[{"data":1,"prerenderedAt":719},["ShallowReactive",2],{"uc-order-status-and-returns-agent":3,"uc-regulations":511},{"useCase":4,"evidence":213,"blitsAiDeployments":373,"benchmarks":374,"indicative":394,"related":397,"indexability":509,"includeUnpublished":220},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":24,"channels":29,"audience":35,"autonomy":36,"adoptionStage":37,"problem":38,"problemStats":39,"howItWorks":47,"valueDrivers":48,"kpis":53,"indicativeValue":60,"macroEstimates":96,"feasibility":97,"implementation":111,"risk":157,"blitsAi":189,"faq":191,"related":201,"datePublished":207,"dateModified":207,"lastVerified":208,"changelog":209,"slug":212},"AI agent for order status, delivery changes and returns","Order status and returns","AI agents for order status and returns","An AI agent answers where is my order, changes deliveries and arranges returns within policy. BARK, Next and Klarna run one; see the results and the playbook.","published","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.",[12,13,14,15,16],"WISMO agent","where is my order chatbot","returns and refunds assistant","ecommerce customer service agent","post purchase support agent",[18,19,20],"cross-industry","retail-and-ecommerce","payments",[22,23],"customer-service","operations",[25,26,27,28],"conversational-agent","agentic-workflow","voice-agent","rag-knowledge-assistant",[30,31,32,33,34],"web-chat","mobile-app","whatsapp","voice","email","customer-facing","supervised-agent","mainstream","After the checkout, the questions start: where is my order, can I change the delivery date, how do\nI return this, when do I get my money back. BARK, which ships tens of thousands of boxes a month,\ndescribes these questions as quick to answer one by one but overwhelming together. They peak\ntogether (a sale, the holidays, a carrier delay) and the answer sits in three or four systems: the\norder management system, the carrier's tracking feed, the warehouse and the returns platform.\n\nReturns are also expensive and emotional. A clumsy returns experience costs the next sale, and\npolicy answers that are wrong (a refund promised outside the return window, a label for an item\nthat cannot be returned) create cost and disputes. First generation chatbots pointed customers to\na tracking link or a returns page. The step change is an agent that identifies the order, reads the\nlive status, and completes the change or the return itself, inside the retailer's rules.",[40,45],{"statement":41,"sourceTitle":42,"sourceUrl":43,"year":44},"The National Retail Federation and Happy Returns projected that total retail returns in the United States would reach USD 890 billion in 2024, with retailers expecting 16.9% of annual sales to be returned.","NRF and Happy Returns Report: 2024 Retail Returns to Total $890 Billion","https://nrf.com/media-center/press-releases/nrf-and-happy-returns-report-2024-retail-returns-total-890-billion",2024,{"statement":46,"sourceTitle":42,"sourceUrl":43,"year":44},"In the same NRF and Happy Returns survey, 67% of consumers said a negative return experience would discourage them from shopping with a retailer again.","1. **Identify the customer and the order.** The agent matches the customer to the order from the\n   logged in session, an email or phone number and a verification step, so the customer does not\n   have to find an order number.\n2. **Read the live status.** It combines the order management system, the warehouse status and\n   the carrier's tracking events, and explains them in plain words: packed, handed to the carrier,\n   delayed at the depot, out for delivery.\n3. **Change what can be changed.** Within set rules it updates the delivery address or slot,\n   cancels an order that has not shipped, or reschedules a delivery through the carrier's API.\n4. **Run the return.** It checks eligibility against the return policy (window, item type,\n   condition), offers the options the retailer allows (refund, exchange, store credit, drop off or\n   pickup), creates the return and sends the label or QR code.\n5. **Explain the refund.** It tells the customer when and how the money comes back, from the\n   payment and refund status, not from a guess.\n6. **Hand over exceptions.** Damaged or missing items above a value threshold, suspected fraud,\n   complaints, repeat failures and emotional conversations go to a person with the order and the\n   conversation attached.",[49,50,51,52],"cost-to-serve","customer-experience","speed","revenue-growth",[54,55,56,57,58,59],"containment-rate","customer-satisfaction","interactions-handled","response-time-reduction","handling-time-reduction","cost-savings",{"referenceOrg":61,"inputs":62,"formula":91,"currency":92,"period":93,"resultLabel":94,"caveat":95},"An online retailer that ships 5 million orders a year",[63,69,76,84],{"key":64,"label":65,"low":66,"high":66,"unit":67,"note":68},"orders","Orders shipped per year",5000000,"orders per year","The reference retailer.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"contactsPerOrder","Post purchase contacts per order",0.1,0.2,"contacts per order","Editorial assumption for order status, delivery and returns contacts. Replace with your own contact reason data.",{"key":77,"label":78,"low":79,"high":80,"unit":81,"note":82,"sourceUrl":83},"resolvedShare","Share of those contacts the agent resolves end to end",0.3,0.5,"fraction of contacts","Editorial assumption within the range of the evidence. BARK's agent handled roughly a quarter of all customer conversations in its first year, Klarna's assistant two thirds of its service chats in its first month, and Ingka Group reports that its Billie chatbot resolved about 47% of all enquiries it received from 2021 to 2023. End to end resolution needs write access to orders and returns.","https://www.ingka.com/newsroom/ai-and-remote-selling-bring-ikea-design-expertise-to-the-many/",{"key":85,"label":86,"low":87,"high":88,"unit":89,"note":90},"costPerContact","Cost of a human handled contact",3,6,"USD per contact","Editorial assumption for a blended chat, email and phone contact. Replace with your own fully loaded cost.","orders * contactsPerOrder * resolvedShare * costPerContact","USD","per year","Human handled contact cost avoided","Gross avoided contact cost only. It leaves out the cost of running the AI and the integrations, the effect on repeat purchase of a better returns experience, fewer refund errors, and any change in return rates.",[],{"complexity":98,"complexityNote":99,"dataPrerequisites":100,"integrations":105},"medium","Reading the order status is easy; acting safely is the work. The agent needs reliable APIs into order management, carriers and the returns platform, identity matching without an order number, and policy rules that are encoded rather than paraphrased.",[101,102,103,104],"Order, shipment and return data reachable through APIs, with carrier tracking events normalised","The return and refund policy as explicit rules (windows, excluded items, conditions, exceptions)","Contact reason data per intent to choose the first scope","Approved content for delivery, returns and warranty questions",[106,107,108,109,110],"Order management system and ecommerce platform","Carrier tracking and delivery management APIs","Returns platform or warehouse returns process, including label generation","Payment service provider for refund status","Contact centre or helpdesk for handover with context",{"steps":112,"guardrails":131,"humanInTheLoop":137,"kpisToInstrument":138,"failureModes":144},[113,116,119,122,125,128],{"title":114,"detail":115},"Start with the contact reason report","Order status, delivery changes and return requests are often a large share. Pick the intents with the highest volume and the clearest rules, and leave damaged goods and disputes with people at first.",{"title":117,"detail":118},"Encode the policy, do not paraphrase it","Turn the return window, excluded categories, condition rules and refund methods into rules the agent's tools enforce. The model explains the outcome; the rule decides it.",{"title":120,"detail":121},"Solve order matching","Most customers do not have the order number at hand. Match on the logged in session, email or phone with a one time code, and confirm the item before acting.",{"title":123,"detail":124},"Give the agent a narrow set of actions","Address or slot change before dispatch, cancellation before dispatch, return creation, label sending and refund status. Each action has its own limits, such as a maximum order value for an automatic refund.",{"title":126,"detail":127},"Plan for peaks and carrier incidents","When a carrier has a regional delay, publish one explanation the agent uses for every affected order instead of letting it improvise, and scale the channel before the peak.",{"title":129,"detail":130},"Measure resolution, not deflection","Count a conversation as resolved only if the customer did not come back on the same order within seven days, and read transcripts of the ones that did.",[132,133,134,135,136],"Refund and exchange decisions come from policy rules in the tools, never from the model's own reading","Value thresholds above which refunds, reshipments or goodwill gestures need a person","Identity verification before any change to address, delivery or refund destination","The agent never promises a delivery date the carrier has not given","Automatic handover for complaints, suspected fraud, damaged goods above a threshold and distress","People own exceptions and judgment calls: damaged or missing items above a threshold, goodwill gestures, suspected return fraud and complaints. A team lead reviews a weekly sample of resolved conversations and every policy answer that led to a refund dispute, and signs off each new action before it goes live.",[139,140,141,142,143],"Resolution rate per intent, counting repeat contacts on the same order within seven days as unresolved","Share of returns created end to end by the agent and their error rate","Satisfaction on agent conversations versus human conversations for the same intents","Average response time and time to refund","Refund disputes and complaints that mention the assistant",[145,148,151,154],{"title":146,"detail":147},"Promising what policy does not allow","A fluent answer that grants a refund outside the window or for an excluded item. Keep eligibility in deterministic rules and have the agent quote the rule it applied.",{"title":149,"detail":150},"Tracking data the agent cannot interpret","Carrier events are cryptic and sometimes wrong. Normalise them into a small set of states and let the agent say \"we do not know yet\" rather than guess.",{"title":152,"detail":153},"Return fraud through an easy channel","An agent that issues refunds without checks becomes a target. Use value limits, customer history signals and a person for high value or repeat claims.",{"title":155,"detail":156},"Deflection dressed up as resolution","Customers who give up look like contained conversations. Track repeat contacts and satisfaction per intent.",{"euAiAct":158,"regulations":161,"guidance":166,"controls":178,"incidents":184},{"tier":159,"basis":160},"limited","A customer facing service agent must disclose that the customer is interacting with AI (Article 50). It is not high risk: it does not decide on access to essential services, credit or employment.",[162,163,164,165],"eu-ai-act","gdpr","pci-dss","eu-accessibility-act",[167,173],{"title":168,"issuer":169,"region":170,"url":171,"note":172},"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":174,"issuer":175,"region":170,"url":176,"note":177},"Consumer rights directive","European Commission","https://commission.europa.eu/law/law-topic/consumer-protection-law/consumer-contract-law/consumer-rights-directive_en","Sets the EU right of withdrawal for distance purchases and the refund rules an agent's answers on returns must respect.",[179,180,181,182,183],"AI disclosure at the start of each conversation","Return and refund rules versioned with an owner, and regression tests on every policy change","Audit log of every order change, return and refund the agent initiates","Masking of payment data and personal data in logs and model prompts","Monitoring of refund value and return volume initiated through the agent, with alerts on outliers",[185],{"title":186,"url":187,"note":188},"Incident 639: Air Canada chatbot reportedly provides inaccurate bereavement fare information, leading to customer overpayment","https://incidentdatabase.ai/cite/639/","Air Canada's website chatbot gave a customer inaccurate information about bereavement fare refunds. A Canadian small claims tribunal held the airline responsible for what its chatbot said and ordered it to pay damages. The same risk applies to any agent that answers return and refund questions without the policy encoded as rules.",{"howToBuild":190},"On Blits.ai this is an **AI agent** with **custom functions** that call the retailer's order\nmanagement, carrier tracking and returns APIs (REST calls with their own authentication and\nlimits), and a **knowledge base** with the approved delivery, returns and warranty content,\nretrieved with hybrid search. Return eligibility and refund rules run as deterministic steps in a\n**flow** or inside the function, so the model explains the decision rather than making it; larger\nrefunds can go through an **agentic workflow with human in the loop approval** above a set\nthreshold.\n\nThe same agent serves **web chat, WhatsApp, email and voice**, and a mobile app through the REST or\nWebSocket API channel; the chat widget can show orders and receipts as rich cards. **Guardrails**\ncheck input and output, **PII masking** and card number tokenization happen at the gateway, and\n**human handover** escalates the conversation to the service team, including through Salesforce,\nFreshdesk or Zoho SalesIQ. **Test suites** run multi turn order and return conversations as\nregression tests after each change, and **monitors** check the order lookup against a test order\non a schedule. The platform is model agnostic, so the model can be chosen or switched per agent.",[192,195,198],{"question":193,"answer":194},"What share of order and returns contacts can an AI agent resolve?","It depends on whether the agent can act. Agents that only link to a tracking page resolve little; agents that can read the live status and create returns can do much more. On this page, BARK's agent handled roughly a quarter of all customer conversations in its first year, and Klarna's assistant, which handles refunds, returns and disputes, took two thirds of its service chats in its first month (in 2025 Klarna began recruiting human agents again).",{"question":196,"answer":197},"Should the AI decide refunds?","No. Put eligibility and refund rules in deterministic tools with value limits, and let the agent explain the outcome. A Canadian tribunal held Air Canada responsible for what its website chatbot told a customer about bereavement fare refunds, so a refund answer must come from the policy itself.",{"question":199,"answer":200},"Does a better returns agent hurt sales?","No source on this page measures the sales effect of a returns agent specifically. What is documented: in the NRF and Happy Returns survey, 67% of consumers said a negative return experience would discourage them from shopping with a retailer again, a measure of stated intent rather than an AI agent's effect. Separately, Sun & Ski Sports extended its agent from returns and order status into product advice, and its vendor reports that shoppers who engage with it convert at three times the rate of those who do not, a self selected comparison, not a controlled measure of the returns agent's effect on sales.",[202,203,204,205,206],"parcel-tracking-and-delivery-exception-agent","conversational-shopping-assistant","chargeback-and-representment","complaints-handling-agent","first-line-contact-centre-agent","2026-09-27","2026-09-26",[210],{"date":207,"note":211},"First published","order-status-and-returns-agent",[214,250,294,320,339],{"title":215,"useCases":216,"organization":218,"vendors":223,"summary":230,"stage":231,"year":44,"channels":232,"languages":233,"metrics":235,"outcomeDisclosed":220,"sources":236,"verification":245,"grade":247,"id":248,"organizationSlug":249},"Best Buy: generative AI virtual assistant for support, deliveries and appointments",[217,212],"branch-and-appointment-booking-agent",{"name":219,"anonymized":220,"country":221,"region":222,"industry":19},"Best Buy",false,"US","north-america",[224,227],{"name":225,"role":226},"Google Cloud","platform",{"name":228,"role":229},"Accenture","integrator","In April 2024 Best Buy announced, with Google Cloud and Accenture, a generative AI virtual assistant for BestBuy.com, its app and its customer support line, expected to launch in late summer 2024, to help customers troubleshoot product issues, change order delivery and scheduling, and manage subscriptions and memberships. Google Cloud reported in its April 2026 list that Best Buy now guides shoppers through technical specifications, issue resolution and appointment scheduling autonomously, using Agent Assist powered by Gemini Enterprise for Customer Experience. It is a retail example of the same job a bank has when it books a branch or specialist appointment. No outcome figures for the assistant were published.","production",[30,31,33],[234],"en",[],[237,241],{"url":238,"title":239,"publisher":219,"date":240},"https://corporate.bestbuy.com/2024/generative-ai-customer-support/","How Best Buy is using generative AI to create better customer support experiences","2024-04-09",{"url":242,"title":243,"publisher":225,"date":244},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","1,302 real-world gen AI use cases from the world's leading organizations","2026-04-22",{"level":246,"checkedAt":208},"source-verified","B","best-buy-appointment-scheduling-agent",null,{"title":251,"useCases":252,"organization":254,"vendors":257,"summary":261,"stage":262,"year":44,"channels":263,"languages":264,"metrics":267,"outcomeDisclosed":283,"sources":284,"verification":292,"grade":247,"id":293,"organizationSlug":249},"Klarna: AI assistant as the first line of customer service",[206,253,212],"card-dispute-and-chargeback-intake",{"name":255,"anonymized":220,"country":256,"region":170,"industry":20},"Klarna","SE",[258],{"name":259,"role":260},"OpenAI","model-provider","Klarna announced in February 2024 that its AI assistant built on OpenAI models had been live globally for a month as the first line of its customer service, handling refunds, returns, payment issues, cancellations and disputes in more than 35 languages across 23 markets. In 2025 the company said it had gone too far in replacing people and began recruiting human agents again so that customers can always reach a person; the assistant still handles the majority of inquiries. The record is useful precisely because it shows both the gain and the correction.","scaled",[31],[234,265,266],"ar","fr",[268,276],{"kpi":56,"value":269,"unit":270,"qualifier":271,"period":272,"claimant":273,"quote":274,"sourceUrl":275},2300000,"count","exact","first month after launch","organization","The AI assistant has had 2.3 million conversations, two-thirds of Klarna’s customer service chats","https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/",{"kpi":57,"value":277,"unit":278,"qualifier":271,"period":279,"baseline":280,"claimant":273,"quote":281,"sourceUrl":282},82,"percent","since launch, as reported in 2025","before the AI assistant","Since launch, response times have improved by 82%, and Klarna has seen a 25% drop in repeat issues.","https://www.customerexperiencedive.com/news/klarna-reinvests-human-talent-customer-service-AI-chatbot/747586/",true,[285,288],{"url":275,"title":286,"publisher":255,"date":287},"Klarna AI assistant handles two-thirds of customer service chats in its first month","2024-02-27",{"url":282,"title":289,"publisher":290,"date":291},"Klarna changes its AI tune and again recruits humans for customer service","CX Dive","2025-05-09",{"level":246,"checkedAt":208},"klarna-ai-assistant-customer-service",{"title":295,"useCases":296,"organization":297,"vendors":299,"summary":302,"stage":231,"year":303,"channels":304,"languages":305,"metrics":306,"outcomeDisclosed":283,"sources":313,"verification":317,"grade":318,"id":319,"organizationSlug":249},"BARK: AI agent Scout for order tracking and shipping questions",[212],{"name":298,"anonymized":220,"country":221,"region":222,"industry":19},"BARK",[300],{"name":301,"role":226},"Sierra","BARK, the company behind BarkBox, ships tens of thousands of boxes a month and deployed an AI agent called Scout to take the high volume, repetitive conversations first: where is my order, what is my tracking number, when will it arrive, and basic account questions. Within the first year the agent handled roughly a quarter of all customer conversations and chat response times fell below two minutes, while emotional conversations, such as the loss of a pet, stay with the human team.",2026,[30],[234],[307],{"kpi":55,"value":308,"unit":278,"qualifier":271,"period":309,"claimant":310,"quote":311,"sourceUrl":312},98,"first year of the agent, customer service overall","vendor","BARK maintained a 98% customer satisfaction rate, holding the same high bar as the Happy Team.","https://sierra.ai/customers/bark",[314],{"url":312,"title":315,"publisher":301,"date":316},"How BARK delivers tail-wagging customer experiences with AI","2026-05-12",{"level":246,"checkedAt":208},"C","bark-scout-order-tracking-agent",{"title":321,"useCases":322,"organization":323,"vendors":326,"summary":328,"stage":231,"year":303,"channels":329,"languages":330,"metrics":331,"outcomeDisclosed":220,"sources":332,"verification":337,"grade":318,"id":338,"organizationSlug":249},"Next: AI agent for arranging returns across brands and markets",[212],{"name":324,"anonymized":220,"country":325,"region":170,"industry":19},"Next","GB",[327],{"name":301,"role":226},"Next, the British fashion retailer that operates across 83 countries and also serves customers of brands such as Gap, Victoria's Secret and Fat Face, launched an AI agent in six weeks across two use cases. The agent handles arrange return queries, matches the customer to the order without asking for an order number and manages identity verification. It adapts to regional preferences, so Next can add languages and processes as it grows, and it meets customers across chat, voice and WhatsApp. No outcome figures were published.",[30,33,32],[234],[],[333],{"url":334,"title":335,"publisher":301,"date":336},"https://sierra.ai/customers/next","How Next transforms global customer service with AI","2026-02-25",{"level":246,"checkedAt":208},"next-returns-ai-agent",{"title":340,"useCases":341,"organization":342,"vendors":344,"summary":346,"stage":231,"year":347,"channels":348,"languages":349,"metrics":350,"outcomeDisclosed":283,"sources":367,"verification":371,"grade":318,"id":372,"organizationSlug":249},"Sun & Ski Sports: AI agent Sunny for order status, returns and product advice",[212,203],{"name":343,"anonymized":220,"country":221,"region":222,"industry":19},"Sun & Ski Sports",[345],{"name":301,"role":226},"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.",2025,[30],[234],[351,357,362],{"kpi":55,"value":352,"unit":278,"qualifier":271,"period":353,"baseline":354,"claimant":310,"quote":355,"sourceUrl":356},90,"conversations handled by the agent, as reported in October 2025","68% for conversations transferred to human agents","Sunny achieves 90% customer satisfaction compared to 68% for conversations transferred to human agents.","https://sierra.ai/customers/sun-and-ski-sports",{"kpi":358,"value":359,"unit":278,"qualifier":271,"period":360,"claimant":310,"quote":361,"sourceUrl":356},"customer-satisfaction-uplift",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":363,"value":87,"unit":364,"qualifier":271,"baseline":365,"claimant":310,"quote":366,"sourceUrl":356},"conversion-rate-uplift","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":356,"title":369,"publisher":301,"date":370},"How Sun & Ski's AI agent \"Sunny\" turns approachability into sales","2025-10-22",{"level":246,"checkedAt":208},"sun-and-ski-sports-sunny-ai-agent",0,[375,383,389],{"kpi":55,"label":376,"unit":278,"aggregate":283,"higherIsBetter":283,"n":377,"nUpTo":373,"median":378,"min":352,"max":308,"byClaimant":379,"vendorOnly":283,"points":380},"Customer satisfaction",2,94,{"organization":373,"vendor":377,"regulator":373,"independent":373},[381,382],{"evidenceId":319,"organization":298,"value":308,"qualifier":271,"claimant":310,"grade":318,"pooled":283},{"evidenceId":372,"organization":343,"value":352,"qualifier":271,"claimant":310,"grade":318,"pooled":283},{"kpi":56,"label":384,"unit":270,"aggregate":220,"higherIsBetter":283,"n":385,"nUpTo":373,"median":269,"min":269,"max":269,"byClaimant":386,"vendorOnly":220,"points":387},"Interactions handled",1,{"organization":385,"vendor":373,"regulator":373,"independent":373},[388],{"evidenceId":293,"organization":255,"value":269,"qualifier":271,"claimant":273,"grade":247,"pooled":283},{"kpi":57,"label":390,"unit":278,"aggregate":283,"higherIsBetter":283,"n":385,"nUpTo":373,"median":277,"min":277,"max":277,"byClaimant":391,"vendorOnly":220,"points":392},"Response time reduction",{"organization":385,"vendor":373,"regulator":373,"independent":373},[393],{"evidenceId":293,"organization":255,"value":277,"qualifier":271,"claimant":273,"grade":247,"pooled":283},{"low":395,"high":396},450000,3000000,[398,419,438,453,476],{"slug":202,"title":399,"shortTitle":400,"definition":401,"status":9,"industries":402,"functions":404,"patterns":405,"audience":35,"autonomy":36,"adoptionStage":407,"evidenceCount":88,"publicEvidenceCount":408,"organizations":409,"bestGrade":247,"headline":415,"lastVerified":207,"indexable":283},"AI agent for parcel tracking and delivery exceptions","Parcel tracking and delivery exceptions","An AI agent that answers \"where is my parcel\" and resolves delivery exceptions for parcel carriers and postal operators, such as missed deliveries, redelivery or a change of address or pickup point, delays, customs holds and lost or damaged parcel claims, on chat, messaging and phone, and hands disputes and claims above set limits to a human with the tracking history attached.",[403],"logistics-and-transportation",[22,23],[25,27,26,406],"classification-and-routing","early-adopters",5,[410,411,412,413,414],"Chronopost","DPD Deutschland","DPD UK","Evri","PostNL",{"kpi":416,"label":417,"unit":278,"n":385,"nUpTo":373,"kind":418,"value":359,"qualifier":271,"claimant":273,"organization":413,"vendorReported":220},"contact-deflection","Contact deflection","reported",{"slug":203,"title":420,"shortTitle":421,"definition":422,"status":9,"industries":423,"functions":424,"patterns":427,"audience":35,"autonomy":429,"adoptionStage":407,"evidenceCount":430,"publicEvidenceCount":408,"organizations":431,"bestGrade":247,"headline":436,"lastVerified":207,"indexable":283},"AI shopping assistant for product discovery and recommendations","Conversational shopping assistant","A conversational assistant on a retailer's site or app that answers product questions, compares items and recommends products from the retailer's own catalog for a need, project or occasion described in the shopper's own words, grounded in product data, reviews and stock, and hands the shopper to a basket, a store or a human expert.",[18,19],[425,426,22],"sales","marketing",[25,428,28,26],"recommendation-and-personalization","autonomous",10,[432,433,343,434,435],"Amazon","Lowe's","Walmart","Zalando",{"kpi":363,"label":437,"unit":364,"n":385,"nUpTo":373,"kind":418,"value":87,"qualifier":271,"claimant":310,"organization":343,"vendorReported":283},"Conversion uplift",{"slug":204,"title":439,"shortTitle":440,"definition":441,"status":9,"industries":442,"functions":444,"patterns":446,"audience":449,"autonomy":36,"adoptionStage":407,"segment":449,"evidenceCount":87,"publicEvidenceCount":377,"organizations":450,"bestGrade":247,"headline":249,"lastVerified":207,"indexable":283},"AI for chargeback and representment operations","Chargeback and representment","AI that runs the dispute engine room for issuers, acquirers and merchants: it maps each dispute to the network reason code, gathers the matching evidence, assembles a network compliant chargeback or representment package, drafts the rebuttal, tracks every deadline and processes pre dispute alerts so a refund can be issued before a chargeback lands.",[20,443,19],"banking",[23,445,22],"fraud-prevention",[26,447,448,406],"document-processing","content-generation","back-office",[451,452],"GitHub","Visa",{"slug":205,"title":454,"shortTitle":455,"definition":456,"status":9,"industries":457,"functions":460,"patterns":463,"audience":465,"autonomy":466,"adoptionStage":407,"segment":467,"evidenceCount":377,"publicEvidenceCount":377,"organizations":468,"bestGrade":247,"headline":471,"lastVerified":207,"indexable":283},"AI agent for complaints recognition, investigation and response","Complaints handling","An AI agent that recognizes when a customer interaction is a complaint, logs it against the regulatory definition, classifies its root cause and severity, gathers the evidence, drafts the acknowledgement and the response for a human handler to approve, and tracks every statutory deadline until the case is closed.",[18,443,20,458,459],"insurance","telecommunications",[461,22,462],"case-management","regulatory-compliance",[406,464,448,26,28],"summarization","employee-facing","copilot","middle-office",[469,470],"Lloyds Banking Group","NatWest Group",{"kpi":472,"label":473,"unit":474,"n":385,"nUpTo":373,"kind":418,"value":408,"qualifier":475,"claimant":273,"organization":469,"vendorReported":220},"time-saved-per-task","Time saved per task","minutes","approximately",{"slug":206,"title":477,"shortTitle":478,"definition":479,"status":9,"industries":480,"functions":483,"patterns":484,"audience":35,"autonomy":36,"adoptionStage":37,"segment":485,"evidenceCount":486,"publicEvidenceCount":487,"organizations":488,"bestGrade":247,"headline":504,"lastVerified":207,"indexable":283},"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.",[18,443,20,459,481,19,482],"travel-and-hospitality","wealth-and-asset-management",[22],[25,27,28,406],"front-office",25,18,[489,490,491,492,493,494,495,496,255,497,498,470,499,500,501,502,503],"Air India","Airbnb","Bank of America","Bank of the Philippine Islands","BT Group","Commonwealth Bank of Australia","Ingka Group","JetBlue","Lufthansa Group","Mobily","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":54,"label":505,"unit":278,"n":506,"nUpTo":373,"kind":507,"value":508,"qualifier":271,"claimant":249,"organization":249,"vendorReported":220},"Containment rate",7,"median",47,{"indexable":283,"reasons":510},[],[512,517,522,530,537,543,550,556,564,570,576,582,589,596,602,607,614,619,625,631,637,643,648,653,658,665,672,677,682,689,696,702,708,713],{"id":162,"label":513,"issuer":169,"region":170,"url":514,"description":515,"useCases":516,"indexable":283},"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":163,"label":518,"issuer":169,"region":170,"url":519,"description":520,"useCases":521,"indexable":283},"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":523,"label":524,"issuer":525,"region":526,"url":527,"description":528,"useCases":529,"indexable":283},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":531,"label":532,"issuer":533,"region":222,"url":534,"description":535,"useCases":536,"indexable":283},"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":538,"label":539,"issuer":169,"region":170,"url":540,"description":541,"useCases":542,"indexable":283},"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":544,"label":545,"issuer":546,"region":170,"url":547,"description":548,"useCases":549,"indexable":283},"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":551,"label":552,"issuer":553,"region":170,"url":554,"description":555,"useCases":508,"indexable":283},"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":557,"label":558,"issuer":559,"region":560,"url":561,"description":562,"useCases":563,"indexable":283},"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":565,"label":566,"issuer":567,"region":560,"url":568,"description":569,"useCases":486,"indexable":283},"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":164,"label":571,"issuer":572,"region":526,"url":573,"description":574,"useCases":575,"indexable":283},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":577,"label":578,"issuer":579,"region":222,"url":580,"description":581,"useCases":575,"indexable":283},"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":583,"label":584,"issuer":585,"region":170,"url":586,"description":587,"useCases":588,"indexable":283},"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":590,"label":591,"issuer":592,"region":526,"url":593,"description":594,"useCases":595,"indexable":283},"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":597,"label":598,"issuer":169,"region":170,"url":599,"description":600,"useCases":601,"indexable":283},"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":603,"label":604,"issuer":169,"region":170,"url":605,"description":606,"useCases":601,"indexable":283},"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":608,"label":609,"issuer":610,"region":222,"url":611,"description":612,"useCases":613,"indexable":283},"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":165,"label":615,"issuer":169,"region":170,"url":616,"description":617,"useCases":618,"indexable":283},"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":620,"label":621,"issuer":622,"region":222,"url":623,"description":624,"useCases":618,"indexable":283},"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":626,"label":627,"issuer":628,"region":526,"url":629,"description":630,"useCases":618,"indexable":283},"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":632,"label":633,"issuer":169,"region":170,"url":634,"description":635,"useCases":636,"indexable":283},"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":638,"label":639,"issuer":640,"region":222,"url":641,"description":642,"useCases":636,"indexable":283},"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":644,"label":645,"issuer":559,"region":560,"url":646,"description":647,"useCases":430,"indexable":283},"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":649,"label":650,"issuer":169,"region":170,"url":651,"description":652,"useCases":430,"indexable":283},"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":654,"label":655,"issuer":169,"region":170,"url":656,"description":657,"useCases":430,"indexable":283},"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":659,"label":660,"issuer":661,"region":170,"url":662,"description":663,"useCases":664,"indexable":283},"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":666,"label":667,"issuer":668,"region":222,"url":669,"description":670,"useCases":671,"indexable":283},"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.",8,{"id":673,"label":674,"issuer":169,"region":170,"url":675,"description":676,"useCases":671,"indexable":283},"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":678,"label":679,"issuer":169,"region":170,"url":680,"description":681,"useCases":88,"indexable":283},"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":683,"label":684,"issuer":685,"region":686,"url":687,"description":688,"useCases":408,"indexable":283},"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":690,"label":691,"issuer":692,"region":170,"url":693,"description":694,"useCases":695,"indexable":283},"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":697,"label":698,"issuer":699,"region":170,"url":700,"description":701,"useCases":695,"indexable":283},"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":703,"label":704,"issuer":705,"region":560,"url":706,"description":707,"useCases":87,"indexable":283},"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":709,"label":710,"issuer":169,"region":170,"url":711,"description":712,"useCases":87,"indexable":283},"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":714,"label":715,"issuer":716,"region":222,"url":717,"description":718,"useCases":87,"indexable":283},"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.",1790598295312]