[{"data":1,"prerenderedAt":661},["ShallowReactive",2],{"uc-product-content-and-catalog-enrichment":3,"uc-regulations":452},{"useCase":4,"evidence":204,"blitsAiDeployments":333,"benchmarks":334,"indicative":356,"related":359,"indexability":450,"includeUnpublished":210},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":18,"functions":21,"patterns":24,"channels":28,"audience":31,"autonomy":32,"adoptionStage":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":42,"indicativeValue":48,"macroEstimates":82,"feasibility":83,"implementation":96,"risk":142,"blitsAi":183,"faq":185,"related":195,"datePublished":199,"dateModified":199,"lastVerified":199,"changelog":200,"slug":203},"AI product content and catalog enrichment for online retail","Product content and catalog enrichment","AI product descriptions and catalog enrichment","AI drafts product titles, descriptions and attributes at catalog scale. Walmart created or improved over 850 million catalog data points using several LLMs.","published","AI that writes and repairs product content at catalog scale: it drafts titles, descriptions and image alt text, and extracts missing attributes such as color, size and material from supplier text and product images, then checks its own output before the content is published to the store and to search engines. A human owns the rules, the quality thresholds and the exceptions.",[12,13,14,15,16,17],"AI product description generator","product attribute extraction","catalog enrichment","AI product listing generation","product information management AI","ecommerce SEO content generation",[19,20],"retail-and-ecommerce","cross-industry",[22,23],"marketing","operations",[25,26,27],"content-generation","computer-vision","classification-and-routing",[29,30],"internal-tools","api","back-office","supervised-agent","mainstream","An online catalog is only as findable as its data. Shoppers filter by size, color and material,\nsearch engines index titles, descriptions and alt text, and marketplaces such as Amazon use\nattributes like color to index products in their own search. Yet most product records arrive thin or inconsistent: a supplier spreadsheet with a\ncryptic name, a few bullet points and a photo. Retailers with hundreds of thousands or millions\nof items cannot write and check every record by hand, so attributes stay empty, descriptions stay\ncopied from the manufacturer, and items that are in stock are never found.\n\nWalmart describes the stakes plainly: the quality of catalog data affects nearly everything it\ndoes, from helping customers find products to sorting inventory and delivering orders. Small\nsellers feel the same problem from the other side. Writing a good listing takes time they do not\nhave, which is why the large marketplaces now offer to draft it for them from a photo, a few words\nor an existing web page.",[],"1. **Collect the inputs.** Supplier feeds, existing descriptions, product images, the category\n   and the attribute specification for that category (allowed values, units, required fields).\n2. **Extract attributes.** A model reads the text and the images and proposes a value for each\n   attribute in the specification, with the source it used.\n3. **Write the content.** A model drafts the title, description, bullet points and image alt\n   text in the house style, using only the extracted attributes and approved claims, and adds\n   the keywords shoppers use in search.\n4. **Check before publishing.** A second model or rule set checks each value and each draft:\n   does the attribute match the image, is the unit valid, does the description claim anything the\n   data does not support? Values above a set accuracy threshold are published; the rest go to a\n   human reviewer, whose decisions become new training and test data.\n5. **Measure and repeat.** Search conversion, returns for \"not as described\" and reviewer\n   corrections show which categories and attributes need better prompts or a human.",[38,39,40,41],"revenue-growth","employee-productivity","customer-experience","speed",[43,44,45,46,47],"interactions-handled","users-served","productivity-gain","quality-score-uplift","accuracy",{"referenceOrg":49,"inputs":50,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"An online retailer with 200,000 active product listings",[51,56,63,70],{"key":52,"label":53,"low":54,"high":54,"unit":52,"note":55},"listings","Active product listings",200000,"The reference retailer.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"shareTouched","Share of listings written or repaired per year",0.3,0.6,"fraction of listings","Editorial assumption covering new items and repairs of thin or inconsistent records. Replace with your own assortment change rate.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"minutesSaved","Content work saved per listing",10,25,"minutes per listing","Editorial assumption. A seller quoted by Amazon in May 2025 (in the Amazon evidence record for this page) said listings used to take an hour and that the AI content is now generated in under 15 minutes; that 15 minutes is generation time, not total listing time. This range is more conservative because a retailer's content team already works faster than a small seller and still reviews each item.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"hourlyCost","Fully loaded cost of a content specialist",30,50,"USD per hour","Editorial assumption, replace with your own cost or agency rate.","listings * shareTouched * minutesSaved / 60 * hourlyCost","USD","per year","Content production cost avoided","Counts only the writing and data entry time saved. It leaves out the running cost of the models, the human review that stays in place, and the revenue effect of better findability, which Etsy reports as 3% more conversions for its sellers (alongside 5% more visits from search engines) from improved alt text, but which depends on the catalog.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":91},"medium","Drafting text is easy; getting reliable attributes at catalog scale is the work. It needs an attribute specification per category, a labelled benchmark set, a quality check that decides what is published automatically, and a connection to the product information system.",[87,88,89,90],"An attribute specification per category, with allowed values and units","A human validated sample of products per category to benchmark accuracy","Brand style guide and a list of claims that may and may not be made","Product images and supplier data linked to each item",[92,93,94,95],"Product information management (PIM) or catalog system","Digital asset management for product images","Ecommerce platform or marketplace listing API","Search and analytics for conversion and search performance",{"steps":97,"guardrails":116,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":129},[98,101,104,107,110,113],{"title":99,"detail":100},"Write the specification first","For each category, list the attributes that matter for search and filters, their allowed values and units, and which ones are safety or compliance relevant. The model is only as good as this list.",{"title":102,"detail":103},"Build a benchmark before a prompt","Have specialists label a few hundred items per large category, keep them out of any tuning data, and measure precision and recall per attribute for every model or prompt change.",{"title":105,"detail":106},"Separate writing from checking","Use one step to extract and write and another to verify, as Walmart describes. Publish automatically only the attributes whose measured accuracy clears your threshold, and send the rest through a further check or to reviewers.",{"title":108,"detail":109},"Ground descriptions in the data","Generate descriptions from the verified attributes and approved claims only, never from the model's general knowledge, so a description cannot promise a feature the item lacks.",{"title":111,"detail":112},"Put people where the risk is","Keep human review for safety relevant attributes (allergens, age ratings, electrical ratings), regulated categories and low confidence items, and sample the automatic output every week.",{"title":114,"detail":115},"Measure on the storefront","Track search conversion, zero result searches, filter usage and returns for \"not as described\" per category, so enrichment is judged by shoppers, not by word count.",[117,118,119,120,121],"Descriptions generated only from verified attributes and an approved claims list","Automatic publication only above a measured accuracy threshold per attribute","Human review for safety, legal and regulated product attributes","Filters that block model refusals, placeholder text and competitor brand names from publication","Versioned prompts and a benchmark run before every change","Content and category specialists own the attribute specifications, label the benchmark sets, review low confidence and safety relevant values, and sample automatically published content. On a marketplace the seller submits each AI draft: Amazon encourages sellers to review drafts before they submit them, and eBay's listing flow asks the seller to review and approve the suggestions.",[124,125,126,127,128],"Attribute precision and recall per category against the human labelled benchmark","Share of AI output published without edits, and the edit rate by reviewers","Attribute fill rate for the fields shoppers filter on","Search conversion and zero result searches before and after, per category","Returns with the reason \"not as described\"",[130,133,136,139],{"title":131,"detail":132},"Confident but wrong attributes","A model fills a color, size or material that the image contradicts, and the item is returned. Measure accuracy per attribute and only automate what clears the threshold.",{"title":134,"detail":135},"Invented features","A fluent description promises a feature the product does not have, which becomes a consumer protection problem. Generate only from verified data and approved claims.",{"title":137,"detail":138},"Unreviewed output goes live","In January 2024 Amazon hosted listings whose titles were model refusal messages. Block refusals and placeholder text automatically and keep a human submit step.",{"title":140,"detail":141},"Thin pages at scale","Thousands of near identical generated pages can be treated as spam by search engines. Write for shoppers with real product facts, not for keyword variants.",{"euAiAct":143,"regulations":146,"guidance":149,"controls":172,"incidents":178},{"tier":144,"basis":145},"context-dependent","Writing product content and extracting catalog attributes is not an Annex III use and makes no decisions about people. When a retailer uses a third party generator, the use is minimal risk for the retailer: the Article 50(2) duty to mark generated text in a machine readable way falls on the provider of that system. When a retailer builds and operates its own generating system and puts it into service under its own name, it is the provider and must mark the output, unless the exception for systems that only assist standard editing or do not substantially alter the input applies. Article 50(4) covers text published to inform the public on matters of public interest, not product listings. Consumer protection law applies to what the listing says in every case.",[147,148],"eu-ai-act","eu-accessibility-act",[150,156,160,166],{"title":151,"issuer":152,"region":153,"url":154,"note":155},"Google Search's guidance about AI generated content","Google Search Central","global","https://developers.google.com/search/blog/2023/02/google-search-and-ai-content","Google rewards helpful content however it is produced, and treats automation used mainly to manipulate rankings as spam.",{"title":157,"issuer":152,"region":153,"url":158,"note":159},"Spam policies for Google web search, scaled content abuse","https://developers.google.com/search/docs/essentials/spam-policies","Lists pages generated at scale with little value for users, including automated transformations such as synonymizing and translating, as scaled content abuse.",{"title":161,"issuer":162,"region":163,"url":164,"note":165},"Final rule banning fake reviews and testimonials","Federal Trade Commission","north-america","https://www.ftc.gov/news-events/news/press-releases/2024/08/federal-trade-commission-announces-final-rule-banning-fake-reviews-testimonials","Prohibits fake reviews and testimonials, including AI generated ones; product content generation must not extend to reviews.",{"title":167,"issuer":168,"region":169,"url":170,"note":171},"Unfair Commercial Practices Directive","European Union","europe","https://eur-lex.europa.eu/eli/dir/2005/29/oj","Misleading product information is an unfair commercial practice, whoever or whatever wrote it.",[173,174,175,176,177],"A named owner per category for the attribute specification and the approved claims list","Accuracy benchmarks per attribute with thresholds for automatic publication","An audit trail that records which model, prompt version and reviewer produced each published value","Alt text that describes the image for people using screen readers, not only for search engines","A takedown route for content reported as wrong by customers or sellers",[179],{"title":180,"url":181,"note":182},"AI refusal messages published as Amazon product titles","https://oecd.ai/en/incidents/2024-01-12-c37b","Amazon hosted listings whose titles were model refusal messages (\"I'm sorry but I cannot fulfill this request\"); Amazon removed them and said it was improving its review systems. The date comes from the incident ID (2024-01-12). It shows what happens when generated content is published without review.",{"howToBuild":184},"On Blits.ai this is an **agentic workflow** that runs on a schedule or through an API token: an\n**AI agent** with **structured output configuration** reads the product record and the category's\nattribute specification from a **knowledge base** (supplier sheets and style guides ingested from\nXLSX, CSV, PDF and DOCX), proposes attribute values and drafts the title, description and alt\ntext. **Custom functions** read from and write back to the product information system or\necommerce platform through REST, and **SQL knowledge bases** can hold the catalog tables the\nagent queries.\n\nA second agent step checks each draft against the specification, and **human in the loop**\nconfirmation asks a content specialist to approve or reject the write back to the catalog before\nit happens. **Guardrails** block unsupported claims and refusal text, **test suites** with LLM\nbased grading run the labelled benchmark on every prompt version, and the run history keeps a\nfull audit trail per run.\n**Machine translation** and multi language support can extend the same content to other\nmarkets, and the platform is model agnostic, so each step can use the model that performs best\non the benchmark.",[186,189,192],{"question":187,"answer":188},"Can AI write product descriptions without a human checking them?","For extracted attributes with measured accuracy, yes, and that is how Walmart describes its catalog work: a second model checks the first, and values for attributes above an accuracy threshold go straight into the catalog. Generated descriptions are a different matter: on Amazon and eBay the seller submits each draft, and Amazon reported in 2025 that sellers accept AI content with little or no edits about 90% of the time.",{"question":190,"answer":191},"Does AI generated product content hurt SEO?","Not by itself. Google says it rewards helpful content however it is produced, but treats pages generated at scale mainly to manipulate rankings as spam. Etsy reports that better alt text generated with Gemini increased visits from search engines by 5% and conversions by 3% for its sellers.",{"question":193,"answer":194},"What is harder, the text or the attributes?","The attributes. Fluent descriptions are easy to generate; correct size, color, material and safety data across millions of items need a specification, a labelled benchmark and a quality check per attribute.",[196,197,198],"conversational-shopping-assistant","marketing-content-compliance-copilot","personalized-marketing-at-scale","2026-09-27",[201],{"date":199,"note":202},"First published","product-content-and-catalog-enrichment",[205,251,283,312],{"title":206,"useCases":207,"organization":208,"vendors":212,"summary":216,"stage":217,"year":218,"channels":219,"languages":220,"metrics":222,"outcomeDisclosed":237,"sources":238,"verification":246,"grade":248,"id":249,"organizationSlug":250},"Amazon: generative AI tools that write product listings for selling partners",[203,198],{"name":209,"anonymized":210,"country":211,"region":153,"industry":19},"Amazon",false,"US",[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,[29],[221],"en",[223,231],{"kpi":44,"value":224,"unit":225,"qualifier":226,"period":227,"claimant":228,"quote":229,"sourceUrl":230},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":46,"value":232,"unit":233,"qualifier":234,"period":235,"claimant":228,"quote":236,"sourceUrl":230},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,[239,242],{"url":230,"title":240,"publisher":209,"date":241},"Amazon sellers can now automatically improve product listings with our new Gen AI tool","2025-05-08",{"url":243,"title":244,"publisher":209,"date":245},"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":247,"checkedAt":199},"source-verified","B","amazon-generative-ai-listing-tools","amazon",{"title":252,"useCases":253,"organization":254,"vendors":256,"summary":259,"stage":217,"year":218,"channels":260,"languages":262,"metrics":264,"outcomeDisclosed":237,"sources":273,"verification":280,"grade":248,"id":281,"organizationSlug":282},"eBay: magical listing, generative AI that drafts listings from a photo",[203],{"name":255,"anonymized":210,"country":211,"region":153,"industry":19},"eBay",[257],{"name":255,"role":258},"in-house","eBay's magical listing tool writes item descriptions from known product attributes and, from a seller's photo, suggests the category and item specifics for the seller to review and approve. The description feature reached all sellers in eBay's top five markets by the end of 2023; a bulk version creates drafts from batches of photos, and in 2025 a simplified mobile flow starting from photos was rolled out in the US, UK and Germany. Early UK tests showed half as many steps to list.",[261,29],"mobile-app",[221,263],"de",[265,270],{"kpi":44,"value":266,"unit":225,"qualifier":226,"period":267,"claimant":228,"quote":268,"sourceUrl":269},10000000,"sellers worldwide who have used any eBay AI feature (eBay AI overall, not only listing generation), by April 2025","Over 10 million sellers worldwide have already used eBay’s AI features, creating well over 100 million listings using AI and generating billions in gross merchandise volume (GMV).","https://innovation.ebayinc.com/stories/ebay-reduces-the-time-to-list-on-mobile-with-new-simplified-selling-tool-now-featuring-magical-listing-ai-technology/",{"kpi":43,"value":271,"unit":225,"qualifier":226,"period":272,"claimant":228,"quote":268,"sourceUrl":269},100000000,"listings created with any eBay AI feature (eBay AI overall, not only listing generation), by April 2025",[274,277],{"url":269,"title":275,"publisher":255,"date":276},"eBay Reduces the Time to List on Mobile With New Simplified Selling Tool, Now Featuring Magical Listing AI Technology","2025-04-09",{"url":278,"title":279,"publisher":255},"https://innovation.ebayinc.com/stories/ebays-magical-listing-tool-wins-ai-breakthrough-award-for-best-overall-generative-ai-solution/","eBay’s Magical Listing Tool Wins AI Breakthrough Award for ‘Best Overall Generative AI Solution’",{"level":247,"checkedAt":199},"ebay-magical-listing-generative-ai",null,{"title":284,"useCases":285,"organization":286,"vendors":288,"summary":291,"stage":217,"year":292,"channels":293,"languages":294,"metrics":295,"outcomeDisclosed":237,"sources":301,"verification":309,"grade":248,"id":310,"organizationSlug":311},"Walmart: large language models that create and check product catalog data",[203],{"name":287,"anonymized":210,"country":211,"region":163,"industry":19},"Walmart",[289],{"name":290,"role":258},"Walmart Global Tech","Walmart uses several large language models to extract product attributes such as color, size and material from item descriptions and images and to create or improve catalog data. One model extracts the values and a second model, tuned on human validated labels, checks them; values for attributes above an accuracy threshold go into the catalog, and the rest are checked by the quality model, with specialists validating samples. Walmart's chief executive told investors in August 2024 that the work covered more than 850 million pieces of catalog data, and estimated that without generative AI the same work would have needed nearly 100 times the current headcount to finish in the same time.",2024,[30],[221],[296],{"kpi":43,"value":297,"unit":225,"qualifier":226,"period":298,"claimant":228,"quote":299,"sourceUrl":300},850000000,"catalog data points created or improved, reported August 2024","We've used multiple large language models to accurately create or improve over 850 million pieces of data in the catalog.","https://corporate.walmart.com/content/dam/corporate/documents/newsroom/2024/08/15/walmart-releases-q2-fy25-earnings/corrected-walmart-inc-wmt-us-q2-2025-earnings-call-15-august-2024.pdf",[302,305],{"url":300,"title":303,"publisher":287,"date":304},"Walmart Inc. Q2 FY25 earnings call, corrected transcript","2024-08-15",{"url":306,"title":307,"publisher":290,"date":308},"https://tech.walmart.com/content/walmart-global-tech/en_us/blog/post/using-llms-to-manage-product-catalogs.html","How Walmart uses LLMs to manage its massive product catalogs","2025-05-20",{"level":247,"checkedAt":199},"walmart-generative-ai-product-catalog","walmart",{"title":313,"useCases":314,"organization":315,"vendors":317,"summary":320,"stage":321,"year":218,"channels":322,"languages":323,"metrics":324,"outcomeDisclosed":237,"sources":325,"verification":330,"grade":331,"id":332,"organizationSlug":282},"Etsy: Gemini enriches listing data and alt text across a 130 million item marketplace",[203],{"name":316,"anonymized":210,"country":211,"region":153,"industry":19},"Etsy",[318],{"name":319,"role":215},"Google Cloud","Etsy uses Gemini models with BigQuery and Dataflow to enrich data about more than 130 million items listed by more than 5 million sellers: classifying items, spotting items linked to emerging trends and generating better image alt text for listings. Etsy's engineering lead for search says the improved alt text increased visits from search engines by 5% and conversions by 3% for sellers.","production",[30],[221],[],[326],{"url":327,"title":328,"publisher":319,"archivedUrl":329},"https://cloud.google.com/customers/etsy-ai","Etsy: Connecting nearly 90 million buyers with special items using gen AI and \"algotorial curation\"","http://web.archive.org/web/20250902172024/https://cloud.google.com/customers/etsy-ai",{"level":247,"checkedAt":199},"C","etsy-gemini-listing-enrichment",0,[335,343,350],{"kpi":43,"label":336,"unit":225,"aggregate":210,"higherIsBetter":237,"n":337,"nUpTo":333,"median":338,"min":271,"max":297,"byClaimant":339,"vendorOnly":210,"points":340},"Interactions handled",2,475000000,{"organization":337,"vendor":333,"regulator":333,"independent":333},[341,342],{"evidenceId":310,"organization":287,"value":297,"qualifier":226,"claimant":228,"grade":248,"pooled":237},{"evidenceId":281,"organization":255,"value":271,"qualifier":226,"claimant":228,"grade":248,"pooled":237},{"kpi":44,"label":344,"unit":225,"aggregate":210,"higherIsBetter":237,"n":337,"nUpTo":333,"median":345,"min":224,"max":266,"byClaimant":346,"vendorOnly":210,"points":347},"Users served",5450000,{"organization":337,"vendor":333,"regulator":333,"independent":333},[348,349],{"evidenceId":281,"organization":255,"value":266,"qualifier":226,"claimant":228,"grade":248,"pooled":237},{"evidenceId":249,"organization":209,"value":224,"qualifier":226,"claimant":228,"grade":248,"pooled":237},{"kpi":46,"label":351,"unit":233,"aggregate":237,"higherIsBetter":237,"n":352,"nUpTo":333,"median":232,"min":232,"max":232,"byClaimant":353,"vendorOnly":210,"points":354},"Quality score uplift",1,{"organization":352,"vendor":333,"regulator":333,"independent":333},[355],{"evidenceId":249,"organization":209,"value":232,"qualifier":234,"claimant":228,"grade":248,"pooled":237},{"low":357,"high":358},300000,2500000,[360,388,412,432],{"slug":196,"title":361,"shortTitle":362,"definition":363,"status":9,"industries":364,"functions":365,"patterns":368,"audience":373,"autonomy":374,"adoptionStage":375,"evidenceCount":66,"publicEvidenceCount":376,"organizations":377,"bestGrade":248,"headline":381,"lastVerified":199,"indexable":237},"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.",[20,19],[366,22,367],"sales","customer-service",[369,370,371,372],"conversational-agent","recommendation-and-personalization","rag-knowledge-assistant","agentic-workflow","customer-facing","autonomous","early-adopters",5,[209,378,379,287,380],"Lowe's","Sun & Ski Sports","Zalando",{"kpi":382,"label":383,"unit":384,"n":352,"nUpTo":333,"kind":385,"value":386,"qualifier":234,"claimant":387,"organization":379,"vendorReported":237},"conversion-rate-uplift","Conversion uplift","multiplier","reported",3,"vendor",{"slug":197,"title":389,"shortTitle":390,"definition":391,"status":9,"industries":392,"functions":398,"patterns":401,"audience":403,"autonomy":404,"adoptionStage":375,"evidenceCount":376,"publicEvidenceCount":386,"organizations":405,"bestGrade":248,"headline":409,"lastVerified":199,"indexable":237},"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.",[20,393,394,395,396,397],"banking","insurance","payments","wealth-and-asset-management","pharma-and-life-sciences",[22,399,400],"regulatory-compliance","legal",[25,371,27,402],"translation","employee-facing","copilot",[406,407,408],"Ally Financial","JPMorgan Chase","Klarna",{"kpi":45,"label":410,"unit":233,"n":352,"nUpTo":333,"kind":385,"value":411,"qualifier":234,"claimant":228,"organization":406,"vendorReported":210},"Productivity gain",34,{"slug":198,"title":413,"shortTitle":414,"definition":415,"status":9,"industries":416,"functions":419,"patterns":420,"audience":31,"autonomy":32,"adoptionStage":33,"evidenceCount":422,"publicEvidenceCount":423,"organizations":424,"bestGrade":248,"headline":431,"lastVerified":199,"indexable":237},"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.",[20,417,418,19,393],"travel-and-hospitality","media-and-entertainment",[22,366],[370,421,25],"prediction-and-scoring",8,7,[209,425,426,427,428,429,430],"Catchtable","Commonwealth Bank of Australia","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":382,"label":383,"unit":233,"n":352,"nUpTo":333,"kind":385,"value":73,"qualifier":234,"claimant":387,"organization":425,"vendorReported":237},{"slug":433,"title":434,"shortTitle":435,"definition":436,"status":9,"industries":437,"functions":439,"patterns":440,"audience":31,"autonomy":32,"adoptionStage":375,"evidenceCount":386,"publicEvidenceCount":386,"organizations":442,"bestGrade":248,"headline":446,"lastVerified":199,"indexable":237},"customs-classification-and-declaration","AI for customs classification and declaration preparation","Customs classification and declarations","AI that reads what is being shipped (the commercial invoice, the product data and sometimes a photo), proposes the tariff classification code with its reasoning and a confidence score, drafts the customs declaration with value, origin and parties, and sends only uncertain or high risk entries to a licensed customs specialist before filing.",[438,19,20],"logistics-and-transportation",[23,399],[27,441,372,26],"document-processing",[443,444,445],"DHL Express","United Parcel Service","ZLS Zoll und Logistikservice GmbH",{"kpi":447,"label":448,"unit":233,"n":352,"nUpTo":333,"kind":385,"value":449,"qualifier":234,"claimant":228,"organization":444,"vendorReported":210},"automation-rate","Automation rate",90,{"indexable":237,"reasons":451},[],[453,458,464,471,478,484,491,498,506,512,519,525,532,539,545,550,557,562,568,574,580,586,591,596,601,608,614,619,625,632,639,645,651,656],{"id":147,"label":454,"issuer":168,"region":169,"url":455,"description":456,"useCases":457,"indexable":237},"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":459,"label":460,"issuer":168,"region":169,"url":461,"description":462,"useCases":463,"indexable":237},"gdpr","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":465,"label":466,"issuer":467,"region":153,"url":468,"description":469,"useCases":470,"indexable":237},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":472,"label":473,"issuer":474,"region":163,"url":475,"description":476,"useCases":477,"indexable":237},"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":479,"label":480,"issuer":168,"region":169,"url":481,"description":482,"useCases":483,"indexable":237},"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":485,"label":486,"issuer":487,"region":169,"url":488,"description":489,"useCases":490,"indexable":237},"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":492,"label":493,"issuer":494,"region":169,"url":495,"description":496,"useCases":497,"indexable":237},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":499,"label":500,"issuer":501,"region":502,"url":503,"description":504,"useCases":505,"indexable":237},"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":507,"label":508,"issuer":509,"region":502,"url":510,"description":511,"useCases":67,"indexable":237},"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":513,"label":514,"issuer":515,"region":153,"url":516,"description":517,"useCases":518,"indexable":237},"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":520,"label":521,"issuer":522,"region":163,"url":523,"description":524,"useCases":518,"indexable":237},"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":526,"label":527,"issuer":528,"region":169,"url":529,"description":530,"useCases":531,"indexable":237},"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":533,"label":534,"issuer":535,"region":153,"url":536,"description":537,"useCases":538,"indexable":237},"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":540,"label":541,"issuer":168,"region":169,"url":542,"description":543,"useCases":544,"indexable":237},"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":546,"label":547,"issuer":168,"region":169,"url":548,"description":549,"useCases":544,"indexable":237},"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":551,"label":552,"issuer":553,"region":163,"url":554,"description":555,"useCases":556,"indexable":237},"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":148,"label":558,"issuer":168,"region":169,"url":559,"description":560,"useCases":561,"indexable":237},"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":563,"label":564,"issuer":565,"region":163,"url":566,"description":567,"useCases":561,"indexable":237},"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":569,"label":570,"issuer":571,"region":153,"url":572,"description":573,"useCases":561,"indexable":237},"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":575,"label":576,"issuer":168,"region":169,"url":577,"description":578,"useCases":579,"indexable":237},"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":581,"label":582,"issuer":583,"region":163,"url":584,"description":585,"useCases":579,"indexable":237},"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":587,"label":588,"issuer":501,"region":502,"url":589,"description":590,"useCases":66,"indexable":237},"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":592,"label":593,"issuer":168,"region":169,"url":594,"description":595,"useCases":66,"indexable":237},"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":597,"label":598,"issuer":168,"region":169,"url":599,"description":600,"useCases":66,"indexable":237},"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":602,"label":603,"issuer":604,"region":169,"url":605,"description":606,"useCases":607,"indexable":237},"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":609,"label":610,"issuer":611,"region":163,"url":612,"description":613,"useCases":422,"indexable":237},"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":615,"label":616,"issuer":168,"region":169,"url":617,"description":618,"useCases":422,"indexable":237},"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":620,"label":621,"issuer":168,"region":169,"url":622,"description":623,"useCases":624,"indexable":237},"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":626,"label":627,"issuer":628,"region":629,"url":630,"description":631,"useCases":376,"indexable":237},"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":633,"label":634,"issuer":635,"region":169,"url":636,"description":637,"useCases":638,"indexable":237},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",4,{"id":640,"label":641,"issuer":642,"region":169,"url":643,"description":644,"useCases":638,"indexable":237},"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":646,"label":647,"issuer":648,"region":502,"url":649,"description":650,"useCases":386,"indexable":237},"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":652,"label":653,"issuer":168,"region":169,"url":654,"description":655,"useCases":386,"indexable":237},"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":657,"label":658,"issuer":162,"region":163,"url":659,"description":660,"useCases":386,"indexable":237},"us-fcra","Fair Credit Reporting Act","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598303263]