[{"data":1,"prerenderedAt":682},["ShallowReactive",2],{"uc-civil-servant-drafting-copilot":3,"uc-regulations":475},{"useCase":4,"evidence":192,"blitsAiDeployments":351,"benchmarks":352,"indicative":367,"related":370,"indexability":473,"includeUnpublished":198},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":26,"audience":30,"autonomy":31,"adoptionStage":32,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":40,"indicativeValue":46,"macroEstimates":76,"feasibility":82,"implementation":94,"risk":140,"blitsAi":169,"faq":171,"related":181,"datePublished":187,"dateModified":187,"lastVerified":187,"changelog":188,"slug":191},"AI drafting copilot for civil servants for correspondence, briefings and ministerial replies","Civil servant drafting copilot","AI drafting assistant for civil servants","AI drafts replies, briefings and summaries that civil servants edit and clear. UK Cabinet Office Assist users report saving about 3 hours a week.","published","A generative AI assistant that drafts replies to correspondence from the public and elected representatives, briefings, submissions and summaries for civil servants, 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.",[12,13,14,15],"ministerial correspondence drafting AI","government briefing drafting assistant","civil service writing assistant","public sector document drafting copilot",[17],"government",[19,20,21],"citizen-services","knowledge-management","case-management",[23,24,25],"content-generation","rag-knowledge-assistant","summarization",[27,28,29],"internal-tools","email","microsoft-teams","employee-facing","copilot","emerging","A large share of civil service time goes into writing: replies to letters and emails from the public\nand from members of parliament, ministerial correspondence, briefings for ministers and senior\nofficials, submissions, meeting notes and summaries of long documents. Much of it follows known\npatterns. A correspondence officer finds the current approved lines in a briefing pack, adapts them\nto the question and routes the draft for clearance; a policy official condenses a stack of papers\ninto a two page brief.\n\nThe work is slow and uneven. Finding the right, current line takes time, service level targets for\nreplies are missed when volumes spike, and quality depends on who drafts. Generative AI can produce a\ngood first draft in seconds, but in government a fluent draft that states a superseded policy, gets a\ncase fact wrong or sounds careless in a ministerial letter is a real problem, so the design has to\nkeep drafts grounded in approved sources and every word owned by an official.",[],"1. **Start from the request.** The official pastes or forwards the incoming letter, or selects the\n   documents to be summarised or briefed on.\n2. **Retrieve approved content.** The assistant searches the department's approved standard lines,\n   briefing packs, policy documents and, for casework, the relevant case record.\n3. **Draft in house style.** It produces a first draft in the right template (reply letter,\n   briefing, submission) with the tone set for the audience, and cites the sources it used.\n4. **Edit and clear.** The official checks facts against the sources, edits the draft and sends it\n   through the normal clearance and approval route; nothing is sent automatically.\n5. **Learn from edits.** Feedback and edits show which lines are missing or outdated, so owners\n   update the approved content rather than the prompt.",[37,38,39],"employee-productivity","speed","customer-experience",[41,42,43,44,45],"productivity-gain","hours-saved","time-saved-per-task","users-served","response-time-reduction",{"referenceOrg":47,"inputs":48,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A ministry that sends 40,000 replies and briefings a year",[49,57,64],{"key":50,"label":51,"low":52,"high":53,"unit":54,"note":55,"sourceUrl":56},"drafts","Replies, briefings and summaries drafted per year",20000,60000,"documents per year","Editorial assumption. For scale, the UK Department for Education says its correspondence teams handle about 1,000 external queries a month that need a reply.","https://www.gov.uk/algorithmic-transparency-records/dfe-correspondence-drafter",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63,"sourceUrl":56},"minutesSaved","Minutes saved per document after human review",10,20,"minutes per document","Editorial assumption, replace with your own. The Department for Education expects its tool to cut drafting from about 30 minutes to about a minute (a calculation made before user testing), and user research for Cabinet Office Assist found users save about 3 hours a week; review, fact checking and clearance still take time.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"hourlyCost","Fully loaded cost of an official's hour",40,60,"EUR per hour","Editorial assumption. Replace with your own staff cost.","drafts * minutesSaved / 60 * hourlyCost","EUR","per year","Drafting time released","Time released, not cash saved, unless headcount or contractor spend changes. It leaves out the cost of licences and assurance, the value of faster replies to citizens and the risk cost of errors that slip through review.",[77],{"statement":78,"sourceTitle":79,"sourceUrl":80,"year":81},"The Alan Turing Institute estimates that AI could support up to 41% of tasks across the UK public sector, according to the UK government.","Landmark government trial shows AI could save civil servants nearly 2 weeks a year","https://www.gov.uk/government/news/landmark-government-trial-shows-ai-could-save-civil-servants-nearly-2-weeks-a-year",2025,{"complexity":83,"complexityNote":84,"dataPrerequisites":85,"integrations":89},"low","A drafting assistant over approved documents is quick to build. The hard parts are organisational: keeping standard lines current and owned, agreeing what may be pasted in at which security classification, and fitting drafts into existing clearance workflows.",[86,87,88],"Current approved standard lines and briefing packs, each with an owner and review date","Templates and style guides for letters, briefings and submissions","For casework replies, read access to the case record under the right permissions",[90,91,92,93],"Correspondence management or case management system","Document management (for example SharePoint) for approved content","Email and Microsoft Teams","Identity and access management for security classifications",{"steps":95,"guardrails":111,"humanInTheLoop":117,"kpisToInstrument":118,"failureModes":124},[96,99,102,105,108],{"title":97,"detail":98},"Start with high volume correspondence on stable lines","Pick a correspondence stream where replies mostly reuse approved lines, as the Department for Education did with its briefing packs, before moving to ministerial submissions.",{"title":100,"detail":101},"Clean up the source of truth","Put every standard line and briefing pack under an owner with a review date, and remove superseded versions; the assistant is only as current as this library.",{"title":103,"detail":104},"Fit the existing clearance route","Keep drafts inside the current approval workflow, as the Crown Prosecution Service did with its multi layer review, rather than creating a side channel.",{"title":106,"detail":107},"Mark AI content and require citations","Show which text is generated and which source each statement came from, so reviewers can check quickly.",{"title":109,"detail":110},"Measure time and quality together","Track drafting time, clearance rework and complaints or corrections after sending, not only user satisfaction.",[112,113,114,115,116],"Drafts only; nothing is sent, published or cleared without an official's approval","Answers grounded in approved lines and case data, with refusal when the library has no answer","Security classification limits enforced on what can be entered and on the model endpoint used","Personal data in correspondence masked in logs and prompts","No political or policy positions beyond the approved lines","The official who owns the reply or briefing edits and approves it, and the normal clearance chain applies. Content owners keep the standard lines current, and a sample of sent replies is reviewed for accuracy and tone.",[119,120,121,122,123],"Drafting time per document, before and after","Share of drafts sent with minor edits only","Replies within service level targets","Corrections, complaints or follow ups caused by errors in replies","Active users as a share of those with access",[125,128,131,134,137],{"title":126,"detail":127},"Superseded lines in fluent prose","The assistant confidently repeats an outdated policy position. Prevent it with owned, dated content and by removing old versions from the index.",{"title":129,"detail":130},"Rubber stamp review","Busy officials approve drafts without reading them properly. Show sources, sample sent replies and keep accountability with the named official.",{"title":132,"detail":133},"Sensitive data in the wrong tool","Staff paste classified or personal information into a tool not cleared for it. Provide a sanctioned tool at the right classification so they have no reason to use public ones.",{"title":135,"detail":136},"Tone that damages trust","Replies that sound generic or evasive to a distressed constituent or a member of parliament. Tune templates by audience and review high profile correspondence closely.",{"title":138,"detail":139},"A general chatbot overtaken by enterprise suites","i.AI withdrew its Redbox assistant at the end of 2025 after tools such as Microsoft Copilot offered similar functionality, and it reports that most interactions were general drafting and chat. Build where a bespoke tool adds value that a suite does not, such as approved lines, case data and clearance workflows.",{"euAiAct":141,"regulations":144,"guidance":150,"controls":162,"incidents":168},{"tier":142,"basis":143},"minimal","An internal drafting assistant that an official reviews is not listed in Annex III. Article 50(4) requires disclosure of AI generated text published to inform the public on matters of public interest, unless it has undergone human review and a person holds editorial responsibility, which this design provides. If the tool is used to evaluate eligibility for public assistance benefits or services rather than to draft, Annex III point 5(a) can apply.",[145,146,147,148,149],"eu-ai-act","gdpr","iso-42001","uk-gdpr","uk-atrs",[151,157],{"title":152,"issuer":153,"region":154,"url":155,"note":156},"Algorithmic Transparency Recording Standard hub","UK government","europe","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","UK departments publish transparency records for drafting tools such as Assist, the Department for Education's Correspondence Drafter and the now withdrawn Redbox.",{"title":158,"issuer":159,"region":154,"url":160,"note":161},"Public attitudes to the use of AI in DfT consultations and correspondence","Department for Transport","https://www.gov.uk/government/publications/public-attitudes-to-the-use-of-ai-in-dft-consultations-and-correspondence","Research on how the public views AI support for drafting replies to their correspondence.",[163,164,165,166,167],"Approved content library with named owners and review dates","Transparency record and staff guidance on permitted use","Security classification controls on inputs and model endpoints","Audit log of drafts, edits and the approving official","Periodic quality sampling of sent correspondence",[],{"howToBuild":170},"On Blits.ai this is an **AI agent** with a **knowledge base** of approved standard lines and\nbriefing packs, retrieved with **hybrid search** so that exact policy wording is found as well as\nrelated content. Approved lines and briefing packs are uploaded to the **document library** with\nversion control, so owners can replace a line and revert if needed; SharePoint is also available\nas a ready made tool for the agent. **Custom functions** read the case record for\ncasework replies, and the agent is instructed to draft in the department's templates and name\nthe documents it drew on.\n\nOfficials use it in **Microsoft Teams**, the web chat or through the **email channel**. **PII\nmasking** runs at the gateway before text reaches a model, and **guardrails** stop the agent from\nanswering outside the approved lines. Flagged real conversations can be added to **test suites**,\nwhich the team runs before publishing a content or prompt change. The platform is **model\nagnostic** and offers EU and UAE data residency, so a\ndepartment can choose the model and region that fits its security classification.",[172,175,178],{"question":173,"answer":174},"How much time does AI drafting save civil servants?","Reported figures vary by task. User research for Cabinet Office Assist found that users save about 3 hours a week on average, and participants in the UK's cross government Copilot trial estimated 26 minutes a day across all tasks (a self reported figure). The Department for Education calculated, before user testing, that its correspondence tool would be 30 times quicker than a manual process of about 30 minutes.",{"question":176,"answer":177},"Can AI send replies to the public on its own?","It should not in this design. Every tool on this page drafts for an official who edits and approves, and replies go through the normal clearance route.",{"question":179,"answer":180},"Is this high risk under the EU AI Act?","Not as designed here. Internal drafting assistance is not listed in Annex III. If the tool is used to evaluate eligibility for essential public assistance benefits and services rather than to draft, Annex III point 5(a) can apply. Public bodies should still tell people how AI is used and keep a named official responsible for what is sent.",[182,183,184,185,186],"correspondence-triage-and-routing","freedom-of-information-request-processing","public-consultation-response-analysis","outbound-notice-drafting","email-and-ticket-reply-drafting","2026-09-27",[189],{"date":187,"note":190},"First published","civil-servant-drafting-copilot",[193,234,263,281,322],{"title":194,"useCases":195,"organization":196,"vendors":200,"summary":207,"stage":208,"year":209,"channels":210,"languages":211,"metrics":213,"outcomeDisclosed":222,"sources":223,"verification":228,"grade":231,"id":232,"organizationSlug":233},"UK Cabinet Office: Assist, a generative AI tool for government communicators",[191],{"name":197,"anonymized":198,"country":199,"region":154,"industry":17},"Cabinet Office (Government Communication Service)",false,"GB",[201,204],{"name":202,"role":203},"Anthropic","model-provider",{"name":205,"role":206},"Amazon Web Services (Amazon Bedrock)","platform","Assist is a bespoke generative AI tool for members of the UK government communications profession. It offers pre built prompts for typical communications tasks, drafts first versions, helps with brainstorming and review, and can ground its answers in Government Communications policies and standards through retrieval. It is not to be used for decisions, and users remain responsible for every output. It is in production and was open to about 8,500 members of the profession in July 2026. The transparency record reports that users save around 3 hours a week on average and that 98% of users find it useful in their role, both from user research.","production",2026,[27],[212],"en",[214],{"kpi":42,"value":215,"unit":216,"qualifier":217,"period":218,"claimant":219,"quote":220,"sourceUrl":221},3,"hours","approximately","per user per week, from user research","organization","User research shows that Assist users on average save around 3 hours per week by using the tool.","https://www.gov.uk/algorithmic-transparency-records/cabinet-office-assist",true,[224],{"url":221,"title":225,"publisher":226,"date":227},"Cabinet Office: Assist (algorithmic transparency record)","GOV.UK","2026-07-30",{"level":229,"checkedAt":230},"source-verified","2026-09-26","B","cabinet-office-assist-government-communications",null,{"title":235,"useCases":236,"organization":237,"vendors":239,"summary":245,"stage":246,"year":81,"channels":247,"languages":248,"metrics":249,"outcomeDisclosed":222,"sources":257,"verification":261,"grade":231,"id":262,"organizationSlug":233},"Crown Prosecution Service: Correspondence Drafting Tool for letters and emails from case data",[191],{"name":238,"anonymized":198,"country":199,"region":154,"industry":17},"Crown Prosecution Service",[240,243],{"name":241,"role":242},"NTT Data UK Limited","integrator",{"name":244,"role":203},"OpenAI (via Microsoft Azure)","The Crown Prosecution Service sends large volumes of letters and emails to people involved in prosecutions. Its Correspondence Drafting Tool pulls key information from the case management system into standard CPS templates and uses a large language model to summarise and pre populate the mandatory content, which authors then refine for the specific case. A built in workflow routes every letter through review and approval by trained staff before it is sent, and the tool plays no part in prosecution decisions. It replaced manual drafting in Word, where copying information by hand could introduce errors, and was in beta with 30 users.","pilot",[27,28],[212],[250],{"kpi":44,"value":251,"unit":252,"qualifier":253,"period":254,"claimant":219,"quote":255,"sourceUrl":256},30,"count","exact","beta phase","Currently in beta phase, being used by small group of users (30 users) the number of users will increase as the tool is implemented.","https://www.gov.uk/algorithmic-transparency-records/the-crown-prosecution-service-correspondence-drafting-tool",[258],{"url":256,"title":259,"publisher":226,"date":260},"The Crown Prosecution Service: Correspondence Drafting Tool (algorithmic transparency record)","2025-08-28",{"level":229,"checkedAt":230},"crown-prosecution-service-correspondence-drafting-tool",{"title":264,"useCases":265,"organization":266,"vendors":268,"summary":271,"stage":246,"year":81,"channels":272,"languages":273,"metrics":274,"outcomeDisclosed":198,"sources":275,"verification":279,"grade":231,"id":280,"organizationSlug":233},"UK Department for Education: Correspondence Drafter for replies to external queries",[191],{"name":267,"anonymized":198,"country":199,"region":154,"industry":17},"Department for Education",[269],{"name":270,"role":203},"Microsoft (Azure OpenAI Service)","The Department for Education's correspondence teams paste an incoming query into the Correspondence Drafter, which retrieves the relevant passages from the department's core briefing packs of approved standard lines and drafts a reply in email form, with options to adjust tone and audience. Staff edit and quality assure every draft before sending. The department says the new process has been calculated to be 30 times quicker than searching briefing packs and copying standard lines by hand, which took about 30 minutes per reply, and expects the final phase to cover about 800 of the roughly 1,000 external queries a month that need a reply. The record describes a private beta that had not yet entered user testing, so no measured result is published.",[27,28],[212],[],[276],{"url":56,"title":277,"publisher":226,"date":278},"DfE: Correspondence Drafter (algorithmic transparency record)","2025-12-16",{"level":229,"checkedAt":230},"department-for-education-correspondence-drafter",{"title":282,"useCases":283,"organization":284,"vendors":286,"summary":290,"stage":291,"year":81,"channels":292,"languages":293,"metrics":294,"outcomeDisclosed":222,"sources":300,"verification":320,"grade":231,"id":321,"organizationSlug":233},"UK government i.AI: Redbox assistant for summarising and redrafting official documents",[191],{"name":285,"anonymized":198,"country":199,"region":154,"industry":17},"Department for Science, Innovation and Technology (Incubator for Artificial Intelligence)",[287],{"name":288,"role":289},"Incubator for Artificial Intelligence (i.AI)","in-house","Redbox was an assistant built by the UK government's Incubator for Artificial Intelligence (i.AI) that let civil servants chat with large language models, with or without their own documents, for work up to OFFICIAL SENSITIVE, and choose between models. It returned summaries, redrafts or translations of material users provided and did not search the internet. In beta in the Cabinet Office, No 10 and DSIT it had about 2,000 users in February 2025, growing by approximately 150 a week. i.AI later decided on a controlled shutdown: tools such as Microsoft Copilot, whose chat became freely available to many departments, offered similar functionality, and the Cabinet Office moved to an enterprise Gemini option, so the service was run only until the end of 2025. In its lessons learned post i.AI reports that around 70% of interactions were general requests such as drafting emails and brainstorming. The code is open source, and the Department for Business and Trade built its own adapted version.","paused",[27],[212],[295],{"kpi":44,"value":296,"unit":252,"qualifier":217,"period":297,"claimant":219,"quote":298,"sourceUrl":299},2000,"February 2025, Cabinet Office, No 10 and DSIT","As of February 2025 Redbox is used by 2,000 Civil Servants across the Cabinet Office, No10 and DSIT.","https://www.gov.uk/algorithmic-transparency-records/dsit-redbox",[301,305,309,312,316],{"url":302,"title":303,"publisher":288,"date":304},"https://ai.gov.uk/blogs/redbox-reflections-5-key-lessons-from-building-and-sunsetting-our-government-ai-chatbot/","Redbox Reflections: 5 Key Lessons from Building (and Sunsetting) Our Government AI Chatbot","2025-10-23",{"url":306,"title":307,"publisher":308},"https://github.com/i-dot-ai/redbox","i-dot-ai/redbox (source code repository, archived)","Incubator for Artificial Intelligence (GitHub)",{"url":299,"title":310,"publisher":226,"date":311},"DSIT: Redbox (algorithmic transparency record)","2025-04-28",{"url":313,"title":314,"publisher":226,"date":315},"https://www.gov.uk/algorithmic-transparency-records/dbt-redbox","DBT: Redbox (algorithmic transparency record)","2025-10-16",{"url":317,"title":318,"publisher":319},"https://github.com/uktrade/redbox","uktrade/redbox (source code repository)","Department for Business and Trade (GitHub)",{"level":229,"checkedAt":230},"uk-cabinet-office-redbox-civil-service-assistant",{"title":323,"useCases":324,"organization":326,"vendors":328,"summary":331,"stage":246,"year":332,"channels":333,"languages":334,"metrics":335,"outcomeDisclosed":222,"sources":344,"verification":349,"grade":231,"id":350,"organizationSlug":233},"UK Government: cross government Microsoft 365 Copilot experiment with 20,000 government employees",[325,191],"meeting-summarization-and-action-items",{"name":327,"anonymized":198,"country":199,"region":154,"industry":17},"Government Digital Service",[329],{"name":330,"role":206},"Microsoft","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 summarising 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.",2024,[29,27],[212],[336],{"kpi":337,"value":338,"unit":339,"qualifier":340,"period":341,"claimant":219,"quote":342,"sourceUrl":343},"employee-adoption",71,"percent","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%.","https://www.gov.uk/government/publications/microsoft-365-copilot-experiment-cross-government-findings-report/microsoft-365-copilot-experiment-cross-government-findings-report-html",[345],{"url":343,"title":346,"publisher":347,"date":348},"Microsoft 365 Copilot Experiment: Cross-Government Findings Report","Government Digital Service (GOV.UK)","2025-06-02",{"level":229,"checkedAt":187},"uk-government-m365-copilot-experiment",0,[353,361],{"kpi":44,"label":354,"unit":252,"aggregate":198,"higherIsBetter":222,"n":355,"nUpTo":351,"median":356,"min":251,"max":296,"byClaimant":357,"vendorOnly":198,"points":358},"Users served",2,1015,{"organization":355,"vendor":351,"regulator":351,"independent":351},[359,360],{"evidenceId":321,"organization":285,"value":296,"qualifier":217,"claimant":219,"grade":231,"pooled":222},{"evidenceId":262,"organization":238,"value":251,"qualifier":253,"claimant":219,"grade":231,"pooled":222},{"kpi":42,"label":362,"unit":216,"aggregate":198,"higherIsBetter":222,"n":363,"nUpTo":351,"median":215,"min":215,"max":215,"byClaimant":364,"vendorOnly":198,"points":365},"Hours saved",1,{"organization":363,"vendor":351,"regulator":351,"independent":351},[366],{"evidenceId":232,"organization":197,"value":215,"qualifier":217,"claimant":219,"grade":231,"pooled":222},{"low":368,"high":369},133333.33333333334,1200000,[371,402,417,433,456],{"slug":182,"title":372,"shortTitle":373,"definition":374,"status":9,"industries":375,"functions":379,"patterns":382,"audience":385,"autonomy":386,"adoptionStage":387,"segment":385,"evidenceCount":388,"publicEvidenceCount":388,"organizations":389,"bestGrade":231,"headline":396,"lastVerified":187,"indexable":222},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[376,377,378,17],"cross-industry","banking","insurance",[380,381,21],"operations","customer-service",[383,384,25],"classification-and-routing","document-processing","back-office","supervised-agent","mainstream",6,[390,391,392,393,394,395],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":397,"label":398,"unit":339,"n":363,"nUpTo":351,"kind":399,"value":400,"qualifier":253,"claimant":401,"organization":394,"vendorReported":222},"accuracy","Accuracy","reported",91,"vendor",{"slug":183,"title":403,"shortTitle":404,"definition":405,"status":9,"industries":406,"functions":407,"patterns":409,"audience":30,"autonomy":31,"adoptionStage":410,"evidenceCount":411,"publicEvidenceCount":411,"organizations":412,"bestGrade":231,"headline":233,"lastVerified":230,"indexable":222},"AI for freedom of information request processing","Freedom of information requests","AI that helps a public body handle freedom of information and open government requests: logging and clarifying requests, spotting duplicates, searching and deduplicating the records in scope, proposing redactions with the exemption that applies, and drafting the response letter, with an FOI officer deciding what is released.",[17],[19,408,21],"legal",[384,383,23],"early-adopters",4,[413,414,415,416],"U.S. Department of Justice","U.S. Food and Drug Administration, Center for Drug Evaluation and Research","Provincie Noord-Holland","U.S. Department of the Interior",{"slug":184,"title":418,"shortTitle":419,"definition":420,"status":9,"industries":421,"functions":422,"patterns":424,"audience":385,"autonomy":31,"adoptionStage":410,"evidenceCount":425,"publicEvidenceCount":425,"organizations":426,"bestGrade":231,"headline":430,"lastVerified":187,"indexable":222},"AI for public consultation response analysis","Consultation response analysis","AI that reads every free text response to a public consultation or rulemaking comment period, proposes themes, maps each response to the themes that officials have validated, flags duplicates, campaign letters and responses that need special attention, and produces counts and summaries for the analysts who write the government's response.",[17],[19,423],"analytics-and-reporting",[25,383,23],5,[427,428,159,285,429],"Centers for Disease Control and Prevention","Board of Governors of the Federal Reserve System","U.S. Department of Transportation, Office of the Secretary",{"kpi":397,"label":398,"unit":339,"n":363,"nUpTo":351,"kind":399,"value":431,"qualifier":432,"claimant":219,"organization":159,"vendorReported":198},92,"at-least",{"slug":185,"title":434,"shortTitle":435,"definition":436,"status":9,"industries":437,"functions":440,"patterns":444,"audience":30,"autonomy":31,"adoptionStage":410,"segment":385,"evidenceCount":425,"publicEvidenceCount":425,"organizations":446,"bestGrade":231,"headline":451,"lastVerified":230,"indexable":222},"AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[376,377,378,17,438,439],"healthcare","wealth-and-asset-management",[380,381,441,442,443],"collections-and-recovery","regulatory-compliance","claims",[23,24,445],"translation",[447,448,449,450],"Acentra Health","Hiscox","Health Resources and Services Administration","SS&C Technologies",{"kpi":452,"label":453,"unit":339,"n":363,"nUpTo":351,"kind":399,"value":454,"qualifier":253,"claimant":401,"organization":455,"vendorReported":222},"processing-time-reduction","Cycle time reduction",25,"SS&C GIDS and RS",{"slug":186,"title":457,"shortTitle":458,"definition":459,"status":9,"industries":460,"functions":463,"patterns":464,"audience":30,"autonomy":31,"adoptionStage":387,"evidenceCount":388,"publicEvidenceCount":388,"organizations":465,"bestGrade":231,"headline":471,"lastVerified":187,"indexable":222},"AI reply drafting for customer email and support tickets","Email and ticket reply drafting","A copilot for asynchronous service work that drafts the reply to an incoming customer email, message or ticket once it has reached an agent: it summarizes the request, pulls the relevant customer data and approved knowledge, and drafts a 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.",[376,17,377,461,462],"telecommunications","technology",[381,380],[23,25,24,383],[427,466,467,468,469,470],"First National Bank","HYPE","Nomad eSIM","Transportation Security Administration","Turing",{"kpi":452,"label":453,"unit":339,"n":355,"nUpTo":351,"kind":399,"value":472,"qualifier":217,"claimant":401,"organization":467,"vendorReported":222},50,{"indexable":222,"reasons":474},[],[476,482,487,494,502,508,514,521,529,535,541,547,552,559,565,570,577,583,589,595,601,607,612,617,622,629,636,641,646,653,659,665,671,676],{"id":145,"label":477,"issuer":478,"region":154,"url":479,"description":480,"useCases":481,"indexable":222},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":146,"label":483,"issuer":478,"region":154,"url":484,"description":485,"useCases":486,"indexable":222},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":147,"label":488,"issuer":489,"region":490,"url":491,"description":492,"useCases":493,"indexable":222},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":495,"label":496,"issuer":497,"region":498,"url":499,"description":500,"useCases":501,"indexable":222},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":503,"label":504,"issuer":478,"region":154,"url":505,"description":506,"useCases":507,"indexable":222},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":148,"label":509,"issuer":510,"region":154,"url":511,"description":512,"useCases":513,"indexable":222},"UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":515,"label":516,"issuer":517,"region":154,"url":518,"description":519,"useCases":520,"indexable":222},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":522,"label":523,"issuer":524,"region":525,"url":526,"description":527,"useCases":528,"indexable":222},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":530,"label":531,"issuer":532,"region":525,"url":533,"description":534,"useCases":454,"indexable":222},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",{"id":536,"label":537,"issuer":538,"region":490,"url":539,"description":540,"useCases":61,"indexable":222},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":542,"label":543,"issuer":544,"region":498,"url":545,"description":546,"useCases":61,"indexable":222},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":149,"label":548,"issuer":549,"region":154,"url":155,"description":550,"useCases":551,"indexable":222},"UK Algorithmic Transparency Recording Standard","UK Government","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":553,"label":554,"issuer":555,"region":490,"url":556,"description":557,"useCases":558,"indexable":222},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":560,"label":561,"issuer":478,"region":154,"url":562,"description":563,"useCases":564,"indexable":222},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":566,"label":567,"issuer":478,"region":154,"url":568,"description":569,"useCases":564,"indexable":222},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":571,"label":572,"issuer":573,"region":498,"url":574,"description":575,"useCases":576,"indexable":222},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":578,"label":579,"issuer":478,"region":154,"url":580,"description":581,"useCases":582,"indexable":222},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":584,"label":585,"issuer":586,"region":498,"url":587,"description":588,"useCases":582,"indexable":222},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":590,"label":591,"issuer":592,"region":490,"url":593,"description":594,"useCases":582,"indexable":222},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":596,"label":597,"issuer":478,"region":154,"url":598,"description":599,"useCases":600,"indexable":222},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":602,"label":603,"issuer":604,"region":498,"url":605,"description":606,"useCases":600,"indexable":222},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-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":608,"label":609,"issuer":524,"region":525,"url":610,"description":611,"useCases":60,"indexable":222},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":613,"label":614,"issuer":478,"region":154,"url":615,"description":616,"useCases":60,"indexable":222},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":618,"label":619,"issuer":478,"region":154,"url":620,"description":621,"useCases":60,"indexable":222},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":623,"label":624,"issuer":625,"region":154,"url":626,"description":627,"useCases":628,"indexable":222},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":630,"label":631,"issuer":632,"region":498,"url":633,"description":634,"useCases":635,"indexable":222},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":637,"label":638,"issuer":478,"region":154,"url":639,"description":640,"useCases":635,"indexable":222},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":642,"label":643,"issuer":478,"region":154,"url":644,"description":645,"useCases":388,"indexable":222},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":647,"label":648,"issuer":649,"region":650,"url":651,"description":652,"useCases":425,"indexable":222},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":654,"label":655,"issuer":656,"region":154,"url":657,"description":658,"useCases":411,"indexable":222},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":660,"label":661,"issuer":662,"region":154,"url":663,"description":664,"useCases":411,"indexable":222},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":666,"label":667,"issuer":668,"region":525,"url":669,"description":670,"useCases":215,"indexable":222},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":672,"label":673,"issuer":478,"region":154,"url":674,"description":675,"useCases":215,"indexable":222},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":677,"label":678,"issuer":679,"region":498,"url":680,"description":681,"useCases":215,"indexable":222},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598298619]