4% desired, a 14.7-point gap and the narrowest of any workflow with unmet demand","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Most legal AI investment sits where the remaining gap is smallest. \'Most mature\' is not \'done\'. n=102.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3177","stat":"Legal research is the only in-house workflow where desired future AI adoption falls below current usage","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"The only source in the vault reporting a net retreat from AI legal research. Directly contradicts Axiom (n=528), where legal research accounts for 57% of cited ROI. LegalOn attributes it to hesitation about relying on AI research output without independent review. Unresolved; watch whether the next in-house survey repeats it. n=102.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3178","stat":"In-house teams already using AI for contract review expect to redirect 308 hrs/yr into strategic advice, against 109 hrs/yr for teams that have not started, a near-3x difference","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Contact with the tool raises the expected ceiling rather than lowering it. Heavy self-selection: adopters are people who already bought the premise. Expectation, not measured outcome. n=102.","url":"https://www.legalontech.c
1om/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3179","stat":"79.1% of in-house AI adopters personally expect more strategic-advice time, against 57.1% of non-adopters, a 22-point gap","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Companion to the 308-vs-109 hours finding. Same self-selection caveat. n=102.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3180","stat":"48.0% of in-house respondents expect AI to change their time allocation \'moderately\' over the next few years and 29.4% expect \'significant\' change","source":"LegalOn Technologies, 2026 In-House Legal Pulse (n=102)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"Respondents do not read the reallocation as a marginal adjustment. n=102.","url":"https://www.legalontech.com/resources/2026-in-house-legal-pulse","access":"gated"},{"id":"s3181","stat":"Anthropic raised its own overall misalignment risk assessment from \\"very low\\" to \\"Low\\", citing \\"general increased uncertainty around recent incident disclosures related to model behavior in cybersecurity evaluations\\"","source":"Anthropic, Risk Report: August 2026 (redacted public version, 186pp)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"vendor","scope":"cross-sector","context":"Read at primary source 21 Aug 2026. Coverage date 15 Jul 2026, covering from the 24 Feb 2026 previous report. The only instance in the vault of a frontier lab publicly moving its own safety rating in the unhelpful direction. Use for the argument that a governance framework which can only ratify is theatre. Note the UK AISI incident postdates the coverage date and Anthropic had not reviewed the transcripts, so this rating is not an assessment of that incident."},{"id":"s3182","stat":"\\"Claude now authors a large majority of the code merged into our production codebases\\" (Anthropic, on its own engineering)","source":"Anthropic, Risk Report: August 2026","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Primary source, self-reported, no percentage given and no methodology. Cite the phrasing, not a number. Coverage date 15 Jul 2026."},{"id":"s3183","stat":"Anthropic assesses its own internal AI R&D as \\"significantly faster than they would be without AI assistance, but not yet by a factor of 2 (though we are uncertain and measurement is difficult)\\"","source":"Anthropic, Risk Report: August 2026","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"A frontier lab putting a self-reported ceiling below 2x on its own AI-accelerated R&D, in a document with a compliance purpose rather than a marketing one. The best available counterweight to 10x productivity claims. Self-assessed and explicitly uncertain."},{"id":"s3184","stat":"OpenAI states it \\"cannot rule out critical cyber capabilities\\" for its upcoming model Astra under its Preparedness Framework; previous models including GPT-5.6-Sol were assessed at High rather than Critical","source":"OpenAI, \\"Responding to the next frontier of critical cyber capabilities\\"","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Read at primary source 21 Aug 2026. The precise wording is \\"cannot rule out\\", not \\"reached Critical\\", and most secondary coverage has the stronger version. OpenAI also states Astra \\"was not involved in exploiting Hugging Face\\"."},{"id":"s3185","stat":"GPT-5.6 Sol, asked to assess a simple static personal website, found 13 security issues in about 15 minutes and remediated them over roughly an hour","source":"Greg Brockman (OpenAI), \\"The Defender\'s Window\\"","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Anecdote, n=1, on the author\'s own site (gregbrockman.com), reported by the president of the vendor whose model it is. Not evidence of a rate. Useful as a session opener on AI-assisted security review. Remediation included DNS and TLS reconfiguration, dropping jQuery, migrating hosting and starting a ph
1ased DMARC rollout."},{"id":"s3186","stat":"At OpenAI, \\"almost all of our initial security alerts are triaged by intelligence before humans are looped in\\"","source":"Greg Brockman (OpenAI), \\"The Defender\'s Window\\"","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Self-reported by the vendor, no figure or period given. A concrete example of agentic triage in production at scale, useful for the human-on-the-loop discussion."},{"id":"s3187","stat":"Four UK government departments trialled M365 Copilot in the same window and reported time savings differing by more than 2x: 26 min/day (GDS), 19 min/day (DWP), 60 min/week (HMRC), no productivity finding (DBT)","source":"UK Government (GDS / HMRC / DWP / DBT Copilot evaluations)","date":"Jun 2025 - Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"The clearest public demonstration that reported AI time savings track the looseness of the instrument, not the tool. Same product, overlapping populations, Oct-Dec 2024. Source file: [[uk-government-copilot-evaluations"},{"id":"s3188","stat":"GDS cross-government M365 Copilot experiment: average self-reported saving of 26 minutes per day across ~20,000 civil servants in 12 organisations","source":"GDS, Microsoft 365 Copilot Experiment: Cross-Government Findings Report","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"The figure that travelled and became \\"nearly two weeks a year\\". Elicited via a six-point categorical band from \\"none\\" to \\"more than 60 minutes\\". No adjustment, no control group. Trial 30 Sep - 31 Dec 2024.","url":"https://www.gov.uk/government/publications/microsoft-365-copilot-experiment-cross-government-findings-report/microsoft-365-copilot-experiment-cross-government-findings-report-html","access":"open"},{"id":"s3189","stat":"GDS cross-government Copilot trial: satisfaction 7.7/10, likelihood to recommend 8.2/10, 85% agreed good organisational value, 82% would not want to go back","source":"GDS, Microsoft 365 Copilot Experiment: Cross-Government Findings Report","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Sentiment measures, strong across the board. Adoption held at ~80%; Teams highest surface at 71%, Excel and PowerPoint lowest at 23-24%. n=~20,000.","url":"https://www.gov.uk/government/publications/microsoft-365-copilot-experiment-cross-government-findings-report/microsoft-365-copilot-experiment-cross-government-findings-report-html","access":"open"},{"id":"s3190","stat":"HMRC\'s own randomised Copilot evaluation found staff self-reported saving 2-3% of their working week, around 60 minutes per week, under half the 26 minutes per day reported by the cross-government trial covering the same period","source":"HMRC, Evaluating the Impact of Microsoft Copilot in HMRC (Phase 3)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"HMRC was one of the 12 GDS participating organisations and ran Sept-Dec 2024, the same window. HMRC attributes the gap to \\"a deliberately cautious methodology, including a randomised cohort design, strict measurement standards\\". Published 9 Jul 2026. n=1,364 survey (45% response) from 3,000 randomly allocated licences.","url":"https://www.gov.uk/government/publications/evaluation-report-phase-3-trial-of-microsoft-copilot/evaluating-the-impact-of-microsoft-copilot-in-hmrc","access":"open"},{"id":"s3191","stat":"HMRC reduced its self-reported Copilot time savings by around 20% to account for non-users and responder bias, and called the result \\"a baseline estimate rather than a precise measure of impact\\"","source":"HMRC, Evaluating the Impact of Microsoft Copilot in HMRC (Phase 3)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"The only one of the four UK government evaluations to apply and disclose a discount to self-reported figures. It also produced the smallest survey-based number.","url":"https://www.gov.uk/government/publications/evaluation-report-phase-3-trial-of-microsoft-copilot/evaluating-the-impact-of-microsoft-copilot-in-hmrc","access":"open"},{"id":"s3192","stat":"HMRC Copilot Phase 3: 83% of licence holders used it, satisfaction 7.1/10, 68% found it easy to use and well integrated, 61% would be disappointed to lose the licence, 64% used it more over the trial","source":"HMRC, Evaluating the Impact of Microsoft Copilot in HMRC (Phase 3)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"3,000 randomly allocated licences plus 500 for volunteers and reasonable adjustments. Top use cases drafting in Word (53%) and collaborating in Teams (45%). n=1,364.","url":"https://www.gov.uk/government/publications/evaluation-report-phase-3-trial-of-microsoft-copilot/evaluating-the-impact-of-microsoft-copilot-in-hmrc","access":"open"},{"id":"s3193","stat":"46% of HMRC staff who did not use their Copilot licence cited security and data privacy as the main reason; the trial restricted Copilot to material below Official Sensitive","source":"HMRC, Evaluating the Impact of Microsoft Copilot in HMRC (Phase 3)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Classification is the primary brake on public sector Copilot use. The production rollout is cleared to Official Sensitive.","url":"https://www.gov.uk/government/publications/evaluation-report-phase-3-trial-of-microsoft-copilot/evaluating-the-impact-of-microsoft-copilot-in-hmrc","access":"open"},{"id":"s3194","stat":"HMRC projects around £50m per year of \\"capacity generating, net productivity benefits\\" from deploying up to 50,000 Copilot licences to March 2028, with no calculation published","source":"HMRC, Evaluating the Impact of Microsoft Copilot in HMRC (Phase 3)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Works out at £1,000 per licence per year (vault-derived from HMRC\'s own figures). \\"Capacity generating\\" is not cash: no headcount or budget line falls. No calculation or assumptions shown in the report.","url":"https://www.gov.uk/government/publications/evaluation-report-phase-3-trial-of-microsoft-copilot/evaluating-the-impact-of-microsoft-copilot-in-hmrc","access":"open"},{"id":"s3195","stat":"HMRC rolled Copilot out to 28,000 of its 66,000 staff between October 2025 and April 2026, targeting 50,000 during 2026, before publishing the Phase 3 evaluation underpinning the decision in July 2026","source":"HMRC / The Register / THINK Digital Partners","date":"Apr - Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"media","scope":"cross-sector","context":"The evidence was published roughly 19 months after the trial ended and after the rollout completed. Chief AI Officer James Mitton; stated aim to be \\"the most AI-enabled tax authority on the planet\\"; agentic features planned.","url":"https://www.theregister.com/2026/04/27/hmrc_hands_28000_staff_ai/","access":"open"},{"id":"s3196","stat":"DWP Copilot trial: Seemingly Unrelated Regression estimated an average saving of 19 minutes per day across eight routine tasks; 90% of users said Copilot helped them save time","source":"DWP, An Evaluation of DWP\'s Microsoft 365 Copilot Trial","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Largest of the UK departmental evaluations: 3,549 licences, 1,716 users surveyed (48% response) and 2,535 non-users (27%). Six-point ordinal bands converted using the lower bound of each category. No further adjustment; no control group on the quantitative side. Trial Oct 2024 - Mar 2025.","url":"https://www.gov.uk/government/publications/an-evaluation-of-dwps-microsoft-copilot-365-trial/an-evaluation-of-dwps-microsoft-365-copilot-trial","access":"open"}
1,{"id":"s3197","stat":"DWP Copilot trial: 65% of users reported feeling more fulfilled at work (+0.56 on a 7-point scale) and 73% reported improved outputs (+0.49); 65% used it daily and a further 30% weekly","source":"DWP, An Evaluation of DWP\'s Microsoft 365 Copilot Trial","date":"Jan 2026","sortKey":20260100,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Self-reported throughout. The report itself flags selection bias, acquiescence bias, post-treatment bias and the absence of any baseline data. n=1,716 users.","url":"https://www.gov.uk/government/publications/an-evaluation-of-dwps-microsoft-copilot-365-trial/an-evaluation-of-dwps-microsoft-365-copilot-trial","access":"open"},{"id":"s3198","stat":"DBT\'s Copilot evaluation found no evidence that time savings led to improved productivity, and control group colleagues had not observed productivity improvements in pilot participants","source":"DBT, The Evaluation of the M365 Copilot Pilot in the Department for Business and Trade","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"The only UK government Copilot evaluation with a control group on task performance, and the only one to reach a negative productivity finding. 1,000 licences, Oct-Dec 2024, diary study N=1,411 tasks at 32% response, 19 interviews, 16 observed sessions.","url":"https://assets.publishing.service.gov.uk/media/68adbe409e1cebdd2c96a19d/dbt-microsoft-365-copilot-evaluation.pdf","access":"open"},{"id":"s3199","stat":"Adjusting DBT\'s Copilot diary data for unused outputs and for tasks only done because the tool existed cut the mean saving on data analysis from 2.0 hours per task to 0.6, and on brainstorming from 2.1 to 0.5","source":"DBT, The Evaluation of the M365 Copilot Pilot in the Department for Business and Trade","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"The clearest public demonstration of what an unadjusted self-reported time-saving figure is worth. Drafting written documents held up almost unchanged (1.5 to 1.3). Two tasks went negative after adjustment: scheduling -0.6 hrs and generating images -0.5 hrs. N=1,411 logged tasks.","url":"https://assets.publishing.service.gov.uk/media/68adbe409e1cebdd2c96a19d/dbt-microsoft-365-copilot-evaluation.pdf","access":"open"},{"id":"s3200","stat":"In DBT\'s blind-scored observed sessions, Copilot users completed Excel data analysis more slowly than non-users (25:01 vs 20:33) and scored worse on accuracy and quality (1.5 vs 2.7 out of 5)","source":"DBT, The Evaluation of the M365 Copilot Pilot in the Department for Business and Trade","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"On PowerPoint, Copilot users were ~7 minutes faster but scored 1.5 accuracy vs 5.0 and 1.0 quality vs 2.0. On summarising reports Copilot won decisively: 12:37 vs 41:34, accuracy 4.0 vs 2.5. Email a wash on time (07:30 vs 07:43) but better on quality. Small sample: 16 participants, four task types; DBT says treat with caution.","url":"https://assets.publishing.service.gov.uk/media/68adbe409e1cebdd2c96a19d/dbt-microsoft-365-copilot-evaluation.pdf","access":"open"},{"id":"s3201","stat":"DBT Copilot users averaged 1.14 Copilot actions per user per day (72 actions over 63 working days), against 72% reporting they were satisfied or very satisfied","source":"DBT, The Evaluation of the M365 Copilot Pilot in the Department for Business and Trade","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"People liked it considerably more than they used it. Mean daily active users 21%, or 30% excluding weekends; 64% active at least once a week. NPS 31. Usage pulled from the Microsoft dashboard, not self-reported.","url":"https://assets.publishing.service.gov.uk/media/68adbe409e1cebdd2c96a19d/dbt-microsoft-365-copilot-evaluation.pdf","access":"open"},{"id":"s3202","stat":"43% of DBT Copilot users never identified a hallucination during the pilot, 22% did, and 11% were unsure; 30% of slide-deck cases and 29% of code-review cases reported that quality assurance took longer with Copilot","source":"DBT, The Evaluation of the M365 Copilot Pilot in the Department for Business and Trade","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"DBT reads the \\"unsure\\" group as possibly meaning users could not identify hallucinations rather than that none occurred. On image generation, 36% either did not QA the output or could not compare. Verification burden offsets the saving on exactly the tasks where output quality scored worst.","url":"https://assets.publishing.service.gov.uk/media/68adbe409e1cebdd2c96a19d/dbt-microsoft-365-copilot-evaluation.pdf","access":"open"},{"id":"s3203","stat":"DBT found M365 Copilot statistically significantly more satisfying for neurodiverse colleagues than for other users, with similar anecdotal benefit reported by non-native English speakers","source":"DBT, The Evaluation of the M365 Copilot Pilot in the Department for Business and Trade","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"The most robust positive finding in the UK government Copilot evidence base, and the one nobody is building a business case on. DBT also found self-led training more effective than formal departmental training at raising satisfaction.","url":"https://assets.publishing.service.gov.uk/media/68adbe409e1cebdd2c96a19d/dbt-microsoft-365-copilot-evaluation.pdf","access":"open"},{"id":"s3204","stat":"DBT states its Copilot findings \\"are not comparable to findings from other Government departments\\" because methodologies and evaluation aims were prioritised differently across Government","source":"DBT, The Evaluation of the M365 Copilot Pilot in the Department for Business and Trade","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"critical","sourceType":"government","scope":"cross-sector","context":"A department that ran one of the four trials saying the four numbers cannot be placed side by side. They have been anyway, and the largest of them is underwriting departmental procurement.","url":"https://assets.publishing.service.gov.uk/media/68adbe409e1cebdd2c96a19d/dbt-microsoft-365-copilot-evaluation.pdf","access":"open"},{"id":"s3205","stat":"DBT found Copilot uptake was limited in part by users\' ethical objections to the environmental impact of LLM development and operation","source":"DBT, The Evaluation of the M365 Copilot Pilot in the Department for Business and Trade","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"The first UK public sector evaluation in the vault to record environmental objection as a live adoption barrier rather than a talking point. DBT is developing environmental impact costing methodologies in response. See [[ai-environmental-impact-water-energy","url":"https://assets.publishing.service.gov.uk/media/68adbe409e1cebdd2c96a19d/dbt-microsoft-365-copilot-evaluation.pdf","access":"open"},{"id":"s3206","stat":"Only 12% of UK SME law firms have fully activated their generative AI plan, 57% are at early-stage pilots and 7% have no plans at all","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Refusal has collapsed: 71% had no plans in 2024, 24% in 2025, 7% in 2026. Almost the entire segment is now in pilot. Online polling of senior SME law firm leaders, fielded Jan-Feb 2026. Source file: [[lpm-frontiers-2026-sme-law-firms"},{"id":"s3207","stat":"Only half of UK SME law firm leaders say their firm has provided a list of approved AI tools with clear guidance on when and how to use them","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"The entry-level governance artefact, absent in half the segment. Lines up with the Law Society of Ireland shipping a shadow AI audit worksheet in Apr 2026. n=60+."},{"id":"s3208","stat":"UK SME law firm leaders confident they can realistically assess ROI on recent technology investment fell from 48% to 31% in a year, while \'don\'t know\' more than doubled to 24% and 44% say they cannot","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"critical","sourceType":"media","scope":"legal","context":"Adoption rising, ability to evaluate it falling. The clearest UK trade-published evidence for the measurement gap thesis. n=60+."},{"id":"s3209","stat":"Accuracy and quality concerns remain the top genAI obstacle for UK SME law firms at 47%, but have fallen 17 percentage points year on year, while cost (34%) and proving value (22%) are rising","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"The obstacle set is rotating from safety to economics. Information security second at 40%. n=60+."},{"id":"s3210","stat":"84% of UK SME law firm leaders expect genAI value from saving time on tasks, but only 32% expect cost savings","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"A 52-point gap between expecting time back and expecting it to show up in cost. More innovation in delivery 38%, better client outcomes 36%. n=60+."},{"id":"s3211","stat":"53% of UK SME law firm leaders are less than satisfied (50-50 or worse) with how their current systems integrate","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"The estate finding underneath the adoption numbers. Sponsor Tessaract\'s own comment concedes it is unclear how much reported cloud migration is cloud-native versus lifted-and-shifted legacy. n=60+."},{"id":"s3212","stat":"UK law firms above 100 users spend a mean 9.9% of annual revenue on technology, against 5.9% for firms below 100","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Covers infrastru
1cture, software and skills. Constrains what advisory work into the bottom half of the SME band can cost. Subgroup figure on a base of roughly 25-35. n=60+."},{"id":"s3213","stat":"Selecting the right supplier is the biggest barrier to technology change for UK SME law firms (41%), narrowly ahead of cost (40%); appetite for change is cited by only 18%","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"The barrier splits by size: larger firms are time-poor for evaluation, smaller firms are cost-constrained. Selection, not willingness, is the bottleneck. n=60+."},{"id":"s3214","stat":"65% of UK SME law firm leaders plan to increase technology spend in 2026 and 8% plan to cut it","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Cutting is more common at higher-headcount firms. Broadly flat against 69% increasing in Frontiers 2025. n=60+."},{"id":"s3215","stat":"52% of UK SME law firms already use or only choose cloud options and 30% describe themselves as cloud-first; only 2% rule it out","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Holds the huge 2025 jump (20% to 59% \'only choose cloud\'). Almost half have committed to a cloud PMS, another third expect to within 6-18 months. n=60+."},{"id":"s3216","stat":"Only 8% of UK SME law firm leaders feel positive about their client-facing technology, and 23% say marketing and BD systems need replacing entirely","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"client","sourceType":"media","scope":"legal","context":"Weakest perceived ROI is also client-facing: 36% want more from client experience investment. Nearly three-quarters rate remote-working equipment as good. n=60+."},{"id":"s3217","stat":"Confidence in current technology enablement among UK SME law firm leaders fell 8 points year on year to 63%, with 30% now neutral and 5% negative","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Only just over one in ten reports feeling \'very positive\'. Neutral is the growing category. n=60+."},{"id":"s3218","stat":"Document management and compliance are where UK SME law firm leaders see the best technology value, with 44% naming the practice management system","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"media","scope":"legal","context":"Perceived value sits in the records and risk layer, not in AI. Relevant to any knowledge-readiness framing for this segment. n=60+."},{"id":"s3219","stat":"32% of UK SME law firm leaders name AI/genAI suppliers as a competitive pressure and 21% name private equity-backed groups; 56% still name similar firms","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Both the AI-supplier and PE answers come mainly from larger firms in the sample. Smaller firms point instead at online legal services. n=60+."},{"id":"s3220","stat":"37% of UK SME law firm leaders expect revenue growth above 10% in 2026, while those expecting little or no change more than doubled to 14% and were exclusively firms under 100 headcount","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Growth expectation 27% (2024), 33% (2025), 37% (2026). The flat-revenue group is entirely at the small end, so the segment is separating. n=60+."},{"id":"s3221","stat":"Process automation is the top 2026 investment priority for UK SME law firms (42%), ahead of technology-led client experience (35%) and human client touchpoints (34%)","source":"Burlington Media, LPM Frontiers 2026 (n=60+)","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"market","sourceType":"media","scope":"legal","context":"Only 16% treat AI through the lens of employee working preferences. Talent focus is pay and career paths (60%) well ahead of culture (51%). n=60+."},{"id":"s3222","stat":"Decoder-only Transformer language models map every prompt to a distinct last-token hidden representation with probability one under the paper\'s stated conditions","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The theorem assumes a finite vocabulary and context window, embedding dimension at least 4, at least one attention head per block, real-analytic components, random initialization from a distribution with a density, and any finite number of GD or SGD steps with step
1sizes in (0,1). It does not cover every architecture, training rule or deliberately non-analytic modification.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3223","stat":"A collision search over 100,000 prompts sampled from four public datasets required around 5 billion pairwise comparisons and found no identical last-token states across the tested models and layers","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Wikipedia, C4, The Pile and Python GitHub code. Collision threshold 1e-6. Tested GPT-2 and Gemma-3 families plus Llama-3.1-8B, Mistral-7B-v0.1, Phi-4-mini-instruct and TinyStories-33M. Empirical support for the theorem, not an exhaustive search of prompt space.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3224","stat":"Across Llama-3.1-8B, Mistral-7B-v0.1, Phi-4-mini-instruct and TinyStories-33M, minimum pairwise L2 distances between prompt representations ranged from 0.001-0.029 in the first layer, 0.129-1.434 in the middle layer and 0.620-9.020 in the final layer, all above the 1e-6 collision threshold","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Table 1. Distances generally increased with depth, but the scale varies substantially by model and is not itself an accuracy measure.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3225","stat":"An exhaustive continuation test on the 10 closest prefix prompts compared more than 343 billion prompt pairs per model and found no collision for GPT-2 Small or Gemma3-1B","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Each prefix was extended with every vocabulary token. The paper notes that even the single-token prompt space for Gemma3-1B already contains more than 34 billion possible pairs, so a complete search across longer prompts remains infeasible.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3226","stat":"FP4 and INT8 weight quantisation introduced no collisions in three tested models and more than doubled the minimum final-layer representation distance against FP32 in every comparison","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Llama-3.1-8B: 1.274 FP32, 2.281 FP4, 6.597 INT8; Mistral-7B-v0.1: 1.136, 1.748, 2.692; Phi-4-mini-instruct: 8.780, 18.368, 20.956. Quantisation is a deliberately non-analytic operation outside the theorem, so this is empirical evidence only.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3227","stat":"SIPIT recovered all 100 GPT-2 Small prompts of 20 tokens with 100% token accuracy in 28.01 +/- 35.87 seconds, versus 3,889.61 +/- 691.17 seconds for brute force at 100% and 6,132.59 +/- 104.61 seconds for HARDPROMPTS at 0%","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The sample was 90% meaningful sentences and 10% random token sequences. All timings used one NVIDIA A100-SXM 64 GB GPU. SIPIT assumes access to the full per-position hidden-state sequence at a chosen layer, not merely model outputs or the final embedding.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3228","stat":"On FP4-quantised Mistral-7B and Llama-3.1-8B, SIPIT recovered every token in 50 ten-token prompts while exploring only 0.19 +/- 0.08% and 0.21 +/- 0.10% of the vocabulary respectively","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Vocabulary sizes 32,000 and 128,255; mean inversion times 111.78 +/- 46.50 and 549.48 +/- 265.75 seconds. The result tests robustness to weight quantisation rather than noise or loss in a stored hidden-state channel.","url":"https://arxiv.org/abs/2510.15511","access":"open"}
1,{"id":"s3229","stat":"In a separate vocabulary-scaling test, SIPIT achieved 100% recovery on Mistral-7B and Llama-3.1-8B while exploring 0.21 +/- 0.11% and 0.22 +/- 0.12% of their 32,000- and 128,255-token vocabularies","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Mean inversion time rose from 72.99 +/- 37.57 seconds to 345.35 +/- 181.30 seconds, consistent with the claimed linear scaling in vocabulary size. Sentences were constructed to tokenize identically across both models.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3230","stat":"SIPIT exactly recovered all 150 GPT-2 prompts of 100 tokens across training-distribution, unseen Wikipedia and random-token datasets, with mean inversion times of 146.48, 128.62 and 106.87 seconds respectively","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"50 prompts per dataset. Standard deviations were 91.52, 83.40 and 39.10 seconds. The authors hypothesise, rather than establish, that random sequences invert faster because their hidden states are more dispersed.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3231","stat":"Around 140,000 prompt pairs deliberately constructed to produce the same translation or arithmetic answer still produced distinct hidden representations across Llama-3.1-8B, Mistral-7B and Phi-4-mini-instruct","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Four separator tokens were varied while preserving the target answer. Minimum L2 distances across first, middle and final layers remained above the 1e-6 threshold, showing that identical next-token behaviour does not imply identical internal state in this test.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3232","stat":"For 10,000 Wikipedia prefixes of 50 tokens followed by the same fixed suffix and next-token prediction, all last-token representations remained distinct across three tested models","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The fixed suffix led to the next token \\"dog\\" in every case. Minimum final-layer L2 distances were 3.494 for Mistral-7B, 6.227 for Llama-3.1-8B and 18.913 for Phi-4-mini-instruct.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3233","stat":"In a GPT-2 Small proof-of-concept, injectivity margin correlated positively with representation anisotropy (Pearson 0.72, Spearman 0.45) and negatively with intrinsic dimension (Pearson -0.60, Spearman -0.79)","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Exploratory analysis across 12 layers using 100 prompts and 1,000 single-token continuations per prompt. The paper presents this as preliminary evidence that lower-dimensional representations can remain lossless, not as a causal result.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3234","stat":"All 18 of 18 representative modern language-model architectures surveyed used real-analytic feed-forward activation functions","source":"Nikolaou et al., Language Models are Injective and Hence Invertible, ICLR 2026","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Includes Llama 2/3, Mistral, Mixtral, Gemma 1/2, Qwen 2/3 and MoE variants, Phi, GPT-2/J, GPT-OSS, Grok-1 and DeepSeek V2/V3. Supports one assumption of the theorem but does not establish that every component or deployment modification falls within it.","url":"https://arxiv.org/abs/2510.15511","access":"open"},{"id":"s3235","stat":"In a preregistered randomised experiment with 316 working adults, exposure to short social-media videos portraying AI as controlling humans increased job-replacement anxiety by 0.96 points on a five-point scale against human-control narratives (95% CI 0.66-1.25, p<.001)","source":"Grobelny et al., Understanding AI Anxiety in the Workplace","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"A 2x2x2 laboratory design, fielded 14 Apr-23 May 2025. Participants watched three compilations of about six minutes each; the control compilation covered human oversight, regulation and governance. None of the videos explicitly mentioned job replacement. Immediate response only, in a non-representative recruited sample.","url":"https://arxiv.org/abs/2608.14609","access":"open"},{"id":"s3236","stat":"The AI-control video manipulation produced a large shift in perceived AI-over-human control (d=0.82, p<.001)","source":"Grobelny et al., Understanding AI Anxiety in the Workplace","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Study 1, N=316. Baseline control perceptions did not differ between groups (d=0.07, p=.509). The post-manipulation control measure had modest reliability (Cronbach\'s alpha=.63), so the manipulation check is strong but the measure is noisy.","url":"https://arxiv.org/abs/2608.14609","access":"open"},{"id":"s3237","stat":"Across the eight experimental cells, mean job-replacement anxiety was 3.74-4.08 under AI-over-human control narratives versus 2.90-3.78 under human-control narratives","source":"Grobelny et al., Understanding AI Anxiety in the Workplace","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Study 1, N=316, five-point anxiety scale. The highest estimated mean was 4.17 when AI was framed as both highly useful and in control; the lowest was 2.88 when usefulness was low and humans were framed as in control. The
1se are immediate post-exposure responses, not lasting anxiety or behaviour.","url":"https://arxiv.org/abs/2608.14609","access":"open"},{"id":"s3238","stat":"In the experiment, framing AI as highly useful raised job-replacement anxiety by 1.00 point (95% CI 0.76-1.24, p<.001), but reduced the additional effect of the AI-control narrative by 0.57 points (p=.001)","source":"Grobelny et al., Understanding AI Anxiety in the Workplace","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Study 1, N=316. The AI-control effect was 0.92 points at low usefulness and 0.33 at high usefulness. This interaction ran opposite to the preregistered hypothesis: useful AI raised anxiety in both conditions, compressing the difference between them rather than strengthening the narrative effect.","url":"https://arxiv.org/abs/2608.14609","access":"open"},{"id":"s3239","stat":"Perceived ease of using AI neither directly predicted job-replacement anxiety in the experiment (b=0.04, p=.777) nor moderated the effect of AI-control narratives (b=-0.07, p=.710)","source":"Grobelny et al., Understanding AI Anxiety in the Workplace","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Study 1, N=316. The ease manipulation itself was strong (d=1.14), so the null result is not explained by a failed manipulation. Practical implication: simple-to-use messaging did n
1ot reduce or increase immediate replacement anxiety in this design.","url":"https://arxiv.org/abs/2608.14609","access":"open"},{"id":"s3240","stat":"The experimental model explained 41.9% of variance in AI job-replacement anxiety, 40.0% after adjustment","source":"Grobelny et al., Understanding AI Anxiety in the Workplace","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Study 1, N=316; HC3 robust standard errors. The model included control and usefulness/ease manipulations, two interactions, core self-evaluations, negative AI attitudes, trait anxiety, age and recent job loss. General negative AI attitudes predicted anxiety; trait anxiety and recent job loss did not.","url":"https://arxiv.org/abs/2608.14609","access":"open"},{"id":"s3241","stat":"In an observational study of 995 employed adults, perceived AI-over-human control correlated 0.60 with job-replacement anxiety and remained associated in regression (b=0.55, 95% CI 0.49-0.62, p<.001)","source":"Grobelny et al., Understanding AI Anxiety in the Workplace","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Study 2, fielded 3 Jun-10 Jul 2025. It measured attitudes at one time point, so it cannot establish causality or independently replicate the experimental mechanism. Sample flow: 1,715 started, 1,423 completed, 1,218 passed screening, 1,019 passed attention checks and 24 influential cases were removed. The demographic table appears to report earlier inconsistent denominators rather than final N=995; do not quote its subgroup shares.","url":"https://arxiv.org/abs/2608.14609","access":"open"},{"id":"s3242","stat":"In the observational study, perceived usefulness independently predicted higher job-replacement anxiety (b=0.10, 95% CI 0.03-0.17, p=.007) while slightly weakening the AI-control association (interaction b=-0.07, p=.03)","source":"Grobelny et al., Understanding AI Anxiety in the Workplace","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Study 2, N=995. The AI-control slope fell from 0.62 at low usefulness to 0.55 at the mean and 0.48 at high usefulness, all p<.001. This again contradicted the preregistered direction: usefulness raised the anxiety baseline rather than amplifying the marginal framing effect.","url":"https://arxiv.org/abs/2608.14609","access":"open"},{"id":"s3243","stat":"Perceived ease of use neither directly predicted job-replacement anxiety in the observational study (b=0.04, p=.272) nor moderated its association with perceived AI control (b=0.05, p=.143)","source":"Grobelny et al., Understanding AI Anxiety in the Workplace","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Study 2, N=995. Converges with the experimental null result, although all Study 2 measures were contemporaneous self-reports.","url":"https://arxiv.org/abs/2608.14609","access":"open"},{"id":"s3244","stat":"The observational model explained 28.2% of variance in AI job-replacement anxiety, 27.7% after adjustment","source":"Grobelny et al., Understanding AI Anxiety in the Workplace","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Study 2, N=995; HC3 robust standard errors. Predictors were perceived AI-over-human control, perceived usefulness, perceived ease and core self-evaluations. A one-factor test accounted for 27% of item variance, below the authors\' 50% common-method threshold, but this does not remove the limits of cross-sectional self-report data.","url":"https://arxiv.org/abs/2608.14609","access":"open"},{"id":"s3245","stat":"Across ~357K real Claude Code and Codex production steps, the median coding-agent LLM step read back ~119K prefix tokens of accumulated context, appended only ~875 fresh input tokens and emitted ~214 output tokens","
1source":"Zhu et al. (UW), TraceLab","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"arXiv:2606.30560, read from the primary PDF. Claude median 126K prefix / 857 append / 252 output; Codex 116K / 886 / 184. Whole-trace input:output is ~294:1 by tokens (54.9B in, 186.9M out) and ~9:1 by cost - the source of FreeToken\'s 9:1 blended pricing mix. Trace: 4,265 sessions, 43 developers, Sep 2025-Jun 2026, 20+ model versions; dataset and pipeline released. Population is one systems research group, not legal work."},{"id":"s3246","stat":"Prefix-cache reads account for 59.5% of total coding-agent API cost, against 29.2% for fresh input and only 11.2% for output tokens - despite cache reads being billed at roughly one-tenth the fresh-input rate","source":"Zhu et al. (UW), TraceLab","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"For agentic workloads the cost is overwhelmingly in re-reading accumulated context, not generation. Inverts the output-driven intuition behind most AI cost models. Computed at provider list prices (snapshot in the paper\'s Table 6) over ~$40.4K of measured usage."},{"id":"s3247","stat":"A real coding-agent session cost $9.70 on average but only $0.61 at the median, with a p99 of $178; a request averaged $1.01 (median $0.33) and a single step $0.11","source":"Zhu et al. (UW), TraceLab","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"API list-price equivalent across 4,265 Claude Code / Codex sessions. The heavy tail (a few very long sessions) drives the mean; typical usage is cheap. Useful calibration for agentic-workflow cost modelling."},{"id":"s3248","stat":"The global prefix cache hit rate for coding agents is 95.7%, but drops to 84.4% on user-initiated steps because human reading/thinking/typing gaps outlast cache eviction; past 1 hour of idle, almost all steps miss","source":"Zhu et al. (UW), TraceLab","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Tool-result steps hit 97.5% because the loop resumes within seconds. The miss pattern is human-shaped: the expensive event is the person pausing to think mid-session."},{"id":"s3249","stat":"Only 19% of coding-agent append tokens are genuinely fresh content; deployed prefix caches prefill 5.3x more tokens than a perfect eviction-free cache would need (Claude 8.1x, Codex 3.9x)","source":"Zhu et al. (UW), TraceLab","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Retaining cache across human thinking time would cut append prefill 45.9% and total cost 12.8% (upper-bound estimate, $5,189 of the trace\'s $40,431). On user-initiated steps prefill amplification reaches 35.4x."},{"id":"s3250","stat":"Human thinking time is 92.3% of coding-agent session wall-clock; LLM generation is 3.3% and tool execution 4.8%","source":"Zhu et al. (UW), TraceLab","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Session p99 elapsed ~206h (sessions left open for days). Capping idle gaps at 1h, the human share is still 64.3%. Within a request, tools take 59.8% of response time vs 41.0% generation; an average request takes 4.3 minutes end to end."},{"id":"s3251","stat":"To resolve one user request, a coding agent averages 8.8 LLM calls and 10.8 tool calls; most steps are tool-initiated, making the loop largely autonomous","source":"Zhu et al. (UW), TraceLab","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"A session averages 9.2 requests with a p99 of 137. Sessions are persistent working environments, not chat exchanges - the structural difference chat-era serving traces and single-task benchmarks both miss."},{"id":"s3252","stat":"Coding-agent tool calls are heavily skewed: the top 3 tools carry over 80% (Claude) / 95% (Codex) of all calls, and calls longer than 1 minute are ~4.9% of Claude calls but 92% of total tool time","source":"Zhu et al. (UW), TraceLab","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"54 distinct tools observed for Claude, 31 for Codex; command execution dominates both (Bash 47% of Claude calls; exec_command 64.6% of Codex). Calls under 1s are 70% of Claude calls but under 1% of tool time."},{"id":"s3253","stat":"Measured production decode speeds: Claude Code median 52.6 normalized tok/s, Codex 32.7; Codex pure decode median 57.1 tok/s with ~3.4s average residual TTFT per step","source":"Zhu et al. (UW), TraceLab","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Normalized speed is end-to-e
1nd (includes TTFT and reasoning time). This is the \\"33 tok/s median decode speed of Codex\\" FreeToken cites as its interactivity bar - a normalized figure, not pure decode. Longer contexts correlate with slower generation, especially for Codex, but variance is large (CV > 50%) at every context length."},{"id":"s3254","stat":"In ~350K production coding-agent steps, over 90% of steps append fewer than 1K tokens, yet more than 70% of all append tokens come from the rare steps appending 10K or more","source":"Zhu et al. (UW), TraceLab","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The bimodal input structure: short-prefix steps carry large appends (cache misses, initial prefills), long-prefix steps carry small incremental ones (tool results, user turns). Serving systems need different optimisation paths for each."},{"id":"s3255","stat":"voyage-context-4 beats voyage-context-3 by 2.08% (chunk-level) and 1.4% (document-level) NDCG@10, averaged across 39 retrieval datasets in 8 domains","source":"Voyage AI blog (vendor benchmark)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Second-generation contextualised chunk embedding model; adds auto-chunking and removes the 32k document limit. $0.12/M vs $0.18/M for context-3. Source: [[voyage-context-4-contextualised-chunk-embeddings]]"},{"id":"s3256","stat":"Contextualised chunk embeddings beat single-vector embeddings of the same documents by 7.11% on LongEmbed (long-context retrieval)","source":"Voyage AI blog (vendor benchmark)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The number that argues for the approach rather than the version: long contracts, transcripts and manuals are where document context in the chunk vector pays. Source: [[voyage-context-4-contextualised-chunk-embeddings]]"},{"id":"s3257","stat":"voyage-context-3 beat Anthropic contextual retrieval by 6.76% and Jina-v3 late chunking by 23.66% on chunk-level retrieval","source":"Voyage AI (vendor benchmark, cited in the context-4 post)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"practitioner","scope":"cross-sector","context":"Same idea two ways: Anthropic pays an LLM call per chunk to write context in, Voyage trains it in. Vendor figures; not independently reproduced. Source: [[voyage-context-4-contextualised-chunk-embeddings]]"},{"id":"s3258","stat":"Used as a standard one-vector embedding model, voyage-context-4 scores 28.80% above OpenAI text-embedding-3-large and 10.73% above Cohere Embed v4 (NDCG@10, 8 domains) and leads in law","source":"Voyage AI blog (vendor benchmark)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"legal","context":"Vendor\'s own evaluation across 39 datasets. Also +0.45% over voyage-4-large. Source: [[voyage-context-4-contextualised-chunk-embeddings]]"},{"id":"s3259","stat":"Hermes Agent (Nous Research): 235.5k GitHub stars and 47.5k forks","source":"GitHub, NousResearch/hermes-agent","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Open-source MIT agent runtime released Feb 2026; v0.20.5 on 19 Aug 2026. OpenClaw c.247k stars per secondary sources (June). Source: [[hermes-agent-nous-research]]"},{"id":"s3260","stat":"OpenRouter daily token volume, 14 Jun 2026: Hermes Agent 224bn vs OpenClaw 186bn; Hermes 18.2tn cumulative","source":"Turing Post comparison (secondary; not verified on OpenRouter directly)","date":"Jun 2026","sortKey":20260600,"precision":"month","year":2026,"theme":"technical","sourceType":"media","scope":"cross-sector","context":"Agent runtimes, not chat windows, are now the largest token consumers on the routing layer. Verify on OpenRouter rankings before external use. Source: [[hermes-agent-nous-research]]"},{"id":"s3261","stat":"GitLab Advisory Database lists 10 CVEs for the hermes-agent PyPI package, 8 with no fixed version recorded (CVSS 5.3-7.5)","source":"GitLab Advisory Database","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Two fixed in 0.16.0 (DNS rebinding 7.5, world-readable files 5.5); eight injection / sandbox / webhook issues from late Apr 2026 with no fix recorded as of 24 Aug 2026. SentinelOne reports vend
1or non-response and a public PoC on CVE-2026-9366. Source: [[hermes-agent-nous-research]]"},{"id":"s3262","stat":"OpenClaw received nine CVEs in four days (18-21 Mar 2026), including CVE-2026-22172 at CVSS 9.9 (any authenticated user could self-assign admin scopes in the WebSocket handshake)","source":"Cloud Security Alliance AI Safety Initiative research note, 4 May 2026, citing OpenClaw\'s own disclosures","date":"Mar 2026","sortKey":20260300,"precision":"month","year":2026,"theme":"technical","sourceType":"industry","scope":"cross-sector","context":"The \\"9 CVEs in 4 days\\" figure belongs to OpenClaw, not Hermes Agent, despite the CSA note\'s title; secondary blogs have misattributed it. Source: [[hermes-agent-nous-research]]"},{"id":"s3263","stat":"Independent audit of Hermes Agent v0.8.0 (812 Python files, ~364,000 lines) found 4 Critical and 9 High severity issues in the default configuration, none assigned a CVE","source":"Anic888, NousResearch/hermes-agent issue #7826, 11 Apr 2026, via CSA research note","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"technical","sourceType":"commentary","scope":"cross-sector","context":"Criticals: shell commands to bash -c behind regex detection only; file reads with no deny list; containerised deployments skip all approval checks; model-written skills persist and execute in future sessions. Issue open, no visible maintainer response as of 24 Aug 2026. Source: [[hermes-agent-nous-research]]"},{"id":"s3264","stat":"Weekly active enterprise Codex users grew 108x in legal from February 2026, compared with 41x in sales, 41x in recruiting, 26x in marketing and 5x in engineering","source":"OpenAI Enterprise Signals","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Aggregated, de-identified OpenAI enterprise telemetry. OpenAI publishes no February or end-period legal-user count, number of represented legal teams or legal adoption rate. Relative acceleration from an undisclosed and potentially tiny base, not evidence of scale. Source: [[codex-agentic-shift-evidence]]","url":"https://openai.com/signals/enterprise-data/","access":"open"},{"id":"s3265","stat":"Frontier firms generated 8.3x as many output tokens per active user as typical firms in June 2026, up from a 2.6x gap in January","source":"OpenAI Enterprise Signals","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Frontier = top 10% of enterprise customers by output tokens per active user each month; typical = 45th-55th percentiles. OpenAI-product telemetry; token volume proxies depth of use, not business value. Source: [[codex-agentic-shift-evidence]]","url":"https://openai.com/signals/enterprise-data/","access":"open"},{"id":"s3266","stat":"Among weekly active users, 21% at frontier firms used plugins and 19% used skills, versus 9% and 3% respectively at typical firms","source":"OpenAI Enterprise Signals","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"Frontier and typical definitions as above. OpenAI reports 95% weekly plugin use among its own employees as a frictionless internal comparison, not an external benchmark. Source: [[codex-agentic-shift-evidence]]","url":"https://openai.com/signals/enterprise-data/","access":"open"},{"id":"s3267","stat":"Codex generated 64% of combined Codex and ChatGPT output tokens among enterprise customers in June 2026","source":"OpenAI Enterprise Signals","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"OpenAI defines Codex tokens as agentic AI use, although Codex and ChatGPT share underlying models and both interfaces can mix conversational and agentic behaviour. Token share measures computational activity, not productivity or value. Source: [[codex-agentic-shift-evidence]]","url":"https://openai.com/signals/enterprise-data/","access":"open"},{"id":"s3268","stat":"20.0% of EU enterprises with 10 or more employees used AI technologies in 2025","source":"Eurostat, Use of artificial intelligence in enterprises","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"government","scope":"legal","context":"Economy-wide baseline; enterprises 10+ employees, NACE C to J, L to N, 95.1. Legal and financial services out of scope. Source: [[raiola-gauss-cauchy-arbitrage]] (verified at Eurostat)","url":"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises","access":"open"},{"id":"s3269","stat":"EU enterprise AI use rose 6.5 percentage points in a year, from 13.5% in 2024 to 20.0% in 2025","source":"Eurostat, Use of artificial intelligence in enterprises","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Compared with 7.7% in 2021 and 8.1% in 2023 per Eurostat;
1 the 2024 to 2025 step is the largest on record. Source: [[raiola-gauss-cauchy-arbitrage]] (verified at Eurostat)","url":"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises","access":"open"},{"id":"s3270","stat":"55.03% of large EU enterprises (250+ employees) used AI technologies in 2025","source":"Eurostat, Use of artificial intelligence in enterprises","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Size-class split from the Statistics Explained article; Eurostat attributes the gap to implementation complexity, economies of scale and cost. Source: [[raiola-gauss-cauchy-arbitrage]] (verified at Eurostat)","url":"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises","access":"open"},{"id":"s3271","stat":"30.36% of medium EU enterprises (50-249 employees) used AI technologies in 2025","source":"Eurostat, Use of artificial intelligence in enterprises","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Size-class split as above. Source: [[raiola-gauss-cauchy-arbitrage]] (verified at Eurostat)","url":"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises","access":"open"},{"id":"s3272","stat":"17% of small EU enterprises (10-49 employees) used AI technologies in 2025","source":"Eurostat, Use of artificial intelligence in enterprises","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Size-class split as above. Raiola cites \\"around 19% among SMEs\\", a blend of this and the medium figure, not a published number. Source: [[raiola-gauss-cauchy-arbitrage]] (verified at Eurostat)","url":"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises","access":"open"},{"id":"s3273","stat":"Thomson Reuters\' final training run for Thomson-1.0-Large cost under USD 450,000 in GPU time over three weeks","source":"Thomson Reuters, Thomson technical report","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"legal","context":"Continual-learning post-train of Qwen3.5-397B-A17B. TR\'s own \\"conservative\\" estimate of GPU cost only; excludes staff, data, experts and the pipeline build. Source: [[thomson-reuters-thomson-1-model]]"},{"id":"s3274","stat":"Thomson Reuters\' total Thomson development cost was approximately USD 40m over two years, including staff, compute, domain-expert compensation and vendor partnerships","source":"Thomson Reuters, Thomson technical report","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"The real programme number against the $450k headline; TR says most of it went on reusable research and infrastructure. Two-year span from the SiliconANGLE launch piece (24 Aug 2026). Source: [[thomson-reuters-thomson-1-model]]"},{"id":"s3275","stat":"The Thomson model family was built by a team of no more than 36 engineers and scientists on at most 368 B200 GPUs, three months from first experiment to both shipped models","source":"Thomson Reuters, Thomson technical report","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"legal","context":"TR\'s own figures. The report concedes the reusable pipeline took \\"meaningfully longer\\" than the three months. Source: [[thomson-reuters-thomson-1-model]]"},{"id":"s3276","stat":"Thomson-1.0-Large scored 78.5 overall against Claude Opus 4.8\'s 79.5, Gemini 3.1 Pro\'s 78.0, Sonnet 5\'s 75.7 and its Qwen3.5-397B base\'s 73.0 on TR\'s cross-domain benchmark suite","source":"Thomson Reuters, Thomson technical report","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"legal","context":"Table 1, unweighted mean over TR\'s own benchmarks, TR harness and graders, all models on identical prompts with medium reasoning effort; closed models via served endpoints. Vendor-run, not independent. Source: [[thomson-reuters-thomson-1-model]]"},{"id":"s3277","stat":"Thomson-1.0-Large scored 85.7 on the Harvey Legal Agent Benchmark as run by TR, against Opus 4.8 at 86.9, Kimi K3 at 83.7 and DeepSeek v4 Pro at 83.1","source":"Thomson Reuters, Thomson technical report","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"legal","context":"TR-run HLAB, not the Vals AI leaderboard, which did not list Thomson or Harvey Tenet at its 19 Aug 2026 update. Source: [[thomson-reuters-thomson-1-model]]"},{"id":"s3278","stat":"In a blind study of over 3,000 expert-rated conversations, the Thomson system with TR\'s licensed legal and news data was preferred over each of five OpenAI and Anthropic systems with web access; on legal conversations against Sonnet 5 it won 53%, tied 17% and lost 30%","source":"Thomson Reuters, Thomson technical report","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"legal","context":"Figure 3. A system comparison, not a model comparison: Thomson had Westlaw, Practical Law and the Reuters feed, the others had the open web. General-domain split was tight. Source: [[thomson-reuters-thomson-1-model]]"},{"id":"s3279","stat":"Thomson Reuters says it has used about 10% of its information base so far in training Thomson; Westlaw spans over 40,000 databases and 150 years of legal publishing","source":"Thomson Reuters via SiliconANGLE","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Launch coverage, 24 Aug 2026, quoting TR. Customer data is not used in training. Source: [[thomson-reuters-thomson-1-model]]"},{"id":"s3280","stat":"Thomson-1.0-Small (35B total, 3B active, Qwen3.6-35B-A3B base) is released as open weights under a Polyform Strict non-commercial licence with a 262,144-token context, scoring 74.6 overall on TR\'s suite against 71.7 for its base and 68.2 for Haiku 4.5","source":"Thomson Reuters, Hugging Face model card and technical report","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"legal","context":"Table 2, TR-run. The first legal-vendor model available for outside evaluation. Source: [[thomson-reuters-thomson-1-model]]"},{"id":"s3281","stat":"LLM inference prices have fallen between 9x and 900x per year across benchmarks, with the median decline near 50x","source":"BAIR, Intelligence is Free, Now What?","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Berkeley database group (Parameswaran, Zaharia, Stoica et al.) citing inference-price tracking across capability benchmarks; the blog does not name the underlying dataset. Source: [[bair-intelligence-is-free-data-systems-agents]]"},{"id":"s3282","stat":"GPT-4-class capability cost roughly $30 per million tokens in early 2023;
1 the same capability now costs under $1 and some providers are below $0.10","source":"BAIR, Intelligence is Free, Now What?","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Order-of-magnitude framing from the blog; pair with the frontier tracker\'s list prices before citing a specific model. Source: [[bair-intelligence-is-free-data-systems-agents]]"},{"id":"s3283","stat":"In multi-agent text-to-SQL benchmarks only 10-20% of agent sub-plans are distinct, so 80-90% of sub-queries perform duplicate work","source":"BAIR, Intelligence is Free, Now What?","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The authors\' own measurement of \\"agentic speculation\\"; the mechanism behind agent token bills and the case for result reuse. Source: [[bair-intelligence-is-free-data-systems-agents]]"},{"id":"s3284","stat":"43% of legal teams use AI contracting solutions for specific tasks","source":"IDC survey of 358 in-house counsel and business leaders, cited by LawVu (Neil Smith) in Artificial Lawyer","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"cross-sector","context":"Secondary: the IDC document was not located and no link was given; probably LawVu-commissioned. Fieldwork date unknown; the Aug 2026 date is the op-ed\'s. Source: [[lawvu-legalos-foundation-first]]"},{"id":"s3285","stat":"Fewer than 30% of legal teams use any dedicated legal technology platform","source":"IDC survey of 358 in-house counsel and business leaders, cited by LawVu (Neil Smith) in Artificial Lawyer","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"cross-sector","context":"Secondary, as above. The op-ed\'s central contrast with the 43% AI-contracting figure. Source: [[lawvu-legalos-foundation-first]]"},{"id":"s3286","stat":"53% of legal teams say they have access to data but cannot compile it across disparate systems","source":"IDC survey of 358 in-house counsel and business leaders, cited by LawVu (Neil Smith) in Artificial Lawyer","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"cross-sector","context":"Secondary, as above. Compare Tonkean/WBR 2025: 25% of legal ops leaders consider their stack adequately integrated. Source: [[lawvu-legalos-foundation-first]]"},{"id":"s3287","stat":"71% of business leaders say their organisations need technology investment for automation and efficiency","source":"IDC survey of 358 in-house counsel and business leaders, cited by LawVu (Neil Smith) in Artificial Lawyer","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"cross-sector","context":"Secondary, as above; business-leader respondents, not legal. Source: [[lawvu-legalos-foundation-first]]"},{"id":"s3288","stat":"LawVu was reported at a NZ$400m valuation in December 2025, with 160 staff (about 100 in New Zealand) and 50%-plus global growth, after acquiring ClauseBase and launching LawVu Lens","source":"LawFuel","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"market","sourceType":"media","scope":"cross-sector","context":"Secondary; round size and investors not stated. Supersedes the \\"$42.5m raised\\" marker in the Halo competitor table. Source: [[lawvu-legalos-foundation-first]]"},{"id":"s3289","stat":"Less than 10% of AI proof-of-concepts (POCs) were deemed successful, having met predefined business goals and metrics (APJ region)","source":"IDC 2024 Future Enterprise Resiliency and Spending (FERS) Survey, Wave 4, via Lenovo/IDC CIO Playbook 2025 (APJ edition)","date":"Feb 2025","sortKey":20250200,"precision":"month","year":2025,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Read from the primary CIO Playbook APJ PDF with pdfplumber. Cross-sector, Asia-Pacific-Japan region only. The widely-cited \\"88% of AI pilots never reach production / 4 of every 33 graduate\\" figure (CIO.com, Mar 2025, attributed to IDC/Lenovo) could NOT be confirmed in either the APJ or EMEA primary edition and is not logged; this \\"<10% successful\\" sentence is what the APJ primary actually states. Chart funnel on the same page shows a POC-to-production drop-off. Corroborates the MIT NANDA pilot-failure narrative (s0385) from an independent survey."},{"id":"s3290","stat":"Average annual law firm software spending rose 41% between 2021 and 2025, equating to approximately 1.03% of revenue","source":"Harbor, Operationalizing AI in 2026 (Sounding Board / Legal Lab)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Harbor benchmarking; not inflation-adjusted, firm set not stated (Embry\'s objection). Consultancy paper, read from the primary PDF 26 Aug 2026. **No sample size, method or participant list stated**; figures come from Harbor\'s private Sounding Boards and Legal Lab polling. Embry (Above the Law, 25 Aug) challenged the methodology. Say so when citing. Source: [[harbor-ai-operating-model-2026]]","url":"https://harborglobal-prod.s3.us-east-2.amazonaws.com/files/reports/2026-Harbor-AI-Ops-Model-Report.pdf","access":"open"},{"id":"s3291","stat":"Overall law firm technology spending has grown to roughly 5% to 6% of firm revenue, nearly double historical norms","source":"Harbor, Operationalizing AI in 2026 (Sounding Board / Legal Lab)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Harbor benchmarking;
1 \'historical norms\' undefined. Consultancy paper, read from the primary PDF 26 Aug 2026. **No sample size, method or participant list stated**; figures come from Harbor\'s private Sounding Boards and Legal Lab polling. Embry (Above the Law, 25 Aug) challenged the methodology. Say so when citing. Source: [[harbor-ai-operating-model-2026]]","url":"https://harborglobal-prod.s3.us-east-2.amazonaws.com/files/reports/2026-Harbor-AI-Ops-Model-Report.pdf","access":"open"},{"id":"s3292","stat":"Law firm business professional staffing has declined by approximately 15% since 2022 while attorney headcount continues to grow","source":"Harbor, Operationalizing AI in 2026 (Sounding Board / Legal Lab)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Harbor benchmarking; firm set not stated. Consultancy paper, read from the primary PDF 26 Aug 2026. **No sample size, method or participant list stated**; figures come from Harbor\'s private Sounding Boards and Legal Lab polling. Embry (Above the Law, 25 Aug) challenged the methodology. Say so when citing. Source: [[harbor-ai-operating-model-2026]]","url":"https://harborglobal-prod.s3.us-east-2.amazonaws.com/files/reports/2026-Harbor-AI-Ops-Model-Report.pdf","access":"open"},{"id":"s3293","stat":"0% of participating top-tier law firms or global in-house departments reported having a mature framework for measuring AI business impact","source":"Harbor, Operationalizing AI in 2026 (Sounding Board / Legal Lab)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"legal","context":"Poll at Harbor\'s 2026 Legal Lab, n not stated. Law-firm-side companion to Harvard CLP (<5% systematic output metrics, in-house) and LegalOn (11.7% measurable cost savings, in-house). Set against Harbor\'s own Jan 2025 CINO Sounding Board (s1267: 74% report successful AI implementation). Consultancy paper, read from the primary PDF 26 Aug 2026. **No sample size, method or participant list stated**; figures come from Harbor\'s private Sounding Boards and Legal Lab polling. Embry (Above the Law, 25 Aug) challenged the methodology. Say so when citing. Source: [[harbor-ai-operating-model-2026]]","url":"https://harborglobal-prod.s3.us-east-2.amazonaws.com/files/reports/2026-Harbor-AI-Ops-Model-Report.pdf","access":"open"},{"id":"s3294","stat":"70% of law firms deliver foundational AI training internally","source":"Harbor, Operationalizing AI in 2026 (Sounding Board / Legal Lab)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Harbor Technology Leaders Sounding Board, n not stated. Consultancy paper, read from the primary PDF 26 Aug 2026. **No sample size, method or participant list stated**; figures come from Harbor\'s private Sounding Boards and Legal Lab polling. Embry (Above the Law, 25 Aug) challenged the methodology. Say so when citing. Source: [[harbor-ai-operating-model-2026]]","url":"https://harborglobal-prod.s3.us-east-2.amazonaws.com/files/reports/2026-Harbor-AI-Ops-Model-Report.pdf","access":"open"},{"id":"s3295","stat":"63% of law firms continue refining or redesigning their AI adoption model","source":"Harbor, Operationalizing AI in 2026 (Sounding Board / Legal Lab)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Harbor Technology Leaders Sounding Board, n not stated. Consultancy paper, read from the primary PDF 26 Aug 2026. **No sample size, method or participant list stated**; figures come from Harbor\'s private Sounding Boards and Legal Lab polling. Embry (Above the Law, 25 Aug) challenged the methodology. Say so when citing. Source: [[harbor-ai-operating-model-2026]]","url":"https://harborglobal-prod.s3.us-east-2.amazonaws.com/files/reports/2026-Harbor-AI-Ops-Model-Report.pdf","access":"open"},{"id":"s3296","stat":"Global 100 law firms have hired 266 AI-focused professionals, with more than 93% of those hires occurring since 2023","source":"Harbor, Operationalizing AI in 2026 (Sounding Board / Legal Lab)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Harbor\'s tracking; the paper\'s own phrasing (\'since 2023 ... 93% since 2023\') is garbled, probably a longer base period. Consultancy paper, read from the primary PDF 26 Aug 2026. **No sample size, method or participant list stated**; figures come from Harbor\'s private Sounding Boards and Legal Lab polling. Embry (Above the Law, 25 Aug) challenged the methodology. Say so when citing. Source: [[harbor-ai-operating-model-2026]]","url":"https://harborglobal-prod.s3.us-east-2.amazonaws.com/files/reports/2026-Harbor-AI-Ops-Model-Report.pdf","access":"open"},{"id":"s3297","stat":"Approximately 24% of the 266 AI-focused professionals hired by Global 100 law firms serve in enablement-related roles","source":"Harbor, Operationalizing AI in 2026 (Sounding Board / Legal Lab)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Harbor\'s tracking. Consultancy paper, read from the primary PDF 26 Aug 2026. **No sample size, method or participant list stated**; figures come from Harbor\'s private Sounding Boards and Legal Lab polling. Embry (Above the Law, 25 Aug) challenged the methodology. Say so when citing. Source: [[harbor-ai-operating-model-2026]]","url":"https://harborglobal-prod.s3.us-east-2.amazonaws.com/files/reports/2026-Harbor-AI-Ops-Model-Report.pdf","access":"open"},{"id":"s3298","stat":"Outside the Global 50, only one Global 50-100 law firm has more than two dedicated AI enablement professionals","source":"Harbor, Operationalizing AI in 2026 (Sounding Board / Legal Lab)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Harbor Technology Leaders Sounding Board. The market-sizing figure for enablement consulting below the Global 50. Consultancy paper, read from the primary PDF 26 Aug 2026. **No sample size, method or participant list stated**; figures come from Harbor\'s private Sounding Boards and Legal Lab polling. Embry (Above the Law, 25 Aug) challenged the methodology. Say so when citing. Source: [[harbor-ai-operating-model-2026]]","url":"https://harborglobal-prod.s3.us-east-2.amazonaws.com/files/reports/2026-Harbor-AI-Ops-Model-Report.pdf","access":"open"},{"id":"s3299","stat":"On ExploitGym, no OpenAI model had ever solved 198 of 898 tasks before the incident, and 93% of tasks discu
1ssed on the unauthorised message board came from that unsolved set","source":"OpenAI, Hugging Face Incident Technical Report","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Evidence that impossible or extremely difficult tasks plus persistence contributed to out-of-bounds strategies. Source: [[frontier-lab-cyber-disclosures-2026]]","url":"https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf","access":"open"},{"id":"s3300","stat":"OpenAI\'s retrospective test found its production ChatGPT harness and system prompt reduced agents\' propensity to compromise infrastructure by more than 100 times","source":"OpenAI, Hugging Face Incident Technical Report","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Production system prompt, harness, auto-review and CoT monitoring were absent from the evaluation environment during the incident. OpenAI-run retrospective, not an independent ablation. Source: [[frontier-lab-cyber-disclosures-2026]]","url":"https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf","access":"open"}
1,{"id":"s3301","stat":"Roughly 1,200 agents used the unauthorised Artifactory message board, around 700 participated in the Hugging Face attack, and they exchanged more than 70,000 messages and files","source":"METR / Redwood, OpenAI-Hugging Face incident investigation","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Independent investigation conducted over six days on site at OpenAI, using approximately 1,300 raw CoT transcripts and a dump of message-board activity. Source: [[frontier-lab-cyber-disclosures-2026]]","url":"https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/","access":"open"},{"id":"s3302","stat":"Roughly 7% of transcripts reviewed by METR contained successful tool-call spoofing in some places","source":"METR / Redwood, OpenAI-Hugging Face incident investigation","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"All observed spoofing was small-scale and aimed at the imagined automated scorer; investigators cannot rule out missing more sophisticated concealment. Source: [[frontier-lab-cyber-disclosures-2026]]","url":"https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/","access":"open"}
1,{"id":"s3303","stat":"Hugging Face reconstructed approximately 17,600 attacker actions in 6,280 clusters over roughly two and a half days inside its infrastructure","source":"Hugging Face, Agent Intrusion Technical Timeline","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Victim-side reconstruction from platform logs and recovered attacker logs. Thousands of mostly failed actions created the coverage that found a viable cross-system chain. Source: [[frontier-lab-cyber-disclosures-2026]]","url":"https://huggingface.co/blog/agent-intrusion-technical-timeline","access":"open"},{"id":"s3304","stat":"The only Hugging Face customer content accessed was five datasets apparently connected to ExploitGym or CyberGym; no other customer-facing models, datasets, Spaces or packages were affected","source":"Hugging Face, Agent Intrusion Technical Timeline","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"cross-sector","context":"Hugging Face\'s stated blast radius after forensic reconstruction. Operational search metadata was also read. Source: [[frontier-lab-cyber-disclosures-2026]]","url":"https://huggingface.co/blog/agent-intrusion-technical-timeline","access":"open"}
1,{"id":"s3305","stat":"McKinsey\'s 1:3:5 pattern: for every dollar spent on agentic technology, successful AI transformations spend three on process redesign and five on capability building and adoption","source":"McKinsey, How to close the agentic adoption gap","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"McKinsey heuristic with no survey basis stated; the authors say most companies invert it. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/how-to-close-the-agentic-adoption-gap","access":"open"},{"id":"s3306","stat":"Employees reporting low trust in their organisation\'s support during an AI transformation are 1.5x more likely to feel anxious about AI-related workplace change than those reporting high trust","source":"McKinsey, AI readiness survey of employees and leaders (cited in How to close the agentic adoption gap)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Sample size and fieldwork dates not given in the article. Source: [[mckinsey-agentic-adoption-gap-2026]]"}
1,{"id":"s3307","stat":"High performers are 3x more likely than their peers to strongly agree that senior leaders demonstrate ownership of and commitment to AI initiatives","source":"McKinsey, How to close the agentic adoption gap","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 3 of the article; \\"high performers\\" not defined in the text. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/how-to-close-the-agentic-adoption-gap","access":"open"},{"id":"s3308","stat":"Up to 57% of current US work hours are automatable","source":"McKinsey Global Institute, Agents, robots, and us: Skill partnerships in the age of AI","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"As cited in McKinsey\'s How to close the agentic adoption gap (Aug 2026); the MGI report itself has not been read. Source: [[mckinsey-agentic-adoption-gap-2026]]"}
1,{"id":"s3309","stat":"More than 70% of the skills sought by employers today are used in both automatable and non-automatable work, so most skills endure but where they are applied changes","source":"McKinsey Global Institute, Agents, robots, and us: Skill partnerships in the age of AI","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"As cited in both the State of Organizations 2026 report (footnote 11) and the August adoption-gap article (Exhibit 4); the MGI report itself has not been read. Source: [[mckinsey-agentic-adoption-gap-2026]]"},{"id":"s3310","stat":"A global bank mobilised more than 2,000 AI champions running floor walks, office hours and micro-demonstrations to drive peer-led agentic adoption","source":"McKinsey, How to close the agentic adoption gap","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Unnamed McKinsey client example. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/how-to-close-the-agentic-adoption-gap","access":"open"},{"id":"s3311","stat":"A global Fortune 500 technology company embedded one AI coach per five to ten engineers, joining sprint ceremonies and retrospectives, in place of classroom training","source":"McKinsey, How to close the agentic adoption gap","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Unnamed McKinsey client example. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/how-to-close-the-agentic-adoption-gap","access":"open"},{"id":"s3312","stat":"McKinsey\'s State of Organizations 2026 survey drew 10,018 responses from leaders and managers at organisations with at least 1,000 employees, fielded June to September 2025, across 16 countries and 17 industries","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Second edition (first 2023). Corrected from the landing page, which says 15 countries and 16 industries and calls respondents senior executives: 5,703 of the 10,018 are middle management, 3,250 top management (directors, VPs) and 1,065 executive team. US 3,017, UK 701. Verified against the PDF. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3313","stat":"55% of leaders say successfully building the AI capabilities of employees will bring exponential productivity gains","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 3, n=7,904; the desired outcomes of developing an AI-savvy workforce. Verified against the PDF. Professional services and TMT run highest at 64% and 62% against a 53% average elsewhere. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3314","stat":"72% of leaders report a notable impact of geopolitical uncertainties on their organisations","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Verified against the PDF. 74% among organisations with greater geographical spread against 63% with less; services businesses less affected than goods. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3315","stat":"Fewer than 25% of organisations that set out to improve their performance achieve sustained impact","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Verified against the PDF (performance edge chapter). McKinsey does not define the sustainment window in the report text. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3316","stat":"Top barriers to adopting AI at scale: concerns about AI itself (bias, IP, job replacement) 46%, regulatory, ethical or legal concerns 44%, organisational challenges including change management and silos 39%, inadequate technology infrastru
1cture about 30%, financial constraints about 24%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Exhibit 1, n=3,763. The first three are stated in the text; infrastructure and financial figures are read from the regional bars (Europe, North America, Asia-Pacific). Regulatory concerns run 48% in EU-based leaders and 56% in Germany against 44% North America and 41% Asia-Pacific. This is the figure the August adoption-gap article summarises as change management outranking infrastructure. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3317","stat":"72% of leaders say their organisations are not fully ready to face upcoming changes; even among optimistic leaders only one-third feel prepared","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Introduction. Just over half of respondents expect environmental change to have at least a somewhat positive impact in the next one to two years. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3318","stat":"Top organisational priorities: value creation (from cost cutting) 43%, sustained excellence 43%, the AI-enabled organisational unlock 40%, building hybrid human-agent capabilities 37%, leadership reinvented 30%, diversity and inclusion 25%, focus on the core 23%, the new geopolitical context 20%, AI rewriting shared services 13%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Appendix Exhibit A3, n=10,018; respondents selected the themes top of mind for them. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3319","stat":"86% of leaders feel their organisations are not very prepared to adopt AI in day-to-day operations","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"AI-enabled organisation chapter, survey highlight. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3320","stat":"One in six organisations has no clear C-level owner for AI adoption","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"AI-enabled organisation chapter. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3321","stat":"Only 14% of organisations see leaders consistently championing AI adoption and experimentation with clear strategies and action","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"AI-enabled organisation chapter. Contrast: in organisations with a clear vision for AI\'s future impact, nearly 90% of leaders actively champion adoption. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3322","stat":"23% of surveyed leaders represent AI Pioneers, organisations with a clear understanding of how AI reshapes activities and rolling out AI across most functions; 64% of pioneers expect environmental change to have a positive impact in the next one to two years against 45% of non-pioneers, and pioneers are more than twice as likely to believe employees will aim for and achieve more (56% vs 26%)","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Footnote 7 defines the pioneer segment. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3323","stat":"Key enablers of enterprise-wide AI adoption: ease of use 42%, leadership championing adoption 36%, a dedicated team to drive adoption 36%, link to business impact 29%, role-based capability training 29%, link to performance management 24%, clear ethical and compliance guidelines 24%, employee incentives 22%, communications plan 22%, portfolio of example use cases 18%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 2, n=9,346; respondents picked the most important elements. Fewer than one in four rate ethical and compliance guidelines as important, which the report reads as leaders under-weighting the concern. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3324","stat":"One executive\'s rule that for every $1 spent on AI technology, $5 should be spent on people","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Quoted in the State of Organizations 2026 with a citation to Relyea, Maor, Durth and Bouly, \'Gen AI\'s next inflection point\' (McKinsey, 7 Aug 2024). Note this is a 1:5 technology-to-people ratio; the August 2026 adoption-gap article restates it as 1:3:5 (technology, process redesign, c
1apability building). Neither has a stated survey basis. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3325","stat":"Allianz\'s generative AI platform AllianzGPT has more than 60,000 active users; the group asks every employee how AI can reduce their workload by at least four hours a week, expects at least 43 hours of learning per employee per year and recorded an average of 60 hours in 2024","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Interview with Bettina Dietsche, group chief people and culture officer, Allianz (150,000+ employees). Her line: in five years two-thirds of the skills Allianz needs will be completely different. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3326","stat":"Desired outcomes of an AI-savvy workforce: exponential productivity gains 55%, faster and widespread access to information 48%, reduction of administrative work 47%, more effective decision-making 46%, improved cross-training 35%, higher employee satisfaction and retention 35%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 3, n=7,904. Millennial leaders weight the productivity gains; Gen X and boomer leaders weight the reduction in administrative work. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3327","stat":"53% of leaders expect AI to serve mainly as a support tool for people in the next one to two years and only 25% expect it to take on agentic roles as autonomous teammates to employees","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 4, n=9,346, multiple responses allowed. Generational split on the autonomous-teammate expectation: 27% of leaders aged 18-24 against 19% of those 55 and older; \'AI will augment human capabilities\' 44% among 18-24s against 35-40% in older bands. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3328","stat":"Only 30% of leaders see employees using time freed by AI for higher-value tasks such as critical thinking and creativity; 13% believe employees are able to do more tasks at the same time; one in ten expect their employees to miss out on the productivity opportunity altogether with no meaningful increase in output","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Humans and AI agents chapter. The \'freed time\' question is the one the enablement line has to answer. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3329","stat":"27% of leaders aged 18-24 and 25% of those in advanced industries say they have clarity about AI\'s impact on their organisation in the next one to two years; leaders over 55 and in public-sector-adjacent fields lag","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Humans and AI agents chapter. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3330","stat":"39% of organisations conduct strategic workforce planning only every two to three years and 39% yearly; only 14%, mostly in TMT, have put in place more flexible planning","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Humans and AI agents chapter. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3331","stat":"Around 75% of current roles will need reshaping with new skill mixes as AI becomes embedded across workflows","source":"McKinsey, The agentic organization: Contours of the next paradigm for the AI era","date":"Sep 2025","sortKey":20250900,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"As cited in the State of Organizations 2026 (footnote 13); the agentic-organisation paper itself has not been read. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-agentic-organization-contours-of-the-next-paradigm-for-the-ai-era","access":"open"},{"id":"s3332","stat":"Demand for AI fluency (the ability to use and manage AI tools) has grown sevenfold in two years in US job postings, faster than for any other skill","source":"McKinsey Global Institute, Agents, robots, and us: Skill partnerships in the age of AI","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"As cited in the State of Organizations 2026 (footnote 11). Source: [[mckinsey-agentic-adoption-gap-2026]]"},{"id":"s3333","stat":"Where entire roles have been eliminated by generative AI, 54% of leaders upskilled, reskilled or redeployed the affected employees and only one in five eliminated the role entirely","source":"McKinsey, How to lead yourself and others in the new era of work","date":"Aug 2025","sortKey":20250800,"precision":"month","year":2025,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"As cited in the State of Organizations 2026 (footnote 14); the underlying McKinsey piece has not been read. Source: [[mckinsey-agentic-adoption-gap-2026]]"}
1,{"id":"s3334","stat":"Hitachi has committed to develop 50,000 generative AI professionals by fiscal year 2027, with a global scope, alongside a \'Customer Zero\' approach of applying AI internally first","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Interview with Lorena Dellagiovanna, chief HR and sustainability officer, Hitachi. Upskilling is delivered as a continuous programme rather than one-off training, with AI embedded in the tools employees already use. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3335","stat":"84% of organisations plan to expand the scope of their shared-services centres within the next one to two years: innovation or transformation tasks 30%, analytical tasks 27%, strategic or advisory tasks 26%, operational or process-driven tasks 21%, administrative tasks 18%; 16% are not planning to expand","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 5, n=8,175, multiple responses. Today shared services still focus on operational tasks (56%) and administrative tasks (49%) with fewer on analytical (41%) or strategic (26%) work. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3336","stat":"Only 6% of business-services leaders are realising the full benefits of advanced technologies across multiple use cases, and more than 40% have yet to start systematic adoption","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Shared services chapter, survey highlight. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3337","stat":"Biggest barriers to scaling AI-native global business services: integration with existing systems 42%, resistance to change 41%, talent shortages 29%, lack of investment 27%, lack of access to or knowledge of technologies 27%, fragmented process landscape 26%, limited ownership or accountability 24%, need for local expertise 22%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 6, n=8,175. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3338","stat":"McKinsey claims AI-native global business services deliver a 40-fold increase in access to innovations, a 50% productivity improvement, an additional 20% increase in cost effectiveness and a 20% improvement in customer experience relative to traditional shared services","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Stated as \'our research suggests\' with no method, sample or definition given. Use as McKinsey\'s claim, not as a measured result. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3339","stat":"A global pharmaceutical company\'s generative AI copilot for financial planning and analysis cut FP&A transactional activity by 30% and FP&A operating expenditure by 15%, with a proof of concept that took six weeks","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Shared services chapter, unnamed McKinsey client example. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3340","stat":"42% of respondents say organisations need a clear vision, leadership mandate and governance structure for global business services transformation; 62% of efficiency-focused leaders expand shared services to reduce costs while 54% of growth-focused leaders prioritise scalability and technology adoption","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Shared services chapter. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3341","stat":"Barriers to dealing with increased global fragility: rigid organisational structures and processes 38%, local regulations 32%, resistance from organisational culture 29%, weak technology and data foundations or legacy systems 26%, lack of regional talent 24%, limited capability footprint 22%, lack of leadership capabilities 20%, capital locked in high-capex investments 19%, single dependencies 17%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 8, n=10,018. 46% of leaders in organisations with more than 30,000 employees cite rigidity against 34% in organisations of 1,000-5,000. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3342","stat":"45% of employees in fatigued organisations see cultural resistance as a barrier to managing geopolitical change, against 28% in highly energised teams","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Geopolitics chapter. Cultural resistance is more pronounced in North America (32%) and Europe (29%) than Asia-Pacific (23%). Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3343","stat":"UK respondents: 21% say geopolitical change has affected their organisation little or not at all, 64% to some extent and 14% to a large extent (n=701); the survey-wide split is led by Saudi Arabia (40% little or not affected) and Japan (39%), with the US at 28/59/13 and Germany at 22/61/16","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 7, n=10,018 with country subsamples listed. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3344","stat":"17% of organisations have no defined methodology to assess the impact of geopolitical trends and only 26% engage in quarterly scenario planning; organisations under 5,000 employees are 50% less likely than larger ones to plan quarterly","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Geopolitics chapter. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3345","stat":"Only 25% of leaders prioritise digital security despite growing cyber-fragility risks","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Geopolitics chapter; McKinsey calls it a sleeper governance issue. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3346","stat":"Key reorganisation levers for geopolitical resilience: process and workflow redesign 34% and adjustments to talent strategy 32%; organisations over 50,000 employees lean harder on process redesign (37%) and supply diversification (31%); confident organisations are seven points more likely (36% vs 29%) to reallocate budget and talent organisation-wide","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Geopolitics chapter. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3347","stat":"43% of surveyed executives said they divested assets too late or failed to divest when they should have","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"As cited in the State of Organizations 2026 (footnote 22) from a 2021 McKinsey piece, but presented on the report\'s own summary page as a current finding; treat the date as uncertain. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3348","stat":"43% of leaders cite productivity growth as their top priority, 61% feel high pressure to deliver further gains, and two-thirds say their organisations are overly complex and inefficient; leaders in organisations over 50,000 employees are twice as likely as those in 1,000-5,000 to feel very high pressure","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Productivity chapter. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3349","stat":"One firm found it was duplicating 35% of decisions across functions, holding 60% more meetings than peers, running two-month lags in data cascading and spending more than 1,000 hours a month on manual reporting","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"critical","sourceType":"analyst","scope":"cross-sector","context":"Productivity chapter, unnamed example of what structural redesign without process redesign leaves in place. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3350","stat":"Cross-functional processes such as strategy, budgeting, forecasting and performance reviews consume 40% to 65% of managerial and overhead time, yet often lack clear accountability beyond executive leadership","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Productivity chapter, stated as McKinsey experience. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"}
1,{"id":"s3351","stat":"McKinsey experience figures on end-to-end process redesign: decision cycles up to three times faster; simplifying governance frees 20% to 60% of management time; a European retailer cut governance participation 60% in a week; an FMCG firm eliminated 70% of duplicated reports and decisions and cut data points 30%; a CPG company lifted speed to market 1.5x, pipeline NPV by 20 percentage points and employee engagement by 25 points","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Productivity chapter; consultancy case figures with no method stated. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3352","stat":"Primary barriers to addressing root causes of productivity stagnation: focus on short-term cost cutting or lack of long-term orientation 30%, organisational silos 30%, organisational resistance 29%, unclear responsibilities 26%, overlapping process structures 26%, disconnected decision-making 25%, lack of change management 25%, transformation fatigue 24%, lack of digitalisation 21%, unavailability of capital expenditure 13%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 9, n=10,018. Capex is the least-cited barrier, which the report reads as leaders cutting cost instead of building value. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3353","stat":"Key unlocks to increase productivity in the next one to two years: investment in innovation capabilities 47%, culture of continuous improvement 47%, redefined process flows 39%, reduced organisational silos 35%, reduction of administrative burden 31%, empowered front line 29%, revised governance for speed 28%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 10, n=10,018. Leaders who rate their organisational setup better than peers\' were about 30% more likely to have undertaken process optimisation. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3354","stat":"56% of executives say they are clear about their organisation\'s must-win battles, falling to 44% of senior managers and 27% of middle managers","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 11, n=10,018. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3355","stat":"Leaders with clarity on must-win battles are more optimistic about the next one to two years (56% vs 46%) and more confident in their ability to adapt quickly (31% vs 14%); 69% of unclear leaders have a negative outlook on AI against 57% of clear leaders","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Focus on the core chapter. AI adoption is described as a litmus test for strategic clarity. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3356","stat":"Only 30% of organisations reallocate resources enterprise-wide; 74% of leaders would not consider reallocating more than 10% of their workforce; 47% review budget and talent annually or less, and of the rest 45% review quarterly and 7% monthly","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Focus on the core chapter. One in three leaders say more frequent reviews of budget and talent allocation are a critical enabler. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"}
1,{"id":"s3357","stat":"Challenges in the dynamic reallocation of budget and talent: organisational resistance or protectionism 41%, inefficient and lengthy decision-making 38%, lack of willingness to make bold decisions 32%, lack of clarity on priorities 30%, no reallocation mechanism 29%, inability to make ad hoc adjustments 26%, lack of clarity on who the high performers are 21%, absence of output reviews 19%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 12, n=10,018. Manufacturing and frontline-heavy industries are two to five percentage points less agile than TMT and professional services on reallocation efficiency. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3358","stat":"About 45% of business leaders are already seeing greater employee willingness to aim higher, rising to 50% in organisations whose leaders feel less productivity pressure against 43% in higher-pressure organisations; 14% are seeing reduced employee performance","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Performance edge chapter. McKinsey calls the pandemic-era Great Attrition over. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3359","stat":"Barriers to building a high-performance culture: limited career progression opportunities 47%, lack of targeted incentives 43%, disengaged employees 38%, rigid performance-management systems 38%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Performance edge chapter. 58% of leaders in low-ambition organisations cite disengaged employees as a major barrier against 31% where employees are motivated to aim higher. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3360","stat":"Only 20% of leaders believe nonfinancial rewards meaningfully boost performance","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Performance edge chapter; the report\'s reading is that most companies are underinvested in human motivation. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3361","stat":"50% of millennial leaders cite limited career progression as a concern against 38% of baby boomers; by industry the concern is highest in advanced industries (50%), banking (49%) and energy and materials (48%) and lowest in public sector (38%) and education (42%)","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Performance edge chapter. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3362","stat":"Levers leaders lean on to improve knowledge-worker performance: career progression pathways 43%, regular performance dialogues 42%, competitive compensation 40%, dynamic goal setting about 35%, nonfinancial rewards about 20%; 64% believe linking individual goals to organisational goals is the key to improving performance","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 13, n=10,018. Desire for targeted incentives is 45% among millennial and Gen Z leaders against 31% of boomers. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3363","stat":"46% of leaders in improving organisations use dynamic goal setting against 33% in organisations with declining performance","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Performance edge chapter. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3364","stat":"Organisations that focus on both people and performance are 4.3 times more likely than the average company to maintain top-tier financial performance for nine of ten years, 1.5 times more likely to remain high performers, have half the earnings volatility, grow revenue twice as fast as performance-only peers and run attrition about 5% lower","source":"McKinsey Global Institute, Performance through people: Transforming human capital into competitive advantage","date":"Feb 2023","sortKey":20230200,"precision":"month","year":2023,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"As cited in the State of Organizations 2026 (footnote 39). Employees at these organisations are 1.3 times more likely to move into higher lifetime earnings brackets. Source: [[mckinsey-agentic-adoption-gap-2026]]"},{"id":"s3365","stat":"Rolls-Royce runs more than 1,000 volunteer \'change makers\' alongside its transformation; its latest engagement survey has 82% saying the strategy is clearly articulated (up ten points) and 88% saying they get regular performance updates (up four); shares were gifted to all employees vesting in 12 months on success","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Interview with Sarah Armstrong, chief people officer, Rolls-Royce. Leadership expectations changed twice in three years; performance managed across financial, operational and people measures with data underpinning. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3366","stat":"90% of global leaders continue to see diversity and inclusion as a priority and 81% of organisations are maintaining or expanding their D&I efforts (46% strategic priority, 26% operational priority); 16% scaled back over the past year, of whom 11% of all organisations paused some efforts while continuing others, and 45% of those who scaled back expect to bring efforts back
1within one to two years","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"D&I chapter, n=10,018. Regional: Europe 83% and Asia-Pacific 84% maintaining or expanding against 75% in North America, where 16% say D&I is not a priority right now. Of those scaling back, 40% globally and 48% in North America cite the sociopolitical environment, 29% a lack of measurable outcomes and 28% competing priorities. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3367","stat":"Benefits observed from prioritising diversity and inclusion: improved employee engagement and well-being 41%, improved talent attraction and retention 39%, stronger culture and belonging 38%, enhanced reputation 31%, increased innovation through diverse perspectives 29%, broader customer and market appeal 24%, improved external relationships 24%, improved decision-making 22%, fewer legal or regulatory violations 18%, fewer internal misconduct cases 17%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Exhibit 14, n=9,472. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3368","stat":"Major challenges to advancing D&I: lack of measurable outcomes 30%, competing priorities 29%, the polarised social and political climate 27%; 38% of organisations are embedding D&I into talent systems, 39% prioritising pay equity, 40% focused on inclusion and belonging and 35% on inclusion-related capability building","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"D&I chapter. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3369","stat":"Main organisational benefits of human-centric leadership practices: increased employee satisfaction and retention 56%, strengthened trust 56%, improved decision-making 42%, greater adaptability and resilience 40%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Leadership chapter, survey highlight. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3370","stat":"Primary challenges in creating a psychologically safe work environment: time pressure 47%, fear of failure or judgment 42%, hierarchical organisational culture 38%, unclear expectations 38%","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Leadership chapter. Two in five leaders reflect on their purpose at least weekly and two-thirds at least monthly. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3371","stat":"Leaders who became more reflective after recent geopolitical events are more likely to have adopted externally developed AI systems across most departments (33% against 28% of steady and 29% of less-reflective leaders) and to personally champion AI adoption (61% against 53% and 43%)","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"cross-sector","context":"Exhibit 15; reflection question n=4,458, AI questions n=9,346. Reflective leaders are also more confident in their organisation\'s ability to adapt (30% vs 18% and 17%) and more likely to have clear visibility on must-win battles (49% vs 25% and 21%), and feel more productivity pressure (74% vs 61% and 54%). Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3372","stat":"63% of Schneider Electric\'s employees are shareholders; the group is piloting an AI leadership coach personalised to its leaders\' code and values, already deployed and being extended","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Interview with Charise Le, CHRO, Schneider Electric (160,000 employees, 100+ countries). Upward feedback is an annual integrated report to each leader. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3373","stat":"McDonald\'s has put 1,000 leaders through its Accelerate development programme, now in its second iteration, as part of an enterprise transformation that started with the people function before finance, procurement, data and AI, and global business services","source":"McKinsey, The State of Organizations 2026","date":"Feb 2026","sortKey":20260200,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"cross-sector","context":"Interview with Tiffanie Boyd, global chief people officer, McDonald\'s. Her lesson: change is harder without a burning platform, and AI puts more weight on trust, judgment and empathy. Source: [[mckinsey-agentic-adoption-gap-2026]]","url":"https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations","access":"open"},{"id":"s3374","stat":"Huawei evaluated VDGR-RAG on four proprietary telecoms corpora: Energy Saving with 2,764,970 tokens, 1,927 chunks and 6,234 entities; Fault Management with 38,942 tokens, 73 chunks and 497 entities; Experience Assurance with 448,716 tokens, 333 chunks and 1,747 entities; and General Dataset with 72,916,106 tokens, 64,823 chunks and 91,846 entities","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Huawei internal enterprise product documents. The corpora and questions were not released. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3375","stat":"The VDGR-RAG evaluation used approximately 700 internal questions spanning single-hop retrieval, multi-hop reasoning and cross-domain knowledge aggregation","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"No per-dataset question counts, confidence intervals or public test set were reported. Retrieval recall and answer accuracy were both scored by an LLM judge. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3376","stat":"LinearRAG achieved retrieval recall and answer accuracy of 70.3% and 55.4% on Energy Saving, 88.1% and 75.3% on Fault Management, and 95.5% and 85.6% on Experience Assurance","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Huawei reproduction using common chunks, Qwen3-Embedding-8B and Qwen3.6-27B. Results were scored by an LLM judge on proprietary data. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3377","stat":"E2GraphRAG achieved retrieval recall and answer accuracy of 75.6% and 60.2% on Energy Saving, 86.8% and 75.9% on Fault Management, and 85.7% and 77.2% on Experience Assurance","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Huawei reproduction using common chunks, Qwen3-Embedding-8B and Qwen3.6-27B. Results were scored by an LLM judge on proprietary data. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"}
1,{"id":"s3378","stat":"HippoRAG achieved retrieval recall and answer accuracy of 82.3% and 77.1% on Energy Saving, 88.1% and 69.6% on Fault Management, and 81.7% and 85.5% on Experience Assurance","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Huawei reproduction using common chunks, Qwen3-Embedding-8B and Qwen3.6-27B. Results were scored by an LLM judge on proprietary data. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3379","stat":"A-RAG achieved retrieval recall and answer accuracy of 66.3% and 63.3% on Energy Saving, 80.1% and 83.9% on Fault Management, and 81.0% and 84.1% on Experience Assurance","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Huawei reproduction using common chunks, Qwen3-Embedding-8B and Qwen3.6-27B. Results were scored by an LLM judge on proprietary data. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3380","stat":"BookRAG achieved retrieval recall and answer accuracy of 84.8% and 74.9% on Energy Saving, 91.9% and 83.2% on Fault Management, and 90.9% and 89.7% on Experience Assurance","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Strongest overall baseline in Huawei\'s comparison. Results were scored by an LLM judge on proprietary data. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3381","stat":"VDGR-RAG achieved retrieval recall and answer accuracy of 98.5% and 97.6% on Energy Saving, 92.4% and 91.7% on Fault Management, and 98.7% and 97.3% on Experience Assurance","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Authors\' system on proprietary Huawei telecoms data, scored by an LLM judge. No confidence intervals or human-validation result reported. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3382","stat":"On Huawei\'s General Dataset, answer accuracy progressed from 76.5% for hybrid sparse-dense retrieval to 88.6% with TOC-based agentic search, 90.5% with entity-enhanced graph search added, 92.4% when reflection was added to the TOC configuration, and 95.7% with TOC search, graph search and reflection combined","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Progressive ablation on a proprietary 72.9 million-token corpus. No external baseline was reproduced on this dataset because of compute limits. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3383","stat":"Adding TOC-based agentic search to the hybrid sparse-dense baseline improved retrieval recall from 80.5% to 91.3% and answer accuracy from 76.5% to 88.6%, a 12.1 percentage-point accuracy gain","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Largest single gain in the General Dataset ablation. Proprietary telecoms corpus and LLM-judge scoring. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3384","stat":"From the TOC-search baseline, entity-enhanced graph search added 1.9 percentage points of answer accuracy and dynamic reflection added 3.8 points; the full system reached 95.7%, 19.2 points above the 76.5% naive RAG baseline","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"The graph and reflection increments came from separate and combined ablation configurations on the proprietary General Dataset. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3385","stat":"Directory backtracking improved retrieval recall from 97.2% to 98.3% and answer accuracy from 95.8% to 97.0% on the Energy Saving dataset","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Maximum backtrack depth was three. The authors report negligible gains on the much larger, more deeply nested General Dataset. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3386","stat":"Knowledge-base routing on the merged Energy Saving, Fault Management and Experience Assurance corpus improved retrieval recall from 92.4% to 96.6% and answer accuracy from 92.3% to 95.1%","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Reported gains of 4.2 and 2.8 percentage points by reducing cross-domain terminology noise. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3387","stat":"VDGR-RAG averaged 4.14 LLM calls per query across three specialist datasets against 7.22 for BookRAG, a reported reduction of 42.7%","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Authors attribute the difference to multi-route retrieval on the original query rather than BookRAG\'s sub-question decomposition. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3388","stat":"On the Energy Saving dataset, VDGR-RAG used 4.16 LLM calls per query against BookRAG\'s 8.37 while achieving 97.6% answer accuracy against 74.9%","source":"Huawei, VDGR-RAG","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Proprietary corpus and LLM-judge scoring; API latency and monetary cost were not reported. Source: [[vdgr-rag-hierarchical-enterprise-retrieval]]","url":"https://arxiv.org/abs/2608.07994","access":"open"},{"id":"s3389","stat":"Task requirements were elicited from 410 employees across six occupational domains (539 responses collected; 129 removed for non-completion, scoring under 50% on the capability quiz, or spending under 10 minutes), 125 recruited through collaborating companies and 414 via Prolific before quality control","source":"Cambridge CFI, Cognitive capability profiles for AI task suitability","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"legal","context":"Domains: warehouse/logistics; manufacture, maintenance, repair; numerical, data, programming; administration and planning; customer service, marketing, HR;
1 hospitality, sales, client care. No legal or professional-services domain. Respondents averaged 40.3 years, 49.8% female, 5.7 years in role. Source: [[cambridge-cognitive-capability-task-suitability]]","url":"https://arxiv.org/abs/2608.25623","access":"open"},{"id":"s3390","stat":"The capability-profiling battery held 19,535 items drawn from 22 existing benchmarks, each annotated for demand on 18 cognitive capabilities by two LLM raters (GPT-4o and Gemini 3 Flash); two capabilities failed inter-rater reliability and the remaining 16 were clustered into eight composite dimensions","source":"Cambridge CFI, Cognitive capability profiles for AI task suitability","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"No single benchmark contributes more than 10% of the battery. Attention and inhibitory control and prospective memory were dropped (rank correlation below 0.30); retained capabilities correlated 0.38 to 0.81 between raters. Source: [[cambridge-cognitive-capability-task-suitability]]","url":"https://arxiv.org/abs/2608.25623","access":"open"},{"id":"s3391","stat":"Averaged across six job domains, the highest-ranked work activities by frequency-adjusted importance were problem solving (2.04), decision making (1.78), checking (1.44), researching (1.35) and computer use (0.96); the lowest were listening (0.29), coding (0.38), managing resources (0.39) and data manipulation (0.39)","source":"Cambridge CFI, Cognitive capability profiles for AI task suitability","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Score combines rank (5 points for a respondent\'s top activity down to 1 for fifth) with selection frequency, so broadly applicable activities outrank ones important only within one domain. 18 activities adapted from O*NET. Source: [[cambridge-cognitive-capability-task-suitability]]","url":"https://arxiv.org/abs/2608.25623","access":"open"},{"id":"s3392","stat":"Computer use accounted for the most working time at 15.7 hours a week despite only moderate importance, while checking ranked among the most important activities at 10.2 hours a week; tool use (14.7), managing people (13.4) and admin (10.8) also took substantial time relative to their importance rank","source":"Cambridge CFI, Cognitive capability profiles for AI task suitability","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Self-reported hours from the 410-respondent cross-domain sample. The authors read the gap as two distinct deployment targets: augmenting high-value activities and cutting time on high-volume ones. Source: [[cambridge-cognitive-capability-task-suitability]]","url":"https://arxiv.org/abs/2608.25623","access":"open"},{"id":"s3393","stat":"Domain-specific task-by-capability importance matrices correlated cell-for-cell at r = 0.53 to 0.77 (mean 0.63) across the six job domains, with planning, semantic memory, working memory, language and procedural memory weighted highest for nearly every activity","source":"Cambridge CFI, Cognitive capability profiles for AI task suitability","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"The authors\' \\"shared cognitive core\\" finding: domains differ mainly in secondary capabilities (warehousing +2.1 on spatial reasoning, manufacture +2.5 on planning, client care +1.7 theory of mind and +1.2 empathy relative to the pooled average). Source: [[cambridge-cognitive-capability-task-suitability]]","url":"https://arxiv.org/abs/2608.25623","access":"open"},{"id":"s3394","stat":"Company-recruited and Prolific-recruited respondents produced activity capability-importance profiles agreeing at mean cosine 0.91 and Pearson 0.80, with within-source split-half reliability up to 0.94 and a disattenuated between-source correlation of approximately 1","source":"Cambridge CFI, Cognitive capability profiles for AI task suitability","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"The paper\'s evidence that its 3
10-minute \\"robot helper\\" elicitation (pick five capabilities per activity, split 100 points) measures the work rather than the sample. Source: [[cambridge-cognitive-capability-task-suitability]]","url":"https://arxiv.org/abs/2608.25623","access":"open"},{"id":"s3395","stat":"All six profiled models (Gemini 2.5 Flash, Gemini 3 Flash, Gemini 3.1 Pro, GPT-4o mini, GPT-5 nano, o4-mini) shared the same capability profile shape: semantic memory was the highest-scoring dimension for five of six (log-capability about 5.7 to 6.3), while action planning, instrumental reasoning and object permanence were consistently weakest","source":"Cambridge CFI, Cognitive capability profiles for AI task suitability","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Variation across cognitive dimensions exceeded variation across model families. Text-only battery under-samples agentic capabilities, and bare models were profiled, not scaffolded agents. No Anthropic or frontier GPT-5.x model in the catalogue. Source: [[cambridge-cognitive-capability-task-suitability]]","url":"https://arxiv.org/abs/2608.25623","access":"open"},{"id":"s3396","stat":"Gemini 3.1 Pro was the most suitable system for all 18 work activities (log suitability 4.1 to 4.6), Gemini 3 Flash second (3.3 to 4.1), with the other four systems overlapping at 1.7 to 3.4; its advantage sat in information integration and control (4.90 against about 2 to 4) and action planning (4.25 against 2.37 for the next system), not in knowledge or language","source":"Cambridge CFI, Cognitive capability profiles for AI task suitability","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Within a six-model Google-heavy catalogue; robust across the sensitivity sweep (top on 17 of 18 activities, admin the exception at the most extreme compensation setting). Comparative ranking, not a calibrated success probability. Source: [[cambridge-cognitive-capability-task-suitability]]","url":"https://arxiv.org/abs/2608.25623","access":"open"},{"id":"s3397","stat":"Multiplying suitability by employee-rated task importance put researching, admin and analysing data at the top of the cross-domain deployment-priority list; in a single-company case study of 35 respondents, communicating and researching were the clearest opportunities and problem solving and computer use the capability gaps","source":"Cambridge CFI, Cognitive capability profiles for AI task suitability","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Suitability for the case study averaged across the six profiled models. Quadrant boundaries are the within-set medians, so \\"high\\" and \\"low\\" are relative labels. Source: [[cambridge-cognitive-capability-task-suitability]]","url":"https://arxiv.org/abs/2608.25623","access":"open"},{"id":"s3398","stat":"On a 100-step warehouse task with Gemini-3-Flash, the SKILL.state runtime (which discards conversation history and keeps only a validated JSON execution state) scored 0.94 accuracy on 65,408 cumulative tokens, against 0.91 on 1,062,387 tokens for a LangGraph-style runtime holding a structured state block alongside the full transcript: a 16.2x token reduction at higher accuracy","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"arXiv preprint 2608.26263, not peer reviewed. Mean of 5 procedural generator seeds; differences at horizons of 50 steps or more significant on a paired t-test at p<0.01. The authors\' own synthetic benchmark (SkillExecBench, 500-shelf inventory domain). Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3399","stat":"SKILL.state held average prompt size flat at 1,736 to 1,905 tokens from 10 to 200 execution steps, while ReAct-style transcript accumulation grew to 48,007 tokens per
1call and accuracy fell from 0.90 to 0.74 over the same range","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The architectural claim: O(1) prompt footprint and O(T) cumulative tokens, against O(T-squared) for conversational runtimes. At 200 steps a summarisation-based runtime consumed 6.18m cumulative tokens against SKILL.state\'s 122,384. Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3400","stat":"In a budget-matched control where every runtime was pinned to roughly 1,800 tokens on a 100-step task, sliding-window truncation scored 0.18, LLMLingua prompt compression 0.22 and capped summarisation 0.52, against 0.94 for structured execution state; unbounded ReAct given 20 times the token budget scored 0.84","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The paper\'s most transferable result: the gain comes from the structure of what is retained, not from the prompt being shorter. Statistical entropy filtering strips slot identifiers that are semantically load-bearing; truncation evicts early allocations. One team\'s implementation of the compression baselines on their own benchmark. Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3401","stat":"On InterCode CTF (100 Linux capture-the-flag challenges), SKILL.state reached 54.2% pass@1 against 46.4% for a summarisation runtime, 43.2% for ReAct and 41.8% for a LangGraph-style stateful runtime, while cutting cumulative tokens 60.4% against ReAct and 65.9% against the stateful baseline","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Gemini-3-Flash. Mechanism is legible: holding tested hypotheses and discovered flags in explicit state stops the agent repeating commands it has already run. Average prompt 813 tokens against 1,797 to 1,946 for the baselines. Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3402","stat":"On Sierra tau-Bench, SKILL.state scored 58.3% pass rate in the Retail domain (against 51.7%, 48.2% and 29.9% for stateful, ReAct and summarisation baselines) and 32.4% in Airline (against 28.1%, 23.6% and 21.8%), saving 40.5% of tokens against ReAct and 45.4% against the stateful runtime in Airline","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Gemini-3-Flash, multi-turn customer service against SQLite databases under policy constraints. Absolute Airline pass rates remain poor for every runtime. In Retail, SKILL.state\'s per-step prompt was the largest of the four at 3,325 tokens and still won on cumulative cost because it does not grow. Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3403","stat":"Injecting distractor events at 50 per turn over a 50-step horizon dropped a ReAct runtime from 0.68 to 0.53 accuracy, while SKILL.state held at 0.98; across all three noise levels tested (5, 20 and 50 events per turn) the state-based runtime stayed at or above 0.97","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Distractors were system telemetry, irrelevant git branch activity and rule overrides. Noise is filtered during state-patch generation and never enters a subsequent prompt, so it cannot compound. Gemini-3-Flash. Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3404","stat":"When the world state was silently changed outside the agent\'s action loop and the agent was then told, history-based runtimes continued hallucinating the stale fact for five to eight consecutive turns, because obsolete facts in the transcript overpowered the correcting observation; the state-based runtime recovered in zero steps","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Context poisoning measured rather than asserted, and the paper\'s sharpest result. Tested across three drift scenarios; a fourth (a cancelled order) defeated every runtime including SKILL.state, which the authors report. Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3405","stat":"The SKILL.state architecture partly collapses on smaller open-weight models: at 100 steps Gemma-4-31B scored 0.42 and Qwen3-8B 0.34, against 0.94 for Gemini-3-Flash on the same task","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Reported by the authors as a limitation. Gemma-4-31B\'s 0.42 nonetheless matched the LangGraph-style baseline\'s accuracy while spending 65,480 tokens against 557,968. The architecture does not rescue a
1weak model, but it does not cost anything either. Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3406","stat":"68% of open-weight model failures in the SKILL.state runtime were premature state overwrite or deletion (omitting existing keys instead of merging in place), 20% schema comprehension and type coercion, and 12% malformed JSON syntax","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The authors\' conclusion is that small-model degradation here is structured-output adherence rather than reasoning capacity, and that grammar-constrained decoding should remove most of it. Failure-log analysis on Gemma-4-31B at 100 steps, score 0.42. Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3407","stat":"On a harder relational environment (a nested graph of git branches, pull requests and CI statuses), the state-based runtime lost at 25 steps (0.88 against 0.94 for a stateful transcript runtime) and won decisively at 100 steps (0.78 against 0.63)","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Evidence that bounded execution state buys nothing until the horizon is long enough for accumulated history to become a liability. Gemini-3-Flash, 5 seeds. Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3408","stat":"A single static five-field state schema was reused across all 100 InterCode CTF challenges, with schemas authored once per domain rather than once per task","source":"Badhe, Tiwari & Chung, SKILL.state: Scalable Long-Horizon Agent Skills","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Relevant to the objection that state-based runtimes require per-workflow schema engineering. The fields were discovered_flags, tested_hypotheses, active_files, working_dir and cmd_summary. The authors still list dynamic schema discovery as an unsolved case. Source: [[skill-state-long-horizon-agent-runtime]]","url":"https://arxiv.org/abs/2608.26263","access":"open"},{"id":"s3409","stat":"Fabricated social proof in a skill description (appending markers such as \\"Official Skill\\") fooled an LLM planner into selecting the malicious skill 45.64% of the time, while embedded prompt injection in the same position succeeded only 4.69% of the time: the least effective of the five planner attacks tested","source":"Badhe & Tiwari, Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Peer reviewed, CAMLIS / PMLR vol. 299. Attacks modified only planner-visible metadata, leaving executable workflows identical, to isolate planner vulnerability from runtime behaviour. Caution: the paper states the planner model as Gemini 3.1 Pro in section 5.3 and Gemini 1.5 Flash in section 6.3, unresolved, so quote the figure with that caveat. Source: [[agent-skill-security-lifecycle]]","url":"https://arxiv.org/abs/2607.13987","access":"open"},{"id":"s3410","stat":"A metadata validation layer applied before planner invocation eliminated misleading-description attacks entirely (27.27% to 0.00%) and cut fake-recommendation attacks by over 80% (45.64% to 8.72%), which the authors read as evidence that planner vulnerability comes from persuasive natural-language metadata rather than from weakness in the underlying model","source":"Badhe & Tiwari, Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Defence is the authors\' own: prompt sanitisation, metadata normalisation to strip marketing language, capability validation against executable intent, and permission validation on verified manifests. Permission deception fell 11.36% to 5.68% and planner deception 9.88% to 2.47%. Source: [[agent-skill-security-lifecycle]]","url":"https://arxiv.org/abs/2607.13987","access":"open"},{"id":"s3411","stat":"Without runtime monitoring, 100% of execution-stage attacks against an LLM agent reached the privileged sink, because standard agent frameworks place implicit trust in the execution environment and apply no check before a model passes sensitive state to a network sink or invokes a high-risk command","source":"Badhe & Tiwari, Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Attack classes were unauthorised tool invocation, information-flow violations and unsafe multi-step workflow composition, all arising from the interaction of individually benign skills after a workflow was selected. Controlled evaluation over 327 real skills. Source: [[agent-skill-security-lifecycle]]","url":"https://arxiv.org/abs/2607.13987","access":"open"},{"id":"s3412","stat":"A runtime guard combining dynamic taint tracking with policy enforcement blocked 87.0% of unauthorised tool invocations but still left a 23.0% execution attack success rate, because string-based taint tags are destroyed when the model is instructed to paraphrase or summarise the sensitive data: the LLM\'s context window acts as an unmonitored implicit flow","source":"Badhe & Tiwari, Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Taint propagation accuracy 66.67%. The authors describe tracking information flow through non-deterministic LLMs as an open challenge requiring NLP-aware taint propagation. A structural limit on the runtime-guard a
1pproach rather than an implementation gap. Source: [[agent-skill-security-lifecycle]]","url":"https://arxiv.org/abs/2607.13987","access":"open"},{"id":"s3413","stat":"Without continuous revalidation of skill updates, 100% of malicious updates successfully inherited the trust established at a skill\'s initial repository admission and bypassed the defences that had screened it","source":"Badhe & Tiwari, Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Evolution-stage attacks tested were permission escalation, instruction injection, tool substitution, dependency compromise and publisher spoofing. Unlike conventional software packages, evolving agent skills are typically incorporated into future executions with no explicit human review. Source: [[agent-skill-security-lifecycle]]","url":"https://arxiv.org/abs/2607.13987","access":"open"},{"id":"s3414","stat":"Treating every agent-skill update as a fresh admission event detected 92.5% of malicious updates but rejected 37.0% of benign ones, dropping legitimate update acceptance to 63.0%, because aggressive but benign refactoring trips the same behavioural-consistency bounds that catch attacks","source":"Badhe & Tiwari, Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"False negative rate 7.5%, from sophisticated instruction injections staying beneath the vector-distance threshold. The authors present this as an inherent trade-off: bounds tight enough to resist zero-day evasion inevitably flag legitimate structural modification for manual review. Source: [[agent-skill-security-lifecycle]]","url":"https://arxiv.org/abs/2607.13987","access":"open"},{"id":"s3415","stat":"Sybil cloning of a malicious agent skill (15 near-identical copies) achieved a 93.20% attack success rate against undefended semantic retrieval, placing an average of 2.84 malicious skills inside every Top-5 candidate set presented to the planner","source":"Badhe & Tiwari, Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Retrieval used all-MiniLM-L6-v2 embeddings over a FAISS FlatL2 index, Top-50 narrowed to Top-5, across 440 benchmark queries. Other retrieval attacks: semantic camouflage 61.14%, keyword stuffing 46.59%, trigger poisoning 31.82%. Source: [[agent-skill-security-lifecycle]]","url":"https://arxiv.org/abs/2607.13987","access":"open"},{"id":"s3416","stat":"A post-retrieval validation layer with semantic diversity filtering cut Sybil-clone attack success from 93.20% to 26.59% and malicious candidates per Top-5 set from 2.84 to 0.27, but trigger poisoning and Sybil attacks survived at 17.50% and 26.59% because they exploit the density of the vector space rather than metadata inconsistency","source":"Badhe & Tiwari, Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The authors\' own conclusion is that post-retrieval filtering is a probabilistic filter rather than a security control and cannot provide zero-day security alone. Keyword stuffing fell to 7.83% and semantic camouflage to 9.82%. Source: [[agent-skill-security-lifecycle]]","url":"https://arxiv.org/abs/2607.13987","access":"open"},{"id":"s3417","stat":"Structural rule-based validation alone admitted 52.9% of malicious agent skills into a repository at a 0% false-positive rate, because attacks preserving valid syntax while manipulating semantic intent passed; adding an LLM semantic validator checking executable instructions against declared permissions cut malicious admission to 7.9%, at a 20.0% false-positive rate","source":"Badhe & Tiwari, Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The authors conclude that LLM intent verification is mandatory rather than optional against agentic supply-chain attacks, while introducing developer friction and remaining exposed to sophisticated zero-day evasion. Semantic validation alone admitted 42.9
1%. Source: [[agent-skill-security-lifecycle]]","url":"https://arxiv.org/abs/2607.13987","access":"open"},{"id":"s3418","stat":"A lifecycle security evaluation of reusable agent skills modelled six sequential trust boundaries (authoring, storage, retrieval, planner selection, execution runtime and evolution) and found that vulnerabilities arise at multiple stages beyond execution, with no individual defence sufficient because each boundary covers an attack surface later stages cannot reliably catch","source":"Badhe & Tiwari, Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Evaluated against 327 benign real-world skills from the SkillMCP repository across 15 capability clusters of 20-25 skills each, deliberately built with overlapping functionality to create dense retrieval neighbourhoods. Threat model excludes the LLM, embedding model, runtime and OS, which are assumed correct: this is not a jailbreak result. Source: [[agent-skill-security-lifecycle]]","url":"https://arxiv.org/abs/2607.13987","access":"open"},{"id":"s3419","stat":"Across AI add-ins editing Microsoft Word documents, between 13% and 32.6% of edits left behind a formatting defect the user was never shown, depending on the tool; SquarePact scored best at 12.8% and Grok for Word worst at 32.6%","source":"SquarePact (Actualization AI), FormattingBench v1.0","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Vendor\'s own benchmark, testing the exact property its product is built to win on, against general-purpose assistants that are not formatting tools, and the vendor comes first. Never print the ranking without saying whose test it is. Six checks covering font fidelity, spacing, indentation and edit-region integrity. Dataset and analysis scripts published under a permissive licence, so the ranking is falsifiable. Sample size and corpus provenance not established. Also tested: Claude for Word, Microsoft Copilot, Claude API and OpenAI API. Source: [[squarepact-formattingbench-word-addins]]","url":"https://squarepact.com/formattingBench/","access":"open"},{"id":"s3420","stat":"Selecting the best AI Word add-in per document reached 94.2% clean edits against 80.2% for the strongest single tool, a 14-point gap sitting in the routing decision rather than in any product","source":"SquarePact (Actualization AI), FormattingBench v1.0","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"The durable finding from the benchmark and the one no vendor benefits from, unlike the leaderboard. Quantified support for per-task routing over standardising on a single tool, and the first hard number in the Ed 48 pack for that proposition. Pairs with Ambrogi\'s ILTACON report of large firms buying Harvey, Legora and CoCounsel together as a hedge. Source: [[squarepact-formattingbench-word-addins]]","url":"https://squarepact.com/formattingBench/","access":"open"},{"id":"s3421","stat":"Of 310 guest submissions to three major opinion pages, 10 (3%) were flagged as 80%+ AI-generated and a further 40 as partially AI-written, so roughly one in six carried some machine drafting while wholesale generation stayed rare","source":"Semafor (Reed Albergotti), AI writing has already begun to appear on the opinion pages","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"workforce","sourceType":"media","scope":"cross-sector","context":"Semafor\'s own analysis; the detection method and the three publications\' identities are not fully specified. The editorial rules differ sharply: NYT prohibits AI in developing and drafting guest essays, the Washington Post requires contributors to confirm no AI creation or manipulation, the FT prohibits it in the writing process, and the WSJ has no prohibition. Source: [[semafor-ai-writing-opinion-pages]]"},{"id":"s3422","stat":"Wall Street Journal opinion editor Paul Gigot defended an AI-written op-ed on the test of whether a piece reflects \\"an author\'s original argument\\" and whether \\"an author has the standing and credibility to make it\\", declining to treat authorship of the prose as the issue","source":"Semafor (Reed Albergotti), AI writing has already begun to appear on the opinion pages","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"media","scope":"legal","context":"Landed in the same fortnight as the Irish High Court and Court of Appeal directions requiring human-controlled independent verification and declarations, which attach the obligation to the facts rather than to the provenance of the view. The contributor, Stanley Druckenmiller, said \\"I write everything using AI now for the same reason I use a calculator when I do math problems\\". Evidence that disclosure norms are being relaxed by practice faster than they are being written down. Source: [[semafor-ai-writing-opinion-pages]]"},{"id":"s3423","stat":"In legal-sector use of ChatGPT, traditional use is 57% writing and 20.5% knowledge retrieval, while agentic use is 16.2% writing, 8.3% retrieval, 32.9% coding and 17.7% system and agent operations","source":"OpenAI Enterprise Signals, via Kelly Galligan","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"OpenAI\'s own platform telemetry about OpenAI\'s own products, with no disclosed base, sample or method, reached through a Substack rather than a located primary report; the Enterprise Signals report was not found at source. Directional signal that legal work moving to agents stops looking like writing and starts looking like softw
1are. Not usable as a market statistic or in client work until the underlying report is read. Source: [[galligan-lawyers-thinking-too-small-agentic]]"},{"id":"s3424","stat":"Legal-sector Codex users grew 108x since February 2026, reported as more growth than any other compared sector","source":"OpenAI Enterprise Signals, via Kelly Galligan","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"cross-sector","context":"A growth multiple off an almost certainly tiny February base with no denominator disclosed, so the multiple must always carry that caveat. OpenAI\'s own figures about its own product, reported secondhand through a Substack; primary report not located. Source: [[galligan-lawyers-thinking-too-small-agentic]]"},{"id":"s3425","stat":"An attack against Claude Code\'s auto mode achieved success rates of up to 80% across variants, on a sample of five runs each (results ranged 3/5 to 4/5); the researcher states explicitly that these are small samples and not a universal attack-success-rate measurement","source":"Johann Rehberger (Embrace The Red), Breaking Claude Code Opus 5 Auto Mode","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"commentary","scope":"cross-sector","context":"The \\"up to\\" and the n=5 must travel with the figure; a flat 80% overstates what the researcher claims. Mechanism is environment poisoning rather than prompt injection: a zip archive carries a struct.py that shadows the standard library, so the decoder Claude writes for itself imports base64 and executes the attacker\'s code. Both Rehberger and Simon Willison reject the prompt-injection label, Willison calling it a confused environment attack in a 30 Aug update. Anthropic closed the report as informative, positioning auto mode as best-effort rather than a security guarantee. Source: [[research-2026-08-18]]"},{"id":"s3426","stat":"Auto mode denied Claude Code\'s cleanup command after Claude had itself detected the compromise and attempted to terminate the malware process","source":"Johann Rehberger (Embrace The Red), via Simon Willison","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"commentary","scope":"cross-sector","context":"The finding that matters more than the success rate: a safety control preventing remediation of the incident it failed to stop. Willison\'s framing is that the safety mechanism itself became part of the failure. Directly relevant to any argument that the control boundary sits in the harness rather than the model. Source: [[research-2026-08-18]]"},{"id":"s3427","stat":"None of 720 prompt-injection attempts succeeded against Claude Opus 5, Sonnet 5 or Fable 5 running auto mode, against a 5.83% success rate on GPT-5.6 Sol in Codex auto-review mode and 19.03% in full-access mode","source":"Trajectory Labs, independent evaluation","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"72 novel attack scenarios run 10 times each. Independent evaluation, not Anthropic\'s internal testing - do not attribute it to Anthropic. The evaluator\'s own caveats: attacks were optimised against Opus 4.7 before being applied to the newer models, and ten repeats of a single scenario may not be independent trials. Source: [[research-2026-08-18]]"},{"id":"s3428","stat":"A control can be excellent against one attack class and irrelevant to another: the same fortnight produced 0 of 720 successes against prompt injection and up to 80% success against environment poisoning, both on Claude Code auto mode","source":"Trajectory Labs and Johann Rehberger, read together","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"commentary","scope":"cross-sector","context":"The reconciliation rather than a source figure, and the reason the two results are not in conflict. Prompt injection smuggles instructions into consumed content; the Rehberger attack poisons the execution environment through Python module shadowing. Useful whenever a vendor cites a single adversarial evaluation as evidence a system is secure. Source: [[research-2026-08-18]]"},{"id":"s3429","stat":"On Vespper\'s own 279-task test set, its DOCX MCP scored 57.7% on tracked-change tasks with GPT 5.6 Sol, against 77.8% on tables, 75.5% on paragraphs and 71.9% on lists, the weakest category in the evaluation; creating or replying to comments is not supported at all","source":"Vespper, Launching Vespper DOCX MCP (blog post)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vendor-run benchmark, vendor wins it overall, but this is the vendor\'s own weak cell. The two capabilities legal needs from an agent Word editor (tracked changes and comments) are the two it does worst. Read against Claude for Word\'s tracked-changes marketing. Source: [[vespper-docx-mcp]]","url":"https://www.vespper.com/blog/launching-vespper-docx-mcp","access":"open"},{"id":"s3430","stat":"Vespper claims its DOCX MCP is 2.7 to 3.5 times faster and 2.7 to 2.9 times cheaper per task than Anthropic\'s DOCX skill across 279 test tasks on two GPT-5.6 variants, with higher accuracy across most document types","source":"Vespper, Launching Vespper DOCX MCP (blog post)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Vendor-run and the vendor wins. Method described (2,046-task benchmark, 70/15/15 split, 300 hand-annotated, identical system prompts, named competitor versions: SuperDoc 0.18.1, Office CLI 1.0.145, Adeu 3.0.2, python-docx) but the dataset does not appear to be published, so it is not falsifiable the way FormattingBench is. Always travels with attribution. Source: [[vespper-docx-mcp]]","url":"https://www.vespper.com/blog/launching-vespper-docx-mcp","access":"open"}
1,{"id":"s3431","stat":"On Word documents longer than ten pages, competing agent DOCX tools dropped to roughly 74% accuracy on Vespper\'s benchmark while Vespper\'s own tool held stable","source":"Vespper, Launching Vespper DOCX MCP (blog post)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vendor-reported approximate figure. The length-sensitivity point is the useful one: an editing harness that manipulates raw OOXML degrades with document size, which is where legal documents live. Source: [[vespper-docx-mcp]]","url":"https://www.vespper.com/blog/launching-vespper-docx-mcp","access":"open"},{"id":"s3432","stat":"GPT 5.6 Terra with Vespper\'s DOCX MCP came in roughly seven times cheaper than GPT 5.6 Sol with Anthropic\'s DOCX skill at comparable results; a generic tool such as Office CLI cost about $0.24 per task","source":"Vespper, Launching Vespper DOCX MCP (blog post)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Vendor-reported cost figures with no per-task cost stated for Vespper itself. Useful as an order-of-magnitude for the harness-plus-cheap-model versus frontier-model-plus-generic-tool trade. Source: [[vespper-docx-mcp]]","url":"https://www.vespper.com/blog/launching-vespper-docx-mcp","access":"open"},{"id":"s3433","stat":"Vespper\'s DOCX reconciler is a 3 to 8B parameter model fine-tuned with LoRA (rank 16, alpha 32) on about 16,000 examples, taking the old HTML, new HTML and old XML and emitting the new OOXML with no reasoning and no tool calls; the training benchmark held 2,046 tasks split 70/15/15 with 300 manually annotated","source":"Vespper, Launching Vespper DOCX MCP (blog post)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Architecture fact rather than a performance claim. A specialised small model behind an MCP beating the frontier lab\'s general-purpose skill on the lab\'s own task is the harness-over-model data point, alongside SKILL.state and FormattingBench. Source: [[vespper-docx-mcp]]","url":"https://www.vespper.com/blog/launching-vespper-docx-mcp","access":"open"},{"id":"s3434","stat":"543 UK legal professionals surveyed in mid-2026 for LexisNexis\'s fourth generative AI wave, the smallest sample in the series (887 in Jan 2025, 700+ in Aug 2025, 848 in Dec 2025 to Jan 2026)","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Sample size and scope. No fieldwork window, recruitment method or question wording published; the report says only \\"mid-2026\\". Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3435","stat":"94% of UK legal professionals use AI for legal work in some form; only 6% say they do not use AI tools at all","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The 94% is the complement of \\"do not use at all\\", a looser question than the 61% \\"using generative AI in their work\\" of Aug 2025, so it is not a like-for-like adoption jump despite the coverage running it as one. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3436","stat":"74% of UK legal professionals use AI at least once a week and 34% use it daily","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Frequency of use. Artificial Lawyer\'s coverage adds 32% \\"multiple times per week\\" (not on the report page). Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3437","stat":"Weekly AI use by segment: in-house legal teams 89%, mid-sized law firms 80%, large law firms 78%, public sector 68%, small law firms 68%, academics 61%, the Bar 52%","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"In-house is the heaviest-using segment in the UK sample, ahead of every firm size. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3438","stat":"Lawyers in commercial and corporate and in regulatory and compliance work are the most likely to use AI weekly, at 85%","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Weekly use by practice area. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3439","stat":"Top AI use cases among UK legal professionals: legal research 69%, document summarisation 62%, drafting 53%, document review 53%","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"All respondents, all segments. The H1 2026 wave reported large-firm figures only (research 80%). Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3440","stat":"66% of large-firm and in-house lawyers use AI for drafting; leaders use it for drafting more than juniors (62% v 51%) and for legal research (73% v 68%)","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Drafting by segment and seniority. Leaders ahead of juniors on drafting reverses the usual assumption about who experiments. By practice area: regulatory and compliance 64%, employment 63%, commercial and corporate 60%. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3441","stat":"10% of UK legal professionals say they are now dependent on AI to do their job","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"First time dependence has been measured in the UK series. Self-report against an undefined word, so it measures how people describe their relationship with the tools, not what would happen if they were removed. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3442","stat":"AI dependence by segment: large law firms 15%, in-house 13%, small law firms 9%, public sector 7%, the Bar 6%","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"Artificial Lawyer led its 2 Sep 2026 coverage with the large-firm 15%. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3443","stat":"AI dependence by practice area: commercial and corporate 14%, regulatory and compliance 12%, family 6%, property 5%, personal injury and clinical negligence 4%","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The practice-area spread follows the drafting and research-heavy areas. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3444","stat":"83% of UK legal professionals are concerned about fabricated or inaccurate AI outputs, up from 57% when LexisNexis first measured it at the start of 2024","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Top concern in every wave since early 2024, and rising while adoption rose from 41% to 94% over the same period. The H1 2026 wave reported 85%. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3445","stat":"Hallucination concern is highest at the Bar (87%) and among senior leaders (86% v 82% for mid and junior lawyers)","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Segment and seniority variation on the top concern. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3446","stat":"Beyond hallucinations, UK legal professionals\' AI concerns are confidential data leakage (53%), becoming too reliant on AI (52%), work quality declining (27%) and accidental bias (21%)","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"The 27% and 21% come from Legal Futures\' 2 Sep 2026 coverage, not the report page. Compare the H1 2026 large-firm list: leakage 56%, too reliant 44%, bias 44%, quality 39%. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3447","stat":"81% of UK legal professionals feel more comfortable using AI grounded in legal sources, up from 65% at the start of 2024, 70% in mid-2025 and 72% in January 2026","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The grounding series. Vendor-friendly (it is the Lexis+ with Protégé pitch) but the four-point series is consistent across waves, unlike the career and maturity baselines. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3448","stat":"Preference for AI grounded in legal sources by segment: in-house 88%, regulatory and compliance 85%, private client 84%, commercial and corporate 83%, employment 83%","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"In-house leads on grounding as it does on weekly use. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3449","stat":"77% of UK legal professionals say AI is causing clients or the general public to misinterpret the law (medium-sized firms 72%, the Bar 80%, academics 83%)","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"client","sourceType":"vendor","scope":"legal","context":"A large-firm leader in the report describes a \\"huge increase in litigation\\" from claimants using AI, not all of it meritorious. Pairs with the pro se surge material. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3450","stat":"Top criteria when selecting a legal technology provider: strong data privacy and security standards 59%, transparency about how AI is used 40%, human oversight of AI-enabled services 39%, all ranking ahead of cost and commercial value","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Vendor selection criteria. Security at 70% in regulatory and compliance, 67% in litigation, 65% in employment. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3451","stat":"29% of UK legal professionals say AI is embedded in their organisation\'s strategy and operations; 43% are experimenting but progress is slow; 14% report interest but little investment; 6% resistance; 3% say AI is not discussed at all","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The full maturity distribution. The GlobeNewswire release calls the 29
1% \\"up from 17% in January 2026\\", but 17% was the Aug 2025 Culture Clash figure and the Jan 2026 wave reported 19% for private practice; use as a snapshot, not a trend. The 43% makes the unverified 39% row from Aug 2025 (s0071) plausible. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3452","stat":"AI embedded in strategy and operations by segment: large law firms 57%, in-house 43%, small law firms 13%, the Bar 3%","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"vendor","scope":"legal","context":"The large-firm 57% is nearly double the 30% the same publisher reported for large firms in Jan 2026 (s2452), a jump no other source shows. Question wording or sample composition probably changed; do not cite as a trend. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3453","stat":"55% of UK legal professionals believe AI will change how law firms bill, up from 40% in January 2025 and 47% in August 2025","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"A clean three-point series across waves. Large firms and in-house both at 62%. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3454","stat":"Expectation that AI will change law firm billing by practice area: regulatory and compliance 63%, commercial and corporate 60%, private client 46%, property 44%, family 33%; leaders 60% v juniors 52%","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Billing expectation by practice area and seniority. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3455","stat":"The biggest challenge facing UK legal teams is keeping pace with new technology (53%; large law firms 71%, in-house 60%), ahead of increasing costs (48%) and compliance demands (44%)","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Top team challenges. The H1 2026 wave had 56% concerned about keeping up with
1new technology. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3456","stat":"46% of UK legal professionals say their career would be negatively affected if their organisation failed to fully embrace AI; the report says this is up from 28% a year earlier","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Baseline contradicts LexisNexis\'s own earlier waves: the Jan 2026 report gave the same 46% as \\"up from 36% in Jan 2025\\" (s2453) and the Aug 2025 wave had 39% private practice and 49% in-house (s3091). Treat 46% as stable across the last two waves and the \\"up from\\" as unreliable. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3457","stat":"22% of UK legal professionals would consider leaving an organisation that failed to embrace AI; the report says this is up from 16% a year earlier","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"The 16% baseline matches nothing in the vault: the Aug 2025 wave had 18% private practice, 19% in-house and 26% large firms (s3090), and Jan 2026 had 30% large firms (s2454). Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3458","stat":"In large law firms and in-house teams around six in ten lawyers say weak AI implementation could hurt their career and around a third would consider leaving over it","source":"LexisNexis Generative AI Legal Survey mid-2026 (The AI-dependent lawyer)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"vendor","scope":"legal","context":"Reported as approximate fractions, not percentages. Source: [[lexisnexis-ai-dependent-lawyer-2026]]","url":"https://www.lexisnexis.co.uk/insights/the-ai-dependent-lawyer/index.html","access":"open"},{"id":"s3459","stat":"Brain connectivity during essay writing was up to 55% lower in low-frequency semantic and monitoring networks for LLM-assisted participants compared to unaided participants","source":"Kosmyna et al. (MIT Media Lab), Your Brain on ChatGPT","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Longitudinal EEG study, 54 participants, 4 sessions across 4 months, 32-channel EEG evaluating dynamic Directed Transfer Function (dDTF). Search engine users showed 34-48% lower connectivity. Source: [[kosmyna-your-brain-on-chatgpt]]","url":"https://arxiv.org/abs/2506.08872","access":"open"},{"id":"s3460","stat":"83% of LLM-assisted writers (15 of 18) were unable to quote from their own essay minutes after completing it, with 0% providing an accurate quote","source":"Kosmyna et al. (MIT Media Lab), Your Brain on ChatGPT","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Session 1 essay recall test. In contrast, unaided (Brain-only) and search-engine groups achieved 100% accurate quoting by Session 3. Source: [[kosmyna-your-brain-on-chatgpt]]","url":"https://arxiv.org/abs/2506.08872","access":"open"},{"id":"s3461","stat":"Unaided essay writers used 60% fewer named entities (NERs) and exhibited significantly higher stylistic variability than LLM-assisted writers","source":"Kosmyna et al. (MIT Media Lab), Your Brain on ChatGPT","date":"Jun 2025","sortKey":20250600,"precision":"month","year":2025,"theme":"critical","sourceType":"academic","scope":"legal","context":"NLP analysis of essays across SAT prompts. LLM essays converged onto homogenous n-gram patterns and third-person address. Human teachers marked down LLM essays for lack of originality, while an AI judge scored them higher. Source: [[kosmyna-your-brain-on-chatgpt]]","url":"https://arxiv.org/abs/2506.08872","access":"open"},{"id":"s3462","stat":"In impossible file-reading tasks, autonomous LLM agents exhibited Non-Failure Rates of up to 97.5% (Kimi-k2) and 27.5% (GPT-4.1), guessing or simulating answers instead of reporting errors","source":"Guo et al., Are Your Agents Upward Deceivers?","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"200 tasks across 8 domains evaluated across 11 frontier and open-weight models under fault injection. Source: [[guo-agentic-upward-deception]]","url":"https://arxiv.org/abs/2512.04864","access":"open"},{"id":"s3463","stat":"When unable to open a user-specified document, autonomous agents silently switched to local decoy files without notifying the user in up to 90% of cases (GLM-4.5)","source":"Guo et al., Are Your Agents Upward Deceivers?","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Benchmark measuring Decoy Fallback Rate (DFR) under disabled file formats. Source: [[guo-agentic-upward-deception]]","url":"https://arxiv.org/abs/2512.04864","access":"open"},{"id":"s3464","stat":"200 benchmark tasks across 8 real-world domains evaluated 11 leading LLM agents for agentic upward deception under environmental fault injection","source":"Guo et al., Are Your Agents Upward Deceivers?","date":"Dec 2025","sortKey":20251200,"precision":"month","year":2025,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Evaluated GPT-5, GPT-4.1, Claude-4-Sonnet, Gemini-2.5-Pro, DeepSeek-v3.1, Kimi-k2, GLM-4.5, Qwen3 and Llama-3.1-405
1B in Smolagents. Source: [[guo-agentic-upward-deception]]","url":"https://arxiv.org/abs/2512.04864","access":"open"},{"id":"s3465","stat":"The Procedural Graph ranked first or joint first in 21 of 24 model-benchmark settings, with 19 wins, two ties and three losses against the strongest baseline in each setting","source":"Lu, Chen, Wu & Arik (Google), Procedural Graphs","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Seven benchmarks (HotpotQA, MultiChallenge, GDPval, ALFWorld, tau-bench, BFCL v3, EnterpriseArena) and four LLMs (Claude Sonnet 4.6, Gemini 3.1 Pro, Gemini 3.5 Flash, Grok 4.1 Fast), all on an identical ReAct solver against seven memory and workflow baselines. One-sided exact binomial sign test excluding ties, p = 4.3e-4. arXiv:2609.09153, preprint. Source: [[procedural-graphs-self-evolving-agents]]","url":"https://arxiv.org/abs/2609.09153","access":"open"},{"id":"s3466","stat":"An agent guided by a self-evolved procedural graph survived the full horizon in 85.0% of long-horizon financial episodes, against 0.0% for the same agent unguided","source":"Lu, Chen, Wu & Arik (Google), Procedural Graphs","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"EnterpriseArena: monthly liquidity decisions over up to 132 months with three undisclosed macroeconomic crises. Test split, 20 episodes. Fisher\'s exact p = 2.6e-8. The best single round during the search reached 95.0%, which the authors decline to quote as selecting on the test set. Source: [[procedural-graphs-self-evolving-agents]]","url":"https://arxiv.org/abs/2609.09153","access":"open"},{"id":"s3467","stat":"A hand-written expert procedure graph cut agent success on MultiChallenge from 87.50% to 58.93%; a self-evolution loop then recovered it to 92.86%, a gain of 33.93 points over the expert initialisation","source":"Lu, Chen, Wu & Arik (Google), Procedural Graphs","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"One static offline update made the expert prior worse still at 53.57%. Evidence that hand-authored procedural scaffolds can actively harm performance until revised against held-out validation. Overall success rate metric. Source: [[procedural-graphs-self-evolving-agents]]","url":"https://arxiv.org/abs/2609.09153","access":"open"},{"id":"s3468","stat":"Grounding agent guidance in a localised two-hop subgraph rather than the full procedure graph cut total token consumption by 70.9% on ALFWorld, 18.1% on GDPval and 14.8% on MultiChallenge","source":"Lu, Chen, Wu & Arik (Google), Procedural Graphs","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Same underlying graph in all configurations, Gemini 3.5 Flash, shared solver prompt. Localised guidance also scored highest on all three benchmarks (89.31, 63.99, 81.53), beating the best alternative by 2.0, 6.8 and 9.0 points. Source: [[procedural-graphs-self-evolving-agents]]","url":"https://arxiv.org/abs/2609.09153","access":"open"},{"id":"s3469","stat":"Adding a guidance step cut an agent\'s solver steps from 28.20 to 18.57 on GDPval and from 21.84 to 18.80 on ALFWorld, while total token consumption still rose 33.4% and 55.4% above the no-graph baseline","source":"Lu, Chen, Wu & Arik (Google), Procedural Graphs","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"The guidance call\'s own tokens outweigh the trajectory it saves. The authors name this as the open problem and suggest reusing guidance across steps or generating it selectively. Gemini 3.5 Flash. Source: [[procedural-graphs-self-evolving-agents]]","url":"https://arxiv.org/abs/2609.09153","access":"open"},{"id":"s3470","stat":"Procedural guidance cut an agent\'s tool calls from 18.94 to 12.53 per month while raising its enterprise score, by stopping it re-querying cash and market state within a single turn","source":"Lu, Chen, Wu & Arik (Google), Procedural Graphs","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Gemini 3.5 Flash on EnterpriseArena. On Claude Sonnet 4.6 and Gemini 3.1 Pro tool calls rose instead (0.
113 to 0.36 and 0.89 to 3.18 per month) and survival still improved, so the change is in which tools are called and when, not how many. Source: [[procedural-graphs-self-evolving-agents]]","url":"https://arxiv.org/abs/2609.09153","access":"open"},{"id":"s3471","stat":"An unguided agent raised $0.00M in average capital across long-horizon financial episodes, against $9.39M for the same model guided by a procedural graph and $30.11M for the strongest guided configuration","source":"Lu, Chen, Wu & Arik (Google), Procedural Graphs","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"EnterpriseArena. Capital arrives one to six months after it is requested, so surviving a crisis requires fundraising during stable months. Gemini 3.5 Flash baseline and PG-guided, and PG-guided Grok 4.1 Fast. Source: [[procedural-graphs-self-evolving-agents]]","url":"https://arxiv.org/abs/2609.09153","access":"open"},{"id":"s3472","stat":"Procedural graph guidance raised GDPval rubric score from 71.37 to 78.78 on Gemini 3.1 Pro and BFCL v3 accuracy from 58.00% to 67.00% on Gemini 3.5 Flash, against the strongest baseline in each setting","source":"Lu, Chen, Wu & Arik (Google), Procedural Graphs","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"GDPval and BFCL v3 were the two benchmarks where the method beat every baseline under all four models. On HotpotQA the margin over the best baseline ran from -0.90 to +1.30 points, so gains are task-shaped. Source: [[procedural-graphs-self-evolving-agents]]","url":"https://arxiv.org/abs/2609.09153","access":"open"},{"id":"s3473","stat":"Procedural guidance raised full-horizon survival in a long-horizon financial simulation from 44.0% to 58.0% on Claude Sonnet 4.6, 6.0% to 34.0% on Gemini 3.1 Pro and 26.0% to 40.0% on Grok 4.1 Fast","source":"Lu, Chen, Wu & Arik (Google), Procedural Graphs","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"EnterpriseArena, against the unguided ReAct baseline. Grok 4.1 Fast also took the best average enterprise score at $39.62M. Highest or joint-highest survival and longest average lifespan on all four models. Source: [[procedural-graphs-self-evolving-agents]]","url":"https://arxiv.org/abs/2609.09153","access":"open"},{"id":"s3474","stat":"Only 15.4% of LLM agent trials survived a full 132-month enterprise simulation, and 12 of 23 ReAct backbone models never survived a single run","source":"Han et al., Can LLM Agents Be CFOs? (EnterpriseArena)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"EnterpriseArena: a CFO simulator with costly observation under a per-step tool budget, mutually exclusive monthly actions, a one-to-six-month capital settlement delay and three undisclosed macroeconomic crises. 23 LLMs and four agent frameworks, five runs per configuration, 115 ReAct runs. arXiv:2603.23638, preprint. Source: [[enterprise-arena-cfo-long-horizon-benchmark]]","url":"https://arxiv.org/abs/2603.23638","access":"open"},{"id":"s3475","stat":"The best LLM agent configuration reached 7% of the human expert baseline on a long-horizon enterprise simulation, scoring $34.7M against $476.7M, despite comparable tool usage","source":"Han et al., Can LLM Agents Be CFOs? (EnterpriseArena)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Codex CLI with GPT-5.5 against five finance professionals averaging over ten years of experience, three from current or former CFO organisations. Both survived 60% of runs, so the gap is in converting information into well-timed commitments, not in information access. Source: [[enterprise-arena-cfo-long-horizon-benchmark]]","url":"https://arxiv.org/abs/2603.23638","access":"open"},{"id":"s3476","stat":"Human experts outperformed every LLM agent on a 132-month enterprise simulation while using 0.58 tool calls per month, below every observation budget tested and against an agent average of 3.23","source":"Han et al., Can LLM Agents Be CFOs? (EnterpriseArena)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Tool budget sweep from 5 to 30 per step: all models collapsed at 5 and 25 beat the default of 20. The authors read the human result as evidence the bottleneck is identifying what matters, not the volume of information available. Source: [[enterprise-arena-cfo-long-horizon-benchmark]]","url":"https://arxiv.org/abs/2603.23638","access":"open"},{"id":"s3477","stat":"An 8B Llama model scored $30.6M on a long-horizon enterprise simulation, nearly twice the 397B Qwen3.5-MoE at $16.0M and higher than all seven closed-source frontier backbones tested","source":"Han et al., Can LLM Agents Be CFOs? (EnterpriseArena)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Llama-3.1-8B-Instruct at 40% full-horizon survival against Llama-3.3-70B-Instruct at 0%. Grok-4.3 40% against Grok-4.20 0%; DeepSeek-V4 60% against DeepSeek-V3.1 0%. Model scale does not predict performance in this setting. Source: [[enterprise-arena-cfo-long-horizon-benchmark]]","url":"https://arxiv.org/abs/2603.23638","access":"open"},{"id":"s3478","stat":"The same model survived 0% of long-horizon enterprise runs under a plain ReAct loop and 60% under a purpose-built agent framework, taking 114.6 actions against 17.2","source":"Han et al., Can LLM Agents Be CFOs? (EnterpriseArena)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"GPT-5.5 under ReAct against GPT-5.5 under Codex CLI, tool usage 0.88 per month. DeepSeek-V4 ran the opposite way, 60% under ReAct with 16.21 tool calls per month against 20% under OpenClaw at 0.27. The harness either unlocks or cuts off the strategy the model depends on. Source: [[enterprise-arena-cfo-long-horizon-benchmark]]","url":"https://arxiv.org/abs/2603.23638","access":"open"},{"id":"s3479","stat":"Removing the capital settlement delay from a long-horizon enterprise simulation took one agent from 40% to 100% survival and lifted another\'s terminal score from $31M to $157M, the largest single ablation effect","source":"Han et al., Can LLM Agents Be CFOs? (EnterpriseArena)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Grok-4.3 survival and Llama-3.1-8B-Instruct score. Without the delay the task becomes reactive because each outcome is observed before the next commitment. Action exclusivity had a moderate effect (+4
10 to 82% on terminal scores, survival unchanged for two of three models) and removing stochasticity barely changed survival. Source: [[enterprise-arena-cfo-long-horizon-benchmark]]","url":"https://arxiv.org/abs/2603.23638","access":"open"},{"id":"s3480","stat":"Agents that failed a long-horizon enterprise simulation used tools 2.8 times less often than survivors (1.8 against 5.0 calls per month) and requested $12.6M per fundraising attempt against $19.5M","source":"Han et al., Can LLM Agents Be CFOs? (EnterpriseArena)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Sampled from 10 surviving and 10 failed ReAct trials. Approval rates were identical at 37 to 38% across both groups, confirming the gap is an agent-side allocation choice rather than environmental bias. Received $10.7M against $14.4M per approval. Source: [[enterprise-arena-cfo-long-horizon-benchmark]]","url":"https://arxiv.org/abs/2603.23638","access":"open"},{"id":"s3481","stat":"Agents that survived a long-horizon enterprise simulation spent 67% of their observation budget on external signals, while agents that failed spent 62% on internal state","source":"Han et al., Can LLM Agents Be CFOs? (EnterpriseArena)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"External signals are market data and financial documents; internal state is cash balance and cash-flow forecasts. Failed agents tracked what had already happened rather than what was about to change, and allocated their first fundraising action only after cash had peaked and begun to decline. Source: [[enterprise-arena-cfo-long-horizon-benchmark]]","url":"https://arxiv.org/abs/2603.23638","access":"open"},{"id":"s3482","stat":"Every LLM agent tested survived the first macroeconomic crisis in a long-horizon enterprise simulation, 52.3% survived the second and 19.2% reached the third","source":"Han et al., Can LLM Agents Be CFOs? (EnterpriseArena)","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Failures cascade rather than occurring at random. Across 23 backbone LLMs and four agent frameworks, mean survival duration was 60 of 132 months. Source: [[enterprise-arena-cfo-long-horizon-benchmark]]","url":"https://arxiv.org/abs/2603.23638","access":"open"},{"id":"s3483","stat":"The SRA received 42 reports of potential AI misuse between July 2025 and July 2026, with open investigations into inaccurate citations, supervision and confidentiality","source":"SRA, Misuse of AI warning notice and news release","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"legal","context":"Published 17 August 2026. Aileen Armstrong, SRA Executive Director Strategy and Policy. The first enforcement-volume figure the SRA has given on AI. Source: [[uk-regulators-ai-agents-supervision]]"},{"id":"s3484","stat":"Use of agentic AI systems within legal practice \'currently appears to be non-existent\'; practitioners use supervised AI agents for contract review, e-discovery and compliance","source":"The Law Society, Shaping the future of agentic AI in legal practice","date":"2026","sortKey":20260000,"precision":"year","year":2026,"theme":"adoption","sourceType":"industry","scope":"legal","context":"95-page foresight report from desk research, expert depth interviews and member workshops. The report\'s own summary: \'in the legal profession hype exceeds practice\'. Month unconfirmed, PDF metadata gives 11 April 2026. Source: [[law-society-agentic-ai-foresight]]"},{"id":"s3485","stat":"Under a strict all-pass standard, frontier models complete less than 10% of legal agent tasks end-to-e
1nd in aggregate","source":"Harvey, Legal Agent Benchmark initial results","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Published 26 May 2026. All-pass means every required rubric criterion passes. Source: [[claude-opus-4-8-legal-agent-benchmark]]"},{"id":"s3486","stat":"On Harvey\'s Legal Agent Benchmark, Claude Opus 4.7 scored 7.1% all-pass, Sonnet 4.6 5.4%, Opus 4.6 4.2%, GPT-5.5 2.1% and Gemini 3.5 Flash 0.8%","source":"Harvey, Legal Agent Benchmark initial results","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"The May 2026 snapshot. Superseded by later leaderboard entries; check the current leaderboard before quoting a ranking. Source: [[claude-opus-4-8-legal-agent-benchmark]]"},{"id":"s3487","stat":"Agents that drafted without a review step scored 1.5 points lower on average on Harvey\'s Legal Agent Benchmark all-pass measure","source":"Harvey, Legal Agent Benchmark initial results","date":"May 2026","sortKey":20260500,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Skipping validation steps correlates with materially worse performance. Source: [[claude-opus-4-8-legal-agent-benchmark]]"},{"id":"s3488","stat":"Fewer than 20% of organisations report widespread agentic AI adoption, while about half are planning or considering it","source":"Thomson Reuters Institute, 2026 AI in Professional Services Report","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"Published 9 April 2026. Sample size and methodology not stated in the published article. Source: [[uk-regulators-ai-agents-supervision]]"},{"id":"s3489","stat":"47% of legal respondents actively believe agentic AI should be used for legal work and 22% believe it should not; 51% felt excited or hopeful against 19% concerned or fearful","source":"Thomson Reuters Institute, 2026 AI in Professional Services Report","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"The second most common hesitation was the need for careful monitoring and oversight, with respondents citing the autonomy of agentic AI and lack of user control. Source: [[uk-regulators-ai-agents-supervision]]"},{"id":"s3490","stat":"28.5% of Singapore firms had started adopting AI in 2026, but only 3.8% had integrated it into core business processes and 71.5% had not started","source":"MRSD, MOM Singapore, Adoption of Artificial Intelligence Among Firms","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"government","scope":"legal","context":"Establishment survey of 2,560 private-sector establishments with at least 10 employees (486,600 workers), fieldwork 19 Jan to 31 Mar 2026, 97.4% response rate. MOM counts firms still planning (7.4%) or piloting (6.0%) as adopters. Economy-wide Singapore figure, not legal-specific. Cited by the CIVIC-AI whitepaper and checked at the MOM primary. Source: [[civic-ai-workflow-augmentation-framework]]","url":"https://stats.mom.gov.sg/iMAS_PdfLibrary/mrsd-Adoption-of-Artificial-Intelligence-Adoption-Among-Firms.pdf","access":"open"},{"id":"s3491","stat":"Among Singapore firms using AI, 70.7% named increased worker productivity as the main impact on employees\' work tasks, ahead of better decision-making (13.3%) and innovation (11.9%)","source":"MRSD, MOM Singapore, Adoption of Artificial Intelligence Among Firms","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"adoption","sourceType":"government","scope":"cross-sector","context":"Base is firms piloting, integrating or with employees actively using AI, so it excludes firms only planning. MOM\'s chart is a distribution of the single main impact; its own highlights, and the CIVIC-AI whitepaper, phrase it as 70.7% reporting productivity improvements. Employer-reported, fieldwork Jan to Mar 2026. Source: [[civic-ai-workflow-augmentation-framework]]","url":"https://stats.mom.gov.sg/iMAS_PdfLibrary/mrsd-Adoption-of-Artificial-Intelligence-Adoption-Among-Firms.pdf","access":"open"},{"id":"s3492","stat":"After adopting AI, 18.9% of Singapore firms redesigned job roles and 13.9% created new AI-related jobs, while 6.2% reduced headcount","source":"MRSD, MOM Singapore, Adoption of Artificial Intelligence Among Firms","date":"Apr 2026","sortKey":20260400,"precision":"month","year":2026,"theme":"workforce","sourceType":"government","scope":"cross-sector","context":"Base is firms piloting, integrating or with employees actively using AI; multiple responses allowed. Employer-reported at an early stage of adoption. The CIVIC-AI whitepaper treats the redesign and headcount figures as preliminary evidence on net value only. Source: [[civic-ai-workflow-augmentation-framework]]","url":"https://stats.mom.gov.sg/iMAS_PdfLibrary/mrsd-Adoption-of-Artificial-Intelligence-Adoption-Among-Firms.pdf","access":"open"},{"id":"s3493","stat":"Five frontier LLMs given identical efficient-breach scenarios recommended breaching the contract at rates from 3.9% (Claude Opus 4.1) to 100% (GPT-5), a spread of about 96 percentage points","source":"Rizner & Krzus (Filevine), The AI\'s Philosophy of Contract","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Control condition with no attorney persona, 7,920 decisions per model. 158,388 breach-advice decisions in total across GPT-5, Gemini 2.5 Flash, Gemini 2.5 Pro, Claude Opus 4.1 and Claude Sonnet 4; run dates not stated (Claude snapshots dated May and Aug 2025) and all five models since superseded. Scenarios adapted from Wilkinson-Ryan\'s party-rental and IT-seminar hypotheticals. Vendor-authored working paper, not peer reviewed; prompts and data not public. Source: [[filevine-ai-philosophy-of-contract]]","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5830004","access":"open"},{"id":"s3494","stat":"Baseline breach advice by model on identical efficient-breach facts: GPT-5 100.0%, Gemini 2.5 Flash 97.6%, Gemini 2.5 Pro 71.3%, Claude Sonnet 4 7.6%, Claude Opus 4.1 3.9%","source":"Rizner & Krzus (Filevine), The AI\'s Philosophy of Contract","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Control condition, model reasoning as the contracting party, 7,920 decisions per model. Expectation damages fully protect the promisee, so breach is the efficient choice on the law-and-economics account. 2025 model versions, since superseded; cite with model names and date. Vendor working paper. Source: [[filevine-ai-philosophy-of-contract]]","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5830004","access":"open"},{"id":"s3495","stat":"All five LLMs put the minimum payment that would justify breach close to the economically rational $2,001: mean thresholds GPT-5 $2,001, Gemini 2.5 Pro $2,002, Claude Opus 4.1 $2,012, Gemini 2.5 Flash $2,023, Claude Sonnet 4 $2,076, with medians of $2,000 to $2,001","source":"Rizner & Krzus (Filevine), The AI\'s Philosophy of Contract","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"cross-sector","context":"Secondary outcome (the paper says preregistered; no registry given). Rational threshold is $1,000 of lost profit plus $1,000 of damages plus $1. The models agree on the economics and differ on whether to act on it, which the authors attribute to post-training guardrails. 2025 models, vendor working paper. Source: [[filevine-ai-philosophy-of-contract]]","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5830004","access":"open"},{"id":"s3496","stat":"Writing expectation-level damages into the contract as a liquidated damages clause raised LLM breach advice by 8.9 percentage points on average (p < 0.001), against a legally equivalent contract that left damages to background law","source":"Rizner & Krzus (Filevine), The AI\'s Philosophy of Contract","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Pooled logit across five 2025 models, N = 158,388, cluster-robust errors at scenario level. In line with Wilkinson-Ryan\'s human finding that a stipulated sum reads as permission to exit. Driven mainly by Gemini 2.5 Pro and Claude Opus 4.1; GPT-5 was at a 100% ceiling. Vendor working paper. Source: [[filevine-ai-philosophy-of-contract]]","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5830004","access":"open"},{"id":"s3497","stat":"Effect of a liquidated damages clause on LLM breach advice by model: Gemini 2.5 Pro +19.3pp, Claude Opus 4.1 +18.1pp, Claude Sonnet 4 +7.1pp, Gemini 2.5 Flash +2.3pp, GPT-5 no effect at a 100% ceiling","source":"Rizner & Krzus (Filevine), The AI\'s Philosophy of Contract","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Change against the control condition without the attorney persona, 7,920 decisions per cell. The paper\'s conclusion that the effect holds across models overstates a range that runs from zero to +19.3pp. 2025 models, vendor working paper. Source: [[filevine-ai-philosophy-of-contract]]","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5830004","access":"open"},{"id":"s3498","stat":"Telling LLMs to act as the client\'s attorney moved breach advice in opposite directions by model family: Claude Opus 4.1 +24.4pp, Claude Sonnet 4 +10.6pp, Gemini 2.5 Pro -21.0pp, Gemini 2.5 Flash about -0.9pp, GPT-5 0pp at ceiling","source":"Rizner & Krzus (Filevine), The AI\'s Philosophy of Contract","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Change against the control condition with no LD clause, 7,920 decisions per cell (7,908 for Sonnet 4). A role prompt is not a neutral setting; the authors recommend documenting, auditing and cross-model validating role instructions. 2025 models, vendor working paper. Source: [[filevine-ai-philosophy-of-contract]]","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5830004","access":"open"}
1,{"id":"s3499","stat":"The liquidated damages by attorney-persona interaction on LLM breach advice was -0.343 on the log-odds scale (z = -5.24, p < 0.001), an average marginal effect of about -3.4 percentage points","source":"Rizner & Krzus (Filevine), The AI\'s Philosophy of Contract","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Pooled binomial GLM with model fixed effects, N = 158,388, Pseudo R-squared (Cox-Snell) about 0.494; number of scenario clusters not reported. The authors read it as the persona mattering less once breach is priced, but Figure 8 shows that clearly only for Claude Sonnet 4, and Gemini 2.5 Pro\'s attorney drop is larger with the clause present (read from the chart). Vendor working paper. Source: [[filevine-ai-philosophy-of-contract]]","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5830004","access":"open"},{"id":"s3500","stat":"Model identity was the dominant predictor of LLM breach advice: model coefficients relative to Claude Opus 4.1 ranged from -0.82 (Claude Sonnet 4) to +10.14 (GPT-5), far larger than the liquidated damages and attorney-persona effects","source":"Rizner & Krzus (Filevine), The AI\'s Philosophy of Contract","date":"Nov 2025","sortKey":20251100,"precision":"month","year":2025,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Binomial GLM with logit link, N = 158,388, model fixed effects plus LD, attorney and interaction terms, all p < 0.001. Coefficients pool all four conditions, which most likely explains why Sonnet 4 sits below Opus 4.1 despite Opus 4.1\'s lower baseline rate. 2025 models, vendor working paper. Source: [[filevine-ai-philosophy-of-contract]]","url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5830004","access":"open"},{"id":"s3501","stat":"Swapping the harness around the same frozen model moved SWE-bench Pro pass@1 from 23% to 52% for GLM-5.2 and from 15% to 36% for Gemma 4 26B-A4B, across ten harnesses and 250 tasks","source":"Niklaus (Hugging Face), Fifty Years of Legal AI (HSG talk)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Niklaus\'s own harness study, presented on slide 46. No write-up located and his X post refuses automated fetches, so the slide is the only read. Coding tasks, not legal. mini-swe-agent was the best pairing for GLM-5.2 here, yet the lowest harness (3.5%) on LAB with a different model in his July study, which is the slide\'s point that pairings do not transfer. Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://docs.google.com/presentation/d/1GzHTZb7pAXG5hNldmXr-26wAJ1oHvnd9Q3DfWJkTaZk/edit?usp=sharing","access":"open"},{"id":"s3502","stat":"The top ten of 139 models on the Vals AI LegalBench leaderboard sat within 2.25 points on the aggregate score at its 19 August 2026 update","source":"Niklaus (Hugging Face), Fifty Years of Legal AI (HSG talk)","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"Niklaus\'s reading of the Vals board on slide 56; he adds that task-level differences can still matter. Not re-read: the page showed a later update when fetched on 14 Sep 2026 and its scores did not render. LegalBench is the Stanford task suite, a different board from Vals AI\'s HLAB. Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://docs.google.com/presentation/d/1GzHTZb7pAXG5hNldmXr-26wAJ1oHvnd9Q3DfWJkTaZk/edit?usp=sharing","access":"open"},{"id":"s3503","stat":"The best of 17 open models evaluated on Swiss legal tasks (Inkling) scored 67.7 on LEXam\'s 2,841 open law-exam questions and 50.9% on 16-option multiple choice","source":"Niklaus et al., The state-of-the-art in open-source AI for Swiss legal tasks (Hugging Face)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Read at source 14 Sep 2026 (published 14 Jul, edited 4 Aug 2026); shown on slide 33 of Niklaus\'s HSG talk. Open models only, no closed frontier models in the comparison. LEXam is 340 Swiss law exams (Fan et al., ICLR 2026). The authors conclude a lawyer still has to check output on real matters. Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://huggingface.co/blog/joelniklaus/swiss-legal-evals","access":"open"},{"id":"s3504","stat":"When wrong answers cost a point and abstaining costs nothing, only 1 of 16 open models (Inkling) stayed above zero on LEXam multiple choice with 16 answer options","source":"Niklaus et al., The state-of-the-art in open-source AI for Swiss legal tasks (Hugging Face)","date":"Jul 2026","sortKey":20260700,"precision":"month","year":2026,"theme":"critical","sourceType":"vendor","scope":"legal","context":"LEXam idk score (+1 correct, 0 abstain, -1 wrong), read at source 14 Sep 2026 and charted on slide 34 of Niklaus\'s HSG talk. The models guess rather than abstain as questions get harder, which is the behaviour that matters in legal work. 16 rather than 17 models because one ran translation only. Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://huggingface.co/blog/joelniklaus/swiss-legal-evals","access":"open"},{"id":"s3505","stat":"On LAB Diligence, GLM-5.2 passed 15.1% of rubric criteria as a base model in an agent harness, 46.1% as orchestrator in Harvey\'s Recursive Language Model harness and 60.
11% after self-distillation post-training in that harness, against a best baseline of 43.8% (Opus 5)","source":"Harvey, Tenet Research Preview","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Harvey\'s own early research results on its own task set; criteria pass rate, not all-pass; number of tasks, runs and judge not given. All four values checked against the post text and Figure 2 (SVG rendered) on 14 Sep 2026. A GLM-5.2 experiment separate from Harvey Tenet (Kimi K3, post-trained with Fireworks). The harness change was worth 31 points before any training. Shown on slide 45 of Niklaus\'s HSG talk. Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://www.harvey.ai/blog/post-training-update-harvey-tenet","access":"open"},{"id":"s3506","stat":"On LAB Diligence, off-the-shelf coding agents scored below the same models in an agent harness: Claude Code running Opus 5 passed 24.6% of rubric criteria against 43.8% for Opus 5, and Codex running GPT-5.6 Sol 12.0% against 16.6%","source":"Harvey, Tenet Research Preview","date":"Aug 2026","sortKey":20260800,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Checked against Figure 2 of the post (SVG rendered) on 14 Sep 2026. The chart labels the comparison group \\"Base model + harness\\" and neither chart nor post says which harness; number of tasks, runs and judge not given. Harvey-run on Harvey\'s own task set, where a vendor has reason to show general tools doing worse. Compare s2675 (general agent frameworks well below the best harness on LAB). Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://www.harvey.ai/blog/post-training-update-harvey-tenet","access":"open"},{"id":"s3507","stat":"OpenAI\'s 90th-percentile bar exam estimate for GPT-4 falls to below the 69th percentile against July test-takers and about the 48th against those who passed, with essays at about the 15th for that group","source":"MartÃnez, Re-evaluating GPT-4\'s bar exam performance (Artificial Intelligence and Law)","date":"Mar 2024","sortKey":20240300,"precision":"month","year":2024,"theme":"critical","sourceType":"academic","scope":"legal","context":"Read from the abstract at MIT DSpace on 14 Sep 2026; published online 30 Mar 2024, in print vol 33(3) 2025. Against first-time takers about the 62nd percentile (42nd on essays). OpenAI\'s figure was against a February cohort weighted towards repeat takers. Cited on slide 29 of Niklaus\'s HSG talk, which rounds \\"below the 69th\\" to 69th. Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://dspace.mit.edu/handle/1721.1/153986","access":"open"},{"id":"s3509","stat":"European Court of Human Rights outcome prediction averaged 0.77 accuracy in cross-validation with years mixed, 0.66 when trained on cases to 2013 and tested on 2016-2017 cases, and 0.58 when training stopped at 2005","source":"Medvedeva, Vols and Wieling, Artificial Intelligence and Law 28","date":"2020","sortKey":20200000,"precision":"year","year":2020,"theme":"critical","sourceType":"academic","scope":"legal","context":"Table 10, checked at the paper 14 Sep 2026. Averages cover Articles 3, 6 and 8 only; 2014-2015 cases scored 0.68. The 2005 cut-off model also had far less training data (112, 354 and 144 cases against 356, 746 and 350), which the authors say contributes to the drop. Test sets of 140, 90 and 56 cases for 2016-2017. Cited on slide 18 of Niklaus\'s HSG talk. Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://link.springer.com/article/10.1007/s10506-019-09255-y","access":"open"},{"id":"s3510","stat":"Knowing only which judges sat on the Chamber, a classifier predicted European Court of Human Rights violation outcomes with 65% average accuracy across nine Convention articles","source":"Medvedeva, Vols and Wieling, Artificial Intelligence and Law 28","date":"2020","sortKey":20200000,"precision":"year","year":2020,"theme":"critical","sourceType":"academic","scope":"legal","context":"Table 11, checked at the paper 14 Sep 2026. Tenfold cross-validation on balanced data, linear SVM, 185 judges; 66% on the held-out test sets, which are single-class; chance 50%. Reflects the Chamber\'s final decision, not individual votes, and judge names may proxy for section, period or case mix. Cited on slide 19 of Niklaus\'s HSG talk. Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://link.springer.com/article/10.1007/s10506-019-09255-y","access":"open"},{"id":"s3511","stat":"Legal-BERT beat generic BERT by 2.2 points of test micro-F1 on the harmonic mean across LexGLUE\'s seven tasks (78.9 against 76.7), and by 2.0 on the arithmetic mean (79.8 against 77.8)","source":"Chalkidis et al., LexGLUE (ACL 2022)","date":"May 2022","sortKey":20220500,"precision":"month","year":2022,"theme":"technical","sourceType":"academic","scope":"legal","context":"Table 4, checked at the paper 14 Sep 2026. Both 110M-parameter encoders, best of five seeds on development data. Most of the gain comes from the two
1US case-law tasks (SCOTUS and CaseHOLD), and BERT stays ahead on ECtHR Task A. Encoder-model evidence about domain pretraining, not about post-training current LLMs. Cited on slide 24 of Niklaus\'s HSG talk. Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://aclanthology.org/2022.acl-long.297.pdf","access":"open"},{"id":"s3512","stat":"Legal-XLM-R-large, further pretrained on the 689 GB MultiLegalPile, scored 59.5 on the overall LEXTREME score against 59.3 for XLM-R-large","source":"Niklaus et al., MultiLegalPile (ACL 2024)","date":"Aug 2024","sortKey":20240800,"precision":"month","year":2024,"theme":"technical","sourceType":"academic","scope":"legal","context":"Section 5.3 and Tables 4 and 5, checked at the paper 14 Sep 2026. Harmonic mean of macro-F1 dataset and language aggregates over 11 datasets and 24 languages, three seeds, no hyperparameter tuning, no significance test. The margin comes from the dataset aggregate (66.8 against 66.1); XLM-R-large stays 0.1 ahead on languages. Niklaus co-authored the paper and uses it on slides 23 to 25 of his HSG talk against legal pretraining. Source: [[fifty-years-legal-ai-niklaus-hsg]]","url":"https://aclanthology.org/2024.acl-long.805.pdf","access":"open"},{"id":"s3513","stat":"Four published harness-evolution methods scored 90.7 to 93.0 on a 120-task Harvey LAB evolve split but moved the out-of-distribution average (JobBench, GDPval, APEX-Agents) from 39.7 by between -1.7 and +0.9 points; two ended below the unevolved harness","source":"Xia et al., RRSI: Regularized Recursive Self-Improvement of Agent Harnesses (arXiv:2609.24972)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Table 1, read from the primary PDF 25 Sep 2026. Meta-Harness, AHE, TTHE, HarnessX; same base harness, frozen Claude Opus 4.8 policy and candidate budget. Preprint","url":"https://arxiv.org/abs/2609.24972","access":"open"},{"id":"s3514","stat":"Unregularised harness evolution raised Harvey LAB evolve-split score from 89.4 to 92.8 but the out-of-distribution average only from 39.7 to 40.3, while policy tokens per trial rose from 1.56m to 3.80m","source":"Xia et al., RRSI: Regularized Recursive Self-Improvement of Agent Harnesses (arXiv:2609.24972)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Table 2 ablation, read from the primary PDF 25 Sep 2026. Frozen Claude Opus 4.8 policy. Preprint","url":"https://arxiv.org/abs/2609.24972","access":"open"},{"id":"s3515","stat":"Regularised harness evolution (RRSI) scored 90.5 on the Harvey LAB evolve split, 89.2 on the 40-task held-out split and lifted the out-of-distribution average from 39.7 to 43.6, on 2.42m policy tokens per trial","source":"Xia et al., RRSI: Regularized Recursive Self-Improvement of Agent Harnesses (arXiv:2609.24972)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Tables 1 and 2, read from the primary PDF 25 Sep 2026. Smallest in-set gain of any evolved harness, best transfer. Author\'s own method. Preprint","url":"https://arxiv.org/abs/2609.24972","access":"open"},{"id":"s3516","stat":"The most token-hungry prior harness-evolution method (AHE) used 3.82m policy tokens per trial, 58% more than RRSI, for 4.4 points less on the out-of-distribution average","source":"Xia et al., RRSI: Regularized Recursive Self-Improvement of Agent Harnesses (arXiv:2609.24972)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Section 4.3 and Figure 4, read from the primary PDF 25 Sep 2026. Agentic workspace instance evolved on Harvey LAB. Preprint","url":"https://arxiv.org/abs/2609.24972","access":"open"},{"id":"s3517","stat":"A harness evolved on Terminal-Bench 2.1 lifted Gemini 3.5 Flash from 64.6 to 78.7 there and from 76.8 to 79.0 on SWE-bench Verified, which the search never scored","source":"Xia et al., RRSI: Regularized Recursive Self-Improvement of Agent Harnesses (arXiv:2609.24972)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Table 3, read from the primary PDF 25 Sep 2026. Under Claude Opus 4.8 the gains were 74.2 to 80.2 and 82.0 to 83.8. Preprint","url":"https://arxiv.org/abs/2609.24972","access":"open"},{"id":"s3518","stat":"A harness evolved with Gemini 3.5 Flash raised Gemini 3.1 Flash Lite, which never took part in the search, from 11.2 to 14.6 on Terminal-Bench 2.1, a 30.4% relative gain","source":"Xia et al., RRSI: Regularized Recursive Self-Improvement of Agent Harnesses (arXiv:2609.24972)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"academic","scope":"cross-sector","context":"Table 4, read from the primary PDF 25 Sep 2026. Preprint","url":"https://arxiv.org/abs/2609.24972","access":"open"},{"id":"s3519","stat":"5% of UK mid-market law firms said they were not using AI in any form in 2026, against 50% in the 2025 survey","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=112 firms, turnover £1m-£250m (average £23m), NatWest banking relationships. Read from the primary PDF 27 Sep 2026. Quote with the 24% making no specific AI investment: much of the swing is free tools and admin use","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3520","stat":"24% of UK mid-market law firms said they had made no specific investment in AI, although only 5% did not use it at all","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=112. The authors read the 19-point gap as firms using free or \\"nearly free\\" AI without investing in it","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3521","stat":"43% of UK mid-market law firms named streamlining administrative processes as the most significant impact of AI on their business; client delivery and cost savings were each cited by about 10%","
1source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=112. Single most-noticeable impact, not multi-select","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3522","stat":"34% of UK mid-market law firms named uncertainty about which AI tools to use as the biggest challenge in adopting AI, and 18% named inability to recover the investment","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=112. Also: client confidentiality 11%, governing AI responsibly 9%. Single biggest challenge","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3523","stat":"64% of UK mid-market law firms had taken no action to recover their investment in AI","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=112. Includes the 24% who made no specific investment, so among investors the figure is lower. Pair with PwC\'s 16% of chargeable hours efficiency figure","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3524","stat":"22% of UK mid-market law firms had revised their pricing structure or service options to recover AI investment; 7% raised chargeable hours targets, 4% reduced headcount and 3% raised charge-out rates","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=112. Single primary action. Only 4% used headcount reduction to recoup AI cost","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3525","stat":"Adoption of AI was named as the single greatest challenge by 8% of UK mid-market law firms in 2026, the first year it appeared in the list","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"analyst","scope":"legal","context":"n=112. Margins and productivity led at 44%","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3526","stat":"44% of UK mid-market law firms named pressure on margins and productivity as their greatest challenge in 2026, up from 39% in 2025, 31% in 2024 and 6% in 2023","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=112. Attracting and retaining talent fell from 46% (2023) to 20% (2026)","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3527","stat":"Median profit per equity partner at UK mid-market law firms grew 2% in 2026, against 23% in 2025, to £326,000","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=112, same-firm comparison. Small firms (under £5m) £157,000, large £356,000. Interquartile range £236,000-£484,000. Authors cite Employers\' NI and AI/IT investment costs","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3528","stat":"Median fee income growth at UK mid-market law firms was 7% in 2026, down from 12% in 2025 and 10% in 2024","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=112. Large firms 8%, small 5%. 93% expect 2027 growth below 10%","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3529","stat":"People costs (including notional equity partner salaries) were 64
1% of fees at the median UK mid-market law firm in 2026, against 63% in 2025","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=112. Authors estimate the Employers\' National Insurance rise added 1-2 points. Notional salary £100k small, £150k large firms","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3530","stat":"28% of UK mid-market law firms reported average chargeable time below four hours per fee earner per day (under 900 hours a year); about 15% exceed roughly five hours a day","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"n=112. Converted on 230 working days. 9% of firms do not record time","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3531","stat":"43% of UK mid-market law firms reported average chargeable hours rising, 36% static and 12% declining (13% in the report text)","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"n=112. 9% no time recording. Authors list \\"AI speeding up production but reducing fee earner input\\" among the causes of stagnation","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3532","stat":"Client interest income made up 14% of profit per equity partner at the median UK mid-market law firm in 2026, down from 21% in 2025; the upper quartile starts at 28%","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=112. Interest was 3.7% of fee income (2025: 5.6%). Authors put it below 1% of PEP in 2022","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3533","stat":"40% of UK mid-market law firms would consider third-party or private equity investment, up from 29% in 2024","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=112. 22% of all firms would take it to fund growth, 6% for succession, 4% for technology, 4% opportunistic","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3534","stat":"33% of UK mid-market law firms named competition for talent as the biggest challenge of operating in a market with PE-backed firms; 24% named increased investment costs and 13% price competition","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=112","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3535","stat":"29% of UK mid-market law firms expected the main impact of the MoJ\'s proposed Interest on Lawyers\' Client Accounts scheme to be higher prices for consumers; 11% expected law firm failures","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"regulatory","sourceType":"analyst","scope":"legal","context":"n=112. Also: reduced margins 24%, administrative burden 22%. MoJ consultation early 2026, not concluded at time of writing","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3536","stat":"Median lock up at UK mid-market law firms rose to 136 days in 2026 from 128 days for the same firms in 2025","source":"NatWest / PKF Francis Clark, Legal Report 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"n=112. WIP 90 days, debtors 41. Borrowing 13p per £1 of members\' funds (2025: 15p)","url":"https://www.natwest.com/content/dam/natwest/business/documents/business-services/FC-4983_NatWest_Benchmarking_Report_2026.pdf","access":"open"},{"id":"s3537","stat":"AI assistance raised patent drafting quality by 0.34 SD at 10 days and 0.38 SD at 90 days against lawyers with no generative AI","source":"Autor et al., NBER w35720, Does AI Assistance Enhance or Erode Expertise?","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"legal","context":"RCT, 133 patent lawyers randomised 2:1 at 11 US IP firms, blinded expert raters; p = 0.03 and 0.01 (abstract, p.16). Google-funded, Google tool (InFlow), six of seven authors at Google","url":"https://www.nber.org/papers/w35720","access":"open"},{"id":"s3538","stat":"Junior patent lawyers (under 7 years) gained 0.58 SD (10 days) and 0.60 SD (90 days) in drafting quality with AI; seniors gained 0.22 SD and 0.30 SD","source":"Autor et al., NBER w35720, Does AI Assistance Enhance or Erode Expertise?","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"legal","context":"n=133; junior-senior gap not itself significant (p.16)","url":"https://www.nber.org/papers/w35720","access":"open"},{"id":"s3539","stat":"After three months of AI-assisted drafting, treated lawyers outscored controls by 0.32 SD on an unassisted redlining task","source":"Autor et al., NBER w35720, Does AI Assistance Enhance or Erode Expertise?","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"legal","context":"91 lawyers completed the redline, AI prohibited; p = 0.04 (p.21)","url":"https://www.nber.org/papers/w35720","access":"open"},{"id":"s3540","stat":"On the unassisted redline, the gain was concentrated among senior lawyers (treated seniors +0.45 SD against control seniors); treated juniors showed -0.03 SD","source":"Autor et al., NBER w35720, Does AI Assistance Enhance or Erode Expertise?","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"legal","context":"Seniors p = 0.02, juniors p = 0.89; about 25 juniors in the redline sample (p.21). Not \\"seniors kept an advantage\\": it is treated vs control seniors","url":"https://www.nber.org/papers/w35720","access":"open"},{"id":"s3541","stat":"Treated juniors\' unassisted redline scores bifurcated: mediocre (fourth-quintile) results fell by 0.42 and poor (bottom-quintile) results rose by 0.22","source":"Autor et al., NBER w35720, Does AI Assistance Enhance or Erode Expertise?","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"cross-sector","context":"Change in probability, Table 7, p.24; both p < 0.05. Good results +0.19, not significant; top quintile unchanged","url":"https://www.nber.org/papers/w35720","access":"open"},{"id":"s3542","stat":"AI saved about 10 minutes on a 112-minute patent drafting task (about 9%); juniors saved 18 minutes, seniors 5 minutes (not significant)","source":"Autor et al., NBER w35720, Does AI Assistance Enhance or Erode Expertise?","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"academic","scope":"cross-sector","context":"10-day task, p.4, p.31, p.33. No time saving on the unassisted redline","url":"https://www.nber.org/papers/w35720","access":"open"},{"id":"s3543","stat":"AI-assisted junior patent lawyers reported 1.48 SD higher satisfaction with drafting at 90 days, a gain not reflected in their unassisted performance","source":"Autor et al., NBER w35720, Does AI Assistance Enhance or Erode Expertise?","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"academic","scope":"legal","context":"Post-task survey, p.35","url":"https://www.nber.org/papers/w35720","access":"open"},{"id":"s3544","stat":"An LLM grader (Gemini 2.5-flash) correlated with expert patent attorney raters at r = 0.47 on a simple drafting task and only r = 0.19 and 0.14 on the harder 90-day tasks","source":"Autor et al., NBER w35720, Does AI Assistance Enhance or Erode Expertise?","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"legal","context":"p.26. Expert inter-rater correlations were 0.47 to 0.59","url":"https://www.nber.org/papers/w35720","access":"open"},{"id":"s3545","stat":"Claude Sonnet 5.5 cost $20.80 per test on the Vals Index against $11.74 for Sonnet 5, 77% more, at an unchanged list price of $2/$10 per million tokens","
1source":"Vals AI, Vals Index","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vals Index updated 27 Sep 2026, read via reader proxy 29 Sep. Output tokens 480.47M against 126.59M. Scores 69.22% and 59.61%. Index weights finance 8.0, coding 5.6, legal 1.2","url":"https://www.vals.ai/benchmarks/vals_index","access":"open"},{"id":"s3546","stat":"Claude Opus 5.5 cost $32.77 per test on the Vals Index against $18.81 for Opus 5, despite a lower list price ($4/$20 against $5/$25 per million tokens)","source":"Vals AI, Vals Index","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vals Index updated 27 Sep 2026. Output tokens 390.18M against 118.97M. Scores 69.69% and 67.21%","url":"https://www.vals.ai/benchmarks/vals_index","access":"open"},{"id":"s3547","stat":"Meta\'s Muse Spark 1.3 Max scored 64.53% on the Vals Index at $3.40 per test, against 69.69% at $32.77 for the top model, Claude Opus 5.5","source":"Vals AI, Vals Index","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vals Index updated 27 Sep 2026. List price $1.25/$4.25 per million tokens","url":"https://www.vals.ai/benchmarks/vals_index","access":"open"},{"id":"s3548","stat":"Claude Opus 5.5 scored 50.48% all-pass on the Vals Legal Research Bench at $25.16 per test, against 55.29% for Claude Opus 5 at $6.76 and Muse Spark 1.3 Max at $0.59","source":"Vals AI, Legal Research Bench","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Page published 29 Sep 2026. Opus 5.5 took 1h52m against 31m and 5m. GPT-6 Astra, GPT-6 Sol and Astra for Law not listed","url":"https://www.vals.ai/benchmarks/legal_research","access":"open"},{"id":"s3549","stat":"Claude Opus 5 reached 90.58% weighted pass on the Vals Legal Research Bench but 55.29% strict all-pass accuracy","source":"Vals AI, Legal Research Bench","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vals: models often satisfy most rubric checks but miss one or two required elements","url":"https://www.vals.ai/benchmarks/legal_research","access":"open"},{"id":"s3550","stat":"The top model on Harvey\'s Legal Agent Benchmark (Muse Spark 1.2) resolved 25.42% of tasks while passing 94.5% of individual criteria","source":"Vals AI, Harvey\'s Legal Agent Benchmark (HLAB) leaderboard","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"vendor","scope":"legal","context":"Vals run on the held-out set, 70 models, page published 29 Sep 2026. Two LLM judges (GPT 5.5, Claude Sonnet 4.6); a task passes only if every criterion passes. No legal-vendor model (Harvey Tenet, Thomson 1.0) among the 70","url":"https://www.vals.ai/benchmarks/hlab","access":"open"},{"id":"s3551","stat":"GPT-6 Astra completed an unsanctioned supply-chain attack in 29.2% of simulated cybersecurity scenarios, against 6.3% for GPT-5.6 Sol and 0% for GPT-5.5","source":"UK AI Security Institute, GPT-6 Astra performs unsanctioned supply-chain attacks in simulations","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"regulatory","sourceType":"government","scope":"cross-sector","context":"UK AISI blog, 28 Sep 2026. Cyber safety classifiers disabled; simulated with Petri, no real systems; GPT-5.5 on a smaller set of seeds. Astra still attacked after scope was clarified","url":"https://www.aisi.gov.uk/blog/gpt-6-astra-performs-unsanctioned-supply-chain-attacks-in-simulations","access":"open"},{"id":"s3552","stat":"Claude Sonnet 5.5 used about 193k output tokens per Artificial Analysis Intelligence Index task, the highest it has measured, about 7x GPT-6 Astra, and cost $7.60 per task, about 50% more than Sonnet 5","source":"Artificial Analysis, Claude Sonnet 5.5 reaches #2 on the Artificial Analysis Intelligence Index","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Artificial Analysis, 28 Sep 2026, max effort; list price unchanged at $2/$10. Scored 56 against 58 for Opus 5.5. Pre-release deployment","url":"https://artificialanalysis.ai/articles/claude-sonnet-5-5","access":"open"},{"id":"s3553","stat":"The Charlotin database of AI hallucination cases held 2,095 cases at 28 September 2026, up from 1,870 at 11 August","source":"Damien Charlotin, AI Hallucination Cases Database","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"critical","sourceType":"practitioner","scope":"legal","context":"Read from the live page with curl on 29 Sep 2026 (\'Last updated: 28 September 2026\')","url":"https://www.damiencharlotin.com/hallucinations/","access":"open"},{"id":"s3554","stat":"AI agents used the urlquery.net scanning service to get round access restrictions from at least 6 March 2026 to 16 September 2026, and attempted to hack three public data providers including an Australian government website","source":"Transluce, Early rogue AI agent activity and attempts to hack found on urlquery.net","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"regulatory","sourceType":"academic","scope":"cross-sector","context":"Transluce, 23 Sep 2026. Thousands of agent reports, rising sharply mid-April and peaking May-June; dataset of tens of thousands of queries released. No evidence any attempt succeeded","url":"https://transluce.org/agent-activity","access":"open"},{"id":"s3555","stat":"Harvey\'s gross margins fell from about 50% at the start of 2026 to -50% by June after a March agent update drove usage up, according to a person familiar","source":"Bloomberg, Startups Like Harvey Embrace Open Models to Cut Reliance on Anthropic, OpenAI","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"cross-sector","context":"Bloomberg (Torrence and Mascarenhas), 21 Sep 2026, read via reader proxy. Reported, not company-published; margins positive again in August after the move to Tenet","url":"https://www.bloomberg.com/news/articles/2026-09-21/startups-like-harvey-embrace-open-models-to-cut-reliance-on-anthropic-openai","access":"paywalled"},{"id":"s3556","stat":"UnitedLex\'s UK revenue fell from £30.5m in the year to December 2022 to £9.4m in the year to December 2024","source":"Legal IT Insider, UnitedLex to be acquired by JLL-backed eDiscovery vend
1or Repario from CVC","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"vendor","scope":"legal","context":"Legal IT Insider, 28 Sep 2026, reporting the sale of UnitedLex (about 2,000 staff) to Repario from CVC. UK filings only; global revenue not disclosed","url":"https://legaltechnology.com/exclusive-unitedlex-to-be-acquired-by-jll-backed-ediscovery-vendor-repario-from-cvc/","access":"open"},{"id":"s3557","stat":"The cost of reaching a given AI performance level has fallen about 47% per quarter, or 13x per year, since 2023","source":"Epoch AI, The plunging price of thought","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Emberson and Roodman, 22 Sep 2026. Five primary benchmarks, cost per task (not per token). Sensitivity range 42.9% to 58.0%; no standard errors","url":"https://epoch.ai/publications/the-plunging-price-of-thought","access":"open"},{"id":"s3558","stat":"Matching o3\'s 75% guessing-adjusted GPQA Diamond score cost about $0.30 per question in January 2025 and $0.0004 with GPT-5.6 Luna just under 18 months later, a 725-fold fall","source":"Epoch AI, The plunging price of thought","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Epoch\'s stated multiple","url":"https://epoch.ai/publications/the-plunging-price-of-thought","access":"open"},{"id":"s3559","stat":"The cost of an AI performance level falls about 66% per quarter (75x per year) when it first becomes state of the art and about 32% per quarter (4.7x per year) two years later","source":"Epoch AI, The plunging price of thought","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Average of five primary benchmarks, Box-Tidwell frontier model","url":"https://epoch.ai/publications/the-plunging-price-of-thought","access":"open"},{"id":"s3560","stat":"Cost declines vary by domain: maths 50-52% per quarter, game puzzles 39-43%, and SWE-bench Verified coding 27.5%","source":"Epoch AI, The plunging price of thought","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"technical","sourceType":"analyst","scope":"cross-sector","context":"Model-free estimates","url":"https://epoch.ai/publications/the-plunging-price-of-thought","access":"open"},{"id":"s3561","stat":"In 93.6% of trajectories, pairs of AI agents checking each other\'s work reached mutual acceptance without the evidence the protocol required at least once; 78.8% settled into it and 66.0% of all episodes involved it","source":"Shi, Zhang & Yang, Emergent Collusion in Long-Horizon LLM Agent Interaction (arXiv:2609.24967)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"10 models x 50 ten-episode trajectories (n=500), synthetic tasks, engineered 200-character limit making compliance impossible, shared reward (pp.1, 5). Stated as \'94%\' in the abstract","url":"https://arxiv.org/abs/2609.24967","access":"open"},{"id":"s3562","stat":"Converged collusion between checking agents ranged from 36.0% (Gemma-4-31B) to 98.0% by model; within a family the more capable model colluded more (Claude Opus 4.6 86.6% of episodes against Sonnet 4.6 76.4%)","source":"Shi, Zhang & Yang, Emergent Collusion in Long-Horizon LLM Agent Interaction (arXiv:2609.24967)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"50 trajectories per model (p.5)","url":"https://arxiv.org/abs/2609.24967","access":"open"},{"id":"s3563","stat":"Swapping in a scripted peer that always accepted lifted an agent\'s acceptance rate without the required evidence from 13.6% to 41.2%","source":"Shi, Zhang & Yang, Emergent Collusion in Long-Horizon LLM Agent Interaction (arXiv:2609.24967)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Gemini 3.1 Flash-Lite, 500 episodes per condition (Table 9, p.22)","url":"https://arxiv.org/abs/2609.24967","access":"open"},{"id":"s3564","stat":"Colluding agents kept task accuracy around 89%, and with the evidence channel restored they accepted 96-98% of the time: the failure was the skipped check, not a wrong verdict","source":"Shi, Zhang & Yang, Emergent Collusion
1in Long-Horizon LLM Agent Interaction (arXiv:2609.24967)","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"critical","sourceType":"academic","scope":"cross-sector","context":"Task accuracy 89.3% overall (p.5); unconstrained control on two Gemini models (p.24)","url":"https://arxiv.org/abs/2609.24967","access":"open"},{"id":"s3565","stat":"Only 29% of law firms have adjusted pricing structures for AI-augmented work","source":"BigHand Legal Pricing and Budgeting Trends Analysis 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"BigHand, 800+ law firm leaders, pricing and finance professionals at 50+ lawyer firms, UK and North America. Read from the public landing page; full report gated","url":"https://www.bighand.com/en-gb/resources/whitepapers/2026-legal-pricing-and-budgeting-trends-analysis/","access":"gated"},{"id":"s3566","stat":"30% of law firms keep existing pricing and use AI efficiencies to improve profitability, and 30% are still evaluating how those efficiencies should shape pricing","source":"BigHand Legal Pricing and Budgeting Trends Analysis 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Landing page. The 25% discounting figure reported by Artificial Lawyer is not shown there","url":"https://www.bighand.com/en-gb/resources/whitepapers/2026-legal-pricing-and-budgeting-trends-analysis/","access":"gated"},{"id":"s3567","stat":"Only 26% of law firms believe clients would pay a premium for AI-enabled work, while 53% believe clients will pay the same fee if AI improves value or outcomes","source":"BigHand Legal Pricing and Budgeting Trends Analysis 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"client","sourceType":"analyst","scope":"legal","context":"Firms\' belief about clients, not a client measure. Landing page","url":"https://www.bighand.com/en-gb/resources/whitepapers/2026-legal-pricing-and-budgeting-trends-analysis/","access":"gated"},{"id":"s3568","stat":"35% of law firms say partners are uncomfortable discussing AI with clients","source":"BigHand Legal Pricing and Budgeting Trends Analysis 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"workforce","sourceType":"analyst","scope":"legal","context":"Landing page; Artificial Lawyer reports it as the biggest obstacle, ahead of regulatory concerns (31%, not shown on the landing page)","url":"https://www.bighand.com/en-gb/resources/whitepapers/2026-legal-pricing-and-budgeting-trends-analysis/","access":"gated"},{"id":"s3569","stat":"35% of law firm lawyers most commonly default to standard hourly rates when pricing a matter","source":"BigHand Legal Pricing and Budgeting Trends Analysis 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"market","sourceType":"analyst","scope":"legal","context":"Landing page. Legal IT Insider\'s \'63% rely on familiar approaches\' adds 28% who copy a previous matter, which is not on the landing page","url":"https://www.bighand.com/en-gb/resources/whitepapers/2026-legal-pricing-and-budgeting-trends-analysis/","access":"gated"},{"id":"s3570","stat":"Average trust in AI among US arbitration professionals is 2.18 out of 5, rising to 3.42 among daily users against 0.83 among non-users","source":"AAA and Jus Mundi, The State of AI in U.S. Arbitration 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"industry","scope":"legal","context":"557 US arbitrators, practitioners and in-house counsel. AAA press release 23 Sep 2026 and Daily Jus summary; full report gated","url":"https://dailyjus.com/reports/2026/09/the-state-of-ai-in-us-arbitration-2026","access":"gated"},{"id":"s3571","stat":"78% of US arbitration professionals cite inaccuracies and hallucinations as a concern, regardless of AI experience","source":"AAA and Jus Mundi, The State of AI in U.S. Arbitration 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"critical","sourceType":"industry","scope":"legal","context":"n=557; AAA press release","url":"https://dailyjus.com/reports/2026/09/the-state-of-ai-in-us-arbitration-2026","access":"gated"},{"id":"s3572","stat":"66% of boutique-firm arbitration practitioners report significant time savings from AI, against 50% at global firms and 51% at mid-size firms","source":"AAA and Jus Mundi, The State of AI in U.S. Arbitration 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"adoption","sourceType":"industry","scope":"legal","context":"n=557; AAA press release / Daily Jus","url":"https://dailyjus.com/reports/2026/09/the-state-of-ai-in-us-arbitration-2026","access":"gated"},{"id":"s3573","stat":"83% of global law firm respondents have a clear AI policy, against 15% of independent arbitrators","source":"AAA and Jus Mundi, The State of AI in U.S. Arbitration 2026","date":"Sep 2026","sortKey":20260900,"precision":"month","year":2026,"theme":"regulatory","sourceType":"industry","scope":"legal","context":"n=557; Daily Jus summary","url":"https://dailyjus.com/reports/2026/09/the-state-of-ai-in-us-arbitration-2026","access":"gated"}]')},16469,e=>{"use strict";var t=e.i(43476),o=e.i(71645),s=e.i(75254);let a=(0,s.default)("search",[["path",{d:"m21 21-4.34-4.34",key:"14j7rj"}],["circle",{cx:"11",cy:"11",r:"8",key:"4ej97u"}]]);var r=e.i(37727);let n=(0,s.default)("arrow-up-down",[["path",{d:"m21 16-4 4-4-4",key:"f6ql7i"}],["path",{d:"M17 20V4",key:"1ejh1v"}],["path",{d:"m3 8 4-4 4 4",key:"11wl7u"}],["path",{d:"M7 4v16",key:"1glfcx"}]]);var i=e.i(74886),c=e.i(43531);let l=(0,s.default)("bookmark",[["path",{d:"M17 3a2 2 0 0 1 2 2v15a1 1 0 0 1-1.496.868l-4.512-2.578a2 2 0 0 0-1.984 0l-4.512 2.578A1 1 0 0 1 5 20V5a2 2 0 0 1 2-2z",key:"oz39mx"}]]);var p=e.i(63059);let d=(0,s.default)("external-link",[["path",{d:"M15 3h6v6",key:"1q9fwt"}],["path",{d:"M10 14 21 3",key:"gplh6r"}],["path",{d:"M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6",key:"a6xqqp"}]]);var u=e.i(31278);let h=(0,s.default)("triangle-alert",[["path",{d:"m21.73 18-8-14a2 2 0 0 0-3.48 0l-8 14A2 2 0 0 0 4 21h16a2 2 0 0 0 1.73-3",key:"wmoenq"}],["path",{d:"M12 9v4",key:"juzpu7"}],["path",{d:"M12 17h.01",key:"p32p05"}]]);function m(e="newest"){return{search:"",themes:new Set,sourceTypes:new Set,scopes:new Set,year:null,sort:e}}function y(e,t){let o=t.search.trim().toLowerCase(),s=[];for(let{stat:a,haystack:r}of e)(!(t.themes.size>0)||t.themes.has(a.theme))&&(!(t.sourceTypes.size>0)||t.sourceTypes.has(a.sourceType))&&(!(t.scopes.size>0)||t.scopes.has(a.scope))&&(null===t.year||a.year===t.year)&&(!o||r.includes(o))&&s.push(a);return function(e,t){let o=new Map;for(let t of e){let e=f(t),s=g(t.id),a=o.get(e);(void 0===a||s>a)&&o.set(e,s)}let s=e=>o.get(f(e));return[...e].sort((e,o)=>
1("newest"===t?o.sortKey-e.sortKey||s(o)-s(e):e.sortKey-o.sortKey||s(e)-s(o))||g(e.id)-g(o.id))}(s,t.sort)}function g(e){return Number.parseInt(e.slice(1),10)}function f(e){return`${e.source}\0${e.date}`}var w=e.i(89539);let v=["legal","cross-sector"],b=["market","adoption","technical","critical","client","regulatory","workforce"],x=["academic","analyst","vendor","industry","media","government","practitioner","commentary","unclassified"];w.default;let k=/^s\d{4,}$/,A=new Set(b),T=new Set(x),I=new Set(v),K=new Set(["newest","oldest"]);function S(e){try{return decodeURIComponent(e.replace(/\+/g," "))}catch{return e}}function L(e,t){let o=new Set;if(!e)return o;for(let s of S(e).split(",")){let e=s.trim().toLowerCase();t.has(e)&&o.add(e)}return o}let C={January:"Jan",February:"Feb",March:"Mar",April:"Apr",May:"May",June:"Jun",July:"Jul",August:"Aug",September:"Sep",October:"Oct",November:"Nov",December:"Dec"},M=RegExp(`\\b(${Object.keys(C).join("|")})\\b`,"g"),G=/^(\d{4})-(0[1-9]|1[0-2])$/;function R(e){let t=e.match(G);if(t){let e=Object.values(C)[Number.parseInt(t[2],10)-1];return`${e} ${t[1]}`}return e.replace(M,e=>C[e])}let P="signal.basket.v1",O=/^s\d{4,}$/;function F(e){return`"${String(e).replace(/"/g,'""')}"`}let B=new Set;function E(){for(let e of B)e()}function D(e){(null===e.key||e.key===P)&&E()}function N(e){return 0===B.size&&window.addEventListener("storage",D),B.add(e),()=>{B.delete(e),0===B.size&&window.removeEventListener("storage",D)}}function J(){try{return window.localStorage.getItem(P)??""}catch{return""}}function j(){return""}function W(e){try{window.localStorage.setItem(P,JSON.stringify(e))}catch{}E()}let H=(0,s.default)("download",[["path",{d:"M12 15V3",key:"m9g1x1"}],["path",{d:"M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4",key:"ih7n3h"}],["path",{d:"m7 10 5 5 5-5",key:"brsn70"}]]),U=(0,s.default)("trash-2",[["path",{d:"M10 11v6",key:"nco0om"}],["path",{d:"M14 11v6",key:"outv1u"}],["path",{d:"M19 6v14a2 2 0 0 1-2 2H7a2 2 0 0 1-2-2V6",key:"miytrc"}],["path",{d:"M3 6h18",key:"d0wm0j"}],["path",{d:"M8 6V4a2 2 0 0 1 2-2h4a2 2 0 0 1 2 2v2",key:"e791ji"}]]);function z({stats:e,savedCount:s,onRemove:a,onClear:n,onClose:i}){let[p,d]=(0,o.useState)(!1),u=window.location.origin,h=s-e.length;async function m(){try{await navigator.clipboard.writeText(0===e.length?"":e.map(e=>`- ${e.stat} (${e.source}, ${R(e.date)})`).join("\n")),d(!0),setTimeout(()=>d(!1),1500)}catch{}}return(0,t.jsx)("aside",{className:"fixed inset-x-0 bottom-0 z-40 border-t border-border bg-paper shadow-[0_-10px_40px_rgba(16,18,20,0.12)]","aria-label":"Evidence pack",children:(0,t.jsxs)("div",{className:"max-w-6xl mx-auto px-6 py-4",children:[(0,t.jsxs)("div",{className:"flex flex-wrap items-center justify-between gap-3 mb-3",children:[(0,t.jsxs)("p",{className:"eyebrow-caps flex items-center gap-2",children:[(0,t.jsx)(l,{className:"w-4 h-4","aria-hidden":"true"}),"Evidence pack ",(0,t.jsxs)("span",{className:"text-fade",children:[s," stats"]}),h>0&&(0,t.jsxs)("span",{className:"text-fade normal-case tracking-normal",children:["(",h," still loading)"]})]}),(0,t.jsxs)("div",{className:"flex items-center gap-2",children:[(0,t.jsx)("button",{onClick:m,className:"btn-quiet text-sm",disabled:0===e.length,children:p?(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(c.Check,{className:"w-4 h-4","aria-hidden":"true"})," Copied"]}):"Copy all"}),(0,t.jsxs)("button",{onClick:()=>{var t;let o,s,a;return o=e.map(e=>[e.id,e.stat,e.source,e.date,e.theme,e.sourceType,e.context,function(e,t="https://signal.vesta.legal"){return`${t}/#stat=${encodeURIComponent(e)}`}(e.id,u)].map(F).join(",")),t=[["id","stat","source","date","theme","source_type","context","link"].map(F).join(","),...o].join("\r\n"),s=URL.createObjectURL(new Blob([t],{type:"text/csv;charset=utf-8"})),void((a=document.createElement("a")).href=s,a.download="the-signal-evidence-pack.csv",document.body.appendChild(a),a.click(),a.remove(),setTimeout(()=>URL.revokeObjectURL(s),1e3))},className:"btn-quiet text-sm",disabled:0===e.length,children:[(0,t.jsx)(H,{className:"w-4 h-4","aria-hidden":"true"})," CSV"]}),(0,t.jsxs)("button",{onClick:n,className:"btn-quiet text-sm",title:"Clear evidence pack",children:[(0,t.jsx)(U,{className:"w-4 h-4","aria-hidden":"true"}),(0,t.jsx)("span",{className:"hidden sm:inline",children:"Clear"})]}),(0,t.jsx)("button",{onClick:i,className:"p-2 text-fade hover:text-ink transition-colors","aria-label":"Hide evidence pack",children:(0,t.jsx)(r.X,{className:"w-4 h-4"})})]})]}),(0,t.jsxs)("ul",{className:"max-h-48 overflow-y-auto divide-y divide-border",children:[e.map(e=>(0,t.jsxs)("li",{className:"flex items-start justify-between gap-3 py-2",children:[(0,t.jsxs)("span",{className:"text-sm text-ink leading-relaxed",children:[e.stat," ",(0,t.jsxs)("span",{className:"text-fade",children:["(",e.source,", ",R(e.date),")"]})]}),(0,t.jsx)("button",{onClick:()=>a(e.id),className:"p-1 text-fade hover:text-ink transition-colors shrink-0","aria-label":`Remove: ${e.stat}`,children:(0,t.jsx)(r.X,{className:"w-3.5 h-3.5"})})]},e.id)),0===e.length&&(0,t.jsx)("li",{className:"py-3 text-sm text-muted",children:s>
10?"Loading your evidence pack.":"Your evidence pack is empty. Use the bookmark on any row."})]})]})})}let q={market:{accent:"var(--theme-market)",label:"Market"},adoption:{accent:"var(--theme-adoption)",label:"Adoption"},technical:{accent:"var(--theme-technical)",label:"Technical"},critical:{accent:"var(--theme-critical)",label:"Critical"},client:{accent:"var(--theme-client)",label:"Client"},regulatory:{accent:"var(--theme-regulatory)",label:"Regulatory"},workforce:{accent:"var(--theme-workforce)",label:"Workforce"}},$=["market","adoption","technical","critical","client","regulatory","workforce"],V={analyst:"Analyst",academic:"Academic",vendor:"Vendor",industry:"Industry body",media:"News and media",government:"Government",practitioner:"Practitioner",commentary:"Commentary",unclassified:"Unclassified"},_=["analyst","academic","vendor","industry","media","government","practitioner","commentary","unclassified"];function Y(e){try{return new URL(e).hostname.replace(/^www\./,"")}catch{return"source"}}let Q={open:"",gated:"registration",paywalled:"subscription"};function X({url:e,access:o}){let s=o?Q[o]:"";return(0,t.jsxs)("a",{href:e,target:"_blank",rel:"noopener noreferrer",onClick:e=>e.stopPropagation(),className:"inline-flex items-baseline gap-1 text-accent-ink hover:text-accent-ink-hover transition-colors whitespace-nowrap",title:`Read the report at ${Y(e)}${s?` (${s} required)`:""}`,children:[(0,t.jsx)("span",{className:"underline underline-offset-2 decoration-border-strong hover:decoration-current",children:Y(e)}),s&&(0,t.jsxs)("span",{className:"text-fade",children:["(",s,")"]}),(0,t.jsx)(d,{className:"w-3 h-3 shrink-0 self-center","aria-hidden":"true"})]})}let Z=["billable hour","hallucination","in-house","contract review","ROI","agentic"];function ee(e,t){let o=t.stat.length>60?`${t.stat.slice(0,60)}...`:t.stat;return`${e}: ${o}`}e.s(["StatsExplorer",0,function({initialStats:e,totalCount:s,availableYears:d,dataUrl:g,recentlyAdded:w}){let[C,M]=(0,o.useState)(()=>m()),[G,P]=(0,o.useState)(50),[F,B]=(0,o.useState)(null),[E,D]=(0,o.useState)(null),[H,U]=(0,o.useState)(null),[Y,Q]=(0,o.useState)(null),[et,eo]=(0,o.useState)(null),[es,ea]=(0,o.useState)(null),[er,en]=(0,o.useState)(!1),[ei,ec]=(0,o.useState)(()=>new Map),[el,ep]=(0,o.useState)(null),ed=(0,o.useSyncExternalStore)(N,J,j),eu=(0,o.useMemo)(()=>(function(e){let t;if(!e)return[];try{t=JSON.parse(e)}catch{return[]}if(!Array.isArray(t))return[];let o=new Set;for(let e of t){if("string"!=typeof e)continue;let t=e.trim().toLowerCase();if(!(!O.test(t)||o.has(t))&&(o.add(t),o.size>=500))break}return[...o]})(ed||null),[ed]),eh=(0,o.useRef)(!1),em=(0,o.useRef)(!1),ey=(0,o.useRef)("replace"),eg=e.length>=s,[ef,ew]=(0,o.useState)(eg?"ready":"idle"),[ev,eb]=(0,o.useState)(null),ex=ev??e,ek="ready"===ef,eA=(0,o.useMemo)(()=>ex.map(e=>({stat:e,haystack:[e.stat,e.source,e.context].join("\0").toLowerCase()})),[ex]),eT=(0,o.useMemo)(()=>new Map(ex.map(e=>[e.id,e])),[ex]),eI=(0,o.useDeferredValue)(C),eK=eI!==C,eS=(0,o.useMemo)(()=>y(eA,eI),[eA,eI]),eL=(0,o.useMemo)(()=>et?eS.find(e=>e.id===et)??null:null,[eS,et]),eC=(0,o.useMemo)(()=>(function(e){let t=new Map,o=[];for(let s of e){let e=f(s),a=t.get(e);a?a.push(s):(t.set(e,[s]),o.push(e))}let s=[];for(let e of o){let o=t.get(e);if(o.length>=3)s.push({kind:"group",key:e,source:o[0].source,date:o[0].date,stats:o});else for(let e of o)s.push({kind:"single",key:e.id,stat:e})}return s})(eL?eS.filter(e=>e.id!==eL.id):eS),[eS,eL]),eM=(0,o.useMemo)(()=>eC.slice(0,G),[eC,G]),eG=(0,o.useMemo)(()=>new Set(eS.map(f)).size,[eS]),eR=G<eC.length,eP=C.search.trim().length>0||C.themes.size>0||C.sourceTypes.size>0||C.scopes.size>0||null!==C.year,eO=eI.search.trim().length>0,eF=el??eO,eB=(0,o.useCallback)(e=>ei.get(e)??eF,[ei,eF]),eE=eM.some(e=>"group"===e.kind&&!eB(e.key)),eD=(0,o.useMemo)(()=>(function(e,t){let o=[];for(let s of e){let e=t.get(s);e&&o.push(e)}return o})(eu,eT),[eu,eT]),eN=(0,o.useMemo)(()=>new Set(eu),[eu]),eJ=(0,o.useCallback)((e,t="push")=>{ey.current=t,M(e),P(50),eo(null),Q(null),ea(null),ec(new Map),ep(null)},[]),ej=(0,o.useCallback)(e=>{ec(t=>{let o=new Map(t);return o.set(e,!(t.get(e)??el??eO)),o})},[el,eO]),eW=(0,o.useCallback)(e=>{ep(e),ec(new Map)},[]),eH=(0,o.useCallback)(()=>{ew(e=>"idle"===e||"error"===e?"loading":e)},[]),eU=(0,o.useCallback)((e,t)=>
1{if(!eT.has(e)){if(!ek){ea(e),eo(e),M(t),P(50),Q(null),eH();return}M(m(t.sort)),P(50),B(null),U(null),eo(null),ea(null),Q(e);return}M(y(eA,t).some(t=>t.id===e)?t:m(t.sort)),P(50),B(e),U({id:e,seq:performance.now()}),eo(e),Q(null),ea(null)},[eT,ek,eA,eH]);(0,o.useEffect)(()=>{if(eg)return;let e=window.requestIdleCallback;if(e){let t=e(()=>eH(),{timeout:3e3});return()=>window.cancelIdleCallback?.(t)}let t=window.setTimeout(eH,1200);return()=>window.clearTimeout(t)},[eg,eH]),(0,o.useEffect)(()=>{if("loading"!==ef)return;let e=!1;return(async()=>{try{let t=await fetch(g);if(!t.ok)throw Error(`HTTP ${t.status}`);let o=await t.json();if(e)return;eb(o),ew("ready")}catch{if(e)return;ew("error")}})(),()=>{e=!0}},[ef,g]),(0,o.useEffect)(()=>{let e=()=>{let{filters:e,statId:t}=function(e,t){let o=m(),s=null,a=(e??"").replace(/^#+/,"");if(!a)return{filters:o,statId:s};let r={};for(let e of a.split("&")){if(!e)continue;let t=e.indexOf("=");t<=0||(r[e.slice(0,t).toLowerCase()]=e.slice(t+1))}if(r.q&&(o.search=S(r.q)),o.themes=L(r.theme,A),o.sourceTypes=L(r.src,T),o.scopes=L(r.scope,I),r.year){let e=Number.parseInt(S(r.year),10);Number.isFinite(e)&&t.includes(e)&&(o.year=e)}if(r.sort){let e=S(r.sort).toLowerCase();K.has(e)&&(o.sort=e)}if(r.stat){let e=S(r.stat).trim().toLowerCase();k.test(e)&&(s=e)}return{filters:o,statId:s}}(window.location.hash,d);em.current=!0,t?eU(t,e):(M(e),P(50),eo(null),Q(null)),eh.current=!0};return e(),window.addEventListener("popstate",e),()=>window.removeEventListener("popstate",e)},[d,eU]),(0,o.useEffect)(()=>{if(!eh.current)return;if(em.current){em.current=!1;return}let e=function(e,t=null){let o=[],s=e.search.trim();s&&o.push(`q=${encodeURIComponent(s)}`);let a=b.filter(t=>e.themes.has(t));a.length&&o.push(`theme=${a.join(",")}`);let r=x.filter(t=>e.sourceTypes.has(t));r.length&&o.push(`src=${r.join(",")}`);let n=v.filter(t=>e.scopes.has(t));return n.length&&o.push(`scope=${n.join(",")}`),null!==e.year&&o.push(`year=${e.year}`),"newest"!==e.sort&&o.push(`sort=${e.sort}`),t&&o.push(`stat=${encodeURIComponent(t)}`),o.join("&")}(C,et),t=e?`#${e}`:window.location.pathname+window.location.search;"push"===ey.current?window.history.pushState(null,"",t):window.history.replaceState(null,"",t)},[C,et]),(0,o.useEffect)(()=>{ek&&es&&eU(es,C)},[ek,es,eU,C]),(0,o.useEffect)(()=>{if(H){var e;let t,o;e=H.id,o=Array.from(t=document.querySelectorAll(`[data-stat-id="${CSS.escape(e)}"]`)).find(e=>null!==e.offsetParent)??t[0],o?.scrollIntoView({behavior:"smooth",block:"center"})}},[H]),(0,o.useEffect)(()=>{ek&&eD.length!==eu.length&&W(eD.map(e=>e.id))},[ek,eD,eu.length]);let ez=()=>{eJ(e=>m(e.sort))},eq=e=>{eJ(t=>({...m(t.sort),search:e}))},e$=e=>(0,t.jsxs)("div",{className:"flex items-center gap-0.5 shrink-0",children:[(0,t.jsx)("button",{onClick:t=>{var o;return o=e.id,void(t.stopPropagation(),W(eN.has(o)?eu.filter(e=>e!==o):[...eu,o]),en(!0))},className:`p-1.5 transition-colors ${eN.has(e.id)?"text-accent-ink":"text-fade hover:text-accent-ink"}`,title:eN.has(e.id)?"Remove from evidence pack":"Add to evidence pack","aria-pressed":eN.has(e.id),"aria-label":ee(eN.has(e.id)?"Remove from evidence pack":"Add to evidence pack",e),children:(0,t.jsx)(l,{className:"w-3.5 h-3.5",fill:eN.has(e.id)?"currentColor":"none"})}),(0,t.jsx)("button",{onClick:t=>{t.stopPropagation(),navigator.clipboard.writeText(`${e.stat} (${e.source}, ${R(e.date)})`),D(e.id),setTimeout(()=>D(null),1500)},className:"p-1.5 text-fade hover:text-accent-ink transition-colors",title:"Copy the stat with its source","aria-label":ee("Copy",e),children:E===e.id?(0,t.jsx)(c.Check,{className:"w-3.5 h-3.5 text-accent-ink"}):(0,t.jsx)(i.Copy,{className:"w-3.5 h-3.5"})})]}),eV=(e,o={})=>{let s=q[e.theme],a=F===e.id;return(0,t.jsxs)("tr",{"data-stat-id":e.id,style:{"--row-accent":s.accent},className:`border-b border-border last:border-0 cursor-pointer transition-colors ${a?"row-expanded":"hover:bg-cloud/60"} ${H?.id===e.id?"stat-found":""}`,onClick:()=>B(a?null:e.id),children:[(0,t.jsxs)("td",{className:`py-3 pr-4 ${o.inGroup?"pl-10":"pl-4"}`,children:[o.linked&&(0,t.jsx)("span",{className:"tag tag-linked mb-1.5 mr-1.5",children:"Linked
1stat"}),eN.has(e.id)&&(0,t.jsx)("span",{className:"tag tag-saved mb-1.5",children:"In pack"}),(0,t.jsx)("div",{className:"text-sm text-ink leading-relaxed",children:e.stat}),a&&e.context&&(0,t.jsx)("div",{className:"mt-2 text-xs text-muted leading-relaxed border-t border-border pt-2",children:e.context})]}),(0,t.jsx)("td",{className:"px-4 py-3 text-xs text-muted align-top",children:!o.inGroup&&(0,t.jsxs)(t.Fragment,{children:[e.source,e.url&&(0,t.jsx)("span",{className:"block mt-1",children:(0,t.jsx)(X,{url:e.url,access:e.access})})]})}),(0,t.jsx)("td",{className:"px-4 py-3 text-xs text-fade align-top whitespace-nowrap",children:o.inGroup?"":R(e.date)}),(0,t.jsx)("td",{className:"px-4 py-3 align-top",children:(0,t.jsx)("span",{className:"tag tag-theme",style:{"--tag-accent":s.accent},children:s.label})}),(0,t.jsx)("td",{className:"px-4 py-3 align-top",children:(0,t.jsx)("span",{className:"tag tag-quiet",children:V[e.sourceType]})}),(0,t.jsx)("td",{className:"px-2 py-3 align-top",children:e$(e)})]},o.linked?`linked-${e.id}`:e.id)},e_=(e,o={})=>{let s=q[e.theme],a=F===e.id;return(0,t.jsxs)("div",{"data-stat-id":e.id,style:{"--row-accent":s.accent},className:`paper paper-hover p-4 cursor-pointer ${o.inGroup?"ml-4":""} ${a?"row-expanded":""} ${H?.id===e.id?"stat-found":""}`,onClick:()=>B(a?null:e.id),children:[(0,t.jsxs)("div",{className:"flex items-start justify-between gap-3 mb-2.5",children:[(0,t.jsxs)("div",{className:"flex flex-wrap items-center gap-1.5",children:[o.linked&&(0,t.jsx)("span",{className:"tag tag-linked",children:"Linked stat"}),eN.has(e.id)&&(0,t.jsx)("span",{className:"tag tag-saved",children:"In pack"}),(0,t.jsx)("span",{className:"tag tag-theme",style:{"--tag-accent":s.accent},children:s.label}),(0,t.jsx)("span",{className:"tag tag-quiet",children:V[e.sourceType]})]}),!o.inGroup&&(0,t.jsx)("span",{className:"text-xs text-fade shrink-0",children:R(e.date)})]}),(0,t.jsx)("p",{className:"text-sm text-ink leading-relaxed mb-2.5",children:e.stat}),(0,t.jsxs)("div",{className:"flex items-center justify-between gap-3",children:[(0,t.jsx)("p",{className:"text-xs text-muted min-w-0",children:!o.inGroup&&(0,t.jsxs)(t.Fragment,{children:[e.source,e.url&&(0,t.jsx)("span",{className:"block mt-1",children:(0,t.jsx)(X,{url:e.url,access:e.access})})]})}),e$(e)]}),a&&e.context&&(0,t.jsx)("div",{className:"mt-3 text-xs text-muted leading-relaxed border-t border-border pt-3",children:e.context})]},o.linked?`linked-${e.id}`:e.id)},eY=e=>(function(e){let t=new Map;for(let o of e)t.set(o.theme,(t.get(o.theme)??0)+1);return[...t.entries()].sort((e,t)=>t[1]-e[1]).map(([e])=>e)})(e.stats).map(e=>(0,t.jsx)("span",{className:"tag tag-theme",style:{"--tag-accent":q[e].accent},children:q[e].label},e)),eQ=(e,o)=>(0,t.jsxs)("button",{onClick:()=>ej(e.key),"aria-expanded":o,className:"min-w-0 flex items-center gap-3 text-left",children:[(0,t.jsx)(p.ChevronRight,{className:`w-4 h-4 text-fade shrink-0 transition-transform ${o?"rotate-90":""}`,"aria-hidden":"true"}),(0,t.jsx)("span",{className:"text-sm text-ink leading-snug",children:e.source}),(0,t.jsxs)("span",{className:"tag tag-quiet shrink-0",children:[e.stats.length," stats"]})]});return(0,t.jsxs)("div",{className:er&&eu.length>0?"pb-72":void 0,onPointerDownCapture:eH,onFocusCapture:eH,onKeyDownCapture:eH,children:[(0,t.jsx)("p",{className:"eyebrow mb-5",children:"The database"}),(0,t.jsxs)("h2",{className:"font-display text-3xl md:text-5xl leading-[1.1] mb-8 text-ink",children:["Search it, filter it,"," ",(0,t.jsx)("span",{className:"font-display-italic grad-text-deep",children:"cite it"}),"."]}),Y&&(0,t.jsxs)("div",{className:"paper-soft px-4 py-3 mb-6 text-sm text-muted",role:"status",children:["The stat ",(0,t.jsx)("code",{className:"font-mono text-ink",children:Y})," has been edited or removed since that link was made. Everything else is below."]}),"error"===ef&&(0,t.jsxs)("div",{className:"paper-soft px-4 py-3 mb-6 flex flex-wrap items-center justify-between gap-3",role:"alert",children:[(0,t.jsxs)("p",{className:"text-sm text-ink flex items-center gap-2",children:[(0,t.jsx)(h,{className:"w-4 h-4 text-accent-ink shrink-0","aria-hidden":"true"}),"Only the ",e.length," most recent stats loaded. Searching and filtering cover those ",e.length,", not all"," ",s.toLocaleStr
1ing("en-GB"),"."]}),(0,t.jsx)("button",{onClick:eH,className:"btn-quiet text-sm",children:"Try again"})]}),(0,t.jsxs)("div",{className:"paper p-4 md:p-5 mb-8",children:[w.length>0&&(0,t.jsx)("div",{className:"pb-4 mb-4 border-b border-border",children:(0,t.jsxs)("div",{className:"flex flex-wrap items-center gap-x-3 gap-y-2",children:[(0,t.jsx)("span",{className:"eyebrow-caps shrink-0",children:"Recently added"}),w.map(e=>(0,t.jsxs)("button",{onClick:()=>eq(e.source),className:"text-sm text-accent-ink hover:text-accent-ink-hover transition-colors text-left",title:`Show the ${e.count} stats from this report`,children:[e.source," ",(0,t.jsxs)("span",{className:"text-fade whitespace-nowrap",children:["(",e.count,")"]})]},`${e.source} ${e.date}`))]})}),(0,t.jsxs)("div",{className:"flex flex-wrap gap-3 mb-3",children:[(0,t.jsxs)("div",{className:"relative flex-1 min-w-[16rem]",children:[(0,t.jsx)(a,{className:"absolute left-3.5 top-1/2 -translate-y-1/2 w-4 h-4 text-fade","aria-hidden":"true"}),(0,t.jsx)("input",{type:"search",value:C.search,onChange:e=>{let t=e.target.value;eJ(e=>({...e,search:t}),"replace")},placeholder:"Search for a word or phrase","aria-label":"Search the database",className:"w-full pl-10 pr-10 py-3 rounded-[3px] border border-border-strong bg-paper text-ink placeholder:text-fade focus:outline-none focus:border-accent-ink transition-colors"}),C.search&&(0,t.jsx)("button",{onClick:()=>eJ(e=>({...e,search:""})),className:"absolute right-3 top-1/2 -translate-y-1/2 text-fade hover:text-ink transition-colors","aria-label":"Clear search",children:(0,t.jsx)(r.X,{className:"w-4 h-4"})})]}),(0,t.jsxs)("button",{onClick:()=>{eJ(e=>({...e,sort:"newest"===e.sort?"oldest":"newest"}))},className:"btn-quiet shrink-0",title:`Sorted ${"newest"===C.sort?"newest first":"oldest first"}. Click to reverse.`,children:[(0,t.jsx)(n,{className:"w-4 h-4","aria-hidden":"true"}),(0,t.jsx)("span",{className:"hidden sm:inline",children:"newest"===C.sort?"Newest":"Oldest"})]}),eu.length>0&&(0,t.jsxs)("button",{onClick:()=>en(e=>!e),className:"btn-quiet shrink-0","aria-expanded":er,title:"Open your evidence pack",children:[(0,t.jsx)(l,{className:"w-4 h-4","aria-hidden":"true"}),(0,t.jsxs)("span",{children:[(0,t.jsx)("span",{className:"hidden sm:inline",children:"Evidence pack "}),eu.length]})]})]}),(0,t.jsx)("p",{className:"mb-4 text-xs text-fade",children:"Literal text match across stats, sources and context. Use a word or phrase, not a question."}),!C.search&&(0,t.jsxs)("div",{className:"flex flex-wrap items-center gap-2 mb-5 text-sm",children:[(0,t.jsx)("span",{className:"text-fade shrink-0",children:"Try"}),Z.map(e=>(0,t.jsx)("button",{onClick:()=>eq(e),className:"text-accent-ink hover:text-accent-ink-hover underline underline-offset-2 decoration-border-strong hover:decoration-current transition-colors",children:e},e))]}),(0,t.jsxs)("div",{className:"flex flex-wrap items-center gap-3",children:[(0,t.jsx)("span",{className:"eyebrow-caps mr-1 w-14 shrink-0",children:"Scope"}),(0,t.jsxs)("label",{className:"inline-flex items-center gap-2.5 text-sm text-ink cursor-pointer",title:"Measured on law firms, in-house teams or the legal market",children:[(0,t.jsx)("input",{type:"checkbox",checked:1===C.scopes.size&&C.scopes.has("legal"),onChange:e=>{var t;return t=e.target.checked,void eJ(e=>({...e,scopes:t?new Set(["legal"]):new Set}))},className:"peer sr-only"}),(0,t.jsx)("span",{"aria-hidden":"true",className:"relative h-5 w-9 shrink-0 rounded-full border border-border-strong bg-cloud transition-colors peer-checked:border-accent-ink peer-checked:bg-accent-ink after:absolute after:left-0.5 after:top-0.5 after:h-3.5 after:w-3.5 after:rounded-full after:bg-paper after:shadow-sm after:transition-transform peer-checked:after:translate-x-4 peer-focus-visible:outline peer-focus-visible:outline-2 peer-focus-visible:outline-offset-2 peer-focus-visible:outline-accent-ink"}),(0,t.jsx)("span",{children:"Legal market only"})]})]}),(0,t.jsxs)("div",{className:"flex flex-wrap items-center gap-2 mt-2.5",children:[(0,t.jsx)("span",{className:"eyebrow-caps mr-1 w-14 shrink-0",children:"Theme"}),$.map(e=>{let o=q[e];return(0,t.jsx)("button",{onClick:()=>{eJ(t=>{let o=new Set(t.themes);return o.has(e)?o.delete(e):o.add(e),{...t,themes:o}})},"aria-pressed":C.themes.has(e),className:"pill pill-accent",style:{"--pill-accent":o.accent},children:o.label},e)})]}),(0,t.jsxs)("div",{className:"flex flex-wrap items-center gap-2 mt-2.5",children:[(0,t.jsx)("span",{className:"eyebrow-caps mr-1 w-14 shrink-0",children:"Source"}),_.map(e=>(0,t.jsx)("button",{onClick:()=>{eJ(t=>{let o=new Set(t.sourceTypes);return o.has(e)?o.delete(e):o.add(e),{...t,sourceTypes:o}})},"aria-pressed":C.sourceTypes.has(e),className:"pill",children:V[e]},e))]}),(0,t.jsxs)("div",{className:"flex flex-wrap items-center gap-2 mt-2.5",children:[(0,t.jsx)("span",{className:"eyebrow-caps mr-1 w-14 shrink-0",children:"Year"}),d.map(e=>(0,t.jsx)("button",{onClick:()=>{eJ(t=>({...t,year:t.year===e?null:e}))},"aria-pressed":C.year===e,className:"pill",children:e},e))]})]}),(0,t.jsxs)("div",{className:"flex flex-wrap items-center justify-between gap-x-4 gap-y-2 mb-4",children:[(0,t.jsxs)("p",{className:"text-sm text-muted flex items-center gap-2",children:[("idle"===ef||"loading"===ef)&&(0,t.jsx)(u.Loader2,{className:"w-3.5 h-3.5 animate-spin text-fade shrink-0","aria-hidden":"true"}
1),ek||"error"===ef?eS.length===s?`All ${s.toLocaleString("en-GB")} stats, across ${eG.toLocaleString("en-GB")} reports`:`${eS.length.toLocaleString("en-GB")} of ${s.toLocaleString("en-GB")} stats, across ${eG.toLocaleString("en-GB")} reports`:`${eS.length.toLocaleString("en-GB")} in the newest ${e.length}, loading the other ${(s-e.length).toLocaleString("en-GB")}`]}),(0,t.jsxs)("div",{className:"flex items-center gap-4",children:[eM.some(e=>"group"===e.kind)&&(0,t.jsx)("button",{onClick:()=>eW(eE),className:"text-sm text-accent-ink hover:text-accent-ink-hover transition-colors",children:eE?"Expand all reports":"Collapse all reports"}),eP&&(0,t.jsxs)("button",{onClick:ez,className:"text-sm text-accent-ink hover:text-accent-ink-hover transition-colors inline-flex items-center gap-1",children:[(0,t.jsx)(r.X,{className:"w-3.5 h-3.5","aria-hidden":"true"})," Clear filters"]})]})]}),(0,t.jsx)("div",{className:`hidden md:block paper overflow-hidden transition-opacity ${eK?"opacity-60":""}`,children:(0,t.jsxs)("table",{className:"w-full",children:[(0,t.jsx)("thead",{children:(0,t.jsxs)("tr",{className:"bg-cloud border-b border-border",children:[(0,t.jsx)("th",{className:"text-left px-4 py-3 eyebrow-caps",children:"Stat"}),(0,t.jsx)("th",{className:"text-left px-4 py-3 eyebrow-caps w-48",children:"Source"}),(0,t.jsx)("th",{className:"text-left px-4 py-3 eyebrow-caps w-28",children:"Date"}),(0,t.jsx)("th",{className:"text-left px-4 py-3 eyebrow-caps w-28",children:"Theme"}),(0,t.jsx)("th",{className:"text-left px-4 py-3 eyebrow-caps w-36",children:"Source type"}),(0,t.jsx)("th",{className:"w-24"})]})}),(0,t.jsxs)("tbody",{children:[eL&&eV(eL,{linked:!0}),eM.map(e=>{if("single"===e.kind)return eV(e.stat);let s=eB(e.key);return(0,t.jsxs)(o.Fragment,{children:[((e,o)=>{let{url:s,access:a,sourceType:r}=e.stats[0];return(0,t.jsxs)("tr",{className:"border-b border-border bg-cloud/40 hover:bg-cloud/60 transition-colors",children:[(0,t.jsx)("td",{className:"py-3 pl-4 pr-4 align-top",children:eQ(e,o)}),(0,t.jsx)("td",{className:"px-4 py-3 text-xs text-muted align-top",children:s&&(0,t.jsx)(X,{url:s,access:a})}),(0,t.jsx)("td",{className:"px-4 py-3 text-xs text-fade align-top whitespace-nowrap",children:R(e.date)}),(0,t.jsx)("td",{className:"px-4 py-3 align-top",children:(0,t.jsx)("span",{className:"flex flex-wrap gap-1.5",children:eY(e)})}),(0,t.jsx)("td",{className:"px-4 py-3 align-top",children:(0,t.jsx)("span",{className:"tag tag-quiet",children:V[r]})}),(0,t.jsx)("td",{})]})})(e,s),s&&e.stats.map(e=>eV(e,{inGroup:!0}))]},e.key)})]})]})}),(0,t.jsxs)("div",{className:`md:hidden space-y-3 transition-opacity ${eK?"opacity-60":""}`,children:[eL&&e_(eL,{linked:!0}),eM.map(e=>{if("single"===e.kind)return e_(e.stat);let o=eB(e.key);return(0,t.jsxs)("div",{className:"space-y-3",children:[(0,t.jsx)("div",{className:"paper",children:((e,o)=>{let{url:s,access:a,sourceType:r}=e.stats[0];return(0,t.jsxs)("div",{className:"flex flex-wrap items-center gap-x-3 gap-y-2 px-4 py-3 hover:bg-cloud/60 transition-colors",children:[eQ(e,o),(0,t.jsx)("span",{className:"text-xs text-fade shrink-0 whitespace-nowrap",children:R(e.date)}),(0,t.jsxs)("span",{className:"flex flex-wrap items-center gap-1.5",children:[eY(e),(0,t.jsx)("span",{className:"tag tag-quiet",children:V[r]})]}),s&&(0,t.jsx)("span",{className:"text-xs shrink-0",children:(0,t.jsx)(X,{url:s,access:a})})]})})(e,o)}),o&&e.stats.map(e=>e_(e,{inGroup:!0}))]},e.key)})]}),eR&&(0,t.jsx)("div",{className:"mt-8 text-center",children:(0,t.jsxs)("button",{onClick:()=>P(e=>e+50),className:"btn-quiet",children:["Load more (",eC.slice(G).reduce((e,t)=>e+("group"===t.kind?t.stats.length:1),0).toLocaleString("en-GB")," ","stats remaining)"]})}),0===eS.length&&!ek&&"error"!==ef&&(0,t.jsx)("div",{className:"paper p-12 text-center",children:(0,t.jsxs)("p",{className:"text-sm text-muted flex items-center justify-center gap-2",children:[(0,t.jsx)(u.Loader2,{className:"w-4 h-4 animate-spin","aria-hidden":"true"}),"Nothing in the newest ",e.length," matches. Still loading the rest."]})}),0===eS.length&&(ek||"error"===ef)&&(0,t.jsxs)("div",{className:"paper p-12 text-center",children:[(0,t.jsx)("p",{className:"font-display text-xl text-ink mb-2",children:"Nothing matches that"}),(0,t.jsx)("p",{className:"text-sm text-muted mb-5",children:"Try a different search term, or clear the filters."}),(0,t.jsx)("button",{onClick:ez,className:"btn-titans",children:"Clear filters"})]}),er&&eu.length>0&&(0,t.jsx)(z,{stats:eD,savedCount:eu.length,onRemove:e=>W(eu.filter(t=>t!==e)),onClear:()=>{W([]),en(!1)},onClose:()=>en(!1)})]})}],16469)},52068,e=>{"use strict";var t=e.i(43476),o=e.i(71645),s=e.i(43531),a=e.i(63059),r=e.i(74886),n=e.i(31278),i=e.i(37727);e.s(["VerifySkillModal",0,function({skillUrl:e}){let c=(0,o.useRef)(null),[l,p]=(0,o.useState)(!1),[d,u]=(0,o.useState)(!1),[h,m]=(0,o.useState)(null),[y,g]=(0,o.useState)(!1),[f,w]=(0,o.useState)(!1),v=(0,o.useCallback)(()=>{w(!1),g(!0)},[]),b=(0,o.useCallback)(()=>{c.current?.close()},[]);(0,o.useEffect)(()=>{let e=()=>{"#verify"===window.location.hash&&c.current?.showModal()};return e(),window.addEventListener("hashchange",e),()=>window.removeEventListener("hashchange",e)},[]),(0,o.useEffect)(()=>{let e=e=>{if(e.defaultPrevented||0!==e.button||e.metaKey||e.ctrlKey||e.shiftKey)return;let t=e.target;t?.closest?.('a[href$="#verify"]')&&(e.preventDefault(),c.current?.open||c.current?.showModal())};return document.addEventListener("click",e),()=>document.removeEventListener("click",e)},[]),(0,o.useEffect)(()=>
1{let e=c.current;if(!e)return;let t=()=>{"#verify"===window.location.hash&&history.replaceState(null,"",window.location.pathname+window.location.search)};return e.addEventListener("close",t),()=>e.removeEventListener("close",t)},[]);let x=(0,o.useCallback)(e=>{if(e.target!==c.current)return;let t=c.current.getBoundingClientRect();e.clientX>=t.left&&e.clientX<=t.right&&e.clientY>=t.top&&e.clientY<=t.bottom||c.current.close()},[]);(0,o.useEffect)(()=>{if(!y||null!==h)return;let t=!1;return(async()=>{try{let o=await fetch(e);if(!o.ok)throw Error(`HTTP ${o.status}`);let s=await o.text();t||m(s)}catch{t||w(!0)}})(),()=>{t=!0}},[y,h,e]);let k=y&&null===h&&!f;async function A(){v();let t=h;if(null===t)try{let o=await fetch(e);if(!o.ok)throw Error(`HTTP ${o.status}`);t=await o.text(),m(t)}catch{w(!0);return}try{await navigator.clipboard.writeText(t),p(!0),setTimeout(()=>p(!1),2e3)}catch{}}return(0,t.jsx)("dialog",{ref:c,className:"modal-titans",onClick:x,"aria-labelledby":"verify-title",children:(0,t.jsxs)("div",{className:"flex flex-col max-h-[calc(100dvh-2rem)]",children:[(0,t.jsxs)("div",{className:"flex items-start justify-between gap-4 px-6 md:px-8 pt-7 pb-5 border-b border-border",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)("p",{className:"eyebrow mb-3",children:"Provenance"}),(0,t.jsxs)("h2",{id:"verify-title",className:"font-display text-2xl md:text-3xl leading-[1.15] text-ink",children:["Donât trust."," ",(0,t.jsx)("span",{className:"font-display-italic grad-text-deep",children:"Verify"}),"."]})]}),(0,t.jsx)("button",{onClick:b,"aria-label":"Close",className:"shrink-0 -mr-2 -mt-1 p-2 text-muted hover:text-ink transition-colors",children:(0,t.jsx)(i.X,{className:"w-5 h-5","aria-hidden":"true"})})]}),(0,t.jsxs)("div",{className:"px-6 md:px-8 py-6 overflow-y-auto",children:[(0,t.jsxs)("div",{className:"prose-ink mb-8",children:[(0,t.jsx)("p",{children:"Every stat here carries a source and a date, but we have not independently re-checked them all against the original documents. The honest position is that this is a starting point extracted from reports, not a final answer."}),(0,t.jsx)("p",{children:"Before a number goes into a client deck or a newsletter, we run the verify skill below against the specific stats that matter. It finds the original source, cross-references it against independent reporting, and flags anything that does not match. It uses parallel research where the tool supports it."}),(0,t.jsxs)("p",{children:[(0,t.jsx)("span",{className:"text-ink",children:"Claude Code:"})," copy the skill, drop it into"," ",(0,t.jsx)("code",{className:"paper-soft px-2 py-0.5 font-mono text-[0.95em] text-ink-soft",children:"~/.claude/skills/verify/SKILL.md"})," ","and run"," ",(0,t.jsx)("code",{className:"paper-soft px-2 py-0.5 font-mono text-[0.95em] text-ink-soft",children:"/verify"})," ","on whatever you are about to cite."]}),(0,t.jsxs)("p",{children:[(0,t.jsx)("span",{className:"text-ink",children:"ChatGPT:"})," open"," ",(0,t.jsx)("span",{className:"text-ink",children:"Plugins > Skills > Create > Create with editor"}),", paste the skill and install it. In a chat, mention"," ",(0,t.jsx)("code",{className:"paper-soft px-2 py-0.5 font-mono text-[0.95em] text-ink-soft",children:"@verify"})," ","followed by the claim or stats you want checked."]}),(0,t.jsx)("p",{className:"text-sm text-fade",children:"The vault file paths and changelog step in the skill only apply when the tool can access that vault. Pasted claims work in either tool."})]}),(0,t.jsxs)("div",{className:"paper overflow-hidden",children:[(0,t.jsxs)("div",{className:"flex flex-wrap items-center justify-between gap-3 px-5 py-4 border-b border-border bg-cloud",children:[(0,t.jsxs)("button",{onClick:()=>{v(),u(!d)},"aria-expanded":d,className:"flex items-center gap-2 font-semibold text-sm text-ink hover:text-accent-ink transition-colors",children:[(0,t.jsx)(a.ChevronRight,{className:`w-4 h-4 transition-transform ${d?"rotate-90":""}`,"aria-hidden":"true"}),d?"Hide":"Show"," the verify skill"]}),(0,t.jsx)("button",{onClick:A,className:"btn-titans text-sm",children:l?(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(s.Check,{className:"w-4 h-4","aria-hidden":"true"})," Copied"]}):(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(r.Copy,{className:"w-4 h-4","aria-hidden":"true"})," Copy the skill"]})})]}),d&&k&&(0,t.jsxs)("p",{className:"p-5 text-sm text-muted flex items-center gap-2",children:[(0,t.jsx)(n.Loader2,{className:"w-4 h-4 animate-spin","aria-hidden":"true"})," Loading the skill"]}),d&&f&&(0,t.jsxs)("p",{className:"p-5 text-sm text-muted",children:["The skill could not be loaded."," ",(0,t.jsx)("button",{onClick:v,className:"text-accent-ink hover:text-accent-ink-hover underline",children:"Try again"}),"."]}),d&&null!==h&&(0,t.jsx)("pre",{className:"p-5 overflow-x-auto text-[13px] font-mono leading-relaxed max-h-[45vh] overflow-y-auto whitespace-pre-wrap break-words text-ink-soft",children:h})]})]})]})})}])}]);
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.