[{"data":1,"prerenderedAt":721},["ShallowReactive",2],{"uc-live-agent-assist":3,"uc-regulations":520},{"useCase":4,"evidence":213,"blitsAiDeployments":361,"benchmarks":362,"indicative":386,"related":389,"indexability":518,"includeUnpublished":219},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":24,"patterns":27,"channels":32,"audience":36,"autonomy":37,"adoptionStage":38,"problem":39,"problemStats":40,"howItWorks":48,"valueDrivers":49,"kpis":54,"indicativeValue":61,"macroEstimates":95,"feasibility":96,"implementation":108,"risk":154,"blitsAi":189,"faq":191,"related":201,"datePublished":208,"dateModified":208,"lastVerified":208,"changelog":209,"slug":212},"Real time AI assist for contact centre agents","Live agent assist","AI agent assist for live contact centre calls","AI agent assist transcribes live calls, surfaces approved knowledge and drafts wrap up notes. Definity says call summaries cut 3.5 minutes from each call.","published","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.",[12,13,14,15],"agent assist","contact centre copilot","real time agent guidance","AI call summarization",[17,18,19,20,21,22,23],"cross-industry","banking","insurance","telecommunications","healthcare","retail-and-ecommerce","technology",[25,26],"customer-service","operations",[28,29,30,31],"speech-analytics","rag-knowledge-assistant","summarization","content-generation",[33,34,35],"agent-desktop","voice","web-chat","employee-facing","assist","mainstream","Even with good self service, the contacts that reach a human are the hard ones: complex products,\nexceptions, complaints, distressed customers. Agents juggle several systems while the customer\nwaits, search the knowledge base mid call, and then spend minutes writing notes after every\ncontact (three to five minutes per call at Definity before it automated summaries). New agents\ntake months to become proficient, and attrition means there are always new agents.\n\nAgent assist attacks the time around the conversation (searching, typing, summarizing) and the\nknowledge gap of less experienced staff, without handing the customer to a machine. That makes it\none of the lower risk ways to bring generative AI into regulated customer service, provided the\nsuggestions are grounded in approved content and the recording is handled correctly.",[41,46],{"statement":42,"sourceTitle":43,"sourceUrl":44,"year":45},"Industry estimates cited by Brynjolfsson, Li and Raymond suggest that 60% of contact centre agents leave each year, costing firms $10,000 to $20,000 per agent.","Generative AI at Work (NBER Working Paper 31161)","https://www.nber.org/system/files/working_papers/w31161/w31161.pdf",2023,{"statement":47,"sourceTitle":43,"sourceUrl":44,"year":45},"In the support operation studied by Brynjolfsson, Li and Raymond, agents without AI assistance needed more than six months of tenure to perform as well as assisted agents with two months.","1. **Transcribe live.** Streaming speech recognition turns both sides of the call into text in\n   real time; on chat the text is already there.\n2. **Understand the moment.** The system detects the customer's intent and key details (product,\n   account type, the problem) as the conversation develops.\n3. **Surface knowledge and next steps.** Relevant procedure snippets, eligibility rules and next\n   best actions appear in the agent desktop, retrieved from approved content.\n4. **Draft, do not send.** On chat and email it drafts replies the agent edits; on voice it\n   suggests wording for disclosures and explanations.\n5. **Wrap up automatically.** After the contact it writes a structured summary, fills CRM fields\n   and service request forms, and the agent confirms them.\n6. **Pause on sensitive data.** Card numbers and authentication answers are not transcribed or\n   stored, using pause and resume or redaction.",[50,51,52,53],"employee-productivity","cost-to-serve","customer-experience","compliance",[55,56,57,58,59,60],"handling-time-reduction","productivity-gain","time-saved-per-task","first-contact-resolution","customer-satisfaction","accuracy",{"referenceOrg":62,"inputs":63,"formula":90,"currency":91,"period":92,"resultLabel":93,"caveat":94},"A contact centre with 500 agents",[64,69,76,83],{"key":65,"label":66,"low":67,"high":67,"unit":65,"note":68},"agents","Agents using the assistant",500,"The reference organization.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"agentCost","Fully loaded cost per agent",40000,60000,"USD per agent per year","Editorial assumption. Replace with your own cost, including outsourced seats.",{"key":77,"label":78,"low":79,"high":80,"unit":81,"note":82},"onContactShare","Share of paid time spent on contacts and wrap up",0.7,0.8,"fraction of paid time","Editorial assumption for occupancy. Replace with your workforce management data.",{"key":84,"label":85,"low":86,"high":87,"unit":88,"note":89},"timeReduction","Reduction in handling time, including wrap up",0.05,0.15,"fraction of handling time","Conservative against the evidence on this page (Definity says automated summaries saved 3.5 minutes per call; Google Cloud reports a 20% cut in call handle time for Definity's whole program, which also automated caller authentication; the NBER field study measured 14% more issues resolved per hour).","agents * agentCost * onContactShare * timeReduction","USD","per year","Agent capacity released","Released capacity, realized only if staffing or service levels are adjusted. It leaves out platform and transcription costs, and quality effects such as fewer errors, better compliance and faster onboarding of new agents.",[],{"complexity":97,"complexityNote":98,"dataPrerequisites":99,"integrations":103},"medium","Summaries after the call are straightforward. Real time guidance needs low latency streaming transcription, integration with the telephony platform and agent desktop, and a knowledge base that is clean enough to surface the right snippet in seconds.",[100,101,102],"Approved knowledge articles and procedures with owners","Call recordings or transcripts to tune intents and test summaries","The CRM fields and disposition codes the summary must fill",[104,105,106,107],"Telephony or contact centre platform with access to the audio stream","Agent desktop and CRM","Knowledge base","Card data redaction or pause and resume for payment calls",{"steps":109,"guardrails":128,"humanInTheLoop":134,"kpisToInstrument":135,"failureModes":141},[110,113,116,119,122,125],{"title":111,"detail":112},"Start with after call summaries","Automated wrap up is the fastest, safest win: the agent reviews and confirms every summary, and time saved is easy to measure.",{"title":114,"detail":115},"Clean the knowledge before surfacing it","Real time suggestions are only as good as the articles behind them. Fix duplicates and outdated procedures for the top call reasons first.",{"title":117,"detail":118},"Add real time guidance for a few intents","Pick high volume intents with clear procedures and add knowledge surfacing and disclosure prompts. Watch whether agents use or ignore suggestions.",{"title":120,"detail":121},"Handle sensitive data by design","Make sure card numbers, authentication answers and special category data are redacted or never captured, and that transcripts are stored in region with defined retention.",{"title":123,"detail":124},"Measure with a control group","Roll out by team and compare handling time, resolution and satisfaction with teams that do not yet have it, on the same contact mix.",{"title":126,"detail":127},"Agree how the data will not be used","Tell agents what is recorded and agree that assist data is not used for individual performance scoring unless that is assessed and disclosed separately.",[129,130,131,132,133],"Suggestions only from approved knowledge, with the source visible to the agent","The agent confirms every summary and every drafted reply before it is saved or sent","Card data and authentication answers redacted or excluded from transcription","No inference of agents' emotions","Transcripts stored in region with a defined retention period","The agent decides what is said and done on every contact and confirms summaries before they are saved. Team leaders review a sample of summaries and suggestions for accuracy, and knowledge owners fix content behind wrong suggestions.",[136,137,138,139,140],"Average handling time and wrap up time, against a control group","Summary accuracy on a weekly sample","Suggestion acceptance rate per intent","First contact resolution and customer satisfaction","Time to proficiency for new agents",[142,145,148,151],{"title":143,"detail":144},"Summaries nobody checks","Agents approve summaries without reading them and errors enter the CRM. Sample and score them, and make edits easy.",{"title":146,"detail":147},"Suggestion noise","Too many prompts distract agents mid call. Limit suggestions to what is relevant and measure acceptance.",{"title":149,"detail":150},"Card data in transcripts","A payment call is transcribed and stored with the card number. Build redaction or pause and resume in from the start.",{"title":152,"detail":153},"Assist becomes surveillance","Transcripts are reused to score individuals without disclosure or assessment. That changes the risk class and erodes trust.",{"euAiAct":155,"regulations":158,"guidance":165,"controls":182,"incidents":188},{"tier":156,"basis":157},"context-dependent","As a pure assist tool for agents it is minimal risk; the customer does not interact with the AI. It becomes high risk under Annex III point 4(b) if its data is used to monitor and evaluate individual agents' performance, and inferring agents' emotions at work is prohibited under Article 5(1)(f).",[159,160,161,162,163,164],"eu-ai-act","gdpr","pci-dss","uk-consumer-duty","dora","mas-ai-risk-management",[166,172,176],{"title":167,"issuer":168,"region":169,"url":170,"note":171},"Article 5, prohibited AI practices","European Union","europe","https://artificialintelligenceact.eu/article/5/","Point 1(f) prohibits AI systems that infer emotions of natural persons in the workplace, except for medical or safety reasons, which rules out emotion scoring of agents.",{"title":173,"issuer":168,"region":169,"url":174,"note":175},"Annex III, high risk AI systems referred to in Article 6(2)","https://artificialintelligenceact.eu/annex/3/","Point 4(b) covers systems that monitor and evaluate the performance and behaviour of workers.",{"title":177,"issuer":178,"region":179,"url":180,"note":181},"Artificial Intelligence (AI) Model Risk Management, information paper","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/publications/monographs-or-information-paper/2024/artificial-intelligence-model-risk-management","Example of supervisory good practice for testing and monitoring generative AI applications at banks.",[183,184,185,186,187],"Data protection impact assessment covering call transcription and retention","PCI DSS scoping of the transcription and storage path","Written limits on the use of assist data for individual performance management","Inventory entry with an accountable owner in customer operations","Regular accuracy sampling of summaries and suggestions",[],{"howToBuild":190},"On Blits.ai the building blocks are **voice** telephony with real time streaming speech\nrecognition across several providers (with **self hosted transcription** and speaker\ndiarization available where recordings must stay on Blits.ai infrastructure), an **AI agent** grounded in a **knowledge base** of approved\nprocedures with hybrid retrieval, and **summarization** through an agent with **structured\noutput** that maps to CRM fields. The assistant is exposed to the agent desktop through the\n**REST or WebSocket API**, and **custom functions** write the confirmed summary to the CRM.\n\n**PII masking** at the gateway keeps sensitive data away from the model and logs, and card\nnumbers typed in chat are detected and tokenized there; for payment calls on voice, plan\nredaction or pause and resume as a design step. **Test suites** grade summaries and suggestions against\nreviewed examples on every change, and the platform is model agnostic with EU or UAE data\nresidency.",[192,195,198],{"question":193,"answer":194},"How much handling time does agent assist save?","Results vary with what is measured. Definity says automated call summaries cut three and a half minutes from each call, which Google Cloud's customer story puts at 33% of average handle time; Google Cloud's customer list reports a 20% cut for Definity's whole program, which also automated caller authentication, and a 15% efficiency gain at SEB. The NBER field study measured 14% more issues resolved per hour, and DBS expects up to 20% for its CSO Assistant once fully deployed, which is a forecast, not yet a result.",{"question":196,"answer":197},"Who benefits most?","Less experienced agents. The NBER field study of 5,179 support agents found 14% more issues resolved per hour on average, with a 34% improvement for novice and low skilled workers and little effect on the most experienced.",{"question":199,"answer":200},"Is agent assist high risk under the EU AI Act?","Not as an assist tool. It becomes high risk if the same data is used to evaluate individual agents (Annex III point 4(b)), and inferring agents' emotions at work is prohibited. Keep those uses separate and assessed.",[202,203,204,205,206,207],"email-and-ticket-reply-drafting","first-line-contact-centre-agent","call-quality-and-compliance-monitoring","enterprise-knowledge-search","conversation-roleplay-training","meeting-summarization-and-action-items","2026-09-27",[210],{"date":208,"note":211},"First published","live-agent-assist",[214,249,294,316,338],{"title":215,"useCases":216,"organization":217,"vendors":221,"summary":224,"stage":225,"year":226,"channels":227,"languages":228,"metrics":229,"outcomeDisclosed":238,"sources":239,"verification":243,"grade":246,"id":247,"organizationSlug":248},"DBS: CSO Assistant, a generative AI copilot for customer service officers",[212],{"name":218,"anonymized":219,"country":220,"region":179,"industry":18},"DBS Bank",false,"SG",[222],{"name":218,"role":223},"in-house","DBS built CSO Assistant in house for its 500 customer service officers in Singapore, who handle queries from more than 250,000 customers a month. It combines a language model tuned to local languages and parlance with telephony and speech recognition: it transcribes the call in real time, searches the knowledge base live, then writes the call summary and prefills service request fields. Pilots began in October 2023; full rollout in Singapore was planned before the end of 2024, followed by Taiwan and Hong Kong. The 20% cut in call handling time in the release is an expectation, not a result.","pilot",2024,[34,33],[],[230],{"kpi":60,"value":231,"unit":232,"qualifier":233,"period":234,"claimant":235,"quote":236,"sourceUrl":237},100,"percent","approximately","pilot, October 2023 to July 2024","organization","Based on data collected since pilots began in October 2023, CSO Assistant has demonstrated transcription and solutioning accuracy of nearly 100%, and when fully deployed, is expected to reduce call handling time by up to 20%.","https://www.dbs.com/newsroom/DBS_empowers_its_Customer_Service_Officers_with_Gen_AI_powered_virtual_assistant_to_reduce_toil_and_enhance_customer_experience",true,[240],{"url":237,"title":241,"publisher":218,"date":242},"DBS empowers its Customer Service Officers with Gen AI powered virtual assistant to reduce toil and enhance customer experience","2024-07-18",{"level":244,"checkedAt":245},"source-verified","2026-09-26","B","dbs-cso-assistant","dbs-bank",{"title":250,"useCases":251,"organization":252,"vendors":256,"summary":263,"stage":264,"year":265,"channels":266,"languages":267,"metrics":269,"outcomeDisclosed":238,"sources":285,"verification":290,"grade":291,"id":292,"organizationSlug":293},"Definity: call summaries and real time recommendations for contact centre staff",[212],{"name":253,"anonymized":219,"country":254,"region":255,"industry":19},"Definity","CA","north-america",[257,260],{"name":258,"role":259},"Google Cloud","platform",{"name":261,"role":262},"Deloitte","integrator","Definity, the parent of several Canadian property and casualty insurers, worked with Deloitte to use Google's AI in its contact centre: summarizing calls, automating caller authentication, analyzing customer sentiment and giving team members real time recommendations. Calls are transcribed, passed through data loss prevention and summarized by language models, with the summaries stored in Salesforce. Definity says automated summaries cut three and a half minutes from each call within about a month; Google Cloud reports shorter call handling times and higher productivity overall.","production",2026,[34,33],[268],"en",[270,276,278],{"kpi":55,"value":271,"unit":232,"qualifier":272,"claimant":273,"quote":274,"sourceUrl":275},20,"exact","vendor","Definity, with help from Google Cloud partner Deloitte, leverages Google’s AI capabilities to summarize calls, automate caller authentication, analyze customer sentiment, and provide real-time recommendations to contact center team members — reducing call handle times by 20% and boosting productivity by 15%.","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",{"kpi":56,"value":277,"unit":232,"qualifier":272,"claimant":273,"quote":274,"sourceUrl":275},15,{"kpi":57,"value":279,"unit":280,"qualifier":272,"period":281,"baseline":282,"claimant":235,"quote":283,"sourceUrl":284},3.5,"minutes","per call, within about a month of automating call summaries","Staff previously spent three to five minutes writing call summary notes","Within about a month, we cut the time agents spend on each call by three and a half minutes.","https://cloud.google.com/customers/definity",[286,288],{"url":275,"title":287,"publisher":258},"Real world gen AI use cases from the world's leading organizations",{"url":284,"title":289,"publisher":258},"Definity: Modernizing call center experiences with AI",{"level":244,"checkedAt":208},"C","definity-contact-centre-agent-assist",null,{"title":295,"useCases":296,"organization":298,"vendors":301,"summary":305,"stage":264,"year":306,"channels":307,"languages":308,"metrics":309,"outcomeDisclosed":238,"sources":312,"verification":314,"grade":291,"id":315,"organizationSlug":293},"SEB: AI agent suggests responses and summarizes calls in wealth management",[212,297],"client-meeting-notes-and-crm-update",{"name":299,"anonymized":219,"country":300,"region":169,"industry":18},"SEB","SE",[302,303],{"name":258,"role":259},{"name":304,"role":262},"Bain & Company","SEB, a Nordic corporate bank, worked with Bain & Company to build an AI agent on Google Cloud for its wealth management division. The agent suggests responses during conversations with customers and generates call summaries afterwards. Google Cloud reports a 15% efficiency gain.",2025,[33],[],[310],{"kpi":56,"value":277,"unit":232,"qualifier":272,"claimant":273,"quote":311,"sourceUrl":275},"The agent, built with Google Cloud, enhances end-customer conversations with suggested responses and generates call summaries, helping to increase efficiency by 15%.",[313],{"url":275,"title":287,"publisher":258},{"level":244,"checkedAt":245},"seb-wealth-advisor-agent-assist",{"title":317,"useCases":318,"organization":319,"vendors":322,"summary":327,"stage":264,"year":306,"channels":328,"languages":329,"metrics":331,"outcomeDisclosed":238,"sources":332,"verification":336,"grade":291,"id":337,"organizationSlug":293},"SIGNAL IDUNA: Co SI knowledge assistant for health insurance service agents",[205,212],{"name":320,"anonymized":219,"country":321,"region":169,"industry":19},"SIGNAL IDUNA","DE",[323,324,326],{"name":258,"role":259},{"name":325,"role":262},"Boston Consulting Group",{"name":261,"role":262},"SIGNAL IDUNA, a German insurer, built Co SI with Google Cloud, BCG and Deloitte: a knowledge assistant that helps customer service agents answer complex health insurance questions. Google Cloud reports that for less experienced agents, information searches are 30% faster and inquiries that previously needed further escalation dropped from 27% to 3%.",[33],[330],"de",[],[333],{"url":275,"title":334,"publisher":258,"archivedUrl":335},"Real-world gen AI use cases from the world's leading organizations","https://web.archive.org/web/20251027121348/https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",{"level":244,"checkedAt":208},"signal-iduna-co-si-knowledge-assistant",{"title":339,"useCases":340,"organization":341,"vendors":344,"summary":347,"stage":348,"year":226,"channels":349,"languages":350,"metrics":351,"outcomeDisclosed":219,"sources":352,"verification":359,"grade":291,"id":360,"organizationSlug":293},"Oportun: from sample based QA to monitoring every call",[204,212],{"name":342,"anonymized":219,"country":343,"region":255,"industry":18},"Oportun","US",[345],{"name":346,"role":259},"Cresta","Oportun, a US consumer lender, replaced manual, sample based QA with Cresta's AI quality management across all calls, combined with real time guidance for agents. Coaching now focuses on the behaviours that drive performance, visible across every call, instead of a small sample reviewed weeks later. No quantified outcome is published.","scaled",[34],[268],[],[353,356],{"url":354,"title":355,"publisher":346},"https://www.cresta.com/customers/oportun","How Oportun moved from sample-based QA to 100% interaction monitoring with Cresta",{"url":357,"title":358,"publisher":346},"https://web.archive.org/web/20240423210739/https://cresta.com/customers/oportun/","How Oportun transformed QM and reduced workload by 50% with Cresta",{"level":244,"checkedAt":208},"oportun-ai-quality-management",1,[363,371,376,381],{"kpi":56,"label":364,"unit":232,"aggregate":238,"higherIsBetter":238,"n":365,"nUpTo":366,"median":277,"min":277,"max":277,"byClaimant":367,"vendorOnly":238,"points":368},"Productivity gain",2,0,{"organization":366,"vendor":365,"regulator":366,"independent":366},[369,370],{"evidenceId":292,"organization":253,"value":277,"qualifier":272,"claimant":273,"grade":291,"pooled":238},{"evidenceId":315,"organization":299,"value":277,"qualifier":272,"claimant":273,"grade":291,"pooled":238},{"kpi":60,"label":372,"unit":232,"aggregate":238,"higherIsBetter":238,"n":361,"nUpTo":366,"median":231,"min":231,"max":231,"byClaimant":373,"vendorOnly":219,"points":374},"Accuracy",{"organization":361,"vendor":366,"regulator":366,"independent":366},[375],{"evidenceId":247,"organization":218,"value":231,"qualifier":233,"claimant":235,"grade":246,"pooled":238},{"kpi":55,"label":377,"unit":232,"aggregate":238,"higherIsBetter":238,"n":361,"nUpTo":366,"median":271,"min":271,"max":271,"byClaimant":378,"vendorOnly":238,"points":379},"Handling time reduction",{"organization":366,"vendor":361,"regulator":366,"independent":366},[380],{"evidenceId":292,"organization":253,"value":271,"qualifier":272,"claimant":273,"grade":291,"pooled":238},{"kpi":57,"label":382,"unit":280,"aggregate":238,"higherIsBetter":238,"n":361,"nUpTo":366,"median":279,"min":279,"max":279,"byClaimant":383,"vendorOnly":219,"points":384},"Time saved per task",{"organization":361,"vendor":366,"regulator":366,"independent":366},[385],{"evidenceId":292,"organization":253,"value":279,"qualifier":272,"claimant":235,"grade":291,"pooled":238},{"low":387,"high":388},700000,3600000,[390,413,454,475,488,505],{"slug":202,"title":391,"shortTitle":392,"definition":393,"status":9,"industries":394,"functions":396,"patterns":397,"audience":36,"autonomy":399,"adoptionStage":38,"evidenceCount":400,"publicEvidenceCount":400,"organizations":401,"bestGrade":246,"headline":408,"lastVerified":208,"indexable":238},"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.",[17,395,18,20,23],"government",[25,26],[31,30,29,398],"classification-and-routing","copilot",6,[402,403,404,405,406,407],"Centers for Disease Control and Prevention","First National Bank","HYPE","Nomad eSIM","Transportation Security Administration","Turing",{"kpi":409,"label":410,"unit":232,"n":365,"nUpTo":366,"kind":411,"value":412,"qualifier":233,"claimant":273,"organization":404,"vendorReported":238},"processing-time-reduction","Cycle time reduction","reported",50,{"slug":203,"title":414,"shortTitle":415,"definition":416,"status":9,"industries":417,"functions":421,"patterns":422,"audience":425,"autonomy":426,"adoptionStage":38,"segment":427,"evidenceCount":428,"publicEvidenceCount":429,"organizations":430,"bestGrade":246,"headline":448,"lastVerified":208,"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.",[17,18,418,20,419,22,420],"payments","travel-and-hospitality","wealth-and-asset-management",[25],[423,424,29,398],"conversational-agent","voice-agent","customer-facing","supervised-agent","front-office",25,18,[431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447],"Air India","Airbnb","Bank of America","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":449,"label":450,"unit":232,"n":451,"nUpTo":366,"kind":452,"value":453,"qualifier":272,"claimant":293,"organization":293,"vendorReported":219},"containment-rate","Containment rate",7,"median",47,{"slug":204,"title":455,"shortTitle":456,"definition":457,"status":9,"industries":458,"functions":460,"patterns":462,"audience":463,"autonomy":426,"adoptionStage":464,"evidenceCount":465,"publicEvidenceCount":465,"organizations":466,"bestGrade":291,"headline":471,"lastVerified":208,"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.",[17,18,19,459,20,22],"energy-and-utilities",[25,461,26],"regulatory-compliance",[28,398,30],"back-office","early-adopters",5,[467,468,469,342,470],"British Gas","Central Bank","DoorDash","VitalityHealth",{"kpi":472,"label":473,"unit":232,"n":361,"nUpTo":366,"kind":411,"value":474,"qualifier":233,"claimant":273,"organization":467,"vendorReported":238},"quality-score-uplift","Quality score uplift",10,{"slug":205,"title":476,"shortTitle":477,"definition":478,"status":9,"industries":479,"functions":481,"patterns":483,"audience":36,"autonomy":37,"adoptionStage":38,"evidenceCount":484,"publicEvidenceCount":484,"organizations":485,"bestGrade":246,"headline":293,"lastVerified":208,"indexable":238},"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.",[17,18,420,19,395,480],"professional-services",[482,26,25],"knowledge-management",[29,423,30],4,[433,486,320,487],"Morgan Stanley","Wells Fargo",{"slug":206,"title":489,"shortTitle":490,"definition":491,"status":9,"industries":492,"functions":493,"patterns":496,"audience":36,"autonomy":37,"adoptionStage":464,"evidenceCount":497,"publicEvidenceCount":497,"organizations":498,"bestGrade":246,"headline":501,"lastVerified":245,"indexable":238},"AI roleplay training for customer conversations","Conversation roleplay training","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.",[17,18,19,20,395,21],[494,25,495],"human-resources","sales",[423,424,31],3,[433,499,500],"GoHealth","U.S. Department of Veterans Affairs",{"kpi":502,"label":503,"unit":232,"n":361,"nUpTo":366,"kind":411,"value":504,"qualifier":272,"claimant":273,"organization":499,"vendorReported":238},"conversion-rate-uplift","Conversion uplift",21,{"slug":207,"title":506,"shortTitle":507,"definition":508,"status":9,"industries":509,"functions":510,"patterns":511,"audience":36,"autonomy":399,"adoptionStage":38,"evidenceCount":465,"publicEvidenceCount":465,"organizations":512,"bestGrade":246,"headline":293,"lastVerified":208,"indexable":238},"AI meeting summarization and action items","Meeting summaries and action items","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.",[17,395,23,480],[482,26],[30,28],[513,514,515,516,517],"U.S. Department of Labor","Ministry of Justice","Softcat","Trace3","Government Digital Service",{"indexable":238,"reasons":519},[],[521,526,531,539,546,551,558,563,568,574,579,585,592,598,604,609,616,622,628,634,640,646,651,656,661,668,675,680,685,692,698,704,710,715],{"id":159,"label":522,"issuer":168,"region":169,"url":523,"description":524,"useCases":525,"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":160,"label":527,"issuer":168,"region":169,"url":528,"description":529,"useCases":530,"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":532,"label":533,"issuer":534,"region":535,"url":536,"description":537,"useCases":538,"indexable":238},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":540,"label":541,"issuer":542,"region":255,"url":543,"description":544,"useCases":545,"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":163,"label":547,"issuer":168,"region":169,"url":548,"description":549,"useCases":550,"indexable":238},"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":552,"label":553,"issuer":554,"region":169,"url":555,"description":556,"useCases":557,"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":162,"label":559,"issuer":560,"region":169,"url":561,"description":562,"useCases":453,"indexable":238},"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.",{"id":164,"label":564,"issuer":178,"region":179,"url":565,"description":566,"useCases":567,"indexable":238},"MAS AI risk management guidelines","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":569,"label":570,"issuer":571,"region":179,"url":572,"description":573,"useCases":428,"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":161,"label":575,"issuer":576,"region":535,"url":577,"description":578,"useCases":271,"indexable":238},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":580,"label":581,"issuer":582,"region":255,"url":583,"description":584,"useCases":271,"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":586,"label":587,"issuer":588,"region":169,"url":589,"description":590,"useCases":591,"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":593,"label":594,"issuer":595,"region":535,"url":596,"description":597,"useCases":277,"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":599,"label":600,"issuer":168,"region":169,"url":601,"description":602,"useCases":603,"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":605,"label":606,"issuer":168,"region":169,"url":607,"description":608,"useCases":603,"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":610,"label":611,"issuer":612,"region":255,"url":613,"description":614,"useCases":615,"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":617,"label":618,"issuer":168,"region":169,"url":619,"description":620,"useCases":621,"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":623,"label":624,"issuer":625,"region":255,"url":626,"description":627,"useCases":621,"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":629,"label":630,"issuer":631,"region":535,"url":632,"description":633,"useCases":621,"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":635,"label":636,"issuer":168,"region":169,"url":637,"description":638,"useCases":639,"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":641,"label":642,"issuer":643,"region":255,"url":644,"description":645,"useCases":639,"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":647,"label":648,"issuer":178,"region":179,"url":649,"description":650,"useCases":474,"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":652,"label":653,"issuer":168,"region":169,"url":654,"description":655,"useCases":474,"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":657,"label":658,"issuer":168,"region":169,"url":659,"description":660,"useCases":474,"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":662,"label":663,"issuer":664,"region":169,"url":665,"description":666,"useCases":667,"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":669,"label":670,"issuer":671,"region":255,"url":672,"description":673,"useCases":674,"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":676,"label":677,"issuer":168,"region":169,"url":678,"description":679,"useCases":674,"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":681,"label":682,"issuer":168,"region":169,"url":683,"description":684,"useCases":400,"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":686,"label":687,"issuer":688,"region":689,"url":690,"description":691,"useCases":465,"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":693,"label":694,"issuer":695,"region":169,"url":696,"description":697,"useCases":484,"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":699,"label":700,"issuer":701,"region":169,"url":702,"description":703,"useCases":484,"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":705,"label":706,"issuer":707,"region":179,"url":708,"description":709,"useCases":497,"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":711,"label":712,"issuer":168,"region":169,"url":713,"description":714,"useCases":497,"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":716,"label":717,"issuer":718,"region":255,"url":719,"description":720,"useCases":497,"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.",1790598307437]