[{"data":1,"prerenderedAt":676},["ShallowReactive",2],{"uc-sales-call-coaching-and-crm-update":3,"uc-regulations":468},{"useCase":4,"evidence":213,"blitsAiDeployments":325,"benchmarks":326,"indicative":344,"related":347,"indexability":466,"includeUnpublished":219},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":22,"patterns":24,"channels":28,"audience":33,"autonomy":34,"adoptionStage":35,"problem":36,"problemStats":37,"howItWorks":45,"valueDrivers":46,"kpis":50,"indicativeValue":56,"macroEstimates":97,"feasibility":98,"implementation":111,"risk":157,"blitsAi":189,"faq":191,"related":201,"datePublished":208,"dateModified":208,"lastVerified":208,"changelog":209,"slug":212},"AI sales call coaching and CRM update","Sales call coaching and CRM update","AI sales call coaching and automatic CRM updates","AI analyses sales calls to coach sellers and proposes CRM updates. Hughes cut call audit costs by 90%; Sandvik sellers save three minutes per Outlook lookup.","published","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.",[12,13,14,15,16],"conversation intelligence for sales","revenue intelligence","AI sales coaching","automatic CRM logging","sales call analysis",[18,19,20,21],"cross-industry","telecommunications","manufacturing","insurance",[23],"sales",[25,26,27],"speech-analytics","summarization","content-generation",[29,30,31,32],"voice","microsoft-teams","email","internal-tools","employee-facing","copilot","early-adopters","Sellers spend most of their week on work that is not selling: writing up calls, updating\nopportunities, logging contacts, searching past emails before a meeting. The CRM suffers first.\nNotes are short, late or missing, stages and next steps are out of date, and forecasts are built on\nwhat sellers remembered to type. Managers coach from the few calls they join and from pipeline\nreports that do not show what was actually said.\n\nAI can take over much of the administration and make coaching evidence based. Call and meeting transcripts\nbecome summaries, next steps and CRM updates the seller confirms in one step. Across many calls,\nthe same analysis shows where deals stall, which questions top performers ask, and where a seller\nneeds help. The sensitive part is the second one: once calls are analysed to judge individual\nsellers, it is worker monitoring, with legal limits and a trust cost if handled badly.",[38,43],{"statement":39,"sourceTitle":40,"sourceUrl":41,"year":42},"Salesforce's State of Sales survey of 7,775 sales professionals found that reps spend 28% of their week actually selling, with most of their time taken by tasks such as deal management and data entry.","New Research Reveals Sales Reps Need a Productivity Overhaul, Spend Less than 30% Of Their Time Actually Selling","https://www.salesforce.com/news/stories/sales-research-2023/",2023,{"statement":44,"sourceTitle":40,"sourceUrl":41,"year":42},"The same Salesforce survey found that only 26% of sales professionals receive one to one coaching at least weekly.","1. **Capture with consent.** Calls and online meetings are recorded and transcribed only where the\n   participants have been informed, and customers can decline.\n2. **Summarize and extract.** The AI writes a summary, the customer's stated needs and objections,\n   agreed next steps, and any changes to contacts, stage, amount or close date.\n3. **Propose the CRM update.** The proposed changes appear next to the opportunity for the seller to\n   confirm or correct, instead of being typed from memory.\n4. **Draft the follow up.** A recap email to the customer with the agreed next steps is drafted for\n   the seller to edit and send.\n5. **Coach against the method.** Across calls, the AI marks moments linked to the team's sales\n   method (discovery questions, next step agreed, pricing discussed) and surfaces examples, for the\n   seller's own review and for coaching conversations with their manager.\n6. **Improve the playbook.** Aggregated, anonymized patterns show which objections are rising and\n   which approaches work, feeding training and enablement content.",[47,48,49],"employee-productivity","revenue-growth","speed",[51,52,53,54,55],"time-saved-per-task","cycle-time-days","cost-reduction","conversion-rate-uplift","users-served",{"referenceOrg":57,"inputs":58,"formula":92,"currency":93,"period":94,"resultLabel":95,"caveat":96},"A sales organization with 300 quota carrying sellers",[59,64,71,78,85],{"key":60,"label":61,"low":62,"high":62,"unit":60,"note":63},"sellers","Sellers using the tool",300,"The reference organization.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"adminHoursPerWeek","Hours per seller per week on call notes, CRM updates and follow up emails",3,5,"hours per seller per week","Editorial assumption. Replace with a time study of your own sellers.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"shareSaved","Share of that administration the AI takes over",0.3,0.5,"fraction of admin hours","Editorial assumption. For scale, Sandvik Coromant reports three minutes saved per transaction several times a day per account manager.",{"key":79,"label":80,"low":81,"high":82,"unit":83,"note":84},"weeks","Working weeks per year",44,46,"weeks per year","Editorial assumption.",{"key":86,"label":87,"low":88,"high":89,"unit":90,"note":91},"hourlyCost","Fully loaded seller cost",60,100,"USD per hour","Editorial assumption, replace with your own.","sellers * adminHoursPerWeek * shareSaved * weeks * hourlyCost","USD","per year","Seller time released from administration","Values seller time at cost. It leaves out the effect on revenue of more selling time and better coaching, the value of a more accurate CRM for forecasting, and the cost of the platform. Time released only becomes value if it goes into customer work.",[],{"complexity":99,"complexityNote":100,"dataPrerequisites":101,"integrations":106},"medium","Summaries and CRM suggestions are available in many CRM and meeting tools. The effort is in consent and recording rules per country, clean CRM field definitions, and a coaching approach that sellers and works councils accept.",[102,103,104,105],"Call and meeting recordings or transcripts, captured with notice and consent","A CRM with defined opportunity stages, fields and next step conventions","The team's sales method or playbook, written down","Agreement with sellers (and employee representatives where required) on how analysis is used",[107,108,109,110],"CRM (for example Salesforce, Microsoft Dynamics 365, HubSpot)","Telephony and meeting platforms (Microsoft Teams, Zoom, dialers)","Email and calendar","Sales enablement and learning content",{"steps":112,"guardrails":131,"humanInTheLoop":137,"kpisToInstrument":138,"failureModes":144},[113,116,119,122,125,128],{"title":114,"detail":115},"Settle consent and purpose first","Decide which calls are recorded, how customers are told and can opt out, and in writing what the analysis will and will not be used for. Involve employee representatives where required.",{"title":117,"detail":118},"Start with summaries and CRM updates","Deliver the part sellers feel immediately: a summary, next steps and proposed CRM changes they confirm in one step. Measure time saved and CRM completeness.",{"title":120,"detail":121},"Define the method you coach against","Turn the sales method into observable moments (for example budget discussed, decision maker identified, next step agreed) and test that the AI detects them reliably on real calls.",{"title":123,"detail":124},"Give sellers their own insight first","Let sellers review their own calls and scores before managers see them, and use insight in coaching conversations rather than as a league table.",{"title":126,"detail":127},"Audit the scoring","Check detection accuracy by language, accent and call type, and make sure no metric depends on tone of voice or inferred emotion.",{"title":129,"detail":130},"Feed enablement","Use aggregated patterns (rising objections, winning questions) to update training and playbooks, with anonymized examples.",[132,133,134,135,136],"Recording and analysis only with notice to all participants and an opt out for customers","CRM changes proposed to the seller, never written without confirmation","No inference of emotions from voice or face; analysis based on what was said","Coaching insight is not used alone for pay, promotion or dismissal decisions","Transcripts masked for payment and sensitive personal data, with defined retention","Sellers confirm every CRM update and follow up message. Managers use the insight in coaching conversations and own any judgment about performance, based on more than the AI's analysis. Sales operations reviews extraction accuracy monthly.",[139,140,141,142,143],"Seller time on administration per week, before and after","Share of opportunities with a next step and updated fields after each meeting","Acceptance rate of proposed CRM updates","Accuracy of detected sales method moments on a reviewed sample","Win rate and cycle length for coached versus not yet coached sellers",[145,148,151,154],{"title":146,"detail":147},"Surveillance, not coaching","Sellers experience scores as monitoring and game or avoid the tool. Agree the purpose up front, show sellers their data first and coach rather than rank.",{"title":149,"detail":150},"Confident but wrong CRM data","The AI records a close date or amount that was never agreed. Propose changes for confirmation, never write them silently.",{"title":152,"detail":153},"Recording without a lawful basis","Calls are recorded in a country or channel where notice or consent was not given. Map the rules per country and enforce them in the tool.",{"title":155,"detail":156},"Biased scoring","Detection works worse for some accents or languages and penalizes those sellers. Test accuracy per group before scores are shown.",{"euAiAct":158,"regulations":161,"guidance":166,"controls":182,"incidents":188},{"tier":159,"basis":160},"context-dependent","Summaries, CRM suggestions and follow up drafts that the seller reviews are not an Annex III use and are minimal risk. Using call analysis to monitor and evaluate the performance and behaviour of individual sellers, or to allocate leads to sellers based on their behaviour or personal traits, is high risk under Annex III point 4(b). Inferring sellers' emotions from their voice is prohibited in the workplace by Article 5(1)(f). Emotion recognition applied to customers' voices is high risk under Annex III point 1(c), and Article 50(3) requires deployers to inform the people exposed to it.",[162,163,164,165],"eu-ai-act","gdpr","uk-gdpr","iso-42001",[167,173,177],{"title":168,"issuer":169,"region":170,"url":171,"note":172},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 4(b) covers monitoring and evaluating workers' performance and behaviour; point 1(c) covers emotion recognition.",{"title":174,"issuer":169,"region":170,"url":175,"note":176},"Article 5, prohibited AI practices","https://artificialintelligenceact.eu/article/5/","Point 1(f) prohibits emotion recognition in the workplace.",{"title":178,"issuer":179,"region":170,"url":180,"note":181},"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/","Expectations for transparency, proportionality and data protection impact assessments when monitoring workers, including a section on monitoring telephone calls.",[183,184,185,186,187],"Data protection impact assessment for call recording and analysis, per country","Written purpose limitation for coaching data, agreed with employee representatives where required","Customer notice and opt out at the start of recorded calls and meetings","Retention limits and access control on recordings and transcripts","Accuracy testing of summaries and detected moments by language",[],{"howToBuild":190},"On Blits.ai the post call part is an **agentic workflow**: the **self hosted transcription with\nspeaker diarization** (who spoke when, processed on Blits.ai infrastructure) or transcripts from\nthe meeting platform feed an **AI agent** with **structured output** that returns the summary,\nnext steps and proposed CRM field changes. **Custom functions** read the opportunity and write the\nconfirmed changes back (ready made tools cover Microsoft Dynamics 365 and HubSpot, and the\nintegration catalog includes Salesforce), with **human in the loop** approval so the seller\nconfirms every update.\n\nCoaching against the sales method runs as a second agent that marks moments in the transcript\nagainst your written playbook, grounded in a **knowledge base** of enablement content. **PII\nmasking** removes payment and personal data before text reaches a model, **test suites** with\nLLM based grading check extraction accuracy on reviewed calls, and **role based access control**\nlimits who in the platform can see transcripts and results. The platform is model agnostic and can run in the EU or\nUAE region.",[192,195,198],{"question":193,"answer":194},"How much time does AI save sellers on admin and CRM updates?","Microsoft reports that adding an email summary as a CRM note takes 10 seconds instead of three minutes or longer with Copilot for Sales. Sandvik Coromant says its account managers save three minutes per transaction multiple times a day, but that saving comes from seeing a customer's full situation in the Outlook side panel, not from updating the CRM. Microsoft also reports that Lumen cut the time sellers spend summarizing past sales interactions and researching an account from up to four hours to 15 minutes.",{"question":196,"answer":197},"Is AI analysis of sales calls high risk under the EU AI Act?","Summaries and CRM updates are not. Using the analysis to monitor and evaluate individual sellers is high risk under Annex III point 4(b), and inferring sellers' emotions from their voice is prohibited in the workplace. Design coaching around what was said, and let managers own judgments about people.",{"question":199,"answer":200},"Can call analysis replace manual call audits?","For coverage, largely. Microsoft reports that Hughes cut the cost of a sales call audit by 90%, from USD 26 to USD 2 per call hour, by replacing manual listening with automated transcription and analysis. Keep people reviewing the calls the analysis flags.",[202,203,204,205,206,207],"meeting-summarization-and-action-items","client-briefing-and-call-report-copilot","client-meeting-notes-and-crm-update","conversation-roleplay-training","call-quality-and-compliance-monitoring","inbound-lead-qualification-agent","2026-09-27",[210],{"date":208,"note":211},"First published","sales-call-coaching-and-crm-update",[214,252,275,303],{"title":215,"useCases":216,"organization":217,"vendors":222,"summary":226,"stage":227,"year":228,"channels":229,"languages":230,"metrics":232,"outcomeDisclosed":241,"sources":242,"verification":246,"grade":249,"id":250,"organizationSlug":251},"Hughes Network Systems: automated auditing and insight on sales calls",[212],{"name":218,"anonymized":219,"country":220,"region":221,"industry":19},"Hughes Network Systems",false,"US","north-america",[223],{"name":224,"role":225},"Microsoft","platform","Hughes, part of EchoStar, replaced manual auditing of sales calls, where auditors listened to hours of recordings, with an automated speech to text and generative AI system on Azure AI Foundry. It produces call insights and directives for sales agents across the whole call, and the team uses automated evaluation tools to check the quality and groundedness of the AI output. Microsoft reports that the cost of a sales call audit fell by 90%, from USD 26 to USD 2 per call hour.","production",2025,[29,32],[231],"en",[233],{"kpi":53,"value":234,"unit":235,"qualifier":236,"baseline":237,"claimant":238,"quote":239,"sourceUrl":240},90,"percent","exact","USD 26 per hour for each call audited manually","vendor","Collectively, these AI initiatives have boosted overall productivity by up to 25%, including automated sales call audit reductions of 90%, from $26 per hour for each call to just $2.","https://www.microsoft.com/en/customers/story/24300-hughes-azure-ai-foundry",true,[243],{"url":240,"title":244,"publisher":245},"EchoStar and Hughes save thousands of work hours, cut costs with Azure AI","Microsoft Customer Stories",{"level":247,"checkedAt":248},"source-verified","2026-09-26","C","hughes-sales-call-auditing",null,{"title":253,"useCases":254,"organization":257,"vendors":260,"summary":262,"stage":227,"year":228,"channels":263,"languages":264,"metrics":265,"outcomeDisclosed":241,"sources":270,"verification":273,"grade":249,"id":274,"organizationSlug":251},"Zurich Insurance Group: Copilot for Sales to keep commercial insurance CRM data current",[212,255,256],"insurance-broker-and-agent-assistant","insurance-renewal-and-retention",{"name":258,"anonymized":219,"country":259,"region":170,"industry":21},"Zurich Insurance Group","CH",[261],{"name":224,"role":225},"Zurich's commercial insurance teams manage more than 100,000 active opportunities in Dynamics 365, and switching applications to copy updates from email into the CRM left data at risk of going stale. With Microsoft 365 Copilot for Sales, 300 users create and update contacts and link emails to opportunities from Outlook, get summaries of relationships and long email threads, and draft emails. Zurich estimates about 14,000 hours saved over the next year; that is an estimate, not a measured result. The story also says user feedback indicates the tool improves Zurich's sales and retention ratios, without figures.",[31,30,32],[231],[266],{"kpi":55,"value":62,"unit":267,"qualifier":236,"claimant":238,"quote":268,"sourceUrl":269},"count","Copilot for Sales has quickly become a critical productivity tool for Zurich’s 300 Copilot users, who find even more benefits as they continue to work with it in Outlook.","https://www.microsoft.com/en/customers/story/23452-zurich-schweiz-microsoft-365-copilot-for-sales",[271],{"url":269,"title":272,"publisher":245},"Zurich Insurance enhances customer relationships with Dynamics 365 and Microsoft 365 Copilot for Sales",{"level":247,"checkedAt":248},"zurich-copilot-for-sales-crm-updates",{"title":276,"useCases":277,"organization":280,"vendors":282,"summary":284,"stage":227,"year":285,"channels":286,"languages":287,"metrics":288,"outcomeDisclosed":241,"sources":297,"verification":301,"grade":249,"id":302,"organizationSlug":251},"Lumen Technologies: Copilot summaries of past sales interactions and account research",[212,278,279],"business-connectivity-quoting-and-service-assistant","outbound-sales-prospecting-agent",{"name":281,"anonymized":219,"country":220,"region":221,"industry":19},"Lumen Technologies",[283],{"name":224,"role":225},"Lumen's sellers use Microsoft Copilot to summarize past sales interactions, gather recent news, identify business challenges and industry trends, and suggest next steps for an account. Microsoft reports that this work took up to four hours per seller and that Lumen cut it to 15 minutes in 2024. Lumen's projected annual value of USD 50 million is a projection and is not recorded as a result.",2024,[32],[231],[289],{"kpi":51,"value":290,"unit":291,"qualifier":292,"period":293,"baseline":294,"claimant":238,"quote":295,"sourceUrl":296},225,"minutes","up-to","summarizing past interactions and researching an account, per seller","up to four hours per seller before","This process traditionally took up to four hours per seller. In 2024, Lumen reduced that time to just 15 minutes, projecting annual time savings worth USD50 million.","https://www.microsoft.com/en-us/microsoft-cloud/blog/2025/07/24/ai-powered-success-with-1000-stories-of-customer-transformation-and-innovation/",[298],{"url":296,"title":299,"publisher":224,"date":300},"AI-powered success, with more than 1,000 stories of customer transformation and innovation","2025-07-24",{"level":247,"checkedAt":248},"lumen-copilot-sales-account-research",{"title":304,"useCases":305,"organization":306,"vendors":309,"summary":311,"stage":227,"year":285,"channels":312,"languages":313,"metrics":314,"outcomeDisclosed":241,"sources":320,"verification":323,"grade":249,"id":324,"organizationSlug":251},"Sandvik Coromant: Copilot for Sales for meeting summaries, email and CRM capture",[212],{"name":307,"anonymized":219,"country":308,"region":170,"industry":20},"Sandvik Coromant","SE",[310],{"name":224,"role":225},"Sandvik Coromant, a supplier of cutting tools with about 8,000 staff, was an early adopter of Microsoft Copilot for Sales on top of Dynamics 365. Sellers use it to summarize email threads and Teams meetings, capture contact details into the CRM in one click, add email summaries as CRM notes, get a summary of next steps after each meeting and a suggested recap for the customer, and draft replies. The company reports that account managers save three minutes per transaction several times a day, and Microsoft reports 20 minutes a day saved on email summaries.",[30,31,32],[231],[315],{"kpi":51,"value":67,"unit":291,"qualifier":236,"period":316,"claimant":317,"quote":318,"sourceUrl":319},"per transaction, several times a day per account manager","organization","With everything on the side panel in Outlook, account managers save three minutes per transaction multiple times a day.","https://customers.microsoft.com/en-us/story/1785448033474736158-sandvik-coromant-microsoft-copilot-for-sales-discrete-manufacturing-en-sweden",[321],{"url":319,"title":322,"publisher":245},"Sandvik Coromant hones sales experience with Microsoft Copilot for Sales",{"level":247,"checkedAt":248},"sandvik-coromant-copilot-for-sales",0,[327,334,339],{"kpi":51,"label":328,"unit":291,"aggregate":241,"higherIsBetter":241,"n":329,"nUpTo":329,"median":67,"min":67,"max":67,"byClaimant":330,"vendorOnly":219,"points":331},"Time saved per task",1,{"organization":329,"vendor":325,"regulator":325,"independent":325},[332,333],{"evidenceId":302,"organization":281,"value":290,"qualifier":292,"claimant":238,"grade":249,"pooled":219},{"evidenceId":324,"organization":307,"value":67,"qualifier":236,"claimant":317,"grade":249,"pooled":241},{"kpi":53,"label":335,"unit":235,"aggregate":241,"higherIsBetter":241,"n":329,"nUpTo":325,"median":234,"min":234,"max":234,"byClaimant":336,"vendorOnly":241,"points":337},"Cost reduction",{"organization":325,"vendor":329,"regulator":325,"independent":325},[338],{"evidenceId":250,"organization":218,"value":234,"qualifier":236,"claimant":238,"grade":249,"pooled":241},{"kpi":55,"label":340,"unit":267,"aggregate":219,"higherIsBetter":241,"n":329,"nUpTo":325,"median":62,"min":62,"max":62,"byClaimant":341,"vendorOnly":241,"points":342},"Users served",{"organization":325,"vendor":329,"regulator":325,"independent":325},[343],{"evidenceId":274,"organization":258,"value":62,"qualifier":236,"claimant":238,"grade":249,"pooled":241},{"low":345,"high":346},712800,3450000,[348,368,385,406,425,448],{"slug":202,"title":349,"shortTitle":350,"definition":351,"status":9,"industries":352,"functions":356,"patterns":359,"audience":33,"autonomy":34,"adoptionStage":360,"evidenceCount":68,"publicEvidenceCount":68,"organizations":361,"bestGrade":367,"headline":251,"lastVerified":208,"indexable":241},"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.",[18,353,354,355],"government","technology","professional-services",[357,358],"knowledge-management","operations",[26,25],"mainstream",[362,363,364,365,366],"U.S. Department of Labor","Ministry of Justice","Softcat","Trace3","Government Digital Service","B",{"slug":203,"title":369,"shortTitle":370,"definition":371,"status":9,"industries":372,"functions":376,"patterns":377,"audience":33,"autonomy":34,"adoptionStage":35,"segment":380,"evidenceCount":67,"publicEvidenceCount":67,"organizations":381,"bestGrade":367,"headline":251,"lastVerified":208,"indexable":241},"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.",[373,374,375],"banking","wealth-and-asset-management","capital-markets",[23,357],[378,26,27,379],"rag-knowledge-assistant","agentic-workflow","specialized-businesses",[382,383,384],"Bank of America","Scotiabank","Standard Chartered",{"slug":204,"title":386,"shortTitle":387,"definition":388,"status":9,"industries":389,"functions":390,"patterns":392,"audience":33,"autonomy":34,"adoptionStage":360,"segment":393,"evidenceCount":394,"publicEvidenceCount":394,"organizations":395,"bestGrade":367,"headline":401,"lastVerified":208,"indexable":241},"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.",[374,373],[23,391,358],"regulatory-compliance",[26,25,379,27],"front-office",6,[382,396,397,398,399,400],"Commerzbank","Morgan Stanley","Quilter","SEB","UniSuper",{"kpi":402,"label":403,"unit":235,"n":329,"nUpTo":325,"kind":404,"value":405,"qualifier":236,"claimant":238,"organization":399,"vendorReported":241},"productivity-gain","Productivity gain","reported",15,{"slug":205,"title":407,"shortTitle":408,"definition":409,"status":9,"industries":410,"functions":412,"patterns":415,"audience":33,"autonomy":418,"adoptionStage":35,"evidenceCount":67,"publicEvidenceCount":67,"organizations":419,"bestGrade":367,"headline":422,"lastVerified":248,"indexable":241},"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.",[18,373,21,19,353,411],"healthcare",[413,414,23],"human-resources","customer-service",[416,417,27],"conversational-agent","voice-agent","assist",[382,420,421],"GoHealth","U.S. Department of Veterans Affairs",{"kpi":54,"label":423,"unit":235,"n":329,"nUpTo":325,"kind":404,"value":424,"qualifier":236,"claimant":238,"organization":420,"vendorReported":241},"Conversion uplift",21,{"slug":206,"title":426,"shortTitle":427,"definition":428,"status":9,"industries":429,"functions":432,"patterns":433,"audience":435,"autonomy":436,"adoptionStage":35,"evidenceCount":68,"publicEvidenceCount":68,"organizations":437,"bestGrade":249,"headline":443,"lastVerified":208,"indexable":241},"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.",[18,373,21,430,19,431],"energy-and-utilities","retail-and-ecommerce",[414,391,358],[25,434,26],"classification-and-routing","back-office","supervised-agent",[438,439,440,441,442],"British Gas","Central Bank","DoorDash","Oportun","VitalityHealth",{"kpi":444,"label":445,"unit":235,"n":329,"nUpTo":325,"kind":404,"value":446,"qualifier":447,"claimant":238,"organization":438,"vendorReported":241},"quality-score-uplift","Quality score uplift",10,"approximately",{"slug":207,"title":449,"shortTitle":450,"definition":451,"status":9,"industries":452,"functions":454,"patterns":456,"audience":457,"autonomy":436,"adoptionStage":35,"evidenceCount":458,"publicEvidenceCount":458,"organizations":459,"bestGrade":249,"headline":464,"lastVerified":208,"indexable":241},"AI agent for inbound lead qualification and meeting booking","Inbound lead qualification","An AI agent that engages inbound prospects the moment they arrive on the website, chat, messaging or the sales phone line, answers their first questions, qualifies them against the organization's criteria, and books a meeting or hands a ready conversation to the right salesperson, with the context written into the CRM.",[18,354,453,373],"automotive",[23,455],"marketing",[416,417,434,379],"customer-facing",4,[460,461,462,463],"8x8","CarMax","Rocket Mortgage","SUSE",{"kpi":54,"label":423,"unit":235,"n":329,"nUpTo":325,"kind":404,"value":465,"qualifier":236,"claimant":238,"organization":460,"vendorReported":241},19,{"indexable":241,"reasons":467},[],[469,474,479,486,493,499,505,512,520,527,534,540,547,553,559,564,571,577,583,589,595,601,606,611,616,623,630,635,640,647,653,659,665,670],{"id":162,"label":470,"issuer":169,"region":170,"url":471,"description":472,"useCases":473,"indexable":241},"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":163,"label":475,"issuer":169,"region":170,"url":476,"description":477,"useCases":478,"indexable":241},"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":165,"label":480,"issuer":481,"region":482,"url":483,"description":484,"useCases":485,"indexable":241},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":487,"label":488,"issuer":489,"region":221,"url":490,"description":491,"useCases":492,"indexable":241},"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":494,"label":495,"issuer":169,"region":170,"url":496,"description":497,"useCases":498,"indexable":241},"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":164,"label":500,"issuer":501,"region":170,"url":502,"description":503,"useCases":504,"indexable":241},"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":506,"label":507,"issuer":508,"region":170,"url":509,"description":510,"useCases":511,"indexable":241},"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":513,"label":514,"issuer":515,"region":516,"url":517,"description":518,"useCases":519,"indexable":241},"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":521,"label":522,"issuer":523,"region":516,"url":524,"description":525,"useCases":526,"indexable":241},"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":528,"label":529,"issuer":530,"region":482,"url":531,"description":532,"useCases":533,"indexable":241},"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":535,"label":536,"issuer":537,"region":221,"url":538,"description":539,"useCases":533,"indexable":241},"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":541,"label":542,"issuer":543,"region":170,"url":544,"description":545,"useCases":546,"indexable":241},"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":548,"label":549,"issuer":550,"region":482,"url":551,"description":552,"useCases":405,"indexable":241},"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":554,"label":555,"issuer":169,"region":170,"url":556,"description":557,"useCases":558,"indexable":241},"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":560,"label":561,"issuer":169,"region":170,"url":562,"description":563,"useCases":558,"indexable":241},"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":565,"label":566,"issuer":567,"region":221,"url":568,"description":569,"useCases":570,"indexable":241},"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":572,"label":573,"issuer":169,"region":170,"url":574,"description":575,"useCases":576,"indexable":241},"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":578,"label":579,"issuer":580,"region":221,"url":581,"description":582,"useCases":576,"indexable":241},"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":584,"label":585,"issuer":586,"region":482,"url":587,"description":588,"useCases":576,"indexable":241},"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":590,"label":591,"issuer":169,"region":170,"url":592,"description":593,"useCases":594,"indexable":241},"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":596,"label":597,"issuer":598,"region":221,"url":599,"description":600,"useCases":594,"indexable":241},"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":602,"label":603,"issuer":515,"region":516,"url":604,"description":605,"useCases":446,"indexable":241},"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":607,"label":608,"issuer":169,"region":170,"url":609,"description":610,"useCases":446,"indexable":241},"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":612,"label":613,"issuer":169,"region":170,"url":614,"description":615,"useCases":446,"indexable":241},"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":617,"label":618,"issuer":619,"region":170,"url":620,"description":621,"useCases":622,"indexable":241},"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":624,"label":625,"issuer":626,"region":221,"url":627,"description":628,"useCases":629,"indexable":241},"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":631,"label":632,"issuer":169,"region":170,"url":633,"description":634,"useCases":629,"indexable":241},"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":636,"label":637,"issuer":169,"region":170,"url":638,"description":639,"useCases":394,"indexable":241},"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":641,"label":642,"issuer":643,"region":644,"url":645,"description":646,"useCases":68,"indexable":241},"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":648,"label":649,"issuer":650,"region":170,"url":651,"description":652,"useCases":458,"indexable":241},"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":654,"label":655,"issuer":656,"region":170,"url":657,"description":658,"useCases":458,"indexable":241},"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":660,"label":661,"issuer":662,"region":516,"url":663,"description":664,"useCases":67,"indexable":241},"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":666,"label":667,"issuer":169,"region":170,"url":668,"description":669,"useCases":67,"indexable":241},"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":671,"label":672,"issuer":673,"region":221,"url":674,"description":675,"useCases":67,"indexable":241},"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.",1790598305889]