[{"data":1,"prerenderedAt":654},["ShallowReactive",2],{"uc-meeting-summarization-and-action-items":3,"uc-regulations":446},{"useCase":4,"evidence":207,"blitsAiDeployments":343,"benchmarks":344,"indicative":351,"related":354,"indexability":444,"includeUnpublished":213},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":22,"patterns":25,"channels":28,"audience":32,"autonomy":33,"adoptionStage":34,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":42,"indicativeValue":46,"macroEstimates":89,"feasibility":90,"implementation":102,"risk":148,"blitsAi":181,"faq":183,"related":196,"datePublished":202,"dateModified":202,"lastVerified":202,"changelog":203,"slug":206},"AI meeting summarization and action items","Meeting summaries and action items","AI meeting notes, summaries and action items","AI turns meeting transcripts into summaries and action items for people to check. UK probation staff summarised over 1.6 million meetings with Justice Transcribe.","published","AI that summarizes internal and operational meetings, such as team, project, board and case meetings: it transcribes an online or in person meeting with the participants' knowledge and produces a summary, decisions and action items with owners and dates 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.",[12,13,14,15,16],"AI meeting notes","AI notetaker","meeting recap","automated minutes","meeting transcription and summary",[18,19,20,21],"cross-industry","government","technology","professional-services",[23,24],"knowledge-management","operations",[26,27],"summarization","speech-analytics",[29,30,31],"microsoft-teams","internal-tools","mobile-app","employee-facing","copilot","mainstream","What a meeting decides is often lost. Someone takes notes while trying to participate, the notes are partial and late, action items live in people's\nheads, and colleagues who missed the meeting ask for a recap or watch a recording. In frontline\nroles such as probation, social work, casework and field inspection, the meeting is the work, and\nwriting it up afterwards takes time that could go to the next person.\n\nThe input is often already there: meeting platforms can produce a transcript. The value is not the\ntranscript but the structured record:\ndecisions, owners, dates and the points that matter for the case or project. The risks are\nspecific too. Summaries can be confidently wrong about who agreed to what, recording people raises\nconsent and privacy questions, and transcripts of sensitive meetings create records that must be\nprotected and retained correctly.",[],"1. **Tell everyone.** Participants are informed that the meeting is being transcribed, and anyone\n   can ask for it to stop; for sensitive meetings the organizer decides whether AI is used at all.\n2. **Transcribe with speakers.** Speech is transcribed live or from the recording, with speaker\n   attribution, in the language spoken.\n3. **Summarize in a fixed structure.** The AI produces a summary, decisions, action items with\n   owners and due dates, open questions and, for casework, the fields the record system requires.\n4. **Organizer reviews.** The organizer or caseworker corrects and approves the summary before it\n   is shared or saved to a record.\n5. **Push the actions.** Approved action items go to task tools or case systems; the summary is\n   stored with the meeting or case.\n6. **Keep what you must, delete what you can.** Transcripts and recordings follow the\n   organization's retention rules; often only the approved summary is kept.",[39,40,41],"employee-productivity","speed","compliance",[43,44,45],"interactions-handled","time-saved-per-task","hours-saved",{"referenceOrg":47,"inputs":48,"formula":84,"currency":85,"period":86,"resultLabel":87,"caveat":88},"An organization with 5,000 employees who use AI meeting summaries",[49,55,62,70,77],{"key":50,"label":51,"low":52,"high":52,"unit":53,"note":54},"users","Employees using meeting summaries",5000,"employees","The reference organization.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"meetingsPerWeek","Summarized meetings per user per week",2,4,"meetings per week","Editorial assumption. Replace with usage data from your meeting platform.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68,"sourceUrl":69},"hoursSavedPerMeeting","Note taking and write up time saved per meeting",0.1,0.2,"hours per meeting","Editorial assumption of 6 to 12 minutes. For Justice Transcribe, HM Prison and Probation Service advised a broad operational assumption of about 10 minutes per meeting; the Ministry of Justice calls the hours total derived from it an illustrative estimate only.","https://www.gov.uk/government/publications/justice-transcribe/justice-transcribe-data-7-october-2025-to-14-september-2026",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"weeks","Working weeks per year",44,46,"weeks per year","Editorial assumption.",{"key":78,"label":79,"low":80,"high":81,"unit":82,"note":83},"hourlyCost","Fully loaded employee cost",40,70,"USD per hour","Editorial assumption, replace with your own.","users * meetingsPerWeek * hoursSavedPerMeeting * weeks * hourlyCost","USD","per year","Employee time released from note taking","Values time at cost and assumes the time is used productively, which trials often cannot confirm. It leaves out licence and platform cost, the time to review summaries, and the harder to measure value of better records and fewer missed actions.",[],{"complexity":91,"complexityNote":92,"dataPrerequisites":93,"integrations":97},"low","Meeting platforms offer it out of the box. The work is in policy (when AI may be used, consent, retention), in structured outputs for casework, and in integration with case or task systems.",[94,95,96],"A policy on which meetings may be transcribed, how participants are told and how long records are kept","Summary templates per meeting type (project, casework, customer, board)","Access rules for transcripts and summaries, aligned with the meeting's confidentiality",[98,99,100,101],"Meeting platforms (Microsoft Teams, Zoom, Google Meet) or a recording app for in person meetings","Task and project tools for action items","Case management or record systems for frontline meetings","Records management for retention and deletion",{"steps":103,"guardrails":122,"humanInTheLoop":128,"kpisToInstrument":129,"failureModes":135},[104,107,110,113,116,119],{"title":105,"detail":106},"Write the policy before the rollout","Decide which meetings may be transcribed, how people are informed, who may switch it on, and which meetings are off limits (HR cases, legal privilege, some board discussions).",{"title":108,"detail":109},"Define summary templates per meeting type","A project meeting needs decisions and actions; a probation or social work meeting needs the fields the case record requires. Structure beats free prose.",{"title":111,"detail":112},"Make review part of the flow","The organizer or caseworker approves before sharing or saving. Make corrections easy and record who approved.",{"title":114,"detail":115},"Connect actions and records","Send approved action items to task tools and approved summaries to the case or project record, instead of leaving them in the meeting chat.",{"title":117,"detail":118},"Train people on what it gets wrong","Show examples of wrong attributions, missed nuance and invented actions, and how to check for them, especially in meetings with several speakers or languages.",{"title":120,"detail":121},"Measure use and quality, not only licences","Track summaries approved, edit rates and user reported time saved, and audit a sample of summaries against recordings for accuracy.",[123,124,125,126,127],"Participants are informed before transcription starts and can object","Summaries are drafts until a named person approves them","Action items and decisions are attributed only to what was said, with low confidence items flagged","No sentiment or emotion scoring of participants, and no use of transcripts to evaluate individual employees","Transcripts and recordings follow retention rules and inherit the meeting's access restrictions","The organizer or caseworker reviews, corrects and approves every summary before it is shared or becomes part of a record, and remains accountable for its content. Records management owns retention; the data protection officer approves the policy for sensitive meeting types.",[130,131,132,133,134],"Meetings summarized and summaries approved per week","Edit rate on summaries and reported errors (wrong owner, invented action)","User reported time saved per meeting, validated by time studies on a sample","Share of action items completed by their due date","Transcripts deleted on schedule",[136,139,142,145],{"title":137,"detail":138},"Confidently wrong attribution","The summary says a person agreed to something they did not. Require review before sharing and flag low confidence attributions.",{"title":140,"detail":141},"Transcripts nobody should have","Sensitive meetings are recorded and transcripts spread through shared channels. Define no AI meetings and inherit access restrictions.",{"title":143,"detail":144},"Licences without adoption","Tools are rolled out but few people use them after the first month. Measure active use per team and train on real meetings.",{"title":146,"detail":147},"Time saved that cannot be found","Self reported savings do not show in any outcome. Pair usage data with outcome measures such as case throughput or actions completed.",{"euAiAct":149,"regulations":152,"guidance":158,"controls":174,"incidents":180},{"tier":150,"basis":151},"context-dependent","Transcribing and summarizing meetings for the participants is minimal risk. It becomes high risk under Annex III point 4(b) if transcripts are analysed to monitor or evaluate individual workers' performance or behaviour, and inferring participants' emotions from their voices or faces at work is prohibited by Article 5(1)(f). Recording and transcription also need a lawful basis and clear information to participants under GDPR.",[153,154,155,156,157],"eu-ai-act","gdpr","iso-42001","uk-gdpr","uk-atrs",[159,165,169],{"title":160,"issuer":161,"region":162,"url":163,"note":164},"Article 5, prohibited AI practices","European Union","europe","https://artificialintelligenceact.eu/article/5/","Point 1(f) prohibits AI that infers people's emotions in the workplace, except for medical or safety reasons, which rules out mood scoring of employees from their voices or faces in meetings.",{"title":166,"issuer":161,"region":162,"url":167,"note":168},"Annex III, high risk AI systems referred to in Article 6(2)","https://artificialintelligenceact.eu/annex/3/","Point 4(b) applies if meeting data is used to monitor or evaluate workers.",{"title":170,"issuer":171,"region":162,"url":172,"note":173},"Employment practices and data protection: monitoring workers","UK Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/employment/monitoring-workers/","Relevant to recording and transcribing employees' meetings and to what the organization may do with the records.",[175,176,177,178,179],"Meeting transcription policy with a list of meeting types where AI is not used","Data protection impact assessment, including for meetings with customers or members of the public","Retention and deletion rules for recordings, transcripts and summaries","Access control that follows the meeting's confidentiality","Periodic accuracy audit of summaries against recordings",[],{"howToBuild":182},"On Blits.ai the transcription can run on the **self hosted WhisperX service with speaker\ndiarization**, which gives word level timestamps, automatic language detection and \"who spoke\nwhen\" on Blits.ai infrastructure for data sovereignty, or on one of the other **speech to text**\nproviders. An **agentic workflow** passes the transcript to an **AI agent** with **structured\noutput** that fills the summary template for the meeting type, including decisions and action\nitems with owners.\n\n**Human in the loop** confirmation holds the next step until the organizer approves the summary,\nafter which **custom functions** push action items to task tools or write the approved summary to\nthe case system (the integration catalog includes Jira, Asana, ServiceNow and Salesforce).\n**PII masking** and **data retention controls** limit what is kept, and **test suites** with\nLLM based grading, run after each prompt change, compare summaries with reviewed transcripts. The platform\nis model agnostic and can run in the EU or UAE region.",[184,187,190,193],{"question":185,"answer":186},"How widely is AI meeting summarization used?","Few organizations publish usage figures for meeting summaries alone. The clearest comes from the UK Ministry of Justice: probation staff summarised more than 1.6 million meetings with Justice Transcribe between October 2025 and September 2026, a count of meetings where the tool was used, not a share of all probation meetings. In the UK government's Microsoft 365 Copilot trial, Teams had the highest Copilot adoption of any application, peaking at 71%, but that figure covers every Copilot feature in Teams, not meeting summaries alone.",{"question":188,"answer":189},"How much time does it save?","Published figures are mostly assumptions or self reported. The Ministry of Justice applies an assumption of about 10 minutes per meeting and calls the result illustrative, and UK trial participants estimated 26 minutes a day across all Copilot tasks. Measure it yourself on a sample before building a business case on it.",{"question":191,"answer":192},"Do we need consent to transcribe meetings?","Under GDPR you need a lawful basis and must inform participants; consent is one possible basis, not the only one, and your data protection officer decides which fits. Tell people before transcription starts, let them object, and do not use transcripts to evaluate employees without a separate assessment.",{"question":194,"answer":195},"How is this different from advisor meeting notes in wealth management?","The wealth version records advice to clients, with suitability and CRM obligations. This page covers the general case: internal, project, casework and frontline meetings, where the output is a checked record and a list of actions.",[197,198,199,200,201],"client-meeting-notes-and-crm-update","sales-call-coaching-and-crm-update","client-briefing-and-call-report-copilot","enterprise-knowledge-search","live-agent-assist","2026-09-27",[204],{"date":202,"note":205},"First published","meeting-summarization-and-action-items",[208,245,276,300,325],{"title":209,"useCases":210,"organization":211,"vendors":215,"summary":216,"stage":217,"year":218,"channels":219,"languages":220,"metrics":222,"outcomeDisclosed":230,"sources":231,"verification":239,"grade":242,"id":243,"organizationSlug":244},"UK Ministry of Justice: Justice Transcribe meeting summaries for probation staff",[206],{"name":212,"anonymized":213,"country":214,"region":162,"industry":19},"Ministry of Justice",false,"GB",[],"Justice Transcribe is an AI transcription and meeting summarisation tool used by probation staff in England and Wales. The Ministry of Justice publishes transparency data on its use: between 7 October 2025 and 14 September 2026 more than 1.6 million meetings were summarised with it. Probation Workforce Transformation within HM Prison and Probation Service advised, as a broad operational assumption, about 10 minutes saved per meeting; the ministry itself labels the resulting hours figure illustrative, so it is not recorded as a result.","scaled",2025,[30],[221],"en",[223],{"kpi":43,"value":224,"unit":225,"qualifier":226,"period":227,"claimant":228,"quote":229,"sourceUrl":69},1600000,"count","at-least","meetings summarised, 7 October 2025 to 14 September 2026","organization","Between 7 October 2025 and 14 September 2026, over 1,600,000 meetings were summarised using Justice Transcribe.",true,[232,236],{"url":69,"title":233,"publisher":234,"date":235},"Justice Transcribe data: 7 October 2025 to 14 September 2026","Ministry of Justice (GOV.UK)","2026-09-16",{"url":237,"title":238,"publisher":234},"https://www.gov.uk/government/publications/justice-transcribe","Justice Transcribe",{"level":240,"checkedAt":241},"source-verified","2026-09-26","B","ministry-of-justice-justice-transcribe",null,{"title":246,"useCases":247,"organization":249,"vendors":251,"summary":255,"stage":256,"year":257,"channels":258,"languages":259,"metrics":260,"outcomeDisclosed":230,"sources":269,"verification":274,"grade":242,"id":275,"organizationSlug":244},"UK Government: cross government Microsoft 365 Copilot experiment with 20,000 government employees",[206,248],"civil-servant-drafting-copilot",{"name":250,"anonymized":213,"country":214,"region":162,"industry":19},"Government Digital Service",[252],{"name":253,"role":254},"Microsoft","platform","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.","pilot",2024,[29,30],[221],[261],{"kpi":262,"value":263,"unit":264,"qualifier":265,"period":266,"claimant":228,"quote":267,"sourceUrl":268},"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",[270],{"url":268,"title":271,"publisher":272,"date":273},"Microsoft 365 Copilot Experiment: Cross-Government Findings Report","Government Digital Service (GOV.UK)","2025-06-02",{"level":240,"checkedAt":202},"uk-government-m365-copilot-experiment",{"title":277,"useCases":278,"organization":279,"vendors":283,"summary":285,"stage":286,"year":257,"channels":287,"languages":288,"metrics":289,"outcomeDisclosed":213,"sources":290,"verification":298,"grade":242,"id":299,"organizationSlug":244},"US Department of Labor: note taking bot for meeting summaries and action items",[206],{"name":280,"anonymized":213,"country":281,"region":282,"industry":19},"U.S. Department of Labor","US","north-america",[284],{"name":253,"role":254},"The Department of Labor's Office of the Chief Information Officer runs a note taking bot that turns meeting transcripts into concise, searchable notes with a summary and action items, to reduce manual note taking and improve information sharing. It is listed as deployed since November 2024 and not high impact, and reports that it has no authority to operate (ATO); no outcome figures are published.","production",[30],[221],[],[291,295],{"url":292,"title":293,"publisher":294},"https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory","2025 Federal Agency AI Use Case Inventory","Office of Management and Budget (GitHub)",{"url":296,"title":297,"publisher":294},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 individually reported AI use cases (entry DOL-32, Note Taking Bot)",{"level":240,"checkedAt":202},"dol-note-taking-bot",{"title":301,"useCases":302,"organization":303,"vendors":305,"summary":307,"stage":217,"year":257,"channels":308,"languages":309,"metrics":310,"outcomeDisclosed":230,"sources":318,"verification":322,"grade":323,"id":324,"organizationSlug":244},"Softcat: organisation wide Microsoft 365 Copilot use for meeting summaries and follow ups",[206],{"name":304,"anonymized":213,"country":214,"region":162,"industry":20},"Softcat",[306],{"name":253,"role":254},"Softcat, the largest Microsoft Solutions Partner in the UK, widened its Microsoft 365 Copilot rollout to 1,500 people. One of its top sellers queries meeting transcripts to pull out information and list actions, so he sends follow ups faster and no longer takes notes during customer meetings; its IT change manager estimates that a third of users use it daily for tasks such as email and meeting summaries. Microsoft reports that 85% of licensed users use it regularly.",[29,30],[221],[311],{"kpi":262,"value":312,"unit":264,"qualifier":313,"period":314,"claimant":315,"quote":316,"sourceUrl":317},85,"exact","licensed users using Copilot regularly","vendor","Eighty-five percent of licenced Softcat users are using Microsoft 365 Copilot regularly.","https://www.microsoft.com/en/customers/story/19912-softcat-microsoft-365-copilot",[319],{"url":317,"title":320,"publisher":321},"Softcat leads Microsoft 365 Copilot adoption to deliver efficiencies and quality improvements","Microsoft Customer Stories",{"level":240,"checkedAt":202},"C","softcat-copilot-meeting-summaries",{"title":326,"useCases":327,"organization":329,"vendors":331,"summary":333,"stage":286,"year":257,"channels":334,"languages":335,"metrics":336,"outcomeDisclosed":213,"sources":337,"verification":341,"grade":323,"id":342,"organizationSlug":244},"Trace3: Microsoft Copilot for first pass resume assessment and meeting highlights",[328,206],"recruitment-screening-and-interview-scheduling",{"name":330,"anonymized":213,"country":281,"region":282,"industry":21},"Trace3",[332],{"name":253,"role":254},"At technology consultancy Trace3, HR managers use Microsoft Copilot for an initial assessment of resumes, so they review submissions faster and respond to applicants within a couple of days instead of the several weeks it could take before. Its practice director for Azure uses it for highlights of Teams meetings and long email chains and for first drafts; colleagues use it for a broad range of tasks. The outcome is described qualitatively, with no measured figure.",[29,30],[221],[],[338],{"url":339,"title":340,"publisher":321},"https://www.microsoft.com/en/customers/story/1790119689031635867-trace3-microsoft-365-professional-services-en-united-states","Trace3 expands the realm of clients' possibilities with Windows 11 Pro and Microsoft Copilot",{"level":240,"checkedAt":241},"trace3-microsoft-copilot-recruiting-and-meetings",0,[345],{"kpi":43,"label":346,"unit":225,"aggregate":213,"higherIsBetter":230,"n":347,"nUpTo":343,"median":224,"min":224,"max":224,"byClaimant":348,"vendorOnly":213,"points":349},"Interactions handled",1,{"organization":347,"vendor":343,"regulator":343,"independent":343},[350],{"evidenceId":243,"organization":212,"value":224,"qualifier":226,"claimant":228,"grade":242,"pooled":230},{"low":352,"high":353},1760000,12880000,[355,382,402,415,428],{"slug":197,"title":356,"shortTitle":357,"definition":358,"status":9,"industries":359,"functions":362,"patterns":365,"audience":32,"autonomy":33,"adoptionStage":34,"segment":368,"evidenceCount":369,"publicEvidenceCount":369,"organizations":370,"bestGrade":242,"headline":377,"lastVerified":202,"indexable":230},"AI meeting notes and CRM update for wealth advisors","Advisor meeting notes","An AI notetaker for wealth advisors that turns a client advice meeting, recorded with the client's consent, into the file note, follow up message and CRM record the firm needs to evidence its advice; unlike a general meeting summarizer, its output becomes part of the regulated client record. It drafts a structured note with the client's goals, circumstances, decisions and action items, and writes it into the CRM once the advisor has approved it.",[360,361],"wealth-and-asset-management","banking",[363,364,24],"sales","regulatory-compliance",[26,27,366,367],"agentic-workflow","content-generation","front-office",6,[371,372,373,374,375,376],"Bank of America","Commerzbank","Morgan Stanley","Quilter","SEB","UniSuper",{"kpi":378,"label":379,"unit":264,"n":347,"nUpTo":343,"kind":380,"value":381,"qualifier":313,"claimant":315,"organization":375,"vendorReported":230},"productivity-gain","Productivity gain","reported",15,{"slug":198,"title":383,"shortTitle":384,"definition":385,"status":9,"industries":386,"functions":390,"patterns":391,"audience":32,"autonomy":33,"adoptionStage":392,"evidenceCount":59,"publicEvidenceCount":59,"organizations":393,"bestGrade":323,"headline":398,"lastVerified":202,"indexable":230},"AI sales call coaching and CRM update","Sales call coaching and CRM update","AI for sales teams that analyses sales calls and meetings against the team's own sales method to coach sellers and their managers, and writes the call summary, next steps and opportunity updates into the CRM for the seller to confirm. Its purpose is winning deals and building selling skill, not the regulated advice record or general meeting notes.",[18,387,388,389],"telecommunications","manufacturing","insurance",[363],[27,26,367],"early-adopters",[394,395,396,397],"Hughes Network Systems","Lumen Technologies","Sandvik Coromant","Zurich Insurance Group",{"kpi":44,"label":399,"unit":400,"n":347,"nUpTo":347,"kind":380,"value":401,"qualifier":313,"claimant":228,"organization":396,"vendorReported":213},"Time saved per task","minutes",3,{"slug":199,"title":403,"shortTitle":404,"definition":405,"status":9,"industries":406,"functions":408,"patterns":409,"audience":32,"autonomy":33,"adoptionStage":392,"segment":411,"evidenceCount":401,"publicEvidenceCount":401,"organizations":412,"bestGrade":242,"headline":244,"lastVerified":202,"indexable":230},"AI copilot for corporate client briefings and call reports","Client briefing and call reports","An AI copilot for relationship managers, mainly in corporate and commercial banking, whose main job is preparation: before a client meeting it assembles a briefing pack from filings, news, internal notes, product holdings and upcoming maturities, and afterwards it turns the banker's notes into a structured call report and CRM update. Unlike a meeting notetaker, which centres on capturing the conversation, it centres on the credit and cross sell context around the meeting; wealth advisor tools that also prepare meetings overlap with it. The banker reviews every output.",[361,360,407],"capital-markets",[363,23],[410,26,367,366],"rag-knowledge-assistant","specialized-businesses",[371,413,414],"Scotiabank","Standard Chartered",{"slug":200,"title":416,"shortTitle":417,"definition":418,"status":9,"industries":419,"functions":420,"patterns":422,"audience":32,"autonomy":424,"adoptionStage":34,"evidenceCount":59,"publicEvidenceCount":59,"organizations":425,"bestGrade":242,"headline":244,"lastVerified":202,"indexable":230},"AI enterprise knowledge search for employees","Enterprise knowledge search","An assistant that lets any employee ask a question in plain language and get a synthesized answer from the organization's own policies, procedures, product manuals and research, with citations to the source documents and only from documents the employee is allowed to see.",[18,361,360,389,19,21],[23,24,421],"customer-service",[410,423,26],"conversational-agent","assist",[371,373,426,427],"SIGNAL IDUNA","Wells Fargo",{"slug":201,"title":429,"shortTitle":430,"definition":431,"status":9,"industries":432,"functions":435,"patterns":436,"audience":32,"autonomy":424,"adoptionStage":34,"evidenceCount":437,"publicEvidenceCount":438,"organizations":439,"bestGrade":242,"headline":443,"lastVerified":202,"indexable":230},"Real time AI assist for contact centre agents","Live agent assist","A real time copilot for human contact centre agents during a live call or chat: it transcribes the conversation as it happens, surfaces the relevant knowledge and next step, drafts responses, and writes the after call summary and CRM notes, while the agent stays in control of what is said and done.",[18,361,389,387,433,434,20],"healthcare","retail-and-ecommerce",[421,24],[27,410,26,367],7,5,[440,441,442,375,426],"DBS Bank","Definity","Oportun",{"kpi":378,"label":379,"unit":264,"n":58,"nUpTo":343,"kind":380,"value":381,"qualifier":313,"claimant":315,"organization":441,"vendorReported":230},{"indexable":230,"reasons":445},[],[447,452,457,464,471,477,483,490,498,505,512,518,524,530,536,541,548,554,560,566,572,578,584,589,594,601,608,613,618,625,631,637,643,648],{"id":153,"label":448,"issuer":161,"region":162,"url":449,"description":450,"useCases":451,"indexable":230},"EU AI Act","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":154,"label":453,"issuer":161,"region":162,"url":454,"description":455,"useCases":456,"indexable":230},"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":155,"label":458,"issuer":459,"region":460,"url":461,"description":462,"useCases":463,"indexable":230},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":465,"label":466,"issuer":467,"region":282,"url":468,"description":469,"useCases":470,"indexable":230},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","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":472,"label":473,"issuer":161,"region":162,"url":474,"description":475,"useCases":476,"indexable":230},"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":156,"label":478,"issuer":479,"region":162,"url":480,"description":481,"useCases":482,"indexable":230},"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":484,"label":485,"issuer":486,"region":162,"url":487,"description":488,"useCases":489,"indexable":230},"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":491,"label":492,"issuer":493,"region":494,"url":495,"description":496,"useCases":497,"indexable":230},"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":499,"label":500,"issuer":501,"region":494,"url":502,"description":503,"useCases":504,"indexable":230},"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.",25,{"id":506,"label":507,"issuer":508,"region":460,"url":509,"description":510,"useCases":511,"indexable":230},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":513,"label":514,"issuer":515,"region":282,"url":516,"description":517,"useCases":511,"indexable":230},"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":157,"label":519,"issuer":520,"region":162,"url":521,"description":522,"useCases":523,"indexable":230},"UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":525,"label":526,"issuer":527,"region":460,"url":528,"description":529,"useCases":381,"indexable":230},"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.",{"id":531,"label":532,"issuer":161,"region":162,"url":533,"description":534,"useCases":535,"indexable":230},"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":537,"label":538,"issuer":161,"region":162,"url":539,"description":540,"useCases":535,"indexable":230},"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":542,"label":543,"issuer":544,"region":282,"url":545,"description":546,"useCases":547,"indexable":230},"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":549,"label":550,"issuer":161,"region":162,"url":551,"description":552,"useCases":553,"indexable":230},"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":555,"label":556,"issuer":557,"region":282,"url":558,"description":559,"useCases":553,"indexable":230},"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":561,"label":562,"issuer":563,"region":460,"url":564,"description":565,"useCases":553,"indexable":230},"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":567,"label":568,"issuer":161,"region":162,"url":569,"description":570,"useCases":571,"indexable":230},"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":573,"label":574,"issuer":575,"region":282,"url":576,"description":577,"useCases":571,"indexable":230},"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":579,"label":580,"issuer":493,"region":494,"url":581,"description":582,"useCases":583,"indexable":230},"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.",10,{"id":585,"label":586,"issuer":161,"region":162,"url":587,"description":588,"useCases":583,"indexable":230},"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":590,"label":591,"issuer":161,"region":162,"url":592,"description":593,"useCases":583,"indexable":230},"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":595,"label":596,"issuer":597,"region":162,"url":598,"description":599,"useCases":600,"indexable":230},"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":602,"label":603,"issuer":604,"region":282,"url":605,"description":606,"useCases":607,"indexable":230},"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":609,"label":610,"issuer":161,"region":162,"url":611,"description":612,"useCases":607,"indexable":230},"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":614,"label":615,"issuer":161,"region":162,"url":616,"description":617,"useCases":369,"indexable":230},"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":619,"label":620,"issuer":621,"region":622,"url":623,"description":624,"useCases":438,"indexable":230},"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":626,"label":627,"issuer":628,"region":162,"url":629,"description":630,"useCases":59,"indexable":230},"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":632,"label":633,"issuer":634,"region":162,"url":635,"description":636,"useCases":59,"indexable":230},"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":638,"label":639,"issuer":640,"region":494,"url":641,"description":642,"useCases":401,"indexable":230},"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":644,"label":645,"issuer":161,"region":162,"url":646,"description":647,"useCases":401,"indexable":230},"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":649,"label":650,"issuer":651,"region":282,"url":652,"description":653,"useCases":401,"indexable":230},"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.",1790598302727]