[{"data":1,"prerenderedAt":659},["ShallowReactive",2],{"uc-inbound-lead-qualification-agent":3,"uc-regulations":452},{"useCase":4,"evidence":210,"blitsAiDeployments":316,"benchmarks":317,"indicative":329,"related":332,"indexability":450,"includeUnpublished":216},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":22,"patterns":25,"channels":30,"audience":35,"autonomy":36,"adoptionStage":37,"problem":38,"problemStats":39,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":52,"macroEstimates":87,"feasibility":88,"implementation":102,"risk":148,"blitsAi":186,"faq":188,"related":198,"datePublished":205,"dateModified":205,"lastVerified":205,"changelog":206,"slug":209},"AI agent for inbound lead qualification and meeting booking","Inbound lead qualification","AI SDR agents for inbound lead qualification","AI SDR agents answer inbound buyers around the clock, qualify them and book meetings. See deployments at 8x8, SUSE, CarMax and Rocket Mortgage.","published","An AI agent that engages inbound prospects the moment they arrive on the website, chat, messaging or the sales phone line, answers their first questions, qualifies them against the organization's criteria, and books a meeting or hands a ready conversation to the right salesperson, with the context written into the CRM.",[12,13,14,15,16],"AI SDR","AI sales development representative","lead qualification chatbot","inbound sales agent","conversational lead capture",[18,19,20,21],"cross-industry","technology","automotive","banking",[23,24],"sales","marketing",[26,27,28,29],"conversational-agent","voice-agent","classification-and-routing","agentic-workflow",[31,32,33,34],"web-chat","voice","email","whatsapp","customer-facing","supervised-agent","early-adopters","Inbound interest comes from people who chose to reach out, and much of it is wasted. Web forms ask\nfor a lot and answer nothing; chat requests go unanswered outside office hours; sales development\nreps spend their day separating buyers from job seekers and support requests, while the real buyer\nwaits for a reply and may book with a competitor. On the phone, callers navigate menus to reach a\nsalesperson who first has to ask the same questions again.\n\nThe deployments on this page show the same pattern in software, car retail and mortgages. At 8x8,\nmore than half of website sessions happened outside business hours, and a meaningful share of chat\nrequests went unanswered, according to its vendor Qualified. A sales team that works office hours\nin one language leaves the rest of the day and the rest of the market to a form. A form or a\nscripted chatbot can capture an email address but cannot hold a discovery conversation. Agents\nthat understand the product, ask the next useful question and act in the CRM and calendar are\nmeant to close that gap.",[],"1. **Engage at the moment of intent.** The agent greets the visitor or caller, in their language,\n   with context from the page they are on or the campaign they came from, and says it is an AI.\n2. **Answer first questions from approved content.** Product, pricing principles, availability and\n   next steps come from the organization's approved knowledge, with a refusal when it does not know.\n3. **Qualify with discovery questions.** It asks what a good salesperson would (need, size,\n   timing, budget, location) and recognises known accounts from the CRM and intent data, instead of\n   presenting a long form.\n4. **Route by rules.** Qualification and routing rules decide the next step: book a meeting with\n   the right rep or specialist, transfer a live call, offer self service, or send a support request\n   or job seeker to the right place.\n5. **Write it down.** The conversation summary, answers and score go into the CRM so the rep starts\n   where the agent stopped.\n6. **Follow up with consent.** Visitors who leave without booking get a follow up by email or\n   messaging only where consent and contact rules allow.",[42,43,44,45],"revenue-growth","speed","cost-to-serve","customer-experience",[47,48,49,50,51],"conversion-rate-uplift","revenue-uplift","interactions-handled","response-time-reduction","productivity-gain",{"referenceOrg":53,"inputs":54,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"A B2B software company with 20,000 inbound leads a year",[55,61,68,75],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"leads","Inbound leads per year",20000,"leads per year","The reference company.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"baseConversion","Lead to closed deal conversion today",0.01,0.02,"fraction of leads","Editorial assumption for B2B inbound. Replace with your own funnel data.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"uplift","Relative uplift in lead to deal conversion",0.05,0.15,"fraction","Conservative against the evidence on this page (Qualified reports that 8x8 saw 19% better MQL to SQL conversion and 24% more MQL to closed won deals in its first nine months), because vendor case studies select their best results.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"dealValue","Average first year deal value",15000,30000,"USD per deal","Editorial assumption. Replace with your own average contract value.","leads * baseConversion * uplift * dealValue","USD","per year","Additional first year revenue from better inbound conversion","Revenue, not margin, and only the conversion effect. It leaves out the cost of the agent, time saved by sales development reps, after hours coverage beyond the leads counted, and the risk that faster qualification also brings forward deals that would have closed anyway.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":96},"medium","A chat that asks questions is simple. The work is in written qualification rules sales agrees with, CRM and calendar integration with the right routing, product answers that stay within approved claims, and consent handling for follow up.",[92,93,94,95],"Written qualification criteria and routing rules agreed between marketing and sales","Approved product, pricing principle and competitive content","CRM account and contact data, and intent data where available","Consent and contact preference records for follow up",[97,98,99,100,101],"CRM (leads, contacts, accounts, activities)","Calendar and meeting booking for reps and specialists","Website, chat and telephony for live transfer","Marketing automation for consented follow up","Intent or enrichment data providers",{"steps":103,"guardrails":122,"humanInTheLoop":128,"kpisToInstrument":129,"failureModes":135},[104,107,110,113,116,119],{"title":105,"detail":106},"Agree the definition of a qualified lead","Write down with sales what makes a lead worth a meeting, who gets which lead, and what happens to the rest. The agent can only be as consistent as the rules.",{"title":108,"detail":109},"Start with after hours and overflow","Put the agent where nobody answers today (nights, weekends, peak campaigns) so the uplift is easy to see and nobody's pipeline is taken away on day one.",{"title":111,"detail":112},"Ground every product answer","Load approved product and pricing content with owners, and make the agent hand over rather than improvise on pricing, discounts, legal terms or roadmap.",{"title":114,"detail":115},"Integrate booking and CRM before launch","A qualified conversation that does not land in the CRM or a rep's calendar is a lost lead. Test routing for every territory and segment.",{"title":117,"detail":118},"Review conversations with sales every week","Sales and marketing read a sample of qualified and disqualified conversations together and adjust questions and rules.",{"title":120,"detail":121},"Measure against a baseline","Compare lead to meeting, meeting to opportunity and closed won rates with the period before, or with a control group, not only the number of conversations.",[123,124,125,126,127],"No price, discount or contractual commitment unless it comes from a system of record","Qualification and routing by written rules, with the reason stored in the CRM","AI disclosure at the start of the conversation and on voice calls","Follow up only with valid consent and within contact rules and quiet hours","Protection against prompt injection and attempts to extract confidential information","Sales owns the qualification rules and every commercial commitment. Reps take over qualified conversations, and a sales and marketing pair reviews a weekly sample of agent conversations, including disqualified ones, to catch good buyers the rules turned away.",[130,131,132,133,134],"Lead to meeting and meeting to opportunity conversion versus baseline","Speed to first response and share of inbound answered outside business hours","Share of conversations disqualified and the reasons, with a sample checked by sales","Closed won revenue from agent qualified leads","Opt outs and complaints from follow up",[136,139,142,145],{"title":137,"detail":138},"The agent makes promises","Prospects push for prices, discounts or commitments and a fluent agent agrees. Keep commercial terms out of the model and test for manipulation.",{"title":140,"detail":141},"Qualifying out good buyers","Rigid rules turn away real buyers who answer one question the wrong way. Review disqualified conversations, not only qualified ones.",{"title":143,"detail":144},"A pipeline that sales does not trust","If rep and agent disagree on what qualified means, reps ignore the leads. Agree the rules first and show the reasoning in the CRM.",{"title":146,"detail":147},"Follow up that breaks consent rules","Automated email and calls to people who did not consent create regulatory and brand risk. Check consent before every follow up.",{"euAiAct":149,"regulations":152,"guidance":157,"controls":175,"incidents":181},{"tier":150,"basis":151},"limited","A customer facing sales agent must make clear that people are talking to an AI system, unless that is obvious (Article 50(1)). Qualifying and routing prospects is not an Annex III use. It becomes high risk where the same system takes on an Annex III task, for example evaluating the creditworthiness of natural persons (Annex III point 5(b)) or assessing risk and pricing for life or health insurance (point 5(c)); those decisions then need the high risk controls.",[153,154,155,156],"eu-ai-act","gdpr","uk-gdpr","us-tcpa",[158,164,169],{"title":159,"issuer":160,"region":161,"url":162,"note":163},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","Providers must design systems that interact directly with people so that they are informed they are dealing with an AI system, unless this is obvious from the context.",{"title":165,"issuer":166,"region":161,"url":167,"note":168},"Direct marketing and privacy and electronic communications","Information Commissioner's Office","https://ico.org.uk/for-organisations/direct-marketing-and-privacy-and-electronic-communications/","UK rules on consent for marketing emails, texts and calls, which govern automated follow up of leads.",{"title":170,"issuer":171,"region":172,"url":173,"note":174},"Declaratory ruling on AI generated voices under the TCPA (FCC 24-17)","Federal Communications Commission","north-america","https://docs.fcc.gov/public/attachments/FCC-24-17A1.pdf","Calls using AI generated voices count as artificial or prerecorded voice calls under the TCPA, so outbound follow up calls need prior consent in the United States.",[176,177,178,179,180],"AI disclosure in chat and at the start of calls","Consent check before every follow up by email, messaging or phone","Qualification rules versioned, with the reason for each decision stored","Audit log of meetings booked and leads routed by the agent","Regular review of disqualified conversations for bias against segments or regions",[182],{"title":183,"url":184,"note":185},"Incident 622: Chevrolet dealer chatbot agrees to sell Tahoe for $1","https://incidentdatabase.ai/cite/622/","A car dealer's sales chatbot was manipulated into agreeing to sell a vehicle for one dollar and recommending a competitor, the typical failure of a sales agent without commercial guardrails.",{"howToBuild":187},"On Blits.ai this is an **AI agent** with a **knowledge base** of approved product and pricing\ncontent, retrieved with hybrid search, and the ready made **HubSpot, Microsoft Dynamics 365 or Zoho** tools\n(or **custom functions** for another CRM such as Salesforce) to look up accounts, create leads and\nlog the conversation. Qualification questions and routing rules can run in a **flow** with deterministic\nconditions, while the agent handles the open conversation; booking a meeting is a function call to\nthe calendar.\n\nThe agent runs on **web chat** (with proactive popup messages), **WhatsApp, email and voice**,\nwhere it can transfer a live call to a rep. **Guardrails** block prompt injection by default, and\nan input and output policy you write can stop commitments on price; **PII masking** protects\ncontact data, and the GDPR toolkit handles consent messages and data removal. **Human handover**\nbrings a rep into the conversation, and a flow can send an AI summarized conversation history to\nthe sales team. **Test suites** replay qualification scenarios on every change, and **analytics**\nshow interactions, satisfaction and sentiment. The platform is model agnostic.",[189,192,195],{"question":190,"answer":191},"How much does an AI SDR improve inbound conversion?","Vendor case studies report gains. Qualified reports that 8x8 saw 19% better MQL to SQL conversion and 24% more MQL to closed won deals in its first nine months with an AI SDR agent. These are vendor selected results with self selection in them; measure your own against a baseline or a control group.",{"question":193,"answer":194},"Should the agent quote prices?","Only list prices and pricing principles from approved content or a system of record. Discounts and commitments belong to people; the Chevrolet dealer chatbot that \"agreed\" to a one dollar car shows what happens otherwise.",{"question":196,"answer":197},"Does it work on the phone as well as in chat?","Yes. CarMax uses AI voice agents on its inbound sales calls to understand what the caller needs, answer common questions such as vehicle availability and pass the caller to the right associate faster, and Rocket Mortgage runs its digital assistant across chat and voice.",[199,200,201,202,203,204],"conversational-shopping-assistant","sales-call-coaching-and-crm-update","proactive-outbound-engagement-agent","home-loan-assistant-and-prequalification","business-connectivity-quoting-and-service-assistant","personalized-marketing-at-scale","2026-09-27",[207],{"date":205,"note":208},"First published","inbound-lead-qualification-agent",[211,240,272,298],{"title":212,"useCases":213,"organization":214,"vendors":218,"summary":222,"stage":223,"year":224,"channels":225,"languages":226,"metrics":228,"outcomeDisclosed":216,"sources":229,"verification":234,"grade":237,"id":238,"organizationSlug":239},"CarMax: AI voice agent on inbound sales calls",[209],{"name":215,"anonymized":216,"country":217,"region":172,"industry":20},"CarMax",false,"US",[219],{"name":220,"role":221},"Sierra","platform","CarMax, the largest used car retailer in the United States, deployed AI voice agents on its inbound sales calls in 2026. The agent asks clarifying questions to understand what the caller needs, answers common questions such as store hours and vehicle availability, and hands the caller to the right associate faster, whatever the call volume or time zone. CarMax reports more calls resolved and fewer unresolved calls without giving figures, and plans appointment management for appraisals, browsing and test drives. It already runs a web virtual assistant, Skye, on CarMax.com.","production",2026,[32],[227],"en",[],[230],{"url":231,"title":232,"publisher":220,"date":233},"https://sierra.ai/customers/carmax","CarMax teams with Sierra to enhance inbound sales call experience","2026-08-06",{"level":235,"checkedAt":236},"source-verified","2026-09-26","C","carmax-inbound-sales-voice-agent",null,{"title":241,"useCases":242,"organization":243,"vendors":245,"summary":248,"stage":223,"year":249,"channels":250,"languages":251,"metrics":252,"outcomeDisclosed":266,"sources":267,"verification":270,"grade":237,"id":271,"organizationSlug":239},"8x8: AI SDR agent that engages and qualifies inbound website buyers",[209],{"name":244,"anonymized":216,"country":217,"region":172,"industry":19},"8x8",[246],{"name":247,"role":221},"Qualified","8x8 had rising inbound traffic but falling meeting volume: chats went unanswered, more than half of website sessions fell outside business hours and sales development reps spent time sorting job seekers and support requests from buyers. It deployed an AI SDR agent that engages visitors around the clock, qualifies them by company size, country and intent, routes meeting and pricing requests and hands ready conversations to sales, with automated email follow up for buyers who did not book. The vendor reports gains across the funnel in the first nine months.",2025,[31,33],[227],[253,262],{"kpi":47,"value":254,"unit":255,"qualifier":256,"period":257,"baseline":258,"claimant":259,"quote":260,"sourceUrl":261},19,"percent","exact","first nine months live","MQL to SQL conversion before the agent","vendor","+19% MQL → SQL conversion","https://www.qualified.com/customers/8x8",{"kpi":47,"value":263,"unit":255,"qualifier":256,"period":257,"baseline":264,"claimant":259,"quote":265,"sourceUrl":261},24,"MQL to closed won deals before the agent","+24% MQL → closed-won deals",true,[268],{"url":261,"title":269,"publisher":247},"8x8 Drives 24% More Closed-Won Inbound Revenue with Piper the AI SDR Agent",{"level":235,"checkedAt":205},"8x8-ai-sdr-inbound-qualification",{"title":273,"useCases":274,"organization":276,"vendors":278,"summary":280,"stage":281,"year":249,"channels":282,"languages":283,"metrics":284,"outcomeDisclosed":266,"sources":292,"verification":296,"grade":237,"id":297,"organizationSlug":239},"Rocket Mortgage: AI Digital Assistant from first question to preapproval",[209,275],"conversational-loan-application-intake",{"name":277,"anonymized":216,"country":217,"region":172,"industry":21},"Rocket Mortgage",[279],{"name":220,"role":221},"Rocket Mortgage runs an AI Digital Assistant across chat and voice that takes prospective borrowers from first questions to preapproval: it answers questions, collects information, pulls credit, presents personalised rates and loan options and hands the client to a human banker. According to the vendor, the programme started as a proof of concept and has grown to more than 400,000 successful chat conversations and over one million outbound dials a month. Clients who start with the assistant close at three times the rate of those who do not. Sierra also reports that clients who use both the AI chat and a banker convert four times better, without stating the comparison group.","scaled",[31,32],[227],[285],{"kpi":49,"value":286,"unit":287,"qualifier":288,"period":289,"claimant":259,"quote":290,"sourceUrl":291},400000,"count","at-least","successful chat conversations per month","What started as a proof of concept in May has grown to more than 400,000 successful chat conversations and over one million outbound dials each month, and both are rising fast.","https://sierra.ai/customers/rocket-mortgage",[293],{"url":291,"title":294,"publisher":220,"date":295},"How Rocket Mortgage is reimagining the journey home with AI","2025-10-27",{"level":235,"checkedAt":205},"rocket-mortgage-digital-assistant",{"title":299,"useCases":300,"organization":301,"vendors":303,"summary":305,"stage":223,"year":306,"channels":307,"languages":308,"metrics":309,"outcomeDisclosed":266,"sources":310,"verification":314,"grade":237,"id":315,"organizationSlug":239},"SUSE: multilingual AI SDR agent for inbound discovery and meeting booking",[209],{"name":302,"anonymized":216,"region":161,"industry":19},"SUSE",[304],{"name":247,"role":221},"SUSE, the open source software company, moved to ungated content and lost the form data that told it who was on its website. It deployed an AI SDR agent that engages every visitor, asks discovery questions adapted to developers, architects or CIOs, recognises returning accounts through Salesforce and intent data, answers in the visitor's language and follows up by email. The vendor reports that the agent turns 70% of qualified conversations into booked meetings and that its email follow up influenced more than USD 10 million in pipeline.",2024,[31,33],[],[],[311],{"url":312,"title":313,"publisher":247},"https://www.qualified.com/customers/suse","SUSE turns 70% of qualified conversations into meetings with Piper the AI SDR Agent",{"level":235,"checkedAt":236},"suse-ai-sdr-inbound-qualification",0,[318,324],{"kpi":47,"label":319,"unit":255,"aggregate":266,"higherIsBetter":266,"n":320,"nUpTo":316,"median":254,"min":254,"max":254,"byClaimant":321,"vendorOnly":266,"points":322},"Conversion uplift",1,{"organization":316,"vendor":320,"regulator":316,"independent":316},[323],{"evidenceId":271,"organization":244,"value":254,"qualifier":256,"claimant":259,"grade":237,"pooled":266},{"kpi":49,"label":325,"unit":287,"aggregate":216,"higherIsBetter":266,"n":320,"nUpTo":316,"median":286,"min":286,"max":286,"byClaimant":326,"vendorOnly":266,"points":327},"Interactions handled",{"organization":316,"vendor":320,"regulator":316,"independent":316},[328],{"evidenceId":297,"organization":277,"value":286,"qualifier":288,"claimant":259,"grade":237,"pooled":266},{"low":330,"high":331},150000,1800000,[333,358,384,398,411,428],{"slug":199,"title":334,"shortTitle":335,"definition":336,"status":9,"industries":337,"functions":339,"patterns":341,"audience":35,"autonomy":344,"adoptionStage":37,"evidenceCount":345,"publicEvidenceCount":346,"organizations":347,"bestGrade":353,"headline":354,"lastVerified":205,"indexable":266},"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.",[18,338],"retail-and-ecommerce",[23,24,340],"customer-service",[26,342,343,29],"recommendation-and-personalization","rag-knowledge-assistant","autonomous",10,5,[348,349,350,351,352],"Amazon","Lowe's","Sun & Ski Sports","Walmart","Zalando","B",{"kpi":47,"label":319,"unit":355,"n":320,"nUpTo":316,"kind":356,"value":357,"qualifier":256,"claimant":259,"organization":350,"vendorReported":266},"multiplier","reported",3,{"slug":200,"title":359,"shortTitle":360,"definition":361,"status":9,"industries":362,"functions":366,"patterns":367,"audience":371,"autonomy":372,"adoptionStage":37,"evidenceCount":373,"publicEvidenceCount":373,"organizations":374,"bestGrade":237,"headline":379,"lastVerified":205,"indexable":266},"AI sales call coaching and CRM update","Sales call coaching and CRM update","AI for sales teams that analyses sales calls and meetings against the team's own sales method to coach sellers and their managers, and writes the call summary, next steps and opportunity updates into the CRM for the seller to confirm. Its purpose is winning deals and building selling skill, not the regulated advice record or general meeting notes.",[18,363,364,365],"telecommunications","manufacturing","insurance",[23],[368,369,370],"speech-analytics","summarization","content-generation","employee-facing","copilot",4,[375,376,377,378],"Hughes Network Systems","Lumen Technologies","Sandvik Coromant","Zurich Insurance Group",{"kpi":380,"label":381,"unit":382,"n":320,"nUpTo":320,"kind":356,"value":357,"qualifier":256,"claimant":383,"organization":377,"vendorReported":216},"time-saved-per-task","Time saved per task","minutes","organization",{"slug":201,"title":385,"shortTitle":386,"definition":387,"status":9,"industries":388,"functions":390,"patterns":391,"audience":35,"autonomy":36,"adoptionStage":392,"segment":393,"evidenceCount":357,"publicEvidenceCount":357,"organizations":394,"bestGrade":353,"headline":239,"lastVerified":205,"indexable":266},"AI agent for proactive customer outreach, activation and retention","Proactive outreach and activation","An AI agent that holds the conversation when a bank reaches out first to change something about the customer's account or products, triggered by an event or a campaign: low balance and fee avoidance alerts, payment and renewal reminders, card activation, dormant account reactivation and offers the customer already qualifies for, over messaging or voice, while the bank's own systems decide who is contacted and why. Reminders about appointments and deliveries the customer booked, and the in app coach the customer opens, are separate use cases.",[21,389],"payments",[24,23,340],[26,27,29,342],"emerging","front-office",[395,396,397],"Bank of America","Capital One","Commonwealth Bank of Australia",{"slug":202,"title":399,"shortTitle":400,"definition":401,"status":9,"industries":402,"functions":404,"patterns":406,"audience":35,"autonomy":36,"adoptionStage":37,"segment":393,"evidenceCount":357,"publicEvidenceCount":357,"organizations":407,"bestGrade":237,"headline":239,"lastVerified":205,"indexable":266},"AI home loan assistant with pre qualification","Home loan assistant","A customer facing assistant that answers home loan questions (rates, loan to value, fees, the documents needed), runs indicative affordability and borrowing estimates from the bank's published rules, and books the customer with a mortgage specialist, grounded in the bank's current, versioned product and policy documents.",[21,403],"real-estate",[405,23,340],"lending-and-credit",[343,26,27],[408,409,410],"Figure","Loft","Safe Rate",{"slug":203,"title":412,"shortTitle":413,"definition":414,"status":9,"industries":415,"functions":416,"patterns":418,"audience":35,"autonomy":36,"adoptionStage":37,"segment":393,"evidenceCount":346,"publicEvidenceCount":346,"organizations":419,"bestGrade":353,"headline":424,"lastVerified":205,"indexable":266},"AI assistant for B2B telecom quoting, sales and service","B2B quoting and service","An AI assistant that serves business customers of a telecom operator and the sellers who look after them: it answers product, pricing and contract questions, prepares configurations and quotes for connectivity, mobile fleets and devices, drafts responses to tenders, and handles routine service requests and fault tickets, with a sales or service specialist approving anything binding.",[363],[23,340,417],"product-and-pricing",[26,343,29,342,370],[376,420,421,422,423],"SoftBank Corp.","Telefónica España","Verizon","Vodafone Business",{"kpi":425,"label":426,"unit":255,"n":320,"nUpTo":316,"kind":356,"value":427,"qualifier":256,"claimant":383,"organization":420,"vendorReported":216},"containment-rate","Containment rate",70,{"slug":204,"title":429,"shortTitle":430,"definition":431,"status":9,"industries":432,"functions":435,"patterns":436,"audience":438,"autonomy":36,"adoptionStage":439,"evidenceCount":440,"publicEvidenceCount":441,"organizations":442,"bestGrade":353,"headline":448,"lastVerified":205,"indexable":266},"AI marketing personalization at scale","Marketing personalization at scale","AI that runs marketing campaigns at the level of the individual: it decides for each customer which product, offer, message or content to show next across email, app, web and paid media, and generates the matching copy and creative variants within brand and compliance rules. It is the marketing team's engine across many campaigns and channels, not an agent that converses with the customer.",[18,433,434,338,21],"travel-and-hospitality","media-and-entertainment",[24,23],[342,437,370],"prediction-and-scoring","back-office","mainstream",8,7,[348,443,397,444,445,446,447],"Catchtable","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":47,"label":319,"unit":255,"n":320,"nUpTo":316,"kind":356,"value":449,"qualifier":256,"claimant":259,"organization":443,"vendorReported":266},30,{"indexable":266,"reasons":451},[],[453,458,463,471,478,484,489,496,504,511,518,524,531,538,544,549,556,562,568,574,580,584,589,594,599,606,612,617,623,630,636,642,648,653],{"id":153,"label":454,"issuer":160,"region":161,"url":455,"description":456,"useCases":457,"indexable":266},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":154,"label":459,"issuer":160,"region":161,"url":460,"description":461,"useCases":462,"indexable":266},"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":464,"label":465,"issuer":466,"region":467,"url":468,"description":469,"useCases":470,"indexable":266},"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":472,"label":473,"issuer":474,"region":172,"url":475,"description":476,"useCases":477,"indexable":266},"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":160,"region":161,"url":481,"description":482,"useCases":483,"indexable":266},"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":155,"label":485,"issuer":166,"region":161,"url":486,"description":487,"useCases":488,"indexable":266},"UK GDPR","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":490,"label":491,"issuer":492,"region":161,"url":493,"description":494,"useCases":495,"indexable":266},"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":497,"label":498,"issuer":499,"region":500,"url":501,"description":502,"useCases":503,"indexable":266},"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":505,"label":506,"issuer":507,"region":500,"url":508,"description":509,"useCases":510,"indexable":266},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":512,"label":513,"issuer":514,"region":467,"url":515,"description":516,"useCases":517,"indexable":266},"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":519,"label":520,"issuer":521,"region":172,"url":522,"description":523,"useCases":517,"indexable":266},"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":525,"label":526,"issuer":527,"region":161,"url":528,"description":529,"useCases":530,"indexable":266},"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":532,"label":533,"issuer":534,"region":467,"url":535,"description":536,"useCases":537,"indexable":266},"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":539,"label":540,"issuer":160,"region":161,"url":541,"description":542,"useCases":543,"indexable":266},"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":545,"label":546,"issuer":160,"region":161,"url":547,"description":548,"useCases":543,"indexable":266},"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":550,"label":551,"issuer":552,"region":172,"url":553,"description":554,"useCases":555,"indexable":266},"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":557,"label":558,"issuer":160,"region":161,"url":559,"description":560,"useCases":561,"indexable":266},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":563,"label":564,"issuer":565,"region":172,"url":566,"description":567,"useCases":561,"indexable":266},"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":467,"url":572,"description":573,"useCases":561,"indexable":266},"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":160,"region":161,"url":577,"description":578,"useCases":579,"indexable":266},"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":156,"label":581,"issuer":171,"region":172,"url":582,"description":583,"useCases":579,"indexable":266},"Telephone Consumer Protection Act","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":585,"label":586,"issuer":499,"region":500,"url":587,"description":588,"useCases":345,"indexable":266},"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":590,"label":591,"issuer":160,"region":161,"url":592,"description":593,"useCases":345,"indexable":266},"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":595,"label":596,"issuer":160,"region":161,"url":597,"description":598,"useCases":345,"indexable":266},"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":600,"label":601,"issuer":602,"region":161,"url":603,"description":604,"useCases":605,"indexable":266},"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":607,"label":608,"issuer":609,"region":172,"url":610,"description":611,"useCases":440,"indexable":266},"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":613,"label":614,"issuer":160,"region":161,"url":615,"description":616,"useCases":440,"indexable":266},"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":618,"label":619,"issuer":160,"region":161,"url":620,"description":621,"useCases":622,"indexable":266},"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":624,"label":625,"issuer":626,"region":627,"url":628,"description":629,"useCases":346,"indexable":266},"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":631,"label":632,"issuer":633,"region":161,"url":634,"description":635,"useCases":373,"indexable":266},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":637,"label":638,"issuer":639,"region":161,"url":640,"description":641,"useCases":373,"indexable":266},"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":643,"label":644,"issuer":645,"region":500,"url":646,"description":647,"useCases":357,"indexable":266},"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":649,"label":650,"issuer":160,"region":161,"url":651,"description":652,"useCases":357,"indexable":266},"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":654,"label":655,"issuer":656,"region":172,"url":657,"description":658,"useCases":357,"indexable":266},"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.",1790598295169]