[{"data":1,"prerenderedAt":658},["ShallowReactive",2],{"uc-conversation-roleplay-training":3,"uc-regulations":453},{"useCase":4,"evidence":213,"blitsAiDeployments":311,"benchmarks":312,"indicative":329,"related":332,"indexability":451,"includeUnpublished":219},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":24,"patterns":28,"channels":32,"audience":36,"autonomy":37,"adoptionStage":38,"problem":39,"problemStats":40,"howItWorks":41,"valueDrivers":42,"kpis":47,"indicativeValue":53,"macroEstimates":94,"feasibility":95,"implementation":107,"risk":153,"blitsAi":189,"faq":191,"related":201,"datePublished":207,"dateModified":207,"lastVerified":208,"changelog":209,"slug":212},"AI roleplay training for customer conversations","Conversation roleplay training","AI roleplay training for sales and service teams","AI roleplay lets staff rehearse hard calls with a simulated customer and get scored feedback. Bank of America staff completed over 1 million simulations in 2024.","published","A training simulator in which generative AI plays a realistic customer, by voice or text, so service, sales and crisis staff can rehearse difficult conversations as often as they need before they handle live ones, and receive structured feedback against the organization's own standards.",[12,13,14,15,16],"AI roleplay","conversation simulator","AI sales roleplay","simulated customer training","AI practice calls",[18,19,20,21,22,23],"cross-industry","banking","insurance","telecommunications","government","healthcare",[25,26,27],"human-resources","customer-service","sales",[29,30,31],"conversational-agent","voice-agent","content-generation",[33,34,35],"internal-tools","voice","web-chat","employee-facing","assist","early-adopters","New contact centre, sales and crisis line staff learn the hardest conversations on real\ncustomers: the angry caller, the fraud victim, the customer in financial hardship, the person in\ncrisis. Classroom roleplay with colleagues or trainers is limited by trainer time, feels\nartificial and rarely covers the full range of situations, so new hires often reach the floor with\nlittle practice. The risk is long ramp up times, inconsistent handling of disclosures and\nvulnerability, and avoidable harm to the first customers each new hire serves.\n\nRegulated firms have an extra reason to care. Conduct rules expect staff to recognise\nvulnerability, give required disclosures and treat customers fairly; in the UK, the FCA's guidance\non vulnerable customers asks firms to ensure frontline staff have the skills and capability to\nrecognise and respond to vulnerability. Trainer led roleplay leaves little evidence of what was\npractised and how well.",[],"1. **Build scenarios from real work.** Training and quality teams write scenarios from real,\n   anonymized contact reasons: a disputed charge, a lost card abroad, a hardship request, a\n   complaint, a sales conversation with required disclosures. Each scenario has a persona, a goal,\n   facts the trainee must find out and behaviours to test.\n2. **The AI plays the customer.** A model plays the persona by voice or text, reacts to what the\n   trainee says, becomes calmer or more upset depending on how the conversation goes, and raises\n   the objections or cues the scenario calls for.\n3. **Score against the rubric.** After the conversation the AI scores the transcript against the\n   organization's rubric (verification steps, required disclosures, empathy, accuracy of\n   information, next steps) and quotes the moments behind each score.\n4. **Give targeted feedback and repeat.** The trainee gets specific feedback and can retry the\n   same scenario or a harder variant immediately.\n5. **Report to trainers, not to discipline.** Trainers see progress per skill and per cohort and\n   spend their time coaching where the simulator shows gaps.",[43,44,45,46],"employee-productivity","customer-experience","compliance","speed",[48,49,50,51,52],"time-to-proficiency-reduction","interactions-handled","conversion-rate-uplift","quality-score-uplift","users-served",{"referenceOrg":54,"inputs":55,"formula":89,"currency":90,"period":91,"resultLabel":92,"caveat":93},"A contact centre that hires 200 new agents a year",[56,62,69,76,82],{"key":57,"label":58,"low":59,"high":59,"unit":60,"note":61},"hires","New agents trained per year",200,"agents per year","The reference organization. Replace with your own hiring volume.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"rampWeeks","Weeks from start to full proficiency today",6,10,"weeks","Editorial assumption. Replace with your own ramp time.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"rampReduction","Share of ramp time removed by simulated practice",0.1,0.3,"fraction of ramp time","Conservative against the evidence on this page (GoHealth's vendor reports onboarding cut from nine weeks to four, a 55% saving), because that figure is a single vendor reported case.",{"key":77,"label":78,"low":73,"high":79,"unit":80,"note":81},"productivityGap","Productivity shortfall of a new agent during ramp up",0.5,"fraction of a fully proficient agent","Editorial assumption.",{"key":83,"label":84,"low":85,"high":86,"unit":87,"note":88},"weeklyCost","Fully loaded weekly cost of an agent",900,1500,"USD per week","Editorial assumption. Replace with your own cost.","hires * rampWeeks * rampReduction * productivityGap * weeklyCost","USD","per year","Value of productive time gained by faster ramp up","Ramp up value only. It leaves out trainer time saved, lower early attrition, fewer complaints and conduct breaches from new hires, and the licence and scenario authoring costs of the simulator.",[],{"complexity":96,"complexityNote":97,"dataPrerequisites":98,"integrations":103},"low","No integration with customer systems is needed. The effort is in writing good scenarios and rubrics with the quality and compliance teams, calibrating the scoring against human assessors and making voice latency low enough to feel like a real call.",[99,100,101,102],"The contact reason report, to choose scenarios by volume and risk","The quality assurance rubric and required disclosures per conversation type","Anonymized example transcripts or call recordings for realistic personas","Vulnerability and complaint handling policies the scenarios must test",[104,105,106],"Learning management system for assignments and completion records","Single sign on for trainees and trainers","Voice or telephony softphone for realistic practice calls",{"steps":108,"guardrails":127,"humanInTheLoop":133,"kpisToInstrument":134,"failureModes":140},[109,112,115,118,121,124],{"title":110,"detail":111},"Pick scenarios by risk and volume","Start with five to ten scenarios that new hires find hardest and that carry conduct risk, such as a hardship request, a scam victim or a complaint. Add routine ones later.",{"title":113,"detail":114},"Write rubrics with quality and compliance","Use the same rubric the quality team uses on live calls, so practice and assessment measure the same things. Mark which items are mandatory, such as identity checks and disclosures.",{"title":116,"detail":117},"Calibrate scoring against humans","Have experienced assessors score a sample of simulated conversations and compare with the AI scores. Fix rubric items where they disagree before trainees see scores.",{"title":119,"detail":120},"Keep personas realistic, not cruel","Let the persona escalate and de escalate in response to the trainee, but keep abuse within what staff actually meet, and give trainees a way to stop a session.",{"title":122,"detail":123},"Blend into the programme","Integrate practice into training weeks rather than bolting it on, as GoHealth did by folding practice calls into training, and let trainers use the reports to target coaching.",{"title":125,"detail":126},"Measure on the floor","Compare ramp time, quality scores and complaint rates of trained cohorts with earlier cohorts on the same contact mix.",[128,129,130,131,132],"Scores are used for practice and coaching, not for promotion, pay or termination decisions without human review","No inference of trainees' emotions from voice or face, which the EU AI Act prohibits in the workplace outside medical or safety reasons","Scenarios and model answers use only approved policies, products and disclosure wording","No real customer data in personas; examples are anonymized before they become scenarios","Trainees can see their transcripts and scores and contest a score with a trainer","Trainers and quality leads own the scenarios and rubrics, review the AI's scoring on a sample every cohort, and make every certification or sign off decision. The AI gives practice and feedback; a human decides whether someone is ready for live customers.",[135,136,137,138,139],"Time to proficiency per cohort, before and after","Practice sessions per trainee and scenario coverage","Agreement between AI scores and human assessor scores on a sample","Live quality scores and complaint rates in the first three months on the floor","Trainee rating of realism and usefulness",[141,144,147,150],{"title":142,"detail":143},"Scoring that trainees do not trust","Scores that disagree with what trainers say undermine the tool. Calibrate against human assessors and show the transcript evidence behind each score.",{"title":145,"detail":146},"Unrealistic customers","Personas that are too easy or cartoonishly hostile teach the wrong lessons. Build personas from real contact reasons and review them with experienced agents.",{"title":148,"detail":149},"Training on outdated policy","The simulated conversation rewards a disclosure or process that has changed. Tie scenarios to the policy owner and review them when policy changes.",{"title":151,"detail":152},"Practice data used as surveillance","Using practice scores in performance management kills honest practice and can make the system high risk. Keep practice and performance evaluation separate by design.",{"euAiAct":154,"regulations":157,"guidance":162,"controls":182,"incidents":188},{"tier":155,"basis":156},"context-dependent","Used only for practice and feedback, the simulator is limited risk. Article 50 requires that people know they are interacting with AI unless that is obvious from the context, as it usually is in a training session, and the provider must mark synthetic voice or text output as AI generated in a machine readable format. It becomes high risk under Annex III point 4(b) if its scores are used to evaluate the performance of workers or to decide on their promotion or termination, and can fall under point 3(b) when a vocational training institution uses it to evaluate learning outcomes. Inferring trainees' emotions from voice or face in the workplace is prohibited under Article 5(1)(f), except for medical or safety reasons.",[158,159,160,161],"eu-ai-act","gdpr","uk-consumer-duty","iso-42001",[163,169,173,177],{"title":164,"issuer":165,"region":166,"url":167,"note":168},"Annex III: High risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 4(b) covers AI used to monitor and evaluate the performance and behaviour of persons in work related relationships, which is where training scores can end up. Point 3(b) covers AI that evaluates learning outcomes in educational and vocational training institutions.",{"title":170,"issuer":165,"region":166,"url":171,"note":172},"Article 5: Prohibited AI practices","https://artificialintelligenceact.eu/article/5/","Point (f) prohibits AI that infers the emotions of a natural person in the workplace or in education institutions, except for medical or safety reasons, which rules out scoring trainees' emotions from their voice or face.",{"title":174,"issuer":165,"region":166,"url":175,"note":176},"Article 50, transparency obligations for providers and deployers of certain AI systems","https://artificialintelligenceact.eu/article/50/","Requires that people are told they are interacting with AI unless it is obvious from the context, and that providers mark synthetic audio and text output as artificially generated.",{"title":178,"issuer":179,"region":166,"url":180,"note":181},"FG21/1: guidance for firms on the fair treatment of vulnerable customers","Financial Conduct Authority","https://www.fca.org.uk/publication/finalised-guidance/fg21-1.pdf","Asks UK financial services firms to ensure frontline staff have the skills and capability to recognise and respond to customers in vulnerable circumstances, which scenario practice can support.",[183,184,185,186,187],"Written purpose limitation that keeps practice scores out of performance management","Periodic calibration of AI scores against human assessors, with results recorded","Scenario and rubric change control owned by training and compliance","Trainee notice of how transcripts and scores are stored, who sees them and for how long","Inventory entry for the simulator with an accountable owner",[],{"howToBuild":190},"On Blits.ai each scenario is an **AI agent** with a persona prompt, a goal and the facts it may\nreveal, with **prompt versioning** so training teams can refine scenarios safely. Trainees\npractise by **voice** with streaming speech recognition and synthesis, including **emotion\naware TTS** so the simulated customer sounds calmer or more upset as the conversation develops,\nor in **web chat** and **Microsoft Teams**; a **digital human** can add a face for in person\ntraining rooms. A **knowledge base** holds the approved policies and disclosure wording the\nrubric checks against.\n\nAfter each session a second agent scores the transcript with **structured output** against the\nrubric, quoting the evidence per item. **Test suites** with **LLM based grading** keep scenarios\nand scoring consistent across changes, **conversation logs** give trainers the transcripts,\nand **role based access control** limits who sees individual results. The platform is model\nagnostic, so the persona and the grader can run on different models.",[192,195,198],{"question":193,"answer":194},"Does AI roleplay actually shorten ramp up time?","Public evidence is early and mostly vendor reported. GoHealth's vendor reports onboarding cut from nine weeks to four after folding AI practice into training, and Bank of America reports more than one million simulations completed by employees in 2024. Measure ramp time and live quality on your own cohorts before and after.",{"question":196,"answer":197},"Is AI roleplay training high risk under the EU AI Act?","Not when it is used only for practice and feedback; then the Article 50 transparency rules apply. It becomes high risk if its scores are used to evaluate employees' performance or decide on promotion or termination (Annex III point 4(b)), and inferring trainees' emotions in the workplace is prohibited (Article 5(1)(f)).",{"question":199,"answer":200},"Can it be used for sensitive conversations such as crisis calls?","Yes, with care. The US Department of Veterans Affairs trains new Veterans Crisis Line responders on AI simulations with eight Veteran personas, and each simulated call produces a scoring summary of strengths and areas of growth. Scenarios for sensitive topics need expert review and a way for trainees to stop a session.",[202,203,204,205,206],"call-quality-and-compliance-monitoring","live-agent-assist","sales-call-coaching-and-crm-update","first-line-contact-centre-agent","employee-onboarding-assistant","2026-09-27","2026-09-26",[210],{"date":207,"note":211},"First published","conversation-roleplay-training",[214,248,278],{"title":215,"useCases":216,"organization":217,"vendors":222,"summary":223,"stage":224,"year":225,"channels":226,"languages":227,"metrics":229,"outcomeDisclosed":238,"sources":239,"verification":243,"grade":245,"id":246,"organizationSlug":247},"Bank of America: AI conversation simulators in The Academy",[212],{"name":218,"anonymized":219,"country":220,"region":221,"industry":19},"Bank of America",false,"US","north-america",[],"The Academy, Bank of America's onboarding, education and professional development organization, uses AI conversation simulators in which employees practise different types of client interactions and receive real time feedback. The bank reports more than one million simulations completed in 2024 and says many employees note that practising client conversations helps them deliver better and more consistent service. No proficiency or client outcome figures are published.","scaled",2024,[33],[228],"en",[230],{"kpi":49,"value":231,"unit":232,"qualifier":233,"period":234,"claimant":235,"quote":236,"sourceUrl":237},1000000,"count","at-least","simulations completed by employees in 2024","organization","Employees completed over 1 million simulations last year, with many noting that practicing client conversations helps them deliver better and more consistent service.","https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/04/ai-adoption-by-bofa-s-global-workforce-improves-productivity--cl.html",true,[240],{"url":237,"title":241,"publisher":218,"date":242},"AI Adoption by BofA's Global Workforce Improves Productivity, Client Service","2025-04-08",{"level":244,"checkedAt":208},"source-verified","B","bank-of-america-academy-conversation-simulators","bank-of-america",{"title":249,"useCases":250,"organization":251,"vendors":253,"summary":257,"stage":258,"year":225,"channels":259,"languages":260,"metrics":261,"outcomeDisclosed":219,"sources":262,"verification":275,"grade":245,"id":276,"organizationSlug":277},"U.S. Department of Veterans Affairs: AI crisis call simulations for Veterans Crisis Line responders",[212],{"name":252,"anonymized":219,"country":220,"region":221,"industry":22},"U.S. Department of Veterans Affairs",[254],{"name":255,"role":256},"ReflexAI","platform","The Veterans Crisis Line trains new crisis responders with ReflexAI simulations in which generative AI plays eight Veteran personas, each with its own configured motivation and crisis, so trainees can practise in a low risk setting before live calls. Each simulated call produces a scoring summary of strengths and areas of growth. The tools launched with the first full cohort of new trainees in May 2024, and VA's 2025 AI use case inventory lists the system as deployed and not high impact.","production",[33],[228],[],[263,266,271],{"url":264,"title":265,"publisher":252},"https://department.va.gov/ai/wp-content/uploads/sites/26/2026/04/VA-AI-Use-Case-Inventory-2025-Web-Compliance-Updates.xlsx","VA AI Use Case Inventory 2025 (entry VA-24-2348, ReflexAI)",{"url":267,"title":268,"publisher":269,"date":270},"https://news.va.gov/133911/ai-technology-is-helping-crisis-line-responders/","AI technology is helping crisis line responders","VA News","2024-09-02",{"url":272,"title":273,"publisher":274},"https://www.missiondaybreak.net/2024-team-spotlights/reflexai/","ReflexAI, Mission Daybreak team spotlight","Mission Daybreak (VA suicide prevention challenge)",{"level":244,"checkedAt":208},"veterans-crisis-line-reflexai-training-simulations","u-s-department-of-veterans-affairs",{"title":279,"useCases":280,"organization":281,"vendors":283,"summary":286,"stage":258,"year":287,"channels":288,"languages":289,"metrics":290,"outcomeDisclosed":238,"sources":304,"verification":307,"grade":308,"id":309,"organizationSlug":310},"GoHealth: AI role play training for licensed benefits consultants",[212],{"name":282,"anonymized":219,"country":220,"region":221,"industry":20},"GoHealth",[284],{"name":285,"role":256},"Second Nature","GoHealth, a health insurance marketplace focused on Medicare, uses AI role play partners so licensed benefits consultants practise sales and compliance heavy conversations, both in onboarding and in ongoing training. For new hires, practice is built into the training weeks instead of a separate three week block of practice calls. After a pilot in the fourth quarter of 2022 GoHealth signed a long term contract. The vendor reports shorter onboarding, a higher sales conversion rate in the pilot and a higher trainee to trainer ratio.",2022,[33],[228],[291,299],{"kpi":48,"value":292,"unit":293,"qualifier":294,"baseline":295,"claimant":296,"quote":297,"sourceUrl":298},55,"percent","exact","onboarding cut from nine weeks to four weeks","vendor","That’s a 55% time saving that allows new hires to start making effective calls five weeks earlier than before.","https://secondnature.ai/resources/gohealth-boosts-productivity-and-cuts-onboarding-time-with-second-nature/",{"kpi":50,"value":300,"unit":293,"qualifier":294,"period":301,"baseline":302,"claimant":296,"quote":303,"sourceUrl":298},21,"Q4 2022 pilot, sales rates 10 days before versus 10 days after about 34 minutes of AI practice","sales rates in the 10 days before the pilot training","Average 21% increase in sales conversions after 34 minutes of practice",[305],{"url":298,"title":306,"publisher":285},"GoHealth Cuts Onboarding 55% with AI Sales Training",{"level":244,"checkedAt":208},"C","gohealth-ai-roleplay-sales-training",null,0,[313,319,324],{"kpi":50,"label":314,"unit":293,"aggregate":238,"higherIsBetter":238,"n":315,"nUpTo":311,"median":300,"min":300,"max":300,"byClaimant":316,"vendorOnly":238,"points":317},"Conversion uplift",1,{"organization":311,"vendor":315,"regulator":311,"independent":311},[318],{"evidenceId":309,"organization":282,"value":300,"qualifier":294,"claimant":296,"grade":308,"pooled":238},{"kpi":49,"label":320,"unit":232,"aggregate":219,"higherIsBetter":238,"n":315,"nUpTo":311,"median":231,"min":231,"max":231,"byClaimant":321,"vendorOnly":219,"points":322},"Interactions handled",{"organization":315,"vendor":311,"regulator":311,"independent":311},[323],{"evidenceId":246,"organization":218,"value":231,"qualifier":233,"claimant":235,"grade":245,"pooled":238},{"kpi":48,"label":325,"unit":293,"aggregate":238,"higherIsBetter":238,"n":315,"nUpTo":311,"median":292,"min":292,"max":292,"byClaimant":326,"vendorOnly":238,"points":327},"Time to proficiency reduction",{"organization":311,"vendor":315,"regulator":311,"independent":311},[328],{"evidenceId":309,"organization":282,"value":292,"qualifier":294,"claimant":296,"grade":308,"pooled":238},{"low":330,"high":331},32400,450000,[333,360,381,401,437],{"slug":202,"title":334,"shortTitle":335,"definition":336,"status":9,"industries":337,"functions":340,"patterns":343,"audience":347,"autonomy":348,"adoptionStage":38,"evidenceCount":349,"publicEvidenceCount":349,"organizations":350,"bestGrade":308,"headline":356,"lastVerified":207,"indexable":238},"AI quality and compliance monitoring of every customer interaction","Call quality and compliance","Automated quality assurance that transcribes and scores every customer interaction, voice and chat, against the organization's own rubric, checking required disclosures and script adherence, flagging conduct and mis selling risk, and surfacing coaching opportunities, instead of the small sample a human QA team can review.",[18,19,20,338,21,339],"energy-and-utilities","retail-and-ecommerce",[26,341,342],"regulatory-compliance","operations",[344,345,346],"speech-analytics","classification-and-routing","summarization","back-office","supervised-agent",5,[351,352,353,354,355],"British Gas","Central Bank","DoorDash","Oportun","VitalityHealth",{"kpi":51,"label":357,"unit":293,"n":315,"nUpTo":311,"kind":358,"value":66,"qualifier":359,"claimant":296,"organization":351,"vendorReported":238},"Quality score uplift","reported","approximately",{"slug":203,"title":361,"shortTitle":362,"definition":363,"status":9,"industries":364,"functions":366,"patterns":367,"audience":36,"autonomy":37,"adoptionStage":369,"evidenceCount":370,"publicEvidenceCount":349,"organizations":371,"bestGrade":245,"headline":376,"lastVerified":207,"indexable":238},"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.",[18,19,20,21,23,339,365],"technology",[26,342],[344,368,346,31],"rag-knowledge-assistant","mainstream",7,[372,373,354,374,375],"DBS Bank","Definity","SEB","SIGNAL IDUNA",{"kpi":377,"label":378,"unit":293,"n":379,"nUpTo":311,"kind":358,"value":380,"qualifier":294,"claimant":296,"organization":373,"vendorReported":238},"productivity-gain","Productivity gain",2,15,{"slug":204,"title":382,"shortTitle":383,"definition":384,"status":9,"industries":385,"functions":387,"patterns":388,"audience":36,"autonomy":389,"adoptionStage":38,"evidenceCount":390,"publicEvidenceCount":390,"organizations":391,"bestGrade":308,"headline":396,"lastVerified":207,"indexable":238},"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.",[18,21,386,20],"manufacturing",[27],[344,346,31],"copilot",4,[392,393,394,395],"Hughes Network Systems","Lumen Technologies","Sandvik Coromant","Zurich Insurance Group",{"kpi":397,"label":398,"unit":399,"n":315,"nUpTo":315,"kind":358,"value":400,"qualifier":294,"claimant":235,"organization":394,"vendorReported":219},"time-saved-per-task","Time saved per task","minutes",3,{"slug":205,"title":402,"shortTitle":403,"definition":404,"status":9,"industries":405,"functions":409,"patterns":410,"audience":411,"autonomy":348,"adoptionStage":369,"segment":412,"evidenceCount":413,"publicEvidenceCount":414,"organizations":415,"bestGrade":245,"headline":432,"lastVerified":207,"indexable":238},"AI agent for first line contact centre service","First line contact centre","An AI agent that answers the first line of inbound customer contact on phone, chat and messaging, resolves general and routine questions end to end in the customer's own language, and routes everything complex, sensitive or regulated to the right human team with the context attached.",[18,19,406,21,407,339,408],"payments","travel-and-hospitality","wealth-and-asset-management",[26],[29,30,368,345],"customer-facing","front-office",25,18,[416,417,218,418,419,420,421,422,423,424,425,426,427,428,429,430,431],"Air India","Airbnb","Bank of the Philippine Islands","BT Group","Commonwealth Bank of Australia","Ingka Group","JetBlue","Klarna","Lufthansa Group","Mobily","NatWest Group","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":433,"label":434,"unit":293,"n":370,"nUpTo":311,"kind":435,"value":436,"qualifier":294,"claimant":310,"organization":310,"vendorReported":219},"containment-rate","Containment rate","median",47,{"slug":206,"title":438,"shortTitle":439,"definition":440,"status":9,"industries":441,"functions":443,"patterns":445,"audience":36,"autonomy":348,"adoptionStage":38,"evidenceCount":390,"publicEvidenceCount":400,"organizations":447,"bestGrade":245,"headline":310,"lastVerified":207,"indexable":238},"AI assistant for employee onboarding","Employee onboarding assistant","An assistant that guides each new employee from signed contract through the first months: it answers first week questions in plain language, tracks the personal onboarding checklist, triggers the paperwork, equipment, access and training steps in the systems that own them, and keeps the manager and HR informed of what is still open.",[18,22,442,23],"professional-services",[25,444],"knowledge-management",[29,368,446],"agentic-workflow",[448,449,450],"American Addiction Centers","KPMG","U.S. Department of Agriculture",{"indexable":238,"reasons":452},[],[454,459,464,471,478,484,491,495,503,509,516,522,529,535,541,546,553,559,565,571,577,583,588,593,598,605,612,617,622,629,635,641,647,652],{"id":158,"label":455,"issuer":165,"region":166,"url":456,"description":457,"useCases":458,"indexable":238},"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":159,"label":460,"issuer":165,"region":166,"url":461,"description":462,"useCases":463,"indexable":238},"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":161,"label":465,"issuer":466,"region":467,"url":468,"description":469,"useCases":470,"indexable":238},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":472,"label":473,"issuer":474,"region":221,"url":475,"description":476,"useCases":477,"indexable":238},"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":479,"label":480,"issuer":165,"region":166,"url":481,"description":482,"useCases":483,"indexable":238},"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":485,"label":486,"issuer":487,"region":166,"url":488,"description":489,"useCases":490,"indexable":238},"uk-gdpr","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":160,"label":492,"issuer":179,"region":166,"url":493,"description":494,"useCases":436,"indexable":238},"FCA Consumer Duty","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",{"id":496,"label":497,"issuer":498,"region":499,"url":500,"description":501,"useCases":502,"indexable":238},"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":504,"label":505,"issuer":506,"region":499,"url":507,"description":508,"useCases":413,"indexable":238},"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":510,"label":511,"issuer":512,"region":467,"url":513,"description":514,"useCases":515,"indexable":238},"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":517,"label":518,"issuer":519,"region":221,"url":520,"description":521,"useCases":515,"indexable":238},"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":523,"label":524,"issuer":525,"region":166,"url":526,"description":527,"useCases":528,"indexable":238},"uk-atrs","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":530,"label":531,"issuer":532,"region":467,"url":533,"description":534,"useCases":380,"indexable":238},"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":536,"label":537,"issuer":165,"region":166,"url":538,"description":539,"useCases":540,"indexable":238},"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":542,"label":543,"issuer":165,"region":166,"url":544,"description":545,"useCases":540,"indexable":238},"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":547,"label":548,"issuer":549,"region":221,"url":550,"description":551,"useCases":552,"indexable":238},"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":554,"label":555,"issuer":165,"region":166,"url":556,"description":557,"useCases":558,"indexable":238},"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":560,"label":561,"issuer":562,"region":221,"url":563,"description":564,"useCases":558,"indexable":238},"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":566,"label":567,"issuer":568,"region":467,"url":569,"description":570,"useCases":558,"indexable":238},"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":572,"label":573,"issuer":165,"region":166,"url":574,"description":575,"useCases":576,"indexable":238},"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":578,"label":579,"issuer":580,"region":221,"url":581,"description":582,"useCases":576,"indexable":238},"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":584,"label":585,"issuer":498,"region":499,"url":586,"description":587,"useCases":66,"indexable":238},"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":589,"label":590,"issuer":165,"region":166,"url":591,"description":592,"useCases":66,"indexable":238},"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":594,"label":595,"issuer":165,"region":166,"url":596,"description":597,"useCases":66,"indexable":238},"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":599,"label":600,"issuer":601,"region":166,"url":602,"description":603,"useCases":604,"indexable":238},"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":606,"label":607,"issuer":608,"region":221,"url":609,"description":610,"useCases":611,"indexable":238},"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":613,"label":614,"issuer":165,"region":166,"url":615,"description":616,"useCases":611,"indexable":238},"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":618,"label":619,"issuer":165,"region":166,"url":620,"description":621,"useCases":65,"indexable":238},"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":623,"label":624,"issuer":625,"region":626,"url":627,"description":628,"useCases":349,"indexable":238},"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":630,"label":631,"issuer":632,"region":166,"url":633,"description":634,"useCases":390,"indexable":238},"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":636,"label":637,"issuer":638,"region":166,"url":639,"description":640,"useCases":390,"indexable":238},"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":642,"label":643,"issuer":644,"region":499,"url":645,"description":646,"useCases":400,"indexable":238},"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":648,"label":649,"issuer":165,"region":166,"url":650,"description":651,"useCases":400,"indexable":238},"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":653,"label":654,"issuer":655,"region":221,"url":656,"description":657,"useCases":400,"indexable":238},"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.",1790598304701]