[{"data":1,"prerenderedAt":687},["ShallowReactive",2],{"uc-size-and-fit-recommendation":3,"uc-regulations":464},{"useCase":4,"evidence":198,"blitsAiDeployments":333,"benchmarks":334,"indicative":360,"related":363,"indexability":462,"includeUnpublished":204},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":26,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":39,"valueDrivers":40,"kpis":44,"indicativeValue":49,"macroEstimates":91,"feasibility":92,"implementation":104,"risk":145,"blitsAi":178,"faq":180,"related":190,"datePublished":193,"dateModified":193,"lastVerified":193,"changelog":194,"slug":197},"AI size and fit recommendation for online fashion","Size and fit recommendation","AI size and fit recommendation for fashion","Size AI helps shoppers pick a size. Zalando says it prevented 8% of size related returns in 2025; Fit Analytics reports 6% more revenue after returns at THE ICONIC.","published","AI size and fit recommendation tells an online shopper which size of a garment or shoe to buy, based on how the item fits and the shopper's history. It draws on the item's measured fit, the shopper's purchase and return history and, optionally, body measurements. It aims to prevent the returns that wrong sizes cause and to raise conversion.",[12,13,14,15,16],"AI size advisor","fit finder","size recommendation engine","AI sizing assistant","body measurement from photos",[18],"retail-and-ecommerce",[20,21],"sales","operations",[23,24,25],"recommendation-and-personalization","prediction-and-scoring","computer-vision",[27],"mobile-app","customer-facing","assist","early-adopters","ecommerce","Sizes mean different things across brands, categories and markets. Zalando says that a size L or\n48 can describe two garments with very different measurements, and that every customer has a\ndifferent body shape and a preference for how clothing should fit. A shopper who is unsure\norders two sizes and returns one, or returns the one that does not fit.\n\nReturns cost the retailer shipping, handling and often the margin of an item that cannot be\nresold at full price, and they add shipments and emissions. Zalando reports that in European\nonline fashion return rates can reach around 50%, with size and fit accounting for up to half of\nthose returns, and that jeans can reach 65%. Retailers also lose the sale when a shopper leaves\nbecause they cannot tell which size to pick.",[34],{"statement":35,"sourceTitle":36,"sourceUrl":37,"year":38},"Zalando says that in European online fashion return rates can reach around 50%, with size and fit accounting for up to half of those returns, and that in jeans return rates can reach 65%.","How Zalando uses technology to help customers find the right size","https:\u002F\u002Fcorporate.zalando.com\u002Fen\u002Fnode\u002F11013",2026,"1. **Learn how each product fits.** The system combines brand supplied measurements, purchase and\n   return behavior, shopper fit feedback and, at Zalando, trained fit models who try garments on.\n   From this it flags items that run small or large.\n2. **Learn the shopper.** It uses the shopper's own history, including return reasons, and a size\n   profile where the shopper says which brands and items fit well, even if bought elsewhere.\n3. **Add measurements if the shopper agrees.** Zalando predicts body measurements from two\n   phone photos or a video, a tool it introduced in July 2023.\n4. **Recommend a size at the moment of choice.** The product page, size chart or profile\n   shows a recommended size or a \"size up\" or \"size down\" flag, as Zalando does.\n5. **Close the loop.** Purchases, returns and fit feedback feed back into the model, and fit data\n   goes back to brands so recurring sizing problems can be fixed at the source.",[41,42,43],"cost-to-serve","revenue-growth","customer-experience",[45,46,47,48],"error-reduction","conversion-rate-uplift","revenue-uplift","users-served",{"referenceOrg":50,"inputs":51,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"An online fashion retailer shipping 5 million orders a year",[52,58,65,72,79],{"key":53,"label":54,"low":55,"high":56,"unit":53,"note":57},"orders","Orders per year",3000000,7000000,"Illustrative size for a mid sized online fashion retailer. Editorial assumption, replace with your own.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"returnRate","Share of orders returned",0.2,0.4,"fraction of orders","Editorial assumption, below the \"around 50%\" that Zalando says European online fashion can reach, since a mixed assortment returns less than a fashion only one.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"sizeShare","Share of returns that are size related",0.33,0.5,"fraction of returns","Zalando reports that one third of its returns are size related and that size and fit account for up to half of returns in European online fashion.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"preventedShare","Share of size related returns prevented",0.01,0.08,"fraction of size related returns","The high end is Zalando's reported 8% of size related returns prevented overall in 2025. The low end is conservative against Fit Analytics' vendor reported return rate reductions of 0.4% at THE ICONIC and 4% at AMARO, which measure the return rate in an A\u002FB test, a different base from the size related share.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"costPerReturn","Cost of handling one return",8,15,"USD per return","Editorial assumption covering return shipping, handling and write down, replace with your own cost per return.","orders * returnRate * sizeShare * preventedShare * costPerReturn","USD","per year","Annual return handling cost avoided","Counts only the cost of handling returns that no longer happen. It leaves out the extra revenue from higher conversion (THE ICONIC's A\u002FB test reports 6% more revenue after returns, a vendor figure), the cost of the tool, the margin effect of fewer markdowns on returned stock, and the emissions from fewer shipments.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":99},"medium","A retailer either buys a size advisor from a specialist vendor and adds its widget to product pages, or builds one as Zalando has since 2018 with a team of 80. Both routes need size charts and garment measurements loaded for the assortment, order and return events sent back to the model, and a privacy review where body photos or measurements are collected.",[96,97,98],"Size charts and garment measurements per product and brand, kept current","Order and return history with return reasons, linked to customer accounts where consent allows","A way for shoppers to give fit feedback and to enter reference items that fit",[100,101,102,103],"Product detail page, wishlist and customer profile in the web shop and app","Order management and returns system, for purchase and return events","Product information system, for size charts and garment measurements","Analytics and A\u002FB testing platform, to measure return rate against a control group",{"steps":105,"guardrails":121,"humanInTheLoop":126,"kpisToInstrument":127,"failureModes":132},[106,109,112,115,118],{"title":107,"detail":108},"Test against a plain size chart","Run an A\u002FB test of the size advisor against the existing size chart on a set of categories with high return rates, as THE ICONIC and AMARO did, and measure return rate and revenue after returns.",{"title":110,"detail":111},"Start where sizing is hardest","Begin with jeans, where Zalando reports some of the highest size related return rates, and with other categories that have high return rates in your own data. Add categories as the model proves itself.",{"title":113,"detail":114},"Fix the product data first","Check garment measurements and size charts for the pilot assortment before launch. A model fed with wrong measurements recommends the wrong size with confidence.",{"title":116,"detail":117},"Make measurement optional and explain it","If you offer body measurement from photos, tell shoppers what is captured, how long it is kept and that they can use size advice without it.",{"title":119,"detail":120},"Report fit data back to buyers and brands","Share products with repeated sizing complaints with buyers and brands, so the brand fixes the garment itself.",[122,123,124,125],"The recommendation is advice, shown with the shopper's free choice of size and a clear return policy","Photos and body measurements are used only for size advice, kept for a stated period and deleted on request","The advisor says when it has too little data for an item instead of showing a confident size","Results are measured against a randomly assigned control group","The shopper makes the final choice. We suggest that inside the retailer, fit specialists and category buyers review items that the model flags as inconsistent, and decide whether to correct the size chart or raise the issue with the brand. Zalando also uses fitting models, a dedicated team that tries on items to help identify sizing issues.",[128,129,130,131],"Return rate and size related return rate, advisor users against a control group","Conversion rate and revenue after returns, per category","Share of product page views where the advisor gives a recommendation","Size bracketing, the share of orders with several sizes of one item",[133,136,139,142],{"title":134,"detail":135},"Selection bias in the headline number","Shoppers who open a size advisor may be more careful buyers to begin with. Compare with a randomized control group.",{"title":137,"detail":138},"Wrong garment data","Mislabelled sizes and wrong measurements flow straight into recommendations. Check the data for the highest return products first.",{"title":140,"detail":141},"Cold start for new shoppers and new items","With no history the model leans on averages. Offer a short size profile or measurement step and show fewer recommendations when confidence is low.",{"title":143,"detail":144},"Privacy backlash on body photos","Photo based measurement can put shoppers off or create a data protection problem. Keep it optional, minimize what is stored and explain it in plain language.",{"euAiAct":146,"regulations":149,"guidance":152,"controls":172,"incidents":177},{"tier":147,"basis":148},"context-dependent","A size advisor is not listed in Annex III, and a size widget on a product page is minimal risk. The design decides the rest. Article 50 transparency applies only if the size advice is delivered through a conversational assistant that interacts directly with shoppers, and then they must be told it is AI unless that is obvious from the context. Body measurements derived from photos can be biometric data (Article 3(34)). Photo based measurement is likely not a biometric categorization system under Article 3(40) while it serves only as an ancillary feature of the sale, for example previewing a product on the shopper, as recital 16 says of body filters on online marketplaces. It would leave that exception if the system inferred sensitive characteristics from the images or reused them for another purpose.",[150,151],"eu-ai-act","gdpr",[153,159,163,167],{"title":154,"issuer":155,"region":156,"url":157,"note":158},"Regulation (EU) 2024\u002F1689 (EU AI Act), Article 50","European Union","europe","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Article 50 sets transparency duties for AI systems that interact directly with people, which reaches a size advisor only when it is delivered as a conversational assistant.",{"title":160,"issuer":155,"region":156,"url":161,"note":162},"EU AI Act, Recital 16, biometric categorization","https:\u002F\u002Fartificialintelligenceact.eu\u002Frecital\u002F16\u002F","Excludes biometric categorization that is a purely ancillary feature of another commercial service, and names filters that categorize body features on online marketplaces, which fits photo based measurement used only for size advice.",{"title":164,"issuer":155,"region":156,"url":165,"note":166},"Regulation (EU) 2016\u002F679 (GDPR)","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","Body measurements and photos are personal data; purpose limitation, data minimization, storage limits and deletion on request apply.",{"title":168,"issuer":169,"region":156,"url":170,"note":171},"A guide to the data protection principles","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002Fdata-protection-principles\u002Fa-guide-to-the-data-protection-principles\u002F","Sets out the UK data protection principles that apply to purchase history, return reasons and body measurements used for size advice.",[173,174,175,176],"A documented purpose, retention period and deletion path for photos, measurements and size profiles","Consent or another lawful basis recorded for each use of purchase and return history","A data protection impact assessment for any photo or video based measurement","Regular checks of recommendation quality by category and size range, including less common sizes",[],{"howToBuild":179},"Blits.ai does not train size models or measure garments; a retailer buys or builds the sizing\nengine and keeps the garment and return data. What Blits.ai adds is a conversational way to use\nit. An **AI agent** with a **custom function** that calls the retailer's sizing service lets a\nshopper ask in plain language which size to order in a given brand, on the web chat or in the\nretailer's app through the **REST API channel**, and a **knowledge base** with **hybrid\nretrieval** over size charts and fit notes answers the questions the sizing service does not.\n\n**Guardrails** keep the agent to approved size guidance and refuse medical or body judgments,\n**PII masking** and **GDPR consent gating** protect measurements and order details, and\n**human handover** passes a disputed fit complaint to customer service. **Analytics** show the\nmost asked sizing questions and the untrained ones, and **test suites** check that\nrecommendations stay consistent when size charts change. Because the platform is **model\nagnostic**, the retailer can pick the model per language, and **EU data residency** keeps\nshopper data in region.",[181,184,187],{"question":182,"answer":183},"How much can size advice reduce returns?","Zalando reports that its size and fit solutions prevented 8% of size related returns overall in 2025. Fit Analytics reports a 0.4% reduction in return rate at THE ICONIC and 4% at AMARO in A\u002FB tests. The figures measure different things and come from the retailer or the vendor without an audit, so treat the Zalando figure as the best documented overall result and the vendor tests as per test results.",{"question":185,"answer":186},"Is photo based body measurement safe to use?","It is personal data under GDPR, so it needs a lawful basis, a stated purpose, a retention limit and a deletion route. Zalando offers it as an optional tool, with two phone photos in tight clothes, and reports that more than 1.5 million customers have tried it. Shoppers who decline should still receive size advice from their purchase history and size profile.",{"question":188,"answer":189},"Does a size advisor also raise sales?","Fit Analytics reports a 1% conversion increase and 6% more revenue after returns for THE ICONIC in a three month A\u002FB test, and a 2% conversion increase at AMARO. These are vendor reported results from single tests, with the test dates not stated.",[191,192],"order-status-and-returns-agent","conversational-shopping-assistant","2026-10-14",[195],{"date":193,"note":196},"First published","size-and-fit-recommendation",[199,242,279,312],{"title":200,"useCases":201,"organization":202,"vendors":207,"summary":210,"stage":211,"year":212,"channels":213,"languages":214,"metrics":216,"outcomeDisclosed":230,"sources":231,"verification":237,"grade":239,"id":240,"organizationSlug":241},"Zalando: Size & Fit advice, body measurements and Virtual Fitting Room",[197],{"name":203,"anonymized":204,"country":205,"region":156,"industry":18,"wikidata":206},"Zalando",false,"DE","Q136570",[208],{"name":203,"role":209},"in-house","Zalando has run an in house Size & Fit team since 2018, now 80 specialists, that combines machine learning, computer vision and 3D technology to give size advice on product pages. Size flags tell a shopper when an item runs small or large, size recommendations use purchase and return history and a customer's size profile, and a body measurement tool predicts measurements from two phone photos or a video. Zalando reports that its foundational size and fit solutions cover around 70% of the assortment and prevented 8% of size related returns overall in 2025. A Virtual Fitting Room that builds a 3D avatar from the measurements is moving from pilots to a permanent experience, and Zalando says its pilots reduced return rates by up to 40%.","scaled",2025,[27],[215],"en",[217,224],{"kpi":45,"value":82,"unit":218,"qualifier":219,"period":220,"baseline":221,"claimant":222,"quote":223,"sourceUrl":37},"percent","exact","in 2025","size related returns that would have occurred without the size and fit solutions","organization","In 2025, these solutions prevented 8% of size-related returns overall.",{"kpi":48,"value":225,"unit":226,"qualifier":227,"period":228,"claimant":222,"quote":229,"sourceUrl":37},1500000,"count","at-least","customers who tried the Body Measurement tool, as of 2026","More than 1.5 million customers have already tried the experience, and the number continues to grow.",true,[232,234],{"url":37,"title":36,"publisher":233},"Zalando Corporate",{"url":235,"title":236,"publisher":233},"https:\u002F\u002Fcorporate.zalando.com\u002Fen\u002Fnode\u002F10416","How Zalando leverages technology to help customers find the right size",{"level":238,"checkedAt":193},"source-verified","B","zalando-size-and-fit-solutions","zalando",{"title":243,"useCases":244,"organization":245,"vendors":249,"summary":253,"stage":254,"year":255,"channels":256,"languages":257,"metrics":259,"outcomeDisclosed":230,"sources":271,"verification":275,"grade":276,"id":277,"organizationSlug":278},"AMARO: Fit Analytics Fit Finder across web, app and stores",[197],{"name":246,"anonymized":204,"country":247,"region":248,"industry":18},"AMARO","BR","latin-america",[250],{"name":251,"role":252},"Fit Analytics","platform","AMARO, a Brazilian digital native fashion brand that designs over 10,000 items a year, put Fit Analytics' Fit Finder size advisor on its website, its iOS and Android apps and in its Guide Stores, localized for Brazil. In an A\u002FB test against a size chart, Fit Analytics reports a 2% increase in conversion rate and a 4% reduction in return rate over two and a half months. The figures come from Fit Analytics' own case study, which gives no date for the test.","production",2022,[27],[258],"pt",[260,267],{"kpi":46,"value":261,"unit":218,"qualifier":219,"period":262,"baseline":263,"claimant":264,"quote":265,"sourceUrl":266},2,"2.5 month A\u002FB test","size chart","vendor","In just two and a half months, Fit Analytics delivered a 2% increase in conversion rate and a 4% reduction in return rate leading to a significant bottom-line boost for Brazil’s hottest digital native brand.","https:\u002F\u002Ffitanalytics.com\u002Fcase-studies\u002Famaro",{"kpi":45,"value":268,"unit":218,"qualifier":219,"period":269,"baseline":270,"claimant":264,"quote":265,"sourceUrl":266},4,"2.5 month A\u002FB test, across all channels","size chart; return rate",[272],{"url":266,"title":273,"publisher":251,"archivedUrl":274},"Amaro case study","https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20221203235355\u002Fhttps:\u002F\u002Ffitanalytics.com\u002Fcase-studies\u002Famaro",{"level":238,"checkedAt":193},"C","amaro-fit-analytics-fit-finder",null,{"title":280,"useCases":281,"organization":282,"vendors":287,"summary":289,"stage":254,"year":255,"channels":290,"languages":291,"metrics":292,"outcomeDisclosed":230,"sources":306,"verification":310,"grade":276,"id":311,"organizationSlug":278},"THE ICONIC: Fit Analytics Fit Finder size advisor A\u002FB test",[197],{"name":283,"anonymized":204,"country":284,"region":285,"industry":18,"wikidata":286},"THE ICONIC","AU","asia-pacific","Q17056430",[288],{"name":251,"role":252},"THE ICONIC, an Australian online fashion and sports retailer with over 700 brands and 45,000 products, tested Fit Analytics' Fit Finder size advisor against a traditional static size chart in a three month A\u002FB test with 250,000 shoppers. Fit Analytics reports that shoppers who used the advisor converted 1% more often, returned 0.4% less and contributed 6% more revenue after returns than the control group. The figures come from Fit Analytics' own case study, which gives no date for the test.",[],[215],[293,299,302],{"kpi":46,"value":294,"unit":218,"qualifier":219,"period":295,"baseline":296,"claimant":264,"quote":297,"sourceUrl":298},1,"three month A\u002FB test with 250,000 shoppers","control group with a static size chart","Shoppers using the Fit Analytics size advisor showed a 1% increase in conversion rate.","https:\u002F\u002Fwww.fitanalytics.com\u002Fcase-studies\u002Fthe-iconic",{"kpi":45,"value":62,"unit":218,"qualifier":219,"period":295,"baseline":300,"claimant":264,"quote":301,"sourceUrl":298},"control group with a static size chart; return rate","Improved fit-related user confidence also led to a 0.4% reduction in return rate.",{"kpi":47,"value":303,"unit":218,"qualifier":219,"period":295,"baseline":304,"claimant":264,"quote":305,"sourceUrl":298},6,"control group with a static size chart; revenue after returns","Overall, Fit Analytics visitors contributed 6% more revenue after returns compared to the control group.",[307],{"url":298,"title":308,"publisher":251,"archivedUrl":309},"The Iconic case study","https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20221203234529\u002Fhttps:\u002F\u002Ffitanalytics.com\u002Fcase-studies\u002Fthe-iconic",{"level":238,"checkedAt":193},"the-iconic-fit-analytics-fit-finder",{"title":313,"useCases":314,"organization":315,"vendors":319,"summary":322,"stage":254,"year":323,"channels":324,"languages":325,"metrics":326,"outcomeDisclosed":204,"sources":327,"verification":331,"grade":276,"id":332,"organizationSlug":278},"ASICS: True Fit fit recommendation across its brands",[197],{"name":316,"anonymized":204,"country":317,"region":285,"industry":18,"wikidata":318},"ASICS","JP","Q327247",[320],{"name":321,"role":252},"True Fit","ASICS launched True Fit's fit recommendation technology in 2018 across all its brands, starting with footwear, to give shoppers a size recommendation on its ecommerce sites. True Fit's case study says ASICS reduced size bracketing, where shoppers order several sizes to keep one, and that its ecommerce director said the return data let ASICS update its return policy and helped mobile conversion. The case study page gives headline claims without the underlying figures, so no metric is recorded.",2018,[27],[215],[],[328],{"url":329,"title":330,"publisher":321},"https:\u002F\u002Finfo.truefit.com\u002Fasics-case-study","True Fit x Asics Case Study",{"level":238,"checkedAt":193},"asics-true-fit-fit-recommendation",0,[335,343,350,355],{"kpi":45,"label":336,"unit":218,"aggregate":230,"higherIsBetter":230,"n":337,"nUpTo":333,"median":268,"min":62,"max":82,"byClaimant":338,"vendorOnly":204,"points":339},"Error reduction",3,{"organization":294,"vendor":261,"regulator":333,"independent":333},[340,341,342],{"evidenceId":240,"organization":203,"value":82,"qualifier":219,"claimant":222,"grade":239,"pooled":230},{"evidenceId":277,"organization":246,"value":268,"qualifier":219,"claimant":264,"grade":276,"pooled":230},{"evidenceId":311,"organization":283,"value":62,"qualifier":219,"claimant":264,"grade":276,"pooled":230},{"kpi":46,"label":344,"unit":218,"aggregate":230,"higherIsBetter":230,"n":261,"nUpTo":333,"median":345,"min":294,"max":261,"byClaimant":346,"vendorOnly":230,"points":347},"Conversion uplift",1.5,{"organization":333,"vendor":261,"regulator":333,"independent":333},[348,349],{"evidenceId":277,"organization":246,"value":261,"qualifier":219,"claimant":264,"grade":276,"pooled":230},{"evidenceId":311,"organization":283,"value":294,"qualifier":219,"claimant":264,"grade":276,"pooled":230},{"kpi":47,"label":351,"unit":218,"aggregate":230,"higherIsBetter":230,"n":294,"nUpTo":333,"median":303,"min":303,"max":303,"byClaimant":352,"vendorOnly":230,"points":353},"Revenue uplift",{"organization":333,"vendor":294,"regulator":333,"independent":333},[354],{"evidenceId":311,"organization":283,"value":303,"qualifier":219,"claimant":264,"grade":276,"pooled":230},{"kpi":48,"label":356,"unit":226,"aggregate":204,"higherIsBetter":230,"n":294,"nUpTo":333,"median":225,"min":225,"max":225,"byClaimant":357,"vendorOnly":204,"points":358},"Users served",{"organization":294,"vendor":333,"regulator":333,"independent":333},[359],{"evidenceId":240,"organization":203,"value":225,"qualifier":227,"claimant":222,"grade":239,"pooled":230},{"low":361,"high":362},15840,1680000,[364,397,422,447],{"slug":191,"title":365,"shortTitle":366,"definition":367,"status":9,"industries":368,"functions":371,"patterns":373,"audience":28,"autonomy":378,"adoptionStage":379,"evidenceCount":380,"publicEvidenceCount":380,"organizations":381,"bestGrade":239,"headline":391,"lastVerified":396,"indexable":230},"AI agent for order status, delivery changes and returns","Order status and returns","An AI order status and returns agent answers \"where is my order\", changes delivery details and arranges returns, exchanges and refunds for online and omnichannel shoppers. It does this end to end, 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.",[369,18,370],"cross-industry","payments",[372,21],"customer-service",[374,375,376,377],"conversational-agent","agentic-workflow","voice-agent","rag-knowledge-assistant","supervised-agent","mainstream",9,[382,383,384,385,386,387,388,389,390],"BARK","Best Buy","Klarna","Next","NOBULL","Nuuly","Sun & Ski Sports","Vivid Seats","Woolworths Group",{"kpi":392,"label":393,"unit":218,"n":268,"nUpTo":333,"kind":394,"value":395,"qualifier":219,"claimant":264,"organization":278,"vendorReported":230},"customer-satisfaction","Customer satisfaction","median",92.5,"2026-09-26",{"slug":192,"title":398,"shortTitle":399,"definition":400,"status":9,"industries":401,"functions":402,"patterns":404,"audience":28,"autonomy":405,"adoptionStage":30,"evidenceCount":406,"publicEvidenceCount":407,"organizations":408,"bestGrade":239,"headline":419,"lastVerified":421,"indexable":230},"AI shopping assistant for product discovery and recommendations","Conversational shopping assistant","An AI conversational shopping assistant answers product questions and recommends items from a retailer's own catalog, for a need, project or occasion a shopper describes. It works on the retailer's site or app, compares items grounded in the retailer's product data, reviews and stock, and hands the shopper to a basket, a store or a human expert.",[369,18],[20,403,372],"marketing",[374,23,377,375],"autonomous",17,12,[409,410,411,412,413,414,415,416,417,388,418,203],"Albertsons Companies","Amazon","Carrefour","The Home Depot","Instacart","Lowe's","Michaels","Omoda","Pick n Pay","Walmart",{"kpi":46,"label":344,"unit":218,"n":337,"nUpTo":333,"kind":394,"value":420,"qualifier":219,"claimant":222,"organization":278,"vendorReported":204},100,"2026-09-27",{"slug":423,"title":424,"shortTitle":425,"definition":426,"status":9,"industries":427,"functions":429,"patterns":430,"audience":432,"autonomy":29,"adoptionStage":30,"segment":433,"evidenceCount":82,"publicEvidenceCount":82,"organizations":434,"bestGrade":239,"headline":443,"lastVerified":446,"indexable":230},"store-and-shelf-monitoring","AI computer vision for store shelf and stock monitoring","Store and shelf monitoring","Store and shelf monitoring is computer vision, on cameras, shelf edge sensors or autonomous robots, that scans store shelves for gaps, misplaced items and wrong prices. It turns what it sees into a prioritized task list for store staff or field sales representatives, so out of stock and shelf compliance problems are caught within hours instead of at the next manual walk.",[18,428],"manufacturing",[21,20],[25,431],"anomaly-detection","back-office","store-operations",[435,436,437,438,439,440,441,442],"BJ's Wholesale Club","B&R Stores","GoGo SqueeZ","Henkel","Schnuck Markets","SPAR Austria","Wakefern Food Corp.","Walmart de México y Centroamérica",{"kpi":47,"label":351,"unit":218,"n":261,"nUpTo":333,"kind":444,"value":445,"qualifier":219,"claimant":264,"organization":437,"vendorReported":230},"reported",7,"2026-09-29",{"slug":448,"title":449,"shortTitle":450,"definition":451,"status":9,"industries":452,"functions":453,"patterns":454,"audience":28,"autonomy":378,"adoptionStage":455,"evidenceCount":303,"publicEvidenceCount":303,"organizations":456,"bestGrade":239,"headline":461,"lastVerified":193,"indexable":230},"agentic-checkout-for-merchants","Agentic checkout for merchants, selling through third party AI shopping agents","Agentic checkout for merchants","Agentic checkout for merchants is how a retailer lets third party AI shopping agents read its catalog, build a cart and take payment while it stays merchant of record. It covers the product data, the cart and checkout endpoints and the order handling behind them.",[18],[20,21],[375,374],"emerging",[457,413,458,459,460,418],"Etsy","PayPal","Shopify","Ulta Beauty",{"kpi":46,"label":344,"unit":218,"n":261,"nUpTo":333,"kind":444,"value":420,"qualifier":219,"claimant":222,"organization":459,"vendorReported":204},{"indexable":230,"reasons":463},[],[465,469,473,481,489,495,501,508,515,522,528,534,540,547,554,559,565,571,577,584,589,594,600,606,613,618,623,629,635,640,647,654,659,665,671,676,681],{"id":150,"label":466,"issuer":155,"region":156,"url":157,"description":467,"useCases":468,"indexable":230},"EU AI Act","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",295,{"id":151,"label":470,"issuer":155,"region":156,"url":165,"description":471,"useCases":472,"indexable":230},"GDPR","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",265,{"id":474,"label":475,"issuer":476,"region":477,"url":478,"description":479,"useCases":480,"indexable":230},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",148,{"id":482,"label":483,"issuer":484,"region":485,"url":486,"description":487,"useCases":488,"indexable":230},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",115,{"id":490,"label":491,"issuer":169,"region":156,"url":492,"description":493,"useCases":494,"indexable":230},"uk-gdpr","UK GDPR","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",84,{"id":496,"label":497,"issuer":155,"region":156,"url":498,"description":499,"useCases":500,"indexable":230},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",73,{"id":502,"label":503,"issuer":504,"region":156,"url":505,"description":506,"useCases":507,"indexable":230},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",53,{"id":509,"label":510,"issuer":511,"region":285,"url":512,"description":513,"useCases":514,"indexable":230},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":516,"label":517,"issuer":518,"region":285,"url":519,"description":520,"useCases":521,"indexable":230},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":523,"label":524,"issuer":155,"region":156,"url":525,"description":526,"useCases":527,"indexable":230},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":529,"label":530,"issuer":531,"region":477,"url":532,"description":533,"useCases":527,"indexable":230},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":535,"label":536,"issuer":537,"region":485,"url":538,"description":539,"useCases":527,"indexable":230},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":541,"label":542,"issuer":543,"region":485,"url":544,"description":545,"useCases":546,"indexable":230},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",20,{"id":548,"label":549,"issuer":550,"region":156,"url":551,"description":552,"useCases":553,"indexable":230},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":555,"label":556,"issuer":155,"region":156,"url":557,"description":558,"useCases":83,"indexable":230},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":560,"label":561,"issuer":562,"region":477,"url":563,"description":564,"useCases":83,"indexable":230},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":566,"label":567,"issuer":568,"region":485,"url":569,"description":570,"useCases":83,"indexable":230},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":572,"label":573,"issuer":155,"region":156,"url":574,"description":575,"useCases":576,"indexable":230},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":578,"label":579,"issuer":580,"region":485,"url":581,"description":582,"useCases":583,"indexable":230},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":585,"label":586,"issuer":155,"region":156,"url":587,"description":588,"useCases":583,"indexable":230},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":590,"label":591,"issuer":155,"region":156,"url":592,"description":593,"useCases":583,"indexable":230},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":595,"label":596,"issuer":597,"region":477,"url":598,"description":599,"useCases":583,"indexable":230},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":601,"label":602,"issuer":603,"region":485,"url":604,"description":605,"useCases":407,"indexable":230},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":607,"label":608,"issuer":609,"region":156,"url":610,"description":611,"useCases":612,"indexable":230},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":614,"label":615,"issuer":155,"region":156,"url":616,"description":617,"useCases":612,"indexable":230},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":619,"label":620,"issuer":155,"region":156,"url":621,"description":622,"useCases":612,"indexable":230},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":624,"label":625,"issuer":511,"region":285,"url":626,"description":627,"useCases":628,"indexable":230},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":630,"label":631,"issuer":632,"region":485,"url":633,"description":634,"useCases":82,"indexable":230},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":636,"label":637,"issuer":155,"region":156,"url":638,"description":639,"useCases":82,"indexable":230},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":641,"label":642,"issuer":643,"region":644,"url":645,"description":646,"useCases":303,"indexable":230},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":648,"label":649,"issuer":650,"region":156,"url":651,"description":652,"useCases":653,"indexable":230},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",5,{"id":655,"label":656,"issuer":155,"region":156,"url":657,"description":658,"useCases":268,"indexable":230},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":660,"label":661,"issuer":662,"region":156,"url":663,"description":664,"useCases":268,"indexable":230},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":666,"label":667,"issuer":668,"region":285,"url":669,"description":670,"useCases":337,"indexable":230},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":672,"label":673,"issuer":155,"region":156,"url":674,"description":675,"useCases":337,"indexable":230},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":677,"label":678,"issuer":511,"region":285,"url":679,"description":680,"useCases":337,"indexable":230},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":682,"label":683,"issuer":684,"region":485,"url":685,"description":686,"useCases":337,"indexable":230},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1792049864851]