[{"data":1,"prerenderedAt":673},["ShallowReactive",2],{"uc-customer-feedback-analysis":3,"uc-regulations":465},{"useCase":4,"evidence":193,"blitsAiDeployments":333,"benchmarks":334,"indicative":354,"related":357,"indexability":463,"includeUnpublished":199},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":22,"patterns":26,"channels":30,"audience":33,"autonomy":34,"adoptionStage":35,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":49,"macroEstimates":84,"feasibility":85,"implementation":99,"risk":145,"blitsAi":167,"faq":169,"related":182,"datePublished":188,"dateModified":188,"lastVerified":188,"changelog":189,"slug":192},"AI for voice of the customer and feedback analysis","Customer feedback analysis","AI customer feedback and VoC analysis","AI tags survey comments, reviews and call transcripts by theme and sentiment. Microsoft reports Majid Al Futtaim cut feedback processing from 7 days to 3 hours.","published","AI that reads every piece of free text customer feedback, such as survey verbatims, NPS comments, reviews, social posts, chat and call transcripts, and turns it into themes, sentiment, drivers and suggested actions that a named owner can act on, so the organization hears all of its customers instead of a sample.",[12,13,14,15,16],"voice of the customer analytics","VoC analysis","survey verbatim analysis","customer feedback text analytics","NPS comment analysis",[18,19,20,21],"cross-industry","retail-and-ecommerce","government","manufacturing",[23,24,25],"customer-service","marketing","analytics-and-reporting",[27,28,29],"classification-and-routing","summarization","speech-analytics",[31,32],"internal-tools","api","back-office","copilot","mainstream","Organizations collect far more feedback than they read. Survey platforms, app store reviews,\nsocial media, chat logs and call recordings produce tens of thousands of comments a week (Majid\nAl Futtaim Retail's marketing team processed 60,000 to 70,000 customer responses a week by hand,\naccording to Microsoft), and most of the value sits in the free text: why a customer gave a low score, what broke, what they wanted\ninstead. Analysts read a sample, tag it by hand against a codebook that drifts over time, and\nreport weeks later, by which time the issue has cost more customers.\n\nKeyword based text analytics helped with volume but struggled with sarcasm, mixed sentiment,\nseveral topics in one comment, other languages and new themes nobody had a keyword for. Language\nmodels change the economics: every comment can be classified against the organization's own\ntaxonomy, summarized per theme and linked to operational data, so a product owner sees the\nproblem in days. The discipline that remains is human: someone has to own each theme, decide what\nit means and close the loop with customers.",[],"1. **Collect every source.** Survey exports, reviews, social mentions, chat logs and transcribed\n   calls land in one store with their metadata (channel, product, date, score, segment).\n2. **Clean and protect.** Personal data is masked before analysis; duplicates, spam and empty\n   answers are removed.\n3. **Classify against your taxonomy.** Each comment is tagged with one or more themes from the\n   organization's own codebook, a sentiment per theme and, where present, a suggested action or a\n   statement of customer effort. New clusters that fit no theme are flagged for review.\n4. **Quantify and explain.** Themes are joined to scores and operational data (store, product,\n   journey step), so dashboards show which themes drive detractors and how they trend.\n5. **Summarize for owners.** Each theme owner gets a short summary with representative, anonymized\n   quotes and the change against last period.\n6. **Close the loop.** Individual comments that need a response (a complaint, a safety issue, a\n   vulnerable customer) are routed to the right team; systemic fixes are tracked to completion.",[40,41,42,43],"customer-experience","employee-productivity","speed","revenue-growth",[45,46,47,48],"cycle-time-days","accuracy","cost-reduction","productivity-gain",{"referenceOrg":50,"inputs":51,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A consumer brand or public service that receives 300,000 free text feedback items a year",[52,58,65,72],{"key":53,"label":54,"low":55,"high":55,"unit":56,"note":57},"feedbackItems","Free text feedback items per year",300000,"items per year","The reference organization, across surveys, reviews and chat.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"hoursPerItem","Analyst time to read and code one item by hand",0.01,0.02,"hours per item","Editorial assumption of 36 to 72 seconds per comment. Replace with your own coding time.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"shareAutomated","Share of manual coding the AI replaces",0.6,0.9,"fraction of items","Conservative against the benchmarks on this page (Google Cloud reports that SBF Group eliminated manual data analysis work and cut annual costs by about 95%), because theme review and quality checks stay with people.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"analystCost","Fully loaded analyst cost",40,70,"USD per hour","Editorial assumption, replace with your own.","feedbackItems * hoursPerItem * shareAutomated * analystCost","USD","per year","Manual feedback coding cost avoided","Counts only the analyst time to read and code comments, and assumes the organization would otherwise read all of them (most read a sample). It leaves out the platform cost and the larger value: issues found and fixed weeks earlier, and churn or complaints avoided.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"low","Classifying text is a mature task and the data is usually already exported from survey and review tools. The effort goes into an agreed taxonomy, joining feedback to operational data, masking personal data and building the habit of acting on the output.",[89,90,91,92],"Exports or APIs from survey, review, social listening and contact centre systems","A theme taxonomy (codebook) agreed with the business, with an owner per theme","Metadata that links feedback to product, location, journey step and score","A labelled sample of a few hundred comments to measure classification accuracy",[94,95,96,97,98],"Survey and experience management platforms","Review sites, app stores and social listening tools","Contact centre recordings and chat logs, with transcription","Data warehouse and BI dashboards","Case or CRM system for comments that need an individual response",{"steps":100,"guardrails":119,"humanInTheLoop":125,"kpisToInstrument":126,"failureModes":132},[101,104,107,110,113,116],{"title":102,"detail":103},"Agree the taxonomy before the model","Start from the themes the business already reports, add the top new clusters found in a sample, and name an owner for each. A model that classifies against themes nobody owns produces dashboards nobody acts on.",{"title":105,"detail":106},"Build a labelled test set","Have two people code a few hundred real comments per language, resolve their disagreements, and use the set to measure accuracy per theme on every prompt or model change.",{"title":108,"detail":109},"Mask personal data at intake","Remove names, account numbers and contact details before analysis and keep the link to the original record only where a response is needed.",{"title":111,"detail":112},"Join feedback to operations","Link each comment to the store, product, order or journey step it concerns, so a theme can be traced to a cause rather than reported as a trend.",{"title":114,"detail":115},"Route individual cases, report systemic ones","Send comments that are complaints, safety issues or signs of vulnerability to the teams that respond to individuals, and give theme owners a periodic summary with trend and quotes.",{"title":117,"detail":118},"Review the codebook every quarter","Look at the unclassified cluster, retire themes that no longer occur and add new ones with an owner, then rerun the test set.",[120,121,122,123,124],"Classification only against an approved taxonomy, with an explicit \"other or new\" bucket reviewed by people","Personal data masked before comments reach a model or a dashboard","Quotes shown to wide audiences are anonymized and checked","Comments that indicate a complaint, a safety risk or a vulnerable customer are routed to a person, not only counted","No inference of emotion from voice or face in call and video feedback without a separate legal assessment","Analysts own the taxonomy, check a weekly sample of classifications against the test set, and validate every theme before it is reported as a finding. Theme owners decide what action to take; the AI suggests, it does not commit changes to products or policies.",[127,128,129,130,131],"Classification accuracy per theme and language on the labelled test set","Share of feedback analysed (coverage) versus the previous sampling approach","Time from feedback received to theme reported to its owner","Share of themes with an owner and a tracked action","Share of individual cases routed correctly (complaints, safety, vulnerability)",[133,136,139,142],{"title":134,"detail":135},"Dashboards without owners","The analysis is excellent and nothing changes. Give every theme an owner and track actions to closure.",{"title":137,"detail":138},"Confident but wrong themes","The model fits comments into the nearest theme and hides new issues. Keep a \"new or other\" bucket and review it.",{"title":140,"detail":141},"Sentiment that misses the point","A positive score on a comment that describes a serious failure. Report themes and drivers, not sentiment alone.",{"title":143,"detail":144},"Personal data spread into reports","Verbatim quotes with names or account details reach wide audiences. Mask at intake and review quotes before sharing.",{"euAiAct":146,"regulations":149,"guidance":153,"controls":160,"incidents":166},{"tier":147,"basis":148},"context-dependent","Classifying and summarizing text feedback is minimal risk. The tier changes if the system infers emotions from customers' voices or faces in calls or video: emotion recognition based on biometric data is listed as high risk in Annex III point 1(c) and triggers the Article 50(3) duty to inform the people exposed. Analysing feedback from employees to evaluate individual workers moves it towards Annex III point 4(b), and emotion recognition in the workplace is prohibited by Article 5(1)(f), except for medical or safety reasons.",[150,151,152],"eu-ai-act","gdpr","iso-42001",[154],{"title":155,"issuer":156,"region":157,"url":158,"note":159},"Regulation (EU) 2024/1689, the Artificial Intelligence Act","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Official text on EUR-Lex. Annex III point 1(c) lists emotion recognition systems and point 4(b) systems that monitor and evaluate the performance and behaviour of workers; Article 5(1)(f) prohibits emotion recognition in the workplace and in education, relevant when employee feedback or staff calls are analysed; Article 50(3) requires deployers of emotion recognition systems to inform the people exposed to them.",[161,162,163,164,165],"Data protection impact assessment where feedback includes personal data or call recordings","Documented taxonomy with owners and a change log","Accuracy testing on a labelled set per language before each change","Retention limits on raw verbatims and recordings","Access control on dashboards that show individual comments",[],{"howToBuild":168},"On Blits.ai this runs as an **agentic workflow** on a schedule: **custom functions** pull new\nfeedback from survey, review and ticketing systems through their REST APIs (the integration\ncatalog includes Zendesk, Salesforce and ServiceNow) and strip names, account numbers and contact\ndetails before the data reaches the agent, and an **AI agent** with **structured output** tags\neach comment with themes from your codebook, sentiment per theme and any suggested action. The results are written to a SQL\ndatabase registered as a **SQL knowledge base**, so an agent can answer plain language questions\nfrom analysts and theme owners over them.\n\nFor feedback that comes in through Blits.ai channels, **PII masking** at the gateway removes\npersonal data from chat input, and the built in **analytics** already show satisfaction and\nsentiment (with engines from Amazon, Google, IBM and Microsoft), thumbs up and down feedback with\ncomments on responses, and conversation level CSAT ratings. **Test suites** with deterministic\nrules for label matching (or LLM based grading for summaries) let you run your labelled comments\nthrough the classifier after every prompt change,\n**human in the loop** approval holds routing of sensitive comments until a person confirms, and\n**monitors** run scheduled checks against the classifier agent and alert when it fails. The\nplatform is model agnostic and can run in the EU or UAE region.",[170,173,176,179],{"question":171,"answer":172},"How accurate is AI at classifying customer feedback?","It can be accurate enough to replace most manual coding, but only your own labelled comments tell you whether it is. Google Cloud reports that SBF Group raised feedback classification accuracy from 16% to 84% with Google Cloud AI. Measure accuracy per theme and per language, because averages hide weak themes.",{"question":174,"answer":175},"How much faster is AI feedback analysis?","Days become minutes or hours. Microsoft reports that Majid Al Futtaim cut feedback processing for Carrefour from seven days to three hours (the same story quotes its Chief Digital Officer as saying three to four minutes), and Google Cloud reports that Mattel cut analysis from a month to a minute. The binding constraint then becomes how fast owners act.",{"question":177,"answer":178},"Is sentiment analysis of customer calls high risk under the EU AI Act?","Analysing the words people say or write is not. Inferring emotions from their voice or face is emotion recognition, which Annex III lists as high risk, with a duty under Article 50(3) to inform the people exposed. Inferring the emotions of your own staff, such as contact centre agents, is prohibited by Article 5(1)(f) except for medical or safety reasons. Keep voice analysis to transcripts unless you have done that assessment.",{"question":180,"answer":181},"How is this different from complaints root cause analysis?","Complaints root cause analysis works on regulated complaints, where every case must be handled and systemic causes reported. Feedback analysis covers the much larger stream of surveys, reviews and comments, most of which are not complaints, to find what drives satisfaction and where to invest.",[183,184,185,186,187],"complaints-root-cause-analysis","public-consultation-response-analysis","call-quality-and-compliance-monitoring","personalized-marketing-at-scale","governed-text-to-sql-analytics","2026-09-27",[190],{"date":188,"note":191},"First published","customer-feedback-analysis",[194,228,246,285,313],{"title":195,"useCases":196,"organization":197,"vendors":202,"summary":208,"stage":209,"year":210,"channels":211,"languages":212,"metrics":213,"outcomeDisclosed":199,"sources":214,"verification":222,"grade":225,"id":226,"organizationSlug":227},"US Department of Housing and Urban Development: Voice of the Customer analytics on surveys, calls and chats",[192],{"name":198,"anonymized":199,"country":200,"region":201,"industry":20},"U.S. Department of Housing and Urban Development",false,"US","north-america",[203,206],{"name":204,"role":205},"Medallia","platform",{"name":207,"role":205},"Qualtrics","The customer experience team in HUD's Office of the Chief Financial Officer runs a Voice of the Customer application that applies transcription, speech and text analytics to customer feedback surveys and to contact centre calls and chats. It produces dashboards that trend sentiment and identify the key drivers of customer sentiment and service delivery performance, which HUD uses to manage its programs and contact centre providers. The department lists it as deployed since August 2024; no outcome figures are published.","production",2024,[31],[],[],[215,219],{"url":216,"title":217,"publisher":218},"https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory","2025 Federal Agency AI Use Case Inventory","Office of Management and Budget (GitHub)",{"url":220,"title":221,"publisher":218},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 individually reported AI use cases (entry HUD-2024-004, Voice of the Customer)",{"level":223,"checkedAt":224},"source-verified","2026-09-26","B","hud-voice-of-the-customer-analytics",null,{"title":229,"useCases":230,"organization":231,"vendors":233,"summary":235,"stage":209,"year":236,"channels":237,"languages":238,"metrics":239,"outcomeDisclosed":199,"sources":240,"verification":244,"grade":225,"id":245,"organizationSlug":227},"US Social Security Administration: Customer Insight Tool for survey text analytics",[192],{"name":232,"anonymized":199,"country":200,"region":201,"industry":20},"U.S. Social Security Administration",[234],{"name":204,"role":205},"SSA's customer survey system includes an AI text analytics capability that reads the free text of survey answers and returns sentiment ratings, categorized themes, suggested actions, customer effort indicators and machine translation. The agency uses it to spot emerging trends early and to see the positive and negative drivers in customer interactions. It is listed as deployed since August 2021; no outcome figures are published.",2021,[31],[],[],[241,242],{"url":216,"title":217,"publisher":218},{"url":220,"title":243,"publisher":218},"2025 individually reported AI use cases (SSA entry, Customer Insight Tool)",{"level":223,"checkedAt":224},"ssa-customer-insight-survey-text-analytics",{"title":247,"useCases":248,"organization":249,"vendors":253,"summary":256,"stage":209,"year":257,"channels":258,"languages":259,"metrics":260,"outcomeDisclosed":273,"sources":274,"verification":282,"grade":283,"id":284,"organizationSlug":227},"SBF Group: AI classification of customer feedback and NPS survey answers",[192],{"name":250,"anonymized":199,"country":251,"region":252,"industry":19},"SBF Group","BR","latin-america",[254],{"name":255,"role":205},"Google Cloud","SBF Group (Grupo SBF), the Brazilian sporting goods retailer behind Centauro and Fisia, the official Nike distributor in Brazil, uses Google Cloud AI to analyse customer feedback and customer satisfaction (NPS) forms. Google Cloud reports that the solution eliminated manual data analysis work, cut annual costs by about 95%, raised feedback classification accuracy from 16% to 84% and made it possible to process daily feedback that previously went unanalysed.",2026,[31],[],[261,269],{"kpi":47,"value":262,"unit":263,"qualifier":264,"period":265,"claimant":266,"quote":267,"sourceUrl":268},95,"percent","approximately","per year (the cost base is not specified)","vendor","The solution reduced annual costs by approximately 95% and eliminated manual data analysis work, in addition to increasing the accuracy rate in feedback classification from 16% to 84%, allowing daily processing of information that was previously not analyzed.","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",{"kpi":46,"value":270,"unit":263,"qualifier":271,"baseline":272,"claimant":266,"quote":267,"sourceUrl":268},84,"exact","16% classification accuracy before",true,[275,278],{"url":268,"title":276,"publisher":255,"date":277},"Real world gen AI use cases from the world's leading organizations","2026-04-22",{"url":279,"title":280,"publisher":281},"https://www.gruposbf.com.br/sobre-nos","Sobre nós","Grupo SBF",{"level":223,"checkedAt":224},"C","sbf-group-nps-feedback-analysis",{"title":286,"useCases":287,"organization":288,"vendors":292,"summary":295,"stage":209,"year":296,"channels":297,"languages":298,"metrics":299,"outcomeDisclosed":273,"sources":306,"verification":311,"grade":283,"id":312,"organizationSlug":227},"Majid Al Futtaim Retail: generative AI analysis of customer feedback for Carrefour",[192],{"name":289,"anonymized":199,"country":290,"region":291,"industry":19},"Majid Al Futtaim Retail","AE","middle-east",[293],{"name":294,"role":205},"Microsoft","Majid Al Futtaim Retail, which runs Carrefour in the Middle East, Africa and Central Asia, built a text analytics solution called \"Excellence\" on Azure OpenAI Service. Before it, the marketing team manually processed 60,000 to 70,000 customer responses a week. The solution captures customer emotion and categorizes feedback by aspects such as delivery, quality, hygiene and checkout queue times, generating actionable insights for improvement. Microsoft reports that feedback processing fell from seven days to three hours; the same story quotes the Chief Digital Officer as saying it now takes three to four minutes.",2025,[31],[],[300],{"kpi":45,"value":301,"unit":302,"qualifier":271,"baseline":303,"claimant":266,"quote":304,"sourceUrl":305},3,"hours","seven days of manual feedback processing","The company saved USD1 million annually, cut feedback processing time from seven days to three hours, and improved geographic targeting, boosting efficiency with AI-driven solutions.","https://www.microsoft.com/en/customers/story/21059-majid-al-futtaim-retail-azure-open-ai-service",[307],{"url":305,"title":308,"publisher":309,"date":310},"From seven days to three minutes: Majid Al Futtaim boosts customer centricity with Azure OpenAI Service","Microsoft Customer Stories","2025-01-16",{"level":223,"checkedAt":188},"majid-al-futtaim-customer-feedback-analysis",{"title":314,"useCases":315,"organization":316,"vendors":318,"summary":320,"stage":209,"year":296,"channels":321,"languages":322,"metrics":323,"outcomeDisclosed":273,"sources":329,"verification":331,"grade":283,"id":332,"organizationSlug":227},"Mattel: generative AI classification of consumer feedback across reviews, social media and contact centre",[192],{"name":317,"anonymized":199,"country":200,"region":201,"industry":21},"Mattel",[319],{"name":255,"role":205},"Mattel built a feedback classification system on BigQuery, Vertex AI and Gemini that analyses millions of consumer feedback points from customer reviews, social media and the contact centre. Google Cloud reports that analysis time fell from a month to a single minute and that data processing capacity rose a hundredfold.",[31],[],[324],{"kpi":45,"value":325,"unit":326,"qualifier":271,"baseline":327,"claimant":266,"quote":328,"sourceUrl":268},1,"minutes","a month per analysis before the system","The system analyzes millions of feedback points from a diverse range of sources (customer reviews, social media, contact center) in seconds — delivering a staggering 100x increase in data processing capacity and slashing analysis times from a month to a single minute.",[330],{"url":268,"title":276,"publisher":255,"date":277},{"level":223,"checkedAt":224},"mattel-feedback-classification",0,[335,340,345,350],{"kpi":46,"label":336,"unit":263,"aggregate":273,"higherIsBetter":273,"n":325,"nUpTo":333,"median":270,"min":270,"max":270,"byClaimant":337,"vendorOnly":273,"points":338},"Accuracy",{"organization":333,"vendor":325,"regulator":333,"independent":333},[339],{"evidenceId":284,"organization":250,"value":270,"qualifier":271,"claimant":266,"grade":283,"pooled":273},{"kpi":47,"label":341,"unit":263,"aggregate":273,"higherIsBetter":273,"n":325,"nUpTo":333,"median":262,"min":262,"max":262,"byClaimant":342,"vendorOnly":273,"points":343},"Cost reduction",{"organization":333,"vendor":325,"regulator":333,"independent":333},[344],{"evidenceId":284,"organization":250,"value":262,"qualifier":264,"claimant":266,"grade":283,"pooled":273},{"kpi":45,"label":346,"unit":302,"aggregate":199,"higherIsBetter":199,"n":325,"nUpTo":333,"median":301,"min":301,"max":301,"byClaimant":347,"vendorOnly":273,"points":348},"Cycle time",{"organization":333,"vendor":325,"regulator":333,"independent":333},[349],{"evidenceId":312,"organization":289,"value":301,"qualifier":271,"claimant":266,"grade":283,"pooled":273},{"kpi":45,"label":346,"unit":326,"aggregate":199,"higherIsBetter":199,"n":325,"nUpTo":333,"median":325,"min":325,"max":325,"byClaimant":351,"vendorOnly":273,"points":352},{"organization":333,"vendor":325,"regulator":333,"independent":333},[353],{"evidenceId":332,"organization":317,"value":325,"qualifier":271,"claimant":266,"grade":283,"pooled":273},{"low":355,"high":356},72000,378000,[358,378,399,419,445],{"slug":183,"title":359,"shortTitle":360,"definition":361,"status":9,"industries":362,"functions":367,"patterns":369,"audience":33,"autonomy":34,"adoptionStage":372,"segment":373,"evidenceCount":301,"publicEvidenceCount":301,"organizations":374,"bestGrade":225,"headline":227,"lastVerified":188,"indexable":273},"AI for complaints root cause and systemic issue analysis","Complaints root cause analysis","AI that reads the free text of complaints across all channels, clusters them into themes, separates systemic causes from one off events, links each theme to the product, process or control behind it and routes the insight to the owner who can fix it, with a human validating every root cause and every remediation.",[18,363,364,365,366,20],"banking","insurance","payments","telecommunications",[368,23,25],"regulatory-compliance",[27,28,370,371],"agentic-workflow","rag-knowledge-assistant","emerging","second-line",[375,376,377],"Centers for Medicare and Medicaid Services","Board of Governors of the Federal Reserve System","Federal Trade Commission",{"slug":184,"title":379,"shortTitle":380,"definition":381,"status":9,"industries":382,"functions":383,"patterns":385,"audience":33,"autonomy":34,"adoptionStage":387,"evidenceCount":388,"publicEvidenceCount":388,"organizations":389,"bestGrade":225,"headline":394,"lastVerified":188,"indexable":273},"AI for public consultation response analysis","Consultation response analysis","AI that reads every free text response to a public consultation or rulemaking comment period, proposes themes, maps each response to the themes that officials have validated, flags duplicates, campaign letters and responses that need special attention, and produces counts and summaries for the analysts who write the government's response.",[20],[384,25],"citizen-services",[28,27,386],"content-generation","early-adopters",5,[390,376,391,392,393],"Centers for Disease Control and Prevention","Department for Transport","Department for Science, Innovation and Technology (Incubator for Artificial Intelligence)","U.S. Department of Transportation, Office of the Secretary",{"kpi":46,"label":336,"unit":263,"n":325,"nUpTo":333,"kind":395,"value":396,"qualifier":397,"claimant":398,"organization":391,"vendorReported":199},"reported",92,"at-least","organization",{"slug":185,"title":400,"shortTitle":401,"definition":402,"status":9,"industries":403,"functions":405,"patterns":407,"audience":33,"autonomy":408,"adoptionStage":387,"evidenceCount":388,"publicEvidenceCount":388,"organizations":409,"bestGrade":283,"headline":415,"lastVerified":188,"indexable":273},"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.",[18,363,364,404,366,19],"energy-and-utilities",[23,368,406],"operations",[29,27,28],"supervised-agent",[410,411,412,413,414],"British Gas","Central Bank","DoorDash","Oportun","VitalityHealth",{"kpi":416,"label":417,"unit":263,"n":325,"nUpTo":333,"kind":395,"value":418,"qualifier":264,"claimant":266,"organization":410,"vendorReported":273},"quality-score-uplift","Quality score uplift",10,{"slug":186,"title":420,"shortTitle":421,"definition":422,"status":9,"industries":423,"functions":426,"patterns":428,"audience":33,"autonomy":408,"adoptionStage":35,"evidenceCount":431,"publicEvidenceCount":432,"organizations":433,"bestGrade":225,"headline":441,"lastVerified":188,"indexable":273},"AI marketing personalization at scale","Marketing personalization at scale","AI that runs marketing campaigns at the level of the individual: it decides for each customer which product, offer, message or content to show next across email, app, web and paid media, and generates the matching copy and creative variants within brand and compliance rules. It is the marketing team's engine across many campaigns and channels, not an agent that converses with the customer.",[18,424,425,19,363],"travel-and-hospitality","media-and-entertainment",[24,427],"sales",[429,430,386],"recommendation-and-personalization","prediction-and-scoring",8,7,[434,435,436,437,438,439,440],"Amazon","Catchtable","Commonwealth Bank of Australia","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":442,"label":443,"unit":263,"n":325,"nUpTo":333,"kind":395,"value":444,"qualifier":271,"claimant":266,"organization":435,"vendorReported":273},"conversion-rate-uplift","Conversion uplift",30,{"slug":187,"title":446,"shortTitle":447,"definition":448,"status":9,"industries":449,"functions":452,"patterns":454,"audience":457,"autonomy":458,"adoptionStage":387,"evidenceCount":301,"publicEvidenceCount":301,"organizations":459,"bestGrade":225,"headline":227,"lastVerified":188,"indexable":273},"Governed text to SQL analytics assistant","Governed SQL analytics","An assistant that turns a business user's plain language question into a query against governed data, runs it under that user's own data permissions and returns the table or chart together with the SQL and the tables used, so routine ad hoc questions no longer queue for the data team.",[18,363,364,19,450,451],"technology","pharma-and-life-sciences",[25,453],"it-and-engineering",[455,456,371],"conversational-agent","code-generation","employee-facing","assist",[460,461,462],"Bayer","LinkedIn","Uber Technologies",{"indexable":273,"reasons":464},[],[466,470,475,482,489,495,502,509,517,524,531,537,544,551,557,562,569,575,581,587,593,599,604,609,614,621,627,632,638,644,651,657,663,668],{"id":150,"label":467,"issuer":156,"region":157,"url":158,"description":468,"useCases":469,"indexable":273},"EU AI Act","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":151,"label":471,"issuer":156,"region":157,"url":472,"description":473,"useCases":474,"indexable":273},"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":152,"label":476,"issuer":477,"region":478,"url":479,"description":480,"useCases":481,"indexable":273},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":483,"label":484,"issuer":485,"region":201,"url":486,"description":487,"useCases":488,"indexable":273},"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":490,"label":491,"issuer":156,"region":157,"url":492,"description":493,"useCases":494,"indexable":273},"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":496,"label":497,"issuer":498,"region":157,"url":499,"description":500,"useCases":501,"indexable":273},"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":503,"label":504,"issuer":505,"region":157,"url":506,"description":507,"useCases":508,"indexable":273},"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":510,"label":511,"issuer":512,"region":513,"url":514,"description":515,"useCases":516,"indexable":273},"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":518,"label":519,"issuer":520,"region":513,"url":521,"description":522,"useCases":523,"indexable":273},"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":525,"label":526,"issuer":527,"region":478,"url":528,"description":529,"useCases":530,"indexable":273},"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":532,"label":533,"issuer":534,"region":201,"url":535,"description":536,"useCases":530,"indexable":273},"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":538,"label":539,"issuer":540,"region":157,"url":541,"description":542,"useCases":543,"indexable":273},"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":545,"label":546,"issuer":547,"region":478,"url":548,"description":549,"useCases":550,"indexable":273},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":552,"label":553,"issuer":156,"region":157,"url":554,"description":555,"useCases":556,"indexable":273},"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":558,"label":559,"issuer":156,"region":157,"url":560,"description":561,"useCases":556,"indexable":273},"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":563,"label":564,"issuer":565,"region":201,"url":566,"description":567,"useCases":568,"indexable":273},"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":570,"label":571,"issuer":156,"region":157,"url":572,"description":573,"useCases":574,"indexable":273},"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":576,"label":577,"issuer":578,"region":201,"url":579,"description":580,"useCases":574,"indexable":273},"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":582,"label":583,"issuer":584,"region":478,"url":585,"description":586,"useCases":574,"indexable":273},"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":588,"label":589,"issuer":156,"region":157,"url":590,"description":591,"useCases":592,"indexable":273},"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":594,"label":595,"issuer":596,"region":201,"url":597,"description":598,"useCases":592,"indexable":273},"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":600,"label":601,"issuer":512,"region":513,"url":602,"description":603,"useCases":418,"indexable":273},"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":605,"label":606,"issuer":156,"region":157,"url":607,"description":608,"useCases":418,"indexable":273},"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":610,"label":611,"issuer":156,"region":157,"url":612,"description":613,"useCases":418,"indexable":273},"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":615,"label":616,"issuer":617,"region":157,"url":618,"description":619,"useCases":620,"indexable":273},"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":622,"label":623,"issuer":624,"region":201,"url":625,"description":626,"useCases":431,"indexable":273},"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.",{"id":628,"label":629,"issuer":156,"region":157,"url":630,"description":631,"useCases":431,"indexable":273},"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":633,"label":634,"issuer":156,"region":157,"url":635,"description":636,"useCases":637,"indexable":273},"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.",6,{"id":639,"label":640,"issuer":641,"region":291,"url":642,"description":643,"useCases":388,"indexable":273},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":645,"label":646,"issuer":647,"region":157,"url":648,"description":649,"useCases":650,"indexable":273},"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.",4,{"id":652,"label":653,"issuer":654,"region":157,"url":655,"description":656,"useCases":650,"indexable":273},"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":658,"label":659,"issuer":660,"region":513,"url":661,"description":662,"useCases":301,"indexable":273},"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":664,"label":665,"issuer":156,"region":157,"url":666,"description":667,"useCases":301,"indexable":273},"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":669,"label":670,"issuer":377,"region":201,"url":671,"description":672,"useCases":301,"indexable":273},"us-fcra","Fair Credit Reporting Act","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.",1790598302186]