[{"data":1,"prerenderedAt":741},["ShallowReactive",2],{"uc-conversational-shopping-assistant":3,"uc-regulations":534},{"useCase":4,"evidence":211,"blitsAiDeployments":374,"benchmarks":375,"indicative":401,"related":404,"indexability":532,"includeUnpublished":217},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":20,"patterns":24,"channels":29,"audience":34,"autonomy":35,"adoptionStage":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":51,"macroEstimates":86,"feasibility":87,"implementation":102,"risk":148,"blitsAi":187,"faq":189,"related":199,"datePublished":206,"dateModified":206,"lastVerified":206,"changelog":207,"slug":210},"AI shopping assistant for product discovery and recommendations","Conversational shopping assistant","AI shopping assistants for product discovery","AI shopping assistants answer product questions and recommend catalog items. Amazon reports over 350 million users in a year; Zalando runs one in 25 markets.","published","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.",[12,13,14,15,16],"AI shopping assistant","conversational commerce assistant","product recommendation chatbot","virtual shopping advisor","agentic shopping assistant",[18,19],"cross-industry","retail-and-ecommerce",[21,22,23],"sales","marketing","customer-service",[25,26,27,28],"conversational-agent","recommendation-and-personalization","rag-knowledge-assistant","agentic-workflow",[30,31,32,33],"mobile-app","web-chat","whatsapp","voice","customer-facing","autonomous","early-adopters","Search boxes and filters work when the shopper knows the product name. They fail when the shopper\nknows the problem: \"what do I need to fix a leaky faucet\", \"what should I wear to a wedding in\nBarcelona in November\", \"will these bindings fit these boots\". In a store an experienced associate\nanswers those questions and sells the right basket; online the shopper reads reviews in ten tabs,\nguesses, or leaves.\n\nRetailers with large or technical assortments (home improvement, sporting goods, fashion,\nelectronics) feel this most, and their best experts are scarce and seasonal. Earlier product\nrecommendation engines ranked items from behaviour but could not hold a conversation or explain a\ntrade off. Generative models grounded in the catalog, reviews and how to content can, and the\nlarger retailers have now put them in front of hundreds of millions of shoppers.",[],"1. **Understand the need.** The assistant takes a free text or spoken question about a product, a\n   project or an occasion, and asks clarifying questions (skill level, budget, size, location) the\n   way a good associate would.\n2. **Retrieve from the retailer's own data.** It searches the product catalog, specifications,\n   customer reviews, questions and answers and the retailer's how to content, and checks price and\n   local stock.\n3. **Recommend and explain.** It proposes a short list or a complete basket, compares options and\n   says why each item fits, including compatibility between items.\n4. **Personalise with consent.** For logged in customers it can use purchase history and\n   preferences, and it adapts to the page the shopper is on.\n5. **Hand over to the purchase.** It adds items to the basket, reserves in store or books a\n   service, and routes complex or high value questions to a human expert.\n6. **Stay inside the rules.** Prices, promotions and availability come from live systems, never\n   from the model, and sponsored products are labelled.",[41,42,43,44],"revenue-growth","customer-experience","cost-to-serve","inclusion-and-access",[46,47,48,49,50],"conversion-rate-uplift","revenue-uplift","users-served","customer-satisfaction","customer-satisfaction-uplift",{"referenceOrg":52,"inputs":53,"formula":81,"currency":82,"period":83,"resultLabel":84,"caveat":85},"An online retailer with EUR 200 million in annual online sales",[54,60,67,74],{"key":55,"label":56,"low":57,"high":57,"unit":58,"note":59},"onlineSales","Annual online sales",200000000,"EUR per year","The reference retailer.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"engagedShare","Share of online sales from sessions where the shopper uses the assistant",0.02,0.06,"fraction of online sales","Editorial assumption for the first years; usage grows slowly because most shoppers still search and browse. Replace with your own adoption data.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"incrementalShare","Share of those sales that is truly incremental",0.05,0.15,"fraction of engaged sales","Editorial assumption, deliberately far below the conversion multiples on this page (Sierra, the vendor, reports triple the conversion for Sun & Ski Sports shoppers who engage), because engaged shoppers are self selected. Measure with a holdout group.",{"key":75,"label":76,"low":77,"high":78,"unit":79,"note":80},"grossMargin","Gross margin on incremental sales",0.3,0.4,"fraction of sales","Editorial assumption for a general merchandise retailer. Replace with your own margin.","onlineSales * engagedShare * incrementalShare * grossMargin","EUR","per year","Incremental gross margin from assisted sessions","A rough estimate of incremental margin only. It leaves out the cost of the assistant and its model usage, service contacts avoided, the effect on returns (better advice can lower them), and it assumes a holdout test confirms the uplift.",[],{"complexity":88,"complexityNote":89,"dataPrerequisites":90,"integrations":96},"medium","The conversation is the easy part. The work is in clean, rich product data, live price and stock, compatibility rules, and evaluation of recommendation quality at the scale of a full catalog.",[91,92,93,94,95],"A product catalog with complete attributes and specifications, not only marketing copy","Customer reviews and questions and answers, where the retailer owns and may use them","Live price, promotion and stock by channel and store","How to and project content, with an owner and review date","Consent records for using purchase history in recommendations",[97,98,99,100,101],"Product information management and search or recommendation engine","Pricing, promotions and inventory systems","Basket, checkout and store reservation APIs","Customer profile and consent management","Human expert chat or video channel for handover",{"steps":103,"guardrails":122,"humanInTheLoop":128,"kpisToInstrument":129,"failureModes":135},[104,107,110,113,116,119],{"title":105,"detail":106},"Pick categories where advice matters","Start where shoppers ask compatibility or project questions and conversion is low (tools, sporting goods, fashion occasions), not in commodity categories where search already works.",{"title":108,"detail":109},"Fix the product data first","Missing attributes produce confident but wrong recommendations. Measure attribute completeness per category and fill the gaps before launch.",{"title":111,"detail":112},"Keep price, stock and promotions live","Call the pricing and inventory systems at answer time. Never let the model state a price or a discount from its own memory or from stale retrieved text.",{"title":114,"detail":115},"Build an evaluation set per category","Write real shopper questions with the right answers, including compatibility traps and questions the assistant should refuse (medical, safety critical), and run them on every change.",{"title":117,"detail":118},"Launch with a holdout group","Measure conversion, basket size and returns against shoppers who do not get the assistant, so the business case rests on incremental effect rather than on self selected users.",{"title":120,"detail":121},"Connect the assistant to the humans","Offer a human expert for high value or complex projects, and give store associates the same assistant so online and in store advice agree.",[123,124,125,126,127],"Prices, promotions and availability only from live systems, never generated","Recommendations only from the retailer's current catalog, with sponsored items labelled","Refusal and a safe pointer for safety critical, medical or legal questions","Personal data used for personalisation only with consent, and never inferred sensitive traits","Protection against prompt injection that tries to obtain unauthorised discounts or commitments","Merchandising and category experts own the content and review a sample of conversations per category every week. Human experts take over complex or high value projects on request. Any new capability that commits the retailer (adding to a basket, reserving stock, booking a service) is signed off before launch.",[130,131,132,133,134],"Conversion and average order value against a holdout group","Share of recommended items in stock and correctly priced at answer time","Return rate of products bought after an assisted session","Satisfaction and thumbs down rate per category","Share of sessions handed to a human expert and why",[136,139,142,145],{"title":137,"detail":138},"Confident recommendations from thin data","When attributes are missing the model fills the gap with plausible text. Measure data completeness and make the assistant say when it does not know.",{"title":140,"detail":141},"Commitments the retailer did not make","Shoppers try to talk the assistant into prices, discounts or promises. Keep all commercial terms in systems of record and test for manipulation.",{"title":143,"detail":144},"Conversion claims built on self selection","Engaged shoppers were already more likely to buy. Without a holdout the business case is overstated.",{"title":146,"detail":147},"Advice beyond the catalog's safety limits","Recipes, chemicals, electrical work and health products need refusals and safety content, not creative answers.",{"euAiAct":149,"regulations":152,"guidance":156,"controls":176,"incidents":182},{"tier":150,"basis":151},"limited","A shopping assistant interacts directly with people, so under Article 50(1) shoppers must be informed that they are dealing with an AI system unless that is obvious. It is not listed in Annex III, so it is not high risk. Manipulative or deceptive techniques that materially distort a shopper's behaviour and cause significant harm are prohibited under Article 5(1)(a), which matters for how persuasion and urgency are designed.",[153,154,155],"eu-ai-act","gdpr","eu-accessibility-act",[157,163,167,172],{"title":158,"issuer":159,"region":160,"url":161,"note":162},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":164,"issuer":159,"region":160,"url":165,"note":166},"Article 5, prohibited AI practices","https://artificialintelligenceact.eu/article/5/","Prohibits AI that uses manipulative or deceptive techniques to materially distort behaviour in a way that causes significant harm; relevant to persuasive recommendation design.",{"title":168,"issuer":169,"region":160,"url":170,"note":171},"Unfair commercial practices directive","European Commission","https://commission.europa.eu/law/law-topic/consumer-protection-law/unfair-commercial-practices-law/unfair-commercial-practices-directive_en","Misleading claims, hidden advertising and aggressive practices are unfair whether a person or an assistant makes them, so sponsored items and claims need the same care.",{"title":173,"issuer":159,"region":160,"url":174,"note":175},"Digital Services Act, Regulation (EU) 2022/2065, Article 27 on recommender system transparency","https://eur-lex.europa.eu/eli/reg/2022/2065/oj/eng","Online platforms that use recommender systems must set out the main parameters in their terms and conditions (Article 27), which applies to marketplaces that add a conversational recommender.",[177,178,179,180,181],"AI disclosure and labelling of sponsored recommendations","Price and stock answers traceable to the live system call","Evaluation set per category run on every model or content change","Consent check before any use of purchase history","Monitoring of conversations for manipulation attempts and unsafe advice",[183],{"title":184,"url":185,"note":186},"Incident 622: Chevrolet dealer chatbot agrees to sell Tahoe for $1","https://incidentdatabase.ai/cite/622/","A dealer's sales chatbot was talked into \"agreeing\" to an absurd price and recommending a competitor's car, showing why commercial terms must stay outside the model.",{"howToBuild":188},"On Blits.ai this is an **AI agent** with a **knowledge base** that holds product guides and how\nto content, retrieved with hybrid search, plus **SQL knowledge bases** or **custom functions** that\nquery the product catalog, live price and stock (REST calls or SQL queries). The agent asks\nclarifying questions, recommends and compares, and the chat widget shows the result as **product\nrecommendation, retail and order cards** or a carousel; basket and reservation actions are custom\nfunctions with their own limits.\n\nThe same assistant runs on **web chat, WhatsApp, voice and a mobile app** (through the REST or\nWebSocket API channel), and can appear as a **digital human**, a photorealistic avatar streamed to\nthe browser. **Guardrails**\nblock prompt injection and apply the retailer's own policies on unsafe advice, **PII masking**\nprotects personal data, and **human handover** routes complex projects to an expert. **Test\nsuites** run category question sets on every change, **analytics** show satisfaction, response\nfeedback and top intents, and the platform is model agnostic, with **EU and UAE data residency**\nwhere needed.",[190,193,196],{"question":191,"answer":192},"Do AI shopping assistants increase sales?","The published figures show strong associations. Sierra, the vendor, reports that Sun & Ski Sports shoppers who engage with its agent convert at triple the rate of those who do not, and Amazon says US customers who use Alexa for Shopping spend over 40% more per order. Those shoppers are self selected, so measure the effect with a holdout group before building a business case on it.",{"question":194,"answer":195},"How many shoppers actually use them?","At the largest retailers, many. Amazon reports that over 350 million customers used its AI shopping assistant in the twelve months to its second quarter 2026 results. Zalando reported in March 2025 that over 2 million customers had used its fashion assistant, which has been live in all 25 of its markets since October 2024.",{"question":197,"answer":198},"Where should the assistant get prices and stock?","Only from live systems at the moment of the answer. Prices, promotions and availability that come from the model or from stale text lead to wrong promises, and a dealer chatbot that \"agreed\" to sell a car for one dollar shows how easily a model can be talked into commitments.",[200,201,202,203,204,205],"order-status-and-returns-agent","personalized-marketing-at-scale","inbound-lead-qualification-agent","travel-and-hotel-booking-concierge","first-line-contact-centre-agent","agentic-payment-initiation","2026-09-27",[208],{"date":206,"note":209},"First published","conversational-shopping-assistant",[212,246,265,303,336],{"title":213,"useCases":214,"organization":215,"vendors":220,"summary":224,"stage":225,"year":226,"channels":227,"languages":228,"metrics":230,"outcomeDisclosed":217,"sources":231,"verification":240,"grade":243,"id":244,"organizationSlug":245},"Lowe's: Mylow home improvement virtual advisor",[210],{"name":216,"anonymized":217,"country":218,"region":219,"industry":19},"Lowe's",false,"US","north-america",[221],{"name":222,"role":223},"OpenAI","model-provider","Lowe's launched Mylow in March 2025, a customer facing virtual advisor built with OpenAI that answers home improvement questions, gives project steps and links the project to product discovery, with recommendations that can be refined by budget and zip code. In May 2025 it rolled out Mylow Companion, built on the same foundation, to associates in more than 1,700 stores, so staff on the floor get the same product, project and inventory answers. No outcome figures were published.","production",2025,[31],[229],"en",[],[232,236],{"url":233,"title":234,"publisher":216,"date":235},"https://corporate.lowes.com/newsroom/press-releases/lowes-launches-first-ai-powered-home-improvement-virtual-advisor-03-05-25","Lowe's Launches First AI-Powered Home Improvement Virtual Advisor","2025-03-05",{"url":237,"title":238,"publisher":216,"date":239},"https://corporate.lowes.com/newsroom/press-releases/lowes-deploys-first-scale-ai-assistant-retail-associates-05-05-25","Lowe's deploys first at-scale AI assistant for retail associates","2025-05-05",{"level":241,"checkedAt":242},"source-verified","2026-09-26","B","lowes-mylow-virtual-advisor",null,{"title":247,"useCases":248,"organization":249,"vendors":251,"summary":252,"stage":253,"year":226,"channels":254,"languages":255,"metrics":256,"outcomeDisclosed":217,"sources":257,"verification":262,"grade":243,"id":263,"organizationSlug":264},"Walmart: Sparky generative AI shopping assistant",[210],{"name":250,"anonymized":217,"country":218,"region":219,"industry":19},"Walmart",[],"Walmart launched Sparky in June 2025 as an \"Ask Sparky\" button in its app across all categories. Sparky answers product questions, compares options, synthesizes reviews and recommends products for an occasion, and Walmart describes a roadmap toward reordering, service booking and multimodal input. It joins Walmart's earlier generative AI features for search, review summaries, product descriptions and comparisons. No outcome figures were published on the launch page.","scaled",[30],[229],[],[258],{"url":259,"title":260,"publisher":250,"date":261},"https://corporate.walmart.com/news/2025/06/06/walmart-the-future-of-shopping-is-agentic-meet-sparky","Walmart: The Future of Shopping Is Agentic. Meet Sparky.","2025-06-06",{"level":241,"checkedAt":242},"walmart-sparky-shopping-assistant","walmart",{"title":266,"useCases":267,"organization":268,"vendors":271,"summary":274,"stage":253,"year":275,"channels":276,"languages":277,"metrics":278,"outcomeDisclosed":287,"sources":288,"verification":300,"grade":243,"id":301,"organizationSlug":302},"Amazon: Rufus and Alexa for Shopping conversational shopping assistant",[210],{"name":269,"anonymized":217,"country":218,"region":270,"industry":19},"Amazon","global",[272],{"name":269,"role":273},"in-house","Amazon launched Rufus in 2024 as a generative AI shopping assistant in its app, trained on the product catalog, customer reviews, community questions and answers and information from the web, to answer product questions, compare items and recommend products for an occasion or need. In 2026 it combined Rufus and Alexa+ into Alexa for Shopping, an agentic assistant that adds price history, price alerts and automated buying. Amazon reports over 350 million customers in twelve months, and says US customers who use Alexa for Shopping spend on average over 40% more per order than those who do not, a comparison between self selected groups rather than a controlled test.",2024,[30,31],[],[279],{"kpi":48,"value":280,"unit":281,"qualifier":282,"period":283,"claimant":284,"quote":285,"sourceUrl":286},350000000,"count","at-least","the 12 months to Q2 2026","organization","Over 350 million customers have used it in the last 12 months, and engagement accelerated in Q2, with active users nearly doubling, and interactions up over 5x year-over-year.","https://www.aboutamazon.com/news/company-news/amazon-ceo-andy-jassy-stores-growth-ai-shopping-q2-2026-earnings",true,[289,292,296],{"url":286,"title":290,"publisher":269,"date":291},"Q2 earnings: CEO Andy Jassy on Amazon Stores growth, delivery speed, and AI shopping","2026-07-31",{"url":293,"title":294,"publisher":269,"date":295},"https://www.aboutamazon.com/news/company-news/amazon-earnings-q2-2026-report","Amazon Q2 2026 earnings report: Read the release","2026-07-30",{"url":297,"title":298,"publisher":269,"date":299},"https://www.aboutamazon.com/news/retail/amazon-rufus","Amazon Rufus AI experience comes to the Amazon Shopping app","2024-02-01",{"level":241,"checkedAt":242},"amazon-rufus-and-alexa-for-shopping","amazon",{"title":304,"useCases":305,"organization":306,"vendors":309,"summary":312,"stage":253,"year":313,"channels":314,"languages":315,"metrics":316,"outcomeDisclosed":287,"sources":322,"verification":334,"grade":243,"id":335,"organizationSlug":245},"Zalando: AI powered fashion assistant in 25 markets",[210],{"name":307,"anonymized":217,"country":308,"region":160,"industry":19},"Zalando","DE",[310,311],{"name":222,"role":223},{"name":307,"role":273},"Zalando opened a beta of its assistant to logged in customers in Germany, Austria, the United Kingdom and Ireland by November 2023, after testing it internally and with selected customers. Since October 2024 it gives logged in customers fashion advice in their local language across all 25 Zalando markets, using Zalando's own models and OpenAI's large language models. It interprets context such as occasion, location and weather (\"what should I wear to my dad's 60th birthday in November in Barcelona?\"), and an update in March 2025 connected it to customer accounts and shopping history and made it aware of the page the customer is browsing. In a pilot of the personalised version Zalando observed 40% more high value interactions, such as likes and add to cart actions.",2023,[31,30],[],[317],{"kpi":48,"value":318,"unit":281,"qualifier":282,"period":319,"claimant":284,"quote":320,"sourceUrl":321},2000000,"cumulative since launch, as reported in March 2025","So far, over 2 million customers have used it to get inspired and find items they love.","https://corporate.zalando.com/en/technology/more-personal-and-smarter-zalando-assistant-enhanced-capabilities-inspire-customers",[323,326,330],{"url":321,"title":324,"publisher":307,"date":325},"More personal and smarter, the Zalando Assistant with enhanced capabilities to inspire customers","2025-03-27",{"url":327,"title":328,"publisher":307,"date":329},"https://corporate.zalando.com/en/technology/zalando-brings-its-ai-powered-assistant-all-markets-and-adds-four-new-cities-its-trend","Zalando brings its AI-powered assistant to all markets and adds four new cities to its Trend Spotter","2024-10-01",{"url":331,"title":332,"publisher":307,"date":333},"https://corporate.zalando.com/en/technology/how-zalando-co-creating-its-new-ai-powered-assistant-together-customers","How Zalando is co-creating its new AI-powered assistant together with customers","2023-11-30",{"level":241,"checkedAt":242},"zalando-ai-fashion-assistant",{"title":337,"useCases":338,"organization":339,"vendors":341,"summary":345,"stage":225,"year":226,"channels":346,"languages":347,"metrics":348,"outcomeDisclosed":287,"sources":367,"verification":371,"grade":372,"id":373,"organizationSlug":245},"Sun & Ski Sports: AI agent Sunny for order status, returns and product advice",[200,210],{"name":340,"anonymized":217,"country":218,"region":219,"industry":19},"Sun & Ski Sports",[342],{"name":343,"role":344},"Sierra","platform","Sun & Ski Sports, a Texas based outdoor retailer with a strongly seasonal business, started its AI agent Sunny on basic returns and order status questions and then extended it to expert product advice on skis, boards, boots and bindings on its product pages. Sierra, the vendor, reports higher satisfaction on conversations the agent handles than on those transferred to humans, higher conversion for shoppers who engage with it, and a winter season without hiring temporary service staff.",[31],[229],[349,358,362],{"kpi":49,"value":350,"unit":351,"qualifier":352,"period":353,"baseline":354,"claimant":355,"quote":356,"sourceUrl":357},90,"percent","exact","conversations handled by the agent, as reported in October 2025","68% for conversations transferred to human agents","vendor","Sunny achieves 90% customer satisfaction compared to 68% for conversations transferred to human agents.","https://sierra.ai/customers/sun-and-ski-sports",{"kpi":50,"value":359,"unit":351,"qualifier":352,"period":360,"claimant":355,"quote":361,"sourceUrl":357},50,"as reported in October 2025, three years after the CMO joined in 2022","Three years later, Sunny, their AI agent, has improved CSAT by 50% and tripled product page conversion rates",{"kpi":46,"value":363,"unit":364,"qualifier":352,"baseline":365,"claimant":355,"quote":366,"sourceUrl":357},3,"multiplier","shoppers who do not engage with the agent","Customers who engage with Sunny convert at triple the rate of those who don't.",[368],{"url":357,"title":369,"publisher":343,"date":370},"How Sun & Ski's AI agent \"Sunny\" turns approachability into sales","2025-10-22",{"level":241,"checkedAt":242},"C","sun-and-ski-sports-sunny-ai-agent",5,[376,385,391,396],{"kpi":48,"label":377,"unit":281,"aggregate":217,"higherIsBetter":287,"n":378,"nUpTo":379,"median":380,"min":318,"max":280,"byClaimant":381,"vendorOnly":217,"points":382},"Users served",2,0,176000000,{"organization":378,"vendor":379,"regulator":379,"independent":379},[383,384],{"evidenceId":301,"organization":269,"value":280,"qualifier":282,"claimant":284,"grade":243,"pooled":287},{"evidenceId":335,"organization":307,"value":318,"qualifier":282,"claimant":284,"grade":243,"pooled":287},{"kpi":46,"label":386,"unit":364,"aggregate":287,"higherIsBetter":287,"n":387,"nUpTo":379,"median":363,"min":363,"max":363,"byClaimant":388,"vendorOnly":287,"points":389},"Conversion uplift",1,{"organization":379,"vendor":387,"regulator":379,"independent":379},[390],{"evidenceId":373,"organization":340,"value":363,"qualifier":352,"claimant":355,"grade":372,"pooled":287},{"kpi":49,"label":392,"unit":351,"aggregate":287,"higherIsBetter":287,"n":387,"nUpTo":379,"median":350,"min":350,"max":350,"byClaimant":393,"vendorOnly":287,"points":394},"Customer satisfaction",{"organization":379,"vendor":387,"regulator":379,"independent":379},[395],{"evidenceId":373,"organization":340,"value":350,"qualifier":352,"claimant":355,"grade":372,"pooled":287},{"kpi":50,"label":397,"unit":351,"aggregate":287,"higherIsBetter":287,"n":387,"nUpTo":379,"median":359,"min":359,"max":359,"byClaimant":398,"vendorOnly":287,"points":399},"Satisfaction uplift",{"organization":379,"vendor":387,"regulator":379,"independent":379},[400],{"evidenceId":373,"organization":340,"value":359,"qualifier":352,"claimant":355,"grade":372,"pooled":287},{"low":402,"high":403},60000,720000,[405,425,449,467,484,516],{"slug":200,"title":406,"shortTitle":407,"definition":408,"status":9,"industries":409,"functions":411,"patterns":413,"audience":34,"autonomy":415,"adoptionStage":416,"evidenceCount":374,"publicEvidenceCount":374,"organizations":417,"bestGrade":243,"headline":422,"lastVerified":242,"indexable":287},"AI agent for order status, delivery changes and returns","Order status and returns","An AI agent that answers \"where is my order\", changes delivery details and arranges returns, exchanges and refunds end to end for online and omnichannel shoppers, by reading and writing to the order, carrier and returns systems within the retailer's policy, and hands exceptions such as damaged goods, disputes and upset customers to a person.",[18,19,410],"payments",[23,412],"operations",[25,28,414,27],"voice-agent","supervised-agent","mainstream",[418,419,420,421,340],"BARK","Best Buy","Klarna","Next",{"kpi":49,"label":392,"unit":351,"n":378,"nUpTo":379,"kind":423,"value":424,"qualifier":352,"claimant":355,"organization":418,"vendorReported":287},"reported",98,{"slug":201,"title":426,"shortTitle":427,"definition":428,"status":9,"industries":429,"functions":433,"patterns":434,"audience":437,"autonomy":415,"adoptionStage":416,"evidenceCount":438,"publicEvidenceCount":439,"organizations":440,"bestGrade":243,"headline":447,"lastVerified":206,"indexable":287},"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.",[18,430,431,19,432],"travel-and-hospitality","media-and-entertainment","banking",[22,21],[26,435,436],"prediction-and-scoring","content-generation","back-office",8,7,[269,441,442,443,444,445,446],"Catchtable","Commonwealth Bank of Australia","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":46,"label":386,"unit":351,"n":387,"nUpTo":379,"kind":423,"value":448,"qualifier":352,"claimant":355,"organization":441,"vendorReported":287},30,{"slug":202,"title":450,"shortTitle":451,"definition":452,"status":9,"industries":453,"functions":456,"patterns":457,"audience":34,"autonomy":415,"adoptionStage":36,"evidenceCount":459,"publicEvidenceCount":459,"organizations":460,"bestGrade":372,"headline":465,"lastVerified":206,"indexable":287},"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.",[18,454,455,432],"technology","automotive",[21,22],[25,414,458,28],"classification-and-routing",4,[461,462,463,464],"8x8","CarMax","Rocket Mortgage","SUSE",{"kpi":46,"label":386,"unit":351,"n":387,"nUpTo":379,"kind":423,"value":466,"qualifier":352,"claimant":355,"organization":461,"vendorReported":287},19,{"slug":203,"title":468,"shortTitle":469,"definition":470,"status":9,"industries":471,"functions":472,"patterns":473,"audience":34,"autonomy":415,"adoptionStage":36,"evidenceCount":474,"publicEvidenceCount":374,"organizations":475,"bestGrade":243,"headline":481,"lastVerified":242,"indexable":287},"AI travel and hotel booking concierge","Travel and hotel booking concierge","A customer facing AI assistant that turns an open travel question into a concrete trip by searching live inventory for flights, hotels, rentals, cruises and activities, comparing options and answering questions about the property and the booking, then completes or hands off the booking and supports the traveller with changes and questions before and during the stay.",[430],[21,23],[25,26,27,28],6,[476,477,478,479,480],"Airbnb","Booking.com","Holland America Line","Priceline","Trip.com",{"kpi":482,"label":483,"unit":351,"n":387,"nUpTo":379,"kind":423,"value":448,"qualifier":352,"claimant":284,"organization":477,"vendorReported":217},"automation-rate","Automation rate",{"slug":204,"title":485,"shortTitle":486,"definition":487,"status":9,"industries":488,"functions":491,"patterns":492,"audience":34,"autonomy":415,"adoptionStage":416,"segment":493,"evidenceCount":494,"publicEvidenceCount":495,"organizations":496,"bestGrade":243,"headline":511,"lastVerified":206,"indexable":287},"AI agent for first line contact centre service","First line contact centre","An AI agent that answers the first line of inbound customer contact on phone, chat and messaging, resolves general and routine questions end to end in the customer's own language, and routes everything complex, sensitive or regulated to the right human team with the context attached.",[18,432,410,489,430,19,490],"telecommunications","wealth-and-asset-management",[23],[25,414,27,458],"front-office",25,18,[497,476,498,499,500,442,501,502,420,503,504,505,506,507,508,509,510],"Air India","Bank of America","Bank of the Philippine Islands","BT Group","Ingka Group","JetBlue","Lufthansa Group","Mobily","NatWest Group","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":512,"label":513,"unit":351,"n":439,"nUpTo":379,"kind":514,"value":515,"qualifier":352,"claimant":245,"organization":245,"vendorReported":217},"containment-rate","Containment rate","median",47,{"slug":205,"title":517,"shortTitle":518,"definition":519,"status":9,"industries":520,"functions":521,"patterns":522,"audience":34,"autonomy":415,"adoptionStage":523,"segment":493,"evidenceCount":438,"publicEvidenceCount":439,"organizations":524,"bestGrade":243,"headline":245,"lastVerified":206,"indexable":287},"AI agent for payment initiation within a customer mandate","Agentic payment initiation","An AI agent that initiates and completes payments or purchases on a customer's behalf, within a mandate the customer set in advance (spending caps, allowed merchants or categories, a tokenized credential and rules for when to ask for confirmation), and then confirms and reconciles every transaction it made.",[410,432,19],[23,21,412],[28,25],"emerging",[525,526,527,528,529,530,531],"DBS Bank","ING","Majid Al Futtaim","PayPal","Banco Santander","Ulta Beauty","Visa",{"indexable":287,"reasons":533},[],[535,540,545,552,559,565,572,578,586,592,599,605,612,619,625,630,637,642,648,654,660,666,672,677,682,689,695,700,705,712,718,724,730,735],{"id":153,"label":536,"issuer":159,"region":160,"url":537,"description":538,"useCases":539,"indexable":287},"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":154,"label":541,"issuer":159,"region":160,"url":542,"description":543,"useCases":544,"indexable":287},"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":546,"label":547,"issuer":548,"region":270,"url":549,"description":550,"useCases":551,"indexable":287},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":553,"label":554,"issuer":555,"region":219,"url":556,"description":557,"useCases":558,"indexable":287},"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":560,"label":561,"issuer":159,"region":160,"url":562,"description":563,"useCases":564,"indexable":287},"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":566,"label":567,"issuer":568,"region":160,"url":569,"description":570,"useCases":571,"indexable":287},"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":573,"label":574,"issuer":575,"region":160,"url":576,"description":577,"useCases":515,"indexable":287},"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.",{"id":579,"label":580,"issuer":581,"region":582,"url":583,"description":584,"useCases":585,"indexable":287},"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":587,"label":588,"issuer":589,"region":582,"url":590,"description":591,"useCases":494,"indexable":287},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",{"id":593,"label":594,"issuer":595,"region":270,"url":596,"description":597,"useCases":598,"indexable":287},"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":600,"label":601,"issuer":602,"region":219,"url":603,"description":604,"useCases":598,"indexable":287},"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":606,"label":607,"issuer":608,"region":160,"url":609,"description":610,"useCases":611,"indexable":287},"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":613,"label":614,"issuer":615,"region":270,"url":616,"description":617,"useCases":618,"indexable":287},"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":620,"label":621,"issuer":159,"region":160,"url":622,"description":623,"useCases":624,"indexable":287},"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":626,"label":627,"issuer":159,"region":160,"url":628,"description":629,"useCases":624,"indexable":287},"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":631,"label":632,"issuer":633,"region":219,"url":634,"description":635,"useCases":636,"indexable":287},"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":155,"label":638,"issuer":159,"region":160,"url":639,"description":640,"useCases":641,"indexable":287},"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":643,"label":644,"issuer":645,"region":219,"url":646,"description":647,"useCases":641,"indexable":287},"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":649,"label":650,"issuer":651,"region":270,"url":652,"description":653,"useCases":641,"indexable":287},"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":655,"label":656,"issuer":159,"region":160,"url":657,"description":658,"useCases":659,"indexable":287},"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":661,"label":662,"issuer":663,"region":219,"url":664,"description":665,"useCases":659,"indexable":287},"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":667,"label":668,"issuer":581,"region":582,"url":669,"description":670,"useCases":671,"indexable":287},"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.",10,{"id":673,"label":674,"issuer":159,"region":160,"url":675,"description":676,"useCases":671,"indexable":287},"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":678,"label":679,"issuer":159,"region":160,"url":680,"description":681,"useCases":671,"indexable":287},"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":683,"label":684,"issuer":685,"region":160,"url":686,"description":687,"useCases":688,"indexable":287},"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":690,"label":691,"issuer":692,"region":219,"url":693,"description":694,"useCases":438,"indexable":287},"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":696,"label":697,"issuer":159,"region":160,"url":698,"description":699,"useCases":438,"indexable":287},"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":701,"label":702,"issuer":159,"region":160,"url":703,"description":704,"useCases":474,"indexable":287},"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":706,"label":707,"issuer":708,"region":709,"url":710,"description":711,"useCases":374,"indexable":287},"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":713,"label":714,"issuer":715,"region":160,"url":716,"description":717,"useCases":459,"indexable":287},"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":719,"label":720,"issuer":721,"region":160,"url":722,"description":723,"useCases":459,"indexable":287},"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":725,"label":726,"issuer":727,"region":582,"url":728,"description":729,"useCases":363,"indexable":287},"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":731,"label":732,"issuer":159,"region":160,"url":733,"description":734,"useCases":363,"indexable":287},"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":736,"label":737,"issuer":738,"region":219,"url":739,"description":740,"useCases":363,"indexable":287},"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.",1790598306232]