[{"data":1,"prerenderedAt":729},["ShallowReactive",2],{"uc-workplace-ai-assistant-rollout":3,"uc-regulations":504},{"useCase":4,"evidence":182,"blitsAiDeployments":354,"benchmarks":355,"indicative":371,"related":374,"indexability":502,"includeUnpublished":188},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":38,"indicativeValue":43,"macroEstimates":83,"feasibility":84,"implementation":96,"risk":137,"blitsAi":160,"faq":162,"related":172,"datePublished":177,"dateModified":177,"lastVerified":177,"changelog":178,"slug":181},"Workplace AI assistant rollout across an organization","Workplace AI assistant rollout","Rolling out a workplace AI assistant","Public evaluations of Microsoft 365 Copilot rollouts: 64% weekly use at a UK department, low use at the Australian Treasury, small time savings.","published","A workplace AI assistant rollout is the program that gives employees a general purpose AI assistant bought with the office suite, such as Microsoft 365 Copilot, and manages licensing, access cleanup, training and the measurement of use and benefit. The page covers the rollout of the assistant as a whole; a governed assistant built to answer from approved policy documents is enterprise knowledge search, and a tool used only for meeting notes is meeting summarization.",[12,13,14,15],"Microsoft 365 Copilot rollout","enterprise copilot deployment","employee AI assistant adoption","office suite AI assistant",[17],"cross-industry",[19,20],"operations","knowledge-management",[22,23,24],"content-generation","summarization","rag-knowledge-assistant",[26,27],"microsoft-teams","internal-tools","employee-facing","copilot","mainstream","back-office","Organizations buy assistant licenses for thousands of employees and then find that usage is uneven and\nthe benefit is hard to prove. Staff get a tool that drafts, summarizes and searches across their mail, files\nand meetings, but no one decides which tasks it should handle, how outputs are checked or what to measure.\n\nThe public evaluations show the pattern. In the UK Department for Business and Trade pilot, around 64% of\nlicense holders used Copilot at least once a week, small time savings appeared on most tasks and the\nevaluation found no evidence that they led to improved productivity. The Australian Treasury found usage\nlower than expected and expectations that were not met. Microsoft reports 93% daily usage at Lloyds Banking\nGroup, which made departments bid for licenses with measurable goals and built a network of volunteer coaches.\n\nEvery deployment on this page is a Microsoft 365 Copilot rollout, because those are the rollouts with\npublished evaluations or reported figures, and two of the sources are Microsoft customer stories. The\nrollout questions (who gets a license, which tasks, what to measure) are the same for other office suite\nassistants.",[],"1. **Choose the groups and the tasks.** Pick departments with a business case and a measurable goal, and\n   name the tasks the assistant should handle, such as meeting summaries, first drafts and document search.\n2. **Prepare the data.** Fix permissions and sensitivity labels on the files the assistant can reach,\n   because it answers from whatever the employee can already open.\n3. **License and train.** Assign licenses, give role specific training and recruit local champions who coach colleagues.\n4. **Measure use and outcome.** Track weekly active users from the usage dashboard, run a diary study or\n   time sampling for tasks, and compare with a control group where possible.\n5. **Check outputs and adjust.** Set rules for human review of anything external or consequential, then\n   reallocate licenses from people who do not use them.",[36,37],"employee-productivity","speed",[39,40,41,42],"employee-adoption","time-saved-per-task","hours-saved","productivity-gain",{"referenceOrg":44,"inputs":45,"formula":78,"currency":79,"period":80,"resultLabel":81,"caveat":82},"A company with 5,000 office employees where 3,000 hold an assistant license",[46,52,59,66,71],{"key":47,"label":48,"low":49,"high":49,"unit":50,"note":51},"licenses","Licensed employees",3000,"employees","The reference company.",{"key":53,"label":54,"low":55,"high":56,"unit":57,"note":58},"activeShare","Share of license holders using it weekly",0.5,0.8,"fraction of license holders","Conservative against the benchmarks on this page: around 64% weekly use at the UK Department for Business and Trade (its own evaluation), 90% active use at Vattenfall (its annual report) and 93% daily use at Lloyds Banking Group (Microsoft's figure), while the Australian Treasury found most participants used it two or three times a week or less.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"hoursPerWeek","Hours saved per active user per week",0.25,1,"hours per week","Editorial assumption. The DBT evaluation found small time savings, and UK government trial participants estimated 26 minutes a day, a self reported figure. Measure your own.",{"key":67,"label":68,"low":69,"high":69,"unit":67,"note":70},"weeks","Working weeks per year",44,"Editorial assumption.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"rate","Cost per working hour",30,60,"USD per hour","Editorial assumption for a loaded office staff cost, replace with your own.","licenses * activeShare * hoursPerWeek * weeks * rate","USD","per year","Value of time saved","Gross value of time, which only becomes a saving if the time is redeployed. It leaves out license cost, training, review of outputs and the DBT finding that time savings did not show up as improved productivity.",[],{"complexity":85,"complexityNote":86,"dataPrerequisites":87,"integrations":91},"medium","The software is bought, not built. The effort sits in cleaning up file permissions and labels, training, review rules for external content, and a measurement plan that goes beyond usage counts.",[88,89,90],"Current permissions and sensitivity labels on mail, files and sites","A list of target roles and the tasks the assistant should handle","A baseline of the time those tasks take today",[92,93,94,95],"Office suite and collaboration platform","Identity and access management","Usage analytics and license management","Data loss prevention and sensitivity labelling",{"steps":97,"guardrails":113,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":124},[98,101,104,107,110],{"title":99,"detail":100},"Pilot with a mix of volunteers and random users","The DBT pilot allocated about 70% of licenses to volunteers and 30% to a random sample, so the findings were less skewed toward enthusiasts.",{"title":102,"detail":103},"Fix access before launch","Review oversharing on files and sites. The assistant shows an employee what they are allowed to see, including things they never knew they could open.",{"title":105,"detail":106},"Train by role","The DBT evaluation found self led training raised satisfaction more than formal sessions. Lloyds trained more than 10,000 people and built a network of volunteer coaches.",{"title":108,"detail":109},"Measure beyond usage","Combine dashboard usage with task level time measurement and a control group, and separate the time saved from the productivity gained.",{"title":111,"detail":112},"Reallocate licenses","Review active use quarterly and move licenses from inactive holders to teams with a case.",[114,115,116,117],"Sensitivity labels and permissions reviewed before licenses are issued","Human review of any output sent outside the organization","Clear rules on what data may be entered and which tasks are out of scope","Logging and retention of assistant use for audit and legal hold","The employee owns every output. Managers review high stakes documents as before, and the program owner reviews usage and quality data every quarter.",[120,121,122,123],"Weekly active share of license holders","Time per task with and without the assistant, from observation or diaries","Reported hallucinations and corrections","Licenses reassigned per quarter",[125,128,131,134],{"title":126,"detail":127},"Licenses without use","At the Australian Treasury most participants used Copilot two or three times a week or less. Measure weekly use and reassign idle licenses.",{"title":129,"detail":130},"Time saved, no productivity gained","The DBT evaluation found no evidence that time savings improved productivity. Decide in advance where saved time goes.",{"title":132,"detail":133},"Output quality","Evaluations record hallucinations and uneven quality on slides and scheduling. Set review rules and exclude weak tasks.",{"title":135,"detail":136},"Oversharing","The assistant exposes files with loose permissions. Clean up access first.",{"euAiAct":138,"regulations":141,"guidance":147,"controls":154,"incidents":159},{"tier":139,"basis":140},"context-dependent","Drafting, summarizing and searching for employees is not an Annex III use. Article 50 puts the marking of generated content on the provider; the organization as deployer must disclose AI generated text only when it publishes it to inform the public on matters of public interest without human review or editorial responsibility (Article 50(4)), and Article 4 requires AI literacy for the staff who use it. It becomes high risk under Annex III point 4 when the organization uses it to recruit, evaluate or monitor employees, for example to screen candidates or draft performance ratings, so the organization must set and enforce the allowed tasks.",[142,143,144,145,146],"eu-ai-act","gdpr","uk-gdpr","iso-42001","nist-ai-rmf",[148],{"title":149,"issuer":150,"region":151,"url":152,"note":153},"Microsoft 365 Copilot Experiment, cross government findings report","Government Digital Service","europe","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fpublications\u002Fmicrosoft-365-copilot-experiment-cross-government-findings-report\u002Fmicrosoft-365-copilot-experiment-cross-government-findings-report-html","Findings from 20,000 UK government employees, including weaker results on complex work and concerns about unverified external sources.",[155,156,157,158],"License register with owner, purpose and review date","Data labelling and access review before rollout","Usage and quality reporting each quarter","Staff guidance on acceptable use",[],{"howToBuild":161},"Blits.ai does not replace an office suite assistant, which is built into the mail, document and meeting\ntools. It covers the assistants an organization builds for specific jobs next to it: an **AI agent** over a\n**knowledge base** with **hybrid retrieval** for policies and procedures, reachable in **Microsoft Teams**,\non the web and through the **REST API**, with **role based access control** over who manages which agents\nand content.\n\n**Guardrails** and **PII masking** keep sensitive data out of prompts, **test suites** run an evaluation set\non every change, and **analytics** show usage, top questions and the questions the agent could not\nanswer. The platform is model agnostic, so the organization picks the model per agent. Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.",[163,166,169],{"question":164,"answer":165},"How many employees actually use the assistant after a rollout?","The UK Department for Business and Trade reports that around 64% of license holders used Copilot at least once a week in a three month pilot, and Microsoft reports 93% daily usage among the 30,000 licensed colleagues at Lloyds Banking Group. The Australian Treasury found most participants used it two or three times a week or less.",{"question":167,"answer":168},"Does the assistant improve productivity?","The evidence is mixed. The DBT evaluation found small time savings, largest on written tasks, and no evidence that they led to improved productivity. In Microsoft's customer story, Ma'aden reports up to 2,200 hours saved in a month in its first phase, and UK government trial participants estimated an average of 26 minutes saved a day, a self reported figure.",{"question":170,"answer":171},"What should be done before issuing licenses?","Review file permissions and sensitivity labels, choose roles and tasks, plan training and decide how to measure results. The assistant answers from whatever the employee can already open.",[173,174,175,176],"enterprise-knowledge-search","meeting-summarization-and-action-items","civil-servant-drafting-copilot","email-and-ticket-reply-drafting","2026-11-12",[179],{"date":177,"note":180},"First published","workplace-ai-assistant-rollout",[183,220,250,271,296,323],{"title":184,"useCases":185,"organization":186,"vendors":192,"summary":196,"stage":197,"year":198,"channels":199,"languages":200,"metrics":201,"outcomeDisclosed":210,"sources":211,"verification":214,"grade":217,"id":218,"organizationSlug":219},"Vattenfall: Microsoft 365 Copilot rollout to 6,000 employees",[181],{"name":187,"anonymized":188,"country":189,"region":151,"industry":190,"wikidata":191},"Vattenfall",false,"SE","energy-and-utilities","Q157675",[193],{"name":194,"role":195},"Microsoft","platform","Vattenfall, the Swedish energy company, rolled out 6,000 Copilot licenses to employees and reports in its 2025 annual and sustainability report that 90 percent of licensed employees actively use the tool. The same report describes language model systems under development that let engineers get answers about earlier service and maintenance on hydro power plants. The report does not disclose a time saving or other outcome from Copilot.","scaled",2025,[26,27],[],[202],{"kpi":39,"value":203,"unit":204,"qualifier":205,"period":206,"claimant":207,"quote":208,"sourceUrl":209},90,"percent","exact","licensed employees who actively use Copilot, 2025 annual report","organization","Vattenfall has successfully rolled out 6,000 Copilot licenses, with 90 percent of licensed employees actively using the tool – an impressive adoption rate.","https:\u002F\u002Fgroup.vattenfall.com\u002Finvestors\u002Ffinancial-reports-and-presentations\u002Fannual-and-sustainability-report-2025-in-highlights\u002Fai-robotics-from-smart-tools-to-smarter-ways-of-working\u002F",true,[212],{"url":209,"title":213,"publisher":187},"AI & Robotics, from smart tools to smarter ways of working",{"level":215,"checkedAt":216},"source-verified","2026-11-11","B","vattenfall-copilot-rollout",null,{"title":221,"useCases":222,"organization":223,"vendors":228,"summary":230,"stage":231,"year":232,"channels":233,"languages":234,"metrics":236,"outcomeDisclosed":210,"sources":243,"verification":248,"grade":217,"id":249,"organizationSlug":219},"UK Department for Business and Trade: Microsoft 365 Copilot pilot with 1,000 licenses",[181],{"name":224,"anonymized":188,"country":225,"region":151,"industry":226,"wikidata":227},"Department for Business and Trade","GB","government","Q116726347",[229],{"name":194,"role":195},"The UK Department for Business and Trade gave 1,000 Microsoft 365 Copilot licenses to staff for a three month pilot from October to December 2024 and published its own evaluation in August 2025. Around 64% of license holders used Copilot at least once a week. The evaluation found small time savings on most tasks, largest on written work, extra time on others such as scheduling and image generation, and no evidence that time savings led to improved productivity. It also reported hallucinations and uneven quality checks.","pilot",2024,[26,27],[235],"en",[237],{"kpi":39,"value":238,"unit":204,"qualifier":239,"period":240,"claimant":207,"quote":241,"sourceUrl":242},64,"approximately","license holders using Copilot at least once a week, October to December 2024","This data implies around 64% of licence holders used M365 Copilot at least once a week, while around 30% used it at least once per working day.","https:\u002F\u002Fassets.publishing.service.gov.uk\u002Fmedia\u002F68adbe409e1cebdd2c96a19d\u002Fdbt-microsoft-365-copilot-evaluation.pdf",[244],{"url":242,"title":245,"publisher":246,"date":247},"The Evaluation of the M365 Copilot Pilot in the Department for Business and Trade","Department for Business and Trade (GOV.UK)","2025-08-01",{"level":215,"checkedAt":177},"uk-dbt-microsoft-365-copilot-pilot-evaluation",{"title":251,"useCases":252,"organization":253,"vendors":258,"summary":260,"stage":231,"year":232,"channels":261,"languages":262,"metrics":263,"outcomeDisclosed":210,"sources":264,"verification":269,"grade":217,"id":270,"organizationSlug":219},"Australian Treasury: 14 week Microsoft 365 Copilot trial with 218 staff",[181],{"name":254,"anonymized":188,"country":255,"region":256,"industry":226,"wikidata":257},"Department of the Treasury (Australia)","AU","asia-pacific","Q3277092",[259],{"name":194,"role":195},"The Australian Department of the Treasury ran a 14 week trial of Microsoft 365 Copilot from 20 May to 23 August 2024 with 218 staff, as part of the whole of government trial coordinated by the Digital Transformation Agency. The Australian Centre for Evaluation found a smooth technical implementation but usage lower than expected, with most participants using Copilot two or three times a week or less, and expectations that were not met. The evaluation did not measure time saved directly.",[26,27],[235],[],[265],{"url":266,"title":267,"publisher":254,"date":268},"https:\u002F\u002Fevaluation.treasury.gov.au\u002Fsites\u002Fevaluation.treasury.gov.au\u002Ffiles\u002F2025-02\u002Fevaluation-generative-artificial-intelligence-summary.pdf","Evaluation of a trial of generative artificial intelligence (Copilot) in the Department of the Treasury, summary report","2025-02-01",{"level":215,"checkedAt":177},"australia-treasury-copilot-trial-evaluation",{"title":272,"useCases":273,"organization":274,"vendors":276,"summary":278,"stage":231,"year":232,"channels":279,"languages":280,"metrics":281,"outcomeDisclosed":210,"sources":287,"verification":292,"grade":217,"id":294,"organizationSlug":295},"UK Government: cross government Microsoft 365 Copilot experiment with 20,000 government employees",[174,175,181],{"name":150,"anonymized":188,"country":225,"region":151,"industry":226,"wikidata":275},"Q5588795",[277],{"name":194,"role":195},"The Government Digital Service ran a trial of Microsoft 365 Copilot with 20,000 employees across UK government from 30 September to 31 December 2024. Copilot in Teams was the most used application throughout, with adoption peaking at 71%, and one participant named summarizing meeting notes among the tasks where it helped. Participants estimated an average saving of 26 minutes a day across all tasks; that figure is self reported, covers every Copilot use and is not recorded as a meeting metric. The report also notes weaker results on complex, nuanced or context heavy work, and concerns that Copilot relied on external sources without built in verification.",[26,27],[235],[282],{"kpi":39,"value":283,"unit":204,"qualifier":284,"period":285,"claimant":207,"quote":286,"sourceUrl":152},71,"up-to","peak share of trial users using Copilot in Teams, October to December 2024","Teams was the most popular tool for M365 Copilot and remained dominant throughout the experiment with a maximum adoption of 71%.",[288],{"url":152,"title":289,"publisher":290,"date":291},"Microsoft 365 Copilot Experiment: Cross-Government Findings Report","Government Digital Service (GOV.UK)","2025-06-02",{"level":215,"checkedAt":293},"2026-09-27","uk-government-m365-copilot-experiment","government-digital-service",{"title":297,"useCases":298,"organization":299,"vendors":303,"summary":305,"stage":197,"year":198,"channels":306,"languages":307,"metrics":308,"outcomeDisclosed":210,"sources":315,"verification":319,"grade":320,"id":321,"organizationSlug":322},"Lloyds Banking Group: Microsoft 365 Copilot for 30,000 colleagues",[181],{"name":300,"anonymized":188,"country":225,"region":151,"industry":301,"wikidata":302},"Lloyds Banking Group","banking","Q1152764",[304],{"name":194,"role":195},"Lloyds Banking Group started with 300 Microsoft 365 Copilot licenses in an early access program, let each department bid for licenses with a business case and measurable goals, and grew to 30,000 licenses, with a commitment in March 2025 to roll out 40,000. Microsoft's customer story reports that 93% of licensed colleagues use Copilot daily. The bank trained more than 10,000 people and built a network of about 1,000 volunteer coaches. Colleagues use it to draft and summarize emails and documents and to prepare for and record meetings.",[26,27],[235],[309],{"kpi":39,"value":310,"unit":204,"qualifier":205,"period":311,"claimant":312,"quote":313,"sourceUrl":314},93,"daily Copilot usage among colleagues with licenses","vendor","More than 10,000 people have now had training, and this has contributed to a 93% daily Copilot usage rate among the 30,000 colleagues with Copilot licenses.","https:\u002F\u002Fukstories.microsoft.com\u002Ffeatures\u002Flloyds-banking-group-using-ai-at-scale-to-transform-operations-and-employee-experience\u002F",[316],{"url":314,"title":317,"publisher":318},"Lloyds Banking Group using AI at scale to transform operations and employee experience","Microsoft UK Stories",{"level":215,"checkedAt":177},"C","lloyds-banking-group-microsoft-365-copilot-rollout","lloyds-banking-group",{"title":324,"useCases":325,"organization":326,"vendors":331,"summary":333,"stage":334,"year":198,"channels":335,"languages":336,"metrics":337,"outcomeDisclosed":210,"sources":344,"verification":351,"grade":320,"id":353,"organizationSlug":219},"Ma'aden: Microsoft 365 Copilot and Copilot Studio policy agents for employees",[173,174,181],{"name":327,"anonymized":188,"country":328,"region":329,"industry":190,"wikidata":330},"Ma'aden","SA","middle-east","Q6721006",[332],{"name":194,"role":195},"Ma'aden, the largest multi commodity mining and metals company in the Middle East, gave employees Microsoft 365 Copilot and built two Copilot Studio agents inside Microsoft Teams: one for governance documents and one for delegation of authority policies. Employees use Copilot to draft emails and presentations, summarize content, take meeting minutes and define actions, and pull figures out of Excel files. Ma'aden started with early adopter champions in each business unit, trained its executives first, and reports the time saved in the first phase of the rollout.","production",[26,27],[],[338],{"kpi":41,"value":339,"unit":340,"qualifier":284,"period":341,"claimant":207,"quote":342,"sourceUrl":343},2200,"hours","per month, first phase of the rollout","We have more than 36,600 interactions using Microsoft 365 Copilot and collectively save up to 2,200 hours in a month in the first phase alone","https:\u002F\u002Fwww.microsoft.com\u002Fen\u002Fcustomers\u002Fstory\u002F21514-maaden-microsoft-365-copilot",[345,347],{"url":343,"title":346,"publisher":194},"Ma'aden empowers employees with Microsoft 365 Copilot and Azure OpenAI Service, saving 2,200 hours monthly",{"url":348,"title":349,"publisher":350},"https:\u002F\u002Fwww.middleeastainews.com\u002Fp\u002Fmaaden-adopts-microsoft-copilot","Ma'aden saves 2,200 hours monthly with Copilot","Middle East AI News",{"level":215,"checkedAt":352},"2026-10-14","maaden-microsoft-365-copilot-and-policy-agents",0,[356,366],{"kpi":39,"label":357,"unit":204,"aggregate":210,"higherIsBetter":210,"n":358,"nUpTo":63,"median":203,"min":238,"max":310,"byClaimant":359,"vendorOnly":188,"points":361},"Employee adoption",3,{"organization":360,"vendor":63,"regulator":354,"independent":354},2,[362,363,364,365],{"evidenceId":321,"organization":300,"value":310,"qualifier":205,"claimant":312,"grade":320,"pooled":210},{"evidenceId":218,"organization":187,"value":203,"qualifier":205,"claimant":207,"grade":217,"pooled":210},{"evidenceId":294,"organization":150,"value":283,"qualifier":284,"claimant":207,"grade":217,"pooled":188},{"evidenceId":249,"organization":224,"value":238,"qualifier":239,"claimant":207,"grade":217,"pooled":210},{"kpi":41,"label":367,"unit":340,"aggregate":188,"higherIsBetter":210,"n":354,"nUpTo":63,"median":219,"min":219,"max":219,"byClaimant":368,"vendorOnly":188,"points":369},"Hours saved",{"organization":354,"vendor":354,"regulator":354,"independent":354},[370],{"evidenceId":353,"organization":327,"value":339,"qualifier":284,"claimant":207,"grade":320,"pooled":188},{"low":372,"high":373},495000,6336000,[375,423,449,479],{"slug":173,"title":376,"shortTitle":377,"definition":378,"status":9,"industries":379,"functions":385,"patterns":387,"audience":28,"autonomy":389,"adoptionStage":30,"evidenceCount":390,"publicEvidenceCount":390,"organizations":391,"bestGrade":418,"headline":419,"lastVerified":293,"indexable":210},"AI enterprise knowledge search for employees","Enterprise knowledge search","Enterprise knowledge search is an AI assistant that lets any employee ask a question in plain language and get a synthesized answer from the organization's own documents. It draws the answer from policies, procedures, product manuals and research, with citations to the source documents, and only from documents the employee is allowed to see.",[17,301,380,381,226,382,383,190,384],"wealth-and-asset-management","insurance","professional-services","telecommunications","travel-and-hospitality",[20,19,386],"customer-service",[24,388,23],"conversational-agent","assist",27,[392,393,394,395,396,397,398,399,400,401,402,403,404,327,405,406,407,408,409,410,411,412,413,414,415,416,417],"All Nippon Airways","Asan Medical Center","Bank of America","BBVA","Booking.com","Bank of the Philippine Islands","Cathay Financial Holdings","Crédit Agricole","DZ BANK AG","EDP","France Travail","Insurance Australia Group","Kuwait Finance House","Morgan Stanley","PLDT","Qatar Ministry of Municipality","Raiffeisen Bank International AG","Rivian","Saudi Energy","SIGNAL IDUNA","Sumitomo Mitsui Financial Group","SNCF","Taiwan Business Bank","Wells Fargo","Zain Jordan","Zillow Group","A",{"kpi":39,"label":357,"unit":204,"n":420,"nUpTo":354,"kind":421,"value":422,"qualifier":205,"claimant":219,"organization":219,"vendorReported":188},6,"median",86.5,{"slug":174,"title":424,"shortTitle":425,"definition":426,"status":9,"industries":427,"functions":429,"patterns":430,"audience":28,"autonomy":29,"adoptionStage":30,"evidenceCount":432,"publicEvidenceCount":432,"organizations":433,"bestGrade":217,"headline":444,"lastVerified":293,"indexable":210},"AI meeting summarization and action items","Meeting summaries and action items","AI meeting summarization and action items is AI that turns an internal or operational meeting into a summary, decisions and action items with owners and dates. It transcribes a team, project, board or case meeting, online or in person, with the participants' knowledge, for the organizer to check and share. It is the general purpose tool; client advice meetings and sales calls, which feed a regulated record or a sales pipeline, have their own pages.",[17,226,428,382],"technology",[20,19],[23,431],"speech-analytics",12,[434,435,436,437,438,327,439,440,441,442,150,443],"Cathay Pacific","Commercial Bank of Dubai","de Alliantie","U.S. Department of Labor","Localiza","Ministry of Justice","PA Consulting","Softcat","Trace3","University of Manchester",{"kpi":40,"label":445,"unit":446,"n":63,"nUpTo":354,"kind":447,"value":448,"qualifier":205,"claimant":207,"organization":436,"vendorReported":188},"Time saved per task","minutes","reported",120,{"slug":175,"title":450,"shortTitle":451,"definition":452,"status":9,"industries":453,"functions":454,"patterns":457,"audience":28,"autonomy":29,"adoptionStage":458,"evidenceCount":459,"publicEvidenceCount":459,"organizations":460,"bestGrade":418,"headline":476,"lastVerified":293,"indexable":210},"AI drafting copilot for civil servants for correspondence, briefings and ministerial replies","Civil servant drafting copilot","A civil servant drafting copilot is a generative AI assistant that drafts replies to correspondence from the public and elected representatives, briefings, submissions and summaries for civil servants. It is grounded in the department's approved lines, policy documents and case data, with the official editing and approving every word before it is sent or cleared.",[226],[455,20,456],"citizen-services","case-management",[22,24,23],"early-adopters",17,[461,462,463,464,465,466,467,468,402,469,470,471,472,473,474,475,150],"Land Baden-Württemberg","Cabinet Office (Government Communication Service)","Centers for Disease Control and Prevention","Crown Prosecution Service","Department for Education","Direction générale des Finances publiques (DGFiP)","DINUM (Direction interministerielle du numerique)","Federal Emergency Management Agency","Government Technology Agency of Singapore","Freie und Hansestadt Hamburg","Home Team Science and Technology Agency","Digital Agency of Japan","The Office for Standards in Education, Children's Services and Skills","Queensland Government","Department for Science, Innovation and Technology (Incubator for Artificial Intelligence)",{"kpi":39,"label":357,"unit":204,"n":63,"nUpTo":63,"kind":447,"value":477,"qualifier":478,"claimant":207,"organization":469,"vendorReported":188},50,"at-least",{"slug":176,"title":480,"shortTitle":481,"definition":482,"status":9,"industries":483,"functions":484,"patterns":485,"audience":28,"autonomy":29,"adoptionStage":30,"evidenceCount":432,"publicEvidenceCount":432,"organizations":487,"bestGrade":217,"headline":499,"lastVerified":293,"indexable":210},"AI reply drafting for customer email and support tickets","Email and ticket reply drafting","AI reply drafting for customer email and support tickets is a copilot that drafts the reply to an incoming email, message or ticket once it reaches a service agent. It summarizes the request, pulls the relevant customer data and approved knowledge, and drafts the reply in the organization's tone and the customer's language for the agent to check, edit and send. Live calls and chats, and the sorting of the inbox itself, are separate use cases.",[17,226,301,383,428],[386,19],[22,23,24,486],"classification-and-routing",[463,488,489,490,491,492,493,494,495,496,497,498],"Deutsche Kreditbank AG (DKB)","First National Bank","Geberit","HYPE","LCL","Les Affaires","L'Oréal","Nomad eSIM","Transportation Security Administration","Turing","Verkehrsbetriebe Zürich (VBZ)",{"kpi":500,"label":501,"unit":204,"n":358,"nUpTo":354,"kind":421,"value":477,"qualifier":205,"claimant":312,"organization":219,"vendorReported":210},"processing-time-reduction","Cycle time reduction",{"indexable":210,"reasons":503},[],[505,511,516,523,530,536,542,548,555,562,568,574,581,587,594,600,606,612,617,623,629,634,640,645,651,658,665,670,675,682,688,694,701,707,712,718,723],{"id":142,"label":506,"issuer":507,"region":151,"url":508,"description":509,"useCases":510,"indexable":210},"EU AI Act","European Union","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",306,{"id":143,"label":512,"issuer":507,"region":151,"url":513,"description":514,"useCases":515,"indexable":210},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",276,{"id":145,"label":517,"issuer":518,"region":519,"url":520,"description":521,"useCases":522,"indexable":210},"ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",149,{"id":146,"label":524,"issuer":525,"region":526,"url":527,"description":528,"useCases":529,"indexable":210},"NIST AI Risk Management Framework","NIST","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",114,{"id":144,"label":531,"issuer":532,"region":151,"url":533,"description":534,"useCases":535,"indexable":210},"UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",87,{"id":537,"label":538,"issuer":507,"region":151,"url":539,"description":540,"useCases":541,"indexable":210},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":543,"label":544,"issuer":545,"region":151,"url":546,"description":547,"useCases":477,"indexable":210},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",{"id":549,"label":550,"issuer":551,"region":256,"url":552,"description":553,"useCases":554,"indexable":210},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":556,"label":557,"issuer":558,"region":256,"url":559,"description":560,"useCases":561,"indexable":210},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":563,"label":564,"issuer":507,"region":151,"url":565,"description":566,"useCases":567,"indexable":210},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":569,"label":570,"issuer":571,"region":519,"url":572,"description":573,"useCases":567,"indexable":210},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":575,"label":576,"issuer":577,"region":526,"url":578,"description":579,"useCases":580,"indexable":210},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",21,{"id":582,"label":583,"issuer":584,"region":526,"url":585,"description":586,"useCases":580,"indexable":210},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":588,"label":589,"issuer":590,"region":151,"url":591,"description":592,"useCases":593,"indexable":210},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":595,"label":596,"issuer":597,"region":526,"url":598,"description":599,"useCases":459,"indexable":210},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":601,"label":602,"issuer":507,"region":151,"url":603,"description":604,"useCases":605,"indexable":210},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",14,{"id":607,"label":608,"issuer":609,"region":519,"url":610,"description":611,"useCases":605,"indexable":210},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":613,"label":614,"issuer":507,"region":151,"url":615,"description":616,"useCases":605,"indexable":210},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":618,"label":619,"issuer":620,"region":519,"url":621,"description":622,"useCases":605,"indexable":210},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":624,"label":625,"issuer":507,"region":151,"url":626,"description":627,"useCases":628,"indexable":210},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",13,{"id":630,"label":631,"issuer":507,"region":151,"url":632,"description":633,"useCases":628,"indexable":210},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":635,"label":636,"issuer":637,"region":526,"url":638,"description":639,"useCases":432,"indexable":210},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",{"id":641,"label":642,"issuer":507,"region":151,"url":643,"description":644,"useCases":432,"indexable":210},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":646,"label":647,"issuer":507,"region":151,"url":648,"description":649,"useCases":650,"indexable":210},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",11,{"id":652,"label":653,"issuer":654,"region":526,"url":655,"description":656,"useCases":657,"indexable":210},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",10,{"id":659,"label":660,"issuer":661,"region":151,"url":662,"description":663,"useCases":664,"indexable":210},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":666,"label":667,"issuer":507,"region":151,"url":668,"description":669,"useCases":664,"indexable":210},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":671,"label":672,"issuer":551,"region":256,"url":673,"description":674,"useCases":664,"indexable":210},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":676,"label":677,"issuer":678,"region":526,"url":679,"description":680,"useCases":681,"indexable":210},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",7,{"id":683,"label":684,"issuer":507,"region":151,"url":685,"description":686,"useCases":687,"indexable":210},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",5,{"id":689,"label":690,"issuer":691,"region":151,"url":692,"description":693,"useCases":687,"indexable":210},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":695,"label":696,"issuer":697,"region":256,"url":698,"description":699,"useCases":700,"indexable":210},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",4,{"id":702,"label":703,"issuer":704,"region":329,"url":705,"description":706,"useCases":700,"indexable":210},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":708,"label":709,"issuer":551,"region":256,"url":710,"description":711,"useCases":700,"indexable":210},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":713,"label":714,"issuer":715,"region":151,"url":716,"description":717,"useCases":700,"indexable":210},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":719,"label":720,"issuer":507,"region":151,"url":721,"description":722,"useCases":358,"indexable":210},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":724,"label":725,"issuer":726,"region":526,"url":727,"description":728,"useCases":358,"indexable":210},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1794572851510]