[{"data":1,"prerenderedAt":766},["ShallowReactive",2],{"uc-personalized-marketing-at-scale":3,"uc-regulations":561},{"useCase":4,"evidence":202,"blitsAiDeployments":418,"benchmarks":419,"indicative":445,"related":448,"indexability":559,"includeUnpublished":209},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":23,"patterns":26,"channels":30,"audience":35,"autonomy":36,"adoptionStage":37,"problem":38,"problemStats":39,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":52,"macroEstimates":80,"feasibility":81,"implementation":95,"risk":141,"blitsAi":178,"faq":180,"related":190,"datePublished":197,"dateModified":197,"lastVerified":197,"changelog":198,"slug":201},"AI marketing personalization at scale","Marketing personalization at scale","AI for personalized marketing campaigns at scale","AI picks the next best offer for each customer and writes copy within approved claims. Google Cloud reports Radisson grew AI campaign revenue by over 20%.","published","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.",[12,13,14,15,16],"hyper personalization","one to one marketing","AI personalized campaigns","personalized recommendations and offers","AI decisioning for marketing",[18,19,20,21,22],"cross-industry","travel-and-hospitality","media-and-entertainment","retail-and-ecommerce","banking",[24,25],"marketing","sales",[27,28,29],"recommendation-and-personalization","prediction-and-scoring","content-generation",[31,32,33,34],"email","mobile-app","web-chat","api","back-office","supervised-agent","mainstream","Marketing teams know that relevant messages work better than broadcast ones, but personalization\nhas been limited by two bottlenecks. The first is decisioning: choosing, for millions of customers,\nwhich of hundreds of offers and messages is most relevant right now, while respecting frequency\ncaps, consent and eligibility. The second is content: even a good decision engine was only as\npersonal as the handful of creative variants the studio could produce and legal could approve.\n\nMachine learning has handled the first bottleneck for years, in recommendation engines and next best\naction systems. Generative AI now attacks the second: copy, images and video variants per segment,\nlanguage and context, without a separate studio brief for each one. Together they make segment of\none campaigns practical. The risks grow with it: invented claims in generated copy, offers that\nexploit vulnerable customers, profiling without a lawful basis, and brand damage from content nobody\nreviewed. The workable approach is a library of approved building blocks within which AI\npersonalizes.",[],"1. **Unify the signals.** Customer profile, consent, product holdings, behaviour on web and app, and\n   context (location, time, channel) are brought into one decisioning layer.\n2. **Decide the next best action.** Models predict propensity and value for each eligible action,\n   including service messages and doing nothing, and arbitration rules pick one within caps,\n   eligibility and business priorities.\n3. **Assemble the content.** The chosen action is rendered from approved building blocks; generative\n   AI produces copy and creative variants within brand, claims and disclosure rules.\n4. **Check before sending.** Automated checks cover required disclosures, prohibited claims,\n   consent and vulnerability flags; new templates and campaigns get human approval.\n5. **Deliver and learn.** The message goes out in the right channel; responses feed back into the\n   models, and experiments with control groups measure the real uplift.",[42,43,44,45],"revenue-growth","customer-experience","employee-productivity","speed",[47,48,49,50,51],"conversion-rate-uplift","revenue-uplift","processing-time-reduction","productivity-gain","interactions-handled",{"referenceOrg":53,"inputs":54,"formula":75,"currency":76,"period":77,"resultLabel":78,"caveat":79},"A consumer business with USD 200 million a year in revenue from marketing driven campaigns",[55,61,68],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"campaignRevenue","Annual revenue attributed to targeted campaigns",200000000,"USD per year","The reference organization. Use revenue measured against a holdout, not last click attribution.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"uplift","Incremental revenue from personalization versus current targeting",0.02,0.06,"fraction of campaign revenue","Editorial assumption, kept well below the benchmarks on this page (Google Cloud reports revenue from Radisson Hotel Group's AI powered campaigns up by more than 20% and Catchtable's reservation conversion up 30%), because those figures are vendor reported and do not state their baseline or whether a control group was used.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"margin","Contribution margin on incremental revenue",0.2,0.4,"fraction of revenue","Editorial assumption, replace with your own.","campaignRevenue * uplift * margin","USD","per year","Incremental contribution from personalization","An uplift estimate that only holds if measured with control groups. It leaves out content production savings, the cost of data, platform and people, and any revenue lost to customers who opt out after poorly judged personalization.",[],{"complexity":82,"complexityNote":83,"dataPrerequisites":84,"integrations":89},"medium","Generating variants is easy; everything around it is not. Unified customer data with consent, a decisioning layer with arbitration, a library of approved content blocks and experiment discipline decide whether personalization pays off.",[85,86,87,88],"Customer profile and behavioural data with recorded marketing consent per channel","Product and offer catalogue with eligibility rules","Approved brand guidelines, claims library and required disclosures","Response history and holdout groups to measure uplift",[90,91,92,93,94],"Customer data platform or data warehouse","Decisioning or next best action engine","Marketing automation, email and push platforms","Content management and digital asset management","Consent and preference management",{"steps":96,"guardrails":115,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":128},[97,100,103,106,109,112],{"title":98,"detail":99},"Fix consent and data first","Know for each customer and channel whether marketing and profiling are allowed, and make the decisioning layer enforce it. Personalization on data you may not use is a liability.",{"title":101,"detail":102},"Build an approved content library","Break campaigns into building blocks (claims, offers, disclosures, images) that legal and brand have approved, and let generative AI personalize wording and combinations within them.",{"title":104,"detail":105},"Start with one journey and a holdout","Pick one high volume journey, such as onboarding, cart abandonment or renewal, and measure uplift against a randomized control group before scaling.",{"title":107,"detail":108},"Add arbitration across campaigns","Move from campaign by campaign targeting to one decision per customer that weighs all eligible actions, including service messages and no message, with frequency caps.",{"title":110,"detail":111},"Automate checks, keep approvals for the new","Run automatic checks for disclosures, prohibited claims and vulnerability flags on every variant, and require human approval for new templates, offers and audiences.",{"title":113,"detail":114},"Review fairness and customer outcomes","Check who receives which offers and prices, and whether customers in vulnerable circumstances are targeted with products that may harm them.",[116,117,118,119,120],"Personalization only on data with a lawful basis and recorded consent for the channel","Generated copy restricted to approved claims and must include required disclosures","No targeting that exploits age, disability or financial difficulty; vulnerable customers excluded from high risk offers","Frequency caps and an easy opt out in every message","Synthetic images and video labelled or marked where the law requires it","Marketing owns the strategy, audiences and offers; brand and legal approve templates, claims and new campaigns; analysts own the experiments. The AI selects and assembles within those approvals, and people review samples of what customers actually received.",[123,124,125,126,127],"Incremental conversion and revenue versus a randomized holdout","Opt out, unsubscribe and complaint rates per campaign","Share of generated variants passing automated compliance checks first time","Campaign production time from brief to launch","Distribution of offers across customer groups, including vulnerable customers",[129,132,135,138],{"title":130,"detail":131},"Uplift that is really attribution","Personalized campaigns take credit for sales that would have happened anyway. Measure against randomized holdouts.",{"title":133,"detail":134},"Invented claims at scale","Generated copy promises a benefit or price that does not exist. Restrict generation to an approved claims library and check every variant.",{"title":136,"detail":137},"Creepy or harmful targeting","Messages reveal inferences customers did not expect, or push credit or gambling to people in difficulty. Limit sensitive inferences and exclude vulnerable customers.",{"title":139,"detail":140},"Too many messages","Each campaign optimizes for itself and customers are flooded. Arbitrate across campaigns with frequency caps.",{"euAiAct":142,"regulations":145,"guidance":151,"controls":171,"incidents":177},{"tier":143,"basis":144},"context-dependent","Most personalization and content generation is minimal risk. Providers of systems that generate synthetic audio, image, video or text content must mark the output as artificially generated, and deployers must disclose deep fakes (Article 50(2) and 50(4)). Personalization that deploys manipulative or deceptive techniques, or exploits vulnerabilities due to age, disability or a specific social or economic situation, in a way that causes or is reasonably likely to cause significant harm, is prohibited under Article 5(1)(a) and (b). Using AI to assess creditworthiness or to price life and health insurance is high risk under Annex III point 5(b) and 5(c) and belongs on its own page. Outside the AI Act, the FCA Consumer Duty applies only to FCA regulated firms (the financial services slice of this use case), and the Telephone Consumer Protection Act applies only to campaigns delivered by call or text message in the US.",[146,147,148,149,150],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty","us-tcpa",[152,158,162,166],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Article 5, prohibited AI practices","European Union","europe","https://artificialintelligenceact.eu/article/5/","Points 1(a) and 1(b) prohibit manipulative techniques and the exploitation of vulnerabilities that cause significant harm.",{"title":159,"issuer":154,"region":155,"url":160,"note":161},"Article 50, transparency obligations for providers and deployers of certain AI systems","https://artificialintelligenceact.eu/article/50/","Marking of synthetic content and disclosure of deep fakes used in campaigns.",{"title":163,"issuer":154,"region":155,"url":164,"note":165},"Article 21 GDPR, right to object","https://gdpr-info.eu/art-21-gdpr/","People may object at any time to processing for direct marketing, including profiling related to it.",{"title":167,"issuer":168,"region":155,"url":169,"note":170},"Direct marketing and privacy and electronic communications","UK Information Commissioner's Office","https://ico.org.uk/for-organisations/direct-marketing-and-privacy-and-electronic-communications/","The UK regulator's guidance hub on direct marketing under PECR and data protection law, covering consent for email and text marketing and choosing a lawful basis.",[172,173,174,175,176],"Consent and preference checks enforced in the decisioning layer","Approved claims and disclosure library with owners","Automated pre send checks plus human approval of new templates and audiences","Holdout based measurement for every personalized journey","Periodic review of offer distribution for vulnerable customers and fairness",[],{"howToBuild":179},"On Blits.ai the decisioning usually stays in the organization's own next best action engine or\ncustomer data platform, which **custom functions** call through REST (the integration catalog\nincludes Salesforce, Snowflake and Databricks). Blits.ai adds the conversational and content side:\nan **agentic workflow** in which an **AI agent** writes copy variants from a **knowledge base** of\napproved claims and brand guidelines, and **guardrails** with admin authored policies check each\nvariant for prohibited claims and missing disclosures before **human in the loop** approval.\n\nWhen a customer responds or asks about an offer, a customer facing agent presents it on **web chat, WhatsApp, SMS or email**,\nor inside the organization's own app through the **REST or WebSocket API channel**, with rich\n**product recommendation cards**; it explains the offer, answers questions and uses **human\nhandover** when needed. **Multi language** bots serve each customer in their language,\n**analytics** and response feedback show how customers react, and **test suites** check the agent's\noutput against your rules before every release. The platform is model agnostic and can run in the EU or\nUAE region.",[181,184,187],{"question":182,"answer":183},"What results do companies report from AI personalization?","Vendor reported results are large. Google Cloud reports that revenue from Radisson Hotel Group's AI powered campaigns rose by more than 20% and ad team productivity by around 50%, that Catchtable's personalized recommendations raised reservation conversion by 30%, and that Virgin Voyages cut campaign creation time by 40%. None of these states its baseline, so treat them as upper bounds and measure against your own holdout.",{"question":185,"answer":186},"How is this different from an offers and rewards agent in banking?","The banking offers page covers choosing and explaining offers and rewards from transaction data inside the bank's app and conversations. This page covers the wider marketing job in any industry: deciding and generating personalized campaign content at scale across email, app, web and paid media.",{"question":188,"answer":189},"Can generative AI write campaign copy without legal review?","Not freely. The workable model is an approved library of claims, offers and disclosures that legal and brand sign off once, with AI personalizing wording and combinations inside it, automated checks on every variant and human approval for anything new.",[191,192,193,194,195,196],"offers-and-rewards-agent","marketing-content-compliance-copilot","churn-prediction-and-retention-offers","customer-feedback-analysis","conversational-shopping-assistant","proactive-outbound-engagement-agent","2026-09-27",[199],{"date":197,"note":200},"First published","personalized-marketing-at-scale",[203,254,283,316,344,366,398],{"title":204,"useCases":205,"organization":207,"vendors":212,"summary":216,"stage":217,"year":218,"channels":219,"languages":221,"metrics":223,"outcomeDisclosed":240,"sources":241,"verification":249,"grade":251,"id":252,"organizationSlug":253},"Amazon: generative AI tools that write product listings for selling partners",[206,201],"product-content-and-catalog-enrichment",{"name":208,"anonymized":209,"country":210,"region":211,"industry":21},"Amazon",false,"US","global",[213],{"name":214,"role":215},"Amazon Web Services","platform","Since the end of 2023 Amazon lets independent sellers create a product listing from a few words or a single image, and since March 2024 also from the URL of their own web page: generative AI on Amazon Bedrock drafts the title, bullet points, description and attributes, and the seller submits the draft, with Amazon encouraging a review first. Bulk creation from a spreadsheet followed, and Enhance My Listing, which Amazon said in May 2025 had begun rolling out in the US, suggests updates to existing listings based on shopping behaviour. In May 2025 Amazon reported that sellers accept the AI generated content with little to no edits about 90% of the time. The same post describes Amazon using generative AI to personalize product recommendation categories and product descriptions shown to customers on the website and in the shopping app, based on a customer's shopping activity; no outcome number is given for that side of the work.","scaled",2025,[220],"internal-tools",[222],"en",[224,233],{"kpi":225,"value":226,"unit":227,"qualifier":228,"period":229,"claimant":230,"quote":231,"sourceUrl":232},"users-served",900000,"count","at-least","selling partners who have used the listing tools, by May 2025","organization","Now, more than 900,000 Amazon selling partners have embraced these tools, with sellers accepting AI-generated content with little to no edits approximately 90% of the time.","https://www.aboutamazon.com/news/innovation-at-amazon/amazon-generative-ai-seller-growth-shopping-experience",{"kpi":234,"value":235,"unit":236,"qualifier":237,"period":238,"claimant":230,"quote":239,"sourceUrl":232},"quality-score-uplift",40,"percent","exact","overall listing quality of listings created with the tools, reported by May 2025","When sellers use our Gen AI tools to create listings, they see a 40% increase in overall listing quality, helping them create content that enhances customer engagement and boosts sales potential.",true,[242,245],{"url":232,"title":243,"publisher":208,"date":244},"Amazon sellers can now automatically improve product listings with our new Gen AI tool","2025-05-08",{"url":246,"title":247,"publisher":208,"date":248},"https://www.aboutamazon.com/news/innovation-at-amazon/amazon-generative-ai-powered-product-listings","Amazon selling partners can now access even more generative AI features to create high-quality product listings","2024-03-13",{"level":250,"checkedAt":197},"source-verified","B","amazon-generative-ai-listing-tools","amazon",{"title":255,"useCases":256,"organization":259,"vendors":263,"summary":266,"stage":217,"year":267,"channels":268,"languages":270,"metrics":271,"outcomeDisclosed":209,"sources":272,"verification":280,"grade":251,"id":281,"organizationSlug":282},"Commonwealth Bank: Customer Engagement Engine for next best conversations",[191,257,258,196,201],"financial-wellbeing-coach","loan-restructuring-recommendations",{"name":260,"anonymized":209,"country":261,"region":262,"industry":22},"Commonwealth Bank of Australia","AU","asia-pacific",[264],{"name":265,"role":215},"Pegasystems","Commonwealth Bank's Customer Engagement Engine (CEE), built on Pega Customer Decision Hub, suggests in real time the next best conversation to have with each customer, whether in the branch, on the phone, online or on a mobile device. Beyond suggesting conversations, the bank uses it to match customers to government benefits and rebates they may be missing (Benefits finder) and to reach customers hit by natural disasters with same day support such as a loan deferral. The same decisions feed digital channels and prompts for branch and contact centre staff.",2022,[32,269],"agent-desktop",[222],[],[273,277],{"url":274,"title":275,"publisher":260,"date":276},"https://www.commbank.com.au/articles/newsroom/2022/06/CBA-artificial-intelligence-usages.html","How artificial intelligence is changing the face of banking","2022-06-24",{"url":278,"title":279,"publisher":265},"https://www.pega.com/customers/cba-marketing","Delivering next best conversations with Pega",{"level":250,"checkedAt":197},"commonwealth-bank-customer-engagement-engine","commonwealth-bank-of-australia",{"title":284,"useCases":285,"organization":286,"vendors":289,"summary":292,"stage":293,"year":218,"channels":294,"languages":295,"metrics":296,"outcomeDisclosed":240,"sources":303,"verification":312,"grade":313,"id":314,"organizationSlug":315},"Catchtable: personalized restaurant recommendations that lift reservation conversion",[201],{"name":287,"anonymized":209,"country":288,"region":262,"industry":19},"Catchtable","KR",[290],{"name":291,"role":215},"Google Cloud","Catchtable, a restaurant discovery and reservation app from South Korea, built personalized recommendation models on Vertex AI that combine language models with custom embeddings to read search intent and recommend restaurants in real time. Google Cloud reports a 30% increase in reservation conversion rates and a 150% increase in impressions per restaurant search.","production",[32],[],[297],{"kpi":47,"value":298,"unit":236,"qualifier":237,"period":299,"claimant":300,"quote":301,"sourceUrl":302},30,"reservation conversion rate","vendor","Catchtable uses Vertex AI and Kubeflow with GPU optimization to build personalized restaurant recommendation models, achieving a 30% increase in reservation conversion rates and a 150% increase in impressions per restaurant search.","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",[304,307],{"url":302,"title":305,"publisher":291,"date":306},"Real-world gen AI use cases from the world's leading organizations","2026-04-22",{"url":308,"title":309,"publisher":310,"date":311},"https://play.google.com/store/apps/details?id=kr.co.catchtable.global.catchtable_global&hl=en","CATCHTABLE: Book Restaurants","WAD Corp.","2026-08-19",{"level":250,"checkedAt":197},"C","catchtable-personalized-recommendations",null,{"title":317,"useCases":318,"organization":320,"vendors":323,"summary":325,"stage":293,"year":218,"channels":326,"languages":327,"metrics":328,"outcomeDisclosed":240,"sources":339,"verification":342,"grade":313,"id":343,"organizationSlug":315},"Swarovski: Génie generative AI portal speeds up campaign localization",[319,201],"marketing-and-product-content-localization",{"name":321,"anonymized":209,"country":322,"region":155,"industry":21},"Swarovski","AT",[324],{"name":291,"role":215},"Swarovski, which sells in more than 140 markets, launched Génie in 2023, a generative AI portal on Vertex AI and Gemini, on top of a BigQuery data foundation consolidated with the partner CloudSufi. More than 1,000 employees use it for tasks including content translation into over 20 languages, creative asset generation and testing visuals and descriptions for different regions. Google Cloud reports that campaign localization became 10 times faster through AI assisted translation and asset adaptation, and that Génie's AI personalized email campaigns see 17% higher open rates and 7% higher click through rates. Every AI application is evaluated against an internal ethics and risk model.",[220],[],[329,335],{"kpi":49,"value":330,"unit":331,"qualifier":237,"period":332,"claimant":300,"quote":333,"sourceUrl":334},10,"multiplier","speed of campaign localization","Campaign localization is 10x faster, thanks to AI-assisted translation and asset adaptation","https://cloud.google.com/customers/swarovski",{"kpi":225,"value":336,"unit":227,"qualifier":228,"period":337,"claimant":300,"quote":338,"sourceUrl":334},1000,"employees using the Génie portal","Over 1,000 employees now utilize Génie for tasks such as contract review, content translation into over 20 languages, creative digital asset generation, and campaign inspirations, and product design cost estimation.",[340],{"url":334,"title":341,"publisher":291},"Letting data shine bright: How Swarovski personalizes luxury with Google Cloud",{"level":250,"checkedAt":197},"swarovski-genie-campaign-localization",{"title":345,"useCases":346,"organization":347,"vendors":350,"summary":352,"stage":293,"year":218,"channels":353,"languages":354,"metrics":355,"outcomeDisclosed":240,"sources":359,"verification":364,"grade":313,"id":365,"organizationSlug":315},"Virgin Voyages: agents and generative media for personalized campaigns",[201],{"name":348,"anonymized":209,"country":210,"region":349,"industry":19},"Virgin Voyages","north-america",[351],{"name":291,"role":215},"Virgin Voyages uses Google's generative video and image models to create thousands of personalized advertisements and emails in its brand voice, and has deployed specialized agents that turn behavioural signals into personalized campaign actions. Google Cloud reports that campaign creation time fell by 40%.",[31],[],[356],{"kpi":49,"value":235,"unit":236,"qualifier":237,"period":357,"claimant":300,"quote":358,"sourceUrl":302},"campaign creation time","Virgin Voyages have already deployed more than 1,000 specialized agents to reduce campaign creation times by 40% by turning behavioral signals into personalized action.",[360,361],{"url":302,"title":305,"publisher":291,"date":306},{"url":362,"title":363,"publisher":348},"https://www.virginvoyages.com/terms-and-conditions","Website Terms & Conditions",{"level":250,"checkedAt":197},"virgin-voyages-personalized-campaigns",{"title":367,"useCases":368,"organization":369,"vendors":372,"summary":377,"stage":293,"year":378,"channels":379,"languages":380,"metrics":381,"outcomeDisclosed":240,"sources":390,"verification":395,"grade":313,"id":397,"organizationSlug":315},"Radisson Hotel Group: personalized advertising at scale with generative AI",[201],{"name":370,"anonymized":209,"country":371,"region":155,"industry":19},"Radisson Hotel Group","BE",[373,374],{"name":291,"role":215},{"name":375,"role":376},"Accenture","integrator","Radisson Hotel Group worked with Accenture to personalize its advertising at scale with Vertex AI and Gemini models, trained on extensive datasets stored in BigQuery. Google Cloud reports that ad team productivity rose by around 50% and that revenue from the AI powered campaigns rose by more than 20%. The comparison group for the revenue figure is not stated.",2024,[],[],[382,386],{"kpi":48,"value":383,"unit":236,"qualifier":228,"period":384,"claimant":300,"quote":385,"sourceUrl":302},20,"revenue from AI powered campaigns","By training them on extensive datasets stored in BigQuery, its ad teams saw productivity rise around 50% while revenue increased from AI-powered campaigns by more than 20%.",{"kpi":50,"value":387,"unit":236,"qualifier":388,"period":389,"claimant":300,"quote":385,"sourceUrl":302},50,"approximately","advertising team productivity",[391,392],{"url":302,"title":305,"publisher":291,"date":306},{"url":393,"title":370,"publisher":394},"https://en.wikipedia.org/wiki/Radisson_Hotel_Group","Wikipedia",{"level":250,"checkedAt":396},"2026-09-26","radisson-hotel-group-personalized-advertising",{"title":399,"useCases":400,"organization":401,"vendors":404,"summary":406,"stage":293,"year":378,"channels":407,"languages":408,"metrics":409,"outcomeDisclosed":240,"sources":410,"verification":416,"grade":313,"id":417,"organizationSlug":315},"Square Enix: AI optimized, personalized player emails",[201],{"name":402,"anonymized":209,"country":403,"region":262,"industry":20},"Square Enix","JP",[405],{"name":291,"role":215},"Square Enix uses customer data to develop AI optimized marketing assets and send players personalized emails matched to their preferences. Google Cloud reports a 20% increase in email opens and a 10% increase in retention.",[31],[],[],[411,412],{"url":302,"title":305,"publisher":291,"date":306},{"url":413,"title":414,"publisher":415},"https://www.hd.square-enix.com/eng/company/","Company","Square Enix Holdings",{"level":250,"checkedAt":197},"square-enix-personalized-emails",1,[420,426,431,435,440],{"kpi":47,"label":421,"unit":236,"aggregate":240,"higherIsBetter":240,"n":418,"nUpTo":422,"median":298,"min":298,"max":298,"byClaimant":423,"vendorOnly":240,"points":424},"Conversion uplift",0,{"organization":422,"vendor":418,"regulator":422,"independent":422},[425],{"evidenceId":314,"organization":287,"value":298,"qualifier":237,"claimant":300,"grade":313,"pooled":240},{"kpi":49,"label":427,"unit":331,"aggregate":240,"higherIsBetter":240,"n":418,"nUpTo":422,"median":330,"min":330,"max":330,"byClaimant":428,"vendorOnly":240,"points":429},"Cycle time reduction",{"organization":422,"vendor":418,"regulator":422,"independent":422},[430],{"evidenceId":343,"organization":321,"value":330,"qualifier":237,"claimant":300,"grade":313,"pooled":240},{"kpi":49,"label":427,"unit":236,"aggregate":240,"higherIsBetter":240,"n":418,"nUpTo":422,"median":235,"min":235,"max":235,"byClaimant":432,"vendorOnly":240,"points":433},{"organization":422,"vendor":418,"regulator":422,"independent":422},[434],{"evidenceId":365,"organization":348,"value":235,"qualifier":237,"claimant":300,"grade":313,"pooled":240},{"kpi":50,"label":436,"unit":236,"aggregate":240,"higherIsBetter":240,"n":418,"nUpTo":422,"median":387,"min":387,"max":387,"byClaimant":437,"vendorOnly":240,"points":438},"Productivity gain",{"organization":422,"vendor":418,"regulator":422,"independent":422},[439],{"evidenceId":397,"organization":370,"value":387,"qualifier":388,"claimant":300,"grade":313,"pooled":240},{"kpi":48,"label":441,"unit":236,"aggregate":240,"higherIsBetter":240,"n":418,"nUpTo":422,"median":383,"min":383,"max":383,"byClaimant":442,"vendorOnly":240,"points":443},"Revenue uplift",{"organization":422,"vendor":418,"regulator":422,"independent":422},[444],{"evidenceId":397,"organization":370,"value":383,"qualifier":228,"claimant":300,"grade":313,"pooled":240},{"low":446,"high":447},800000,4800000,[449,467,493,513,534,548],{"slug":191,"title":450,"shortTitle":451,"definition":452,"status":9,"industries":453,"functions":455,"patterns":457,"audience":459,"autonomy":460,"adoptionStage":37,"segment":461,"evidenceCount":462,"publicEvidenceCount":463,"organizations":464,"bestGrade":251,"headline":315,"lastVerified":197,"indexable":240},"AI agent for personalized offers and rewards","Offers and rewards","A customer facing AI agent for banks and card issuers that picks the offer, reward or loyalty action most relevant to each customer at each moment from their transactions and context, delivers it in the app, in messaging or through a colleague, and helps the customer understand, track and redeem rewards in conversation. Unlike campaign personalization, it works inside the customer's own account and loyalty relationship, one moment at a time.",[22,454],"payments",[24,25,456],"customer-service",[27,28,458],"conversational-agent","customer-facing","autonomous","front-office",8,3,[465,260,466],"Bank of America","DBS Bank",{"slug":192,"title":468,"shortTitle":469,"definition":470,"status":9,"industries":471,"functions":475,"patterns":478,"audience":482,"autonomy":483,"adoptionStage":484,"evidenceCount":485,"publicEvidenceCount":463,"organizations":486,"bestGrade":251,"headline":490,"lastVerified":197,"indexable":240},"AI copilot for marketing content with compliance pre review","Marketing content and compliance","A copilot that drafts campaign copy, product explainers and social posts on brand and in the customer's language from approved product facts, then runs a first pass compliance check against advertising rules and required disclosures, flagging unsupported claims and missing warnings before a human in marketing compliance approves publication.",[18,22,472,454,473,474],"insurance","wealth-and-asset-management","pharma-and-life-sciences",[24,476,477],"regulatory-compliance","legal",[29,479,480,481],"rag-knowledge-assistant","classification-and-routing","translation","employee-facing","copilot","early-adopters",5,[487,488,489],"Ally Financial","JPMorgan Chase","Klarna",{"kpi":50,"label":436,"unit":236,"n":418,"nUpTo":422,"kind":491,"value":492,"qualifier":237,"claimant":230,"organization":487,"vendorReported":209},"reported",34,{"slug":193,"title":494,"shortTitle":495,"definition":496,"status":9,"industries":497,"functions":499,"patterns":501,"audience":35,"autonomy":36,"adoptionStage":37,"segment":502,"evidenceCount":503,"publicEvidenceCount":503,"organizations":504,"bestGrade":251,"headline":509,"lastVerified":197,"indexable":240},"AI for telecom churn prediction and retention offers","Churn prediction and retention","AI for telecom operators that scores each subscriber's risk of leaving from usage, service, billing and contact signals, explains the likely reason, and chooses the next best retention action, such as fixing a problem, adjusting a plan or making an offer, delivered through the app, messaging, an agent or an advisor within approved offer budgets.",[498],"telecommunications",[24,456,25,500],"analytics-and-reporting",[28,27,458],"middle-office",4,[505,506,507,508],"Etisalat","Telenet","Virgin Media O2","Vodafone UK",{"kpi":510,"label":511,"unit":236,"n":512,"nUpTo":422,"kind":491,"value":383,"qualifier":237,"claimant":300,"organization":506,"vendorReported":240},"churn-reduction","Churn reduction",2,{"slug":194,"title":514,"shortTitle":515,"definition":516,"status":9,"industries":517,"functions":520,"patterns":521,"audience":35,"autonomy":483,"adoptionStage":37,"evidenceCount":485,"publicEvidenceCount":485,"organizations":524,"bestGrade":251,"headline":530,"lastVerified":197,"indexable":240},"AI for voice of the customer and feedback analysis","Customer feedback analysis","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.",[18,21,518,519],"government","manufacturing",[456,24,500],[480,522,523],"summarization","speech-analytics",[525,526,527,528,529],"U.S. Department of Housing and Urban Development","Majid Al Futtaim Retail","Mattel","SBF Group","U.S. Social Security Administration",{"kpi":531,"label":532,"unit":236,"n":418,"nUpTo":422,"kind":491,"value":533,"qualifier":237,"claimant":300,"organization":528,"vendorReported":240},"accuracy","Accuracy",84,{"slug":195,"title":535,"shortTitle":536,"definition":537,"status":9,"industries":538,"functions":539,"patterns":540,"audience":459,"autonomy":460,"adoptionStage":484,"evidenceCount":330,"publicEvidenceCount":485,"organizations":542,"bestGrade":251,"headline":547,"lastVerified":197,"indexable":240},"AI shopping assistant for product discovery and recommendations","Conversational shopping assistant","A conversational assistant on a retailer's site or app that answers product questions, compares items and recommends products from the retailer's own catalog for a need, project or occasion described in the shopper's own words, grounded in product data, reviews and stock, and hands the shopper to a basket, a store or a human expert.",[18,21],[25,24,456],[458,27,479,541],"agentic-workflow",[208,543,544,545,546],"Lowe's","Sun & Ski Sports","Walmart","Zalando",{"kpi":47,"label":421,"unit":331,"n":418,"nUpTo":422,"kind":491,"value":463,"qualifier":237,"claimant":300,"organization":544,"vendorReported":240},{"slug":196,"title":549,"shortTitle":550,"definition":551,"status":9,"industries":552,"functions":553,"patterns":554,"audience":459,"autonomy":36,"adoptionStage":556,"segment":461,"evidenceCount":463,"publicEvidenceCount":463,"organizations":557,"bestGrade":251,"headline":315,"lastVerified":197,"indexable":240},"AI agent for proactive customer outreach, activation and retention","Proactive outreach and activation","An AI agent that holds the conversation when a bank reaches out first to change something about the customer's account or products, triggered by an event or a campaign: low balance and fee avoidance alerts, payment and renewal reminders, card activation, dormant account reactivation and offers the customer already qualifies for, over messaging or voice, while the bank's own systems decide who is contacted and why. Reminders about appointments and deliveries the customer booked, and the in app coach the customer opens, are separate use cases.",[22,454],[24,25,456],[458,555,541,27],"voice-agent","emerging",[465,558,260],"Capital One",{"indexable":240,"reasons":560},[],[562,567,572,579,586,592,598,604,611,618,624,630,637,644,650,655,662,668,674,680,686,691,696,701,706,713,719,724,730,737,743,749,755,760],{"id":146,"label":563,"issuer":154,"region":155,"url":564,"description":565,"useCases":566,"indexable":240},"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":147,"label":568,"issuer":154,"region":155,"url":569,"description":570,"useCases":571,"indexable":240},"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":573,"label":574,"issuer":575,"region":211,"url":576,"description":577,"useCases":578,"indexable":240},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":580,"label":581,"issuer":582,"region":349,"url":583,"description":584,"useCases":585,"indexable":240},"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":587,"label":588,"issuer":154,"region":155,"url":589,"description":590,"useCases":591,"indexable":240},"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":148,"label":593,"issuer":594,"region":155,"url":595,"description":596,"useCases":597,"indexable":240},"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":149,"label":599,"issuer":600,"region":155,"url":601,"description":602,"useCases":603,"indexable":240},"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":605,"label":606,"issuer":607,"region":262,"url":608,"description":609,"useCases":610,"indexable":240},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":612,"label":613,"issuer":614,"region":262,"url":615,"description":616,"useCases":617,"indexable":240},"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":619,"label":620,"issuer":621,"region":211,"url":622,"description":623,"useCases":383,"indexable":240},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":625,"label":626,"issuer":627,"region":349,"url":628,"description":629,"useCases":383,"indexable":240},"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":631,"label":632,"issuer":633,"region":155,"url":634,"description":635,"useCases":636,"indexable":240},"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":638,"label":639,"issuer":640,"region":211,"url":641,"description":642,"useCases":643,"indexable":240},"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":645,"label":646,"issuer":154,"region":155,"url":647,"description":648,"useCases":649,"indexable":240},"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":651,"label":652,"issuer":154,"region":155,"url":653,"description":654,"useCases":649,"indexable":240},"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":656,"label":657,"issuer":658,"region":349,"url":659,"description":660,"useCases":661,"indexable":240},"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":663,"label":664,"issuer":154,"region":155,"url":665,"description":666,"useCases":667,"indexable":240},"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":669,"label":670,"issuer":671,"region":349,"url":672,"description":673,"useCases":667,"indexable":240},"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":675,"label":676,"issuer":677,"region":211,"url":678,"description":679,"useCases":667,"indexable":240},"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":681,"label":682,"issuer":154,"region":155,"url":683,"description":684,"useCases":685,"indexable":240},"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":150,"label":687,"issuer":688,"region":349,"url":689,"description":690,"useCases":685,"indexable":240},"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":692,"label":693,"issuer":607,"region":262,"url":694,"description":695,"useCases":330,"indexable":240},"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":697,"label":698,"issuer":154,"region":155,"url":699,"description":700,"useCases":330,"indexable":240},"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":702,"label":703,"issuer":154,"region":155,"url":704,"description":705,"useCases":330,"indexable":240},"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":707,"label":708,"issuer":709,"region":155,"url":710,"description":711,"useCases":712,"indexable":240},"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":714,"label":715,"issuer":716,"region":349,"url":717,"description":718,"useCases":462,"indexable":240},"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":720,"label":721,"issuer":154,"region":155,"url":722,"description":723,"useCases":462,"indexable":240},"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":725,"label":726,"issuer":154,"region":155,"url":727,"description":728,"useCases":729,"indexable":240},"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":731,"label":732,"issuer":733,"region":734,"url":735,"description":736,"useCases":485,"indexable":240},"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":738,"label":739,"issuer":740,"region":155,"url":741,"description":742,"useCases":503,"indexable":240},"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":744,"label":745,"issuer":746,"region":155,"url":747,"description":748,"useCases":503,"indexable":240},"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":750,"label":751,"issuer":752,"region":262,"url":753,"description":754,"useCases":463,"indexable":240},"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":756,"label":757,"issuer":154,"region":155,"url":758,"description":759,"useCases":463,"indexable":240},"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":761,"label":762,"issuer":763,"region":349,"url":764,"description":765,"useCases":463,"indexable":240},"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.",1790598302624]