[{"data":1,"prerenderedAt":692},["ShallowReactive",2],{"uc-outbound-sales-prospecting-agent":3,"uc-regulations":487},{"useCase":4,"evidence":223,"blitsAiDeployments":380,"benchmarks":381,"indicative":382,"related":385,"indexability":485,"includeUnpublished":229},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":24,"channels":29,"audience":33,"autonomy":34,"adoptionStage":35,"problem":36,"problemStats":37,"howItWorks":43,"valueDrivers":44,"kpis":48,"indicativeValue":54,"macroEstimates":94,"feasibility":95,"implementation":110,"risk":157,"blitsAi":197,"faq":199,"related":212,"datePublished":218,"dateModified":218,"lastVerified":218,"changelog":219,"slug":222},"AI agent for outbound sales prospecting and personalized outreach","Outbound sales prospecting","AI agents for outbound sales prospecting","AI agents research target accounts and draft outreach that reps approve. Clay reports Merge SDRs got 10+ hours back a week and 20% higher response rates.","published","An AI agent that researches target accounts and contacts, drafts personalized outbound outreach (emails, LinkedIn messages and call scripts) from the campaign, the prospect's context and the sales goals, and sequences the follow ups, with a sales development rep approving or sending every message.",[12,13,14,15,16],"AI sales prospecting assistant","outbound prospecting agent","personalized cold outreach generator","account research agent for sellers","AI outbound sequencing",[18,19,20],"cross-industry","technology","professional-services",[22,23],"sales","marketing",[25,26,27,28],"content-generation","agentic-workflow","recommendation-and-personalization","prediction-and-scoring",[30,31,32],"email","internal-tools","api","employee-facing","copilot","early-adopters","Outbound prospecting is mostly research and writing. Before a sales development rep sends a\nfirst message, they have to decide which accounts in a large territory deserve attention this\nweek, find the right people, read the company's news, filings and job posts, and work out why the\nproduct matters to this account now. Then they write the message, and the follow ups, and adapt\nit for email, LinkedIn and a call. Lumen's chief revenue officer says research for customer\noutreach typically takes a seller four hours, so reps either cover few accounts well or many\naccounts with generic templates that buyers ignore.\n\nTemplate automation made the problem worse: sequencing tools make it easy to send thousands of\nnear identical emails, and that volume puts reply rates, sender reputation and complaint levels\nat risk. The\nalternative is an agent that does the research and the first draft per account, grounded in real\nsignals and the organization's own positioning, while the rep keeps judgment over who to contact,\nwhat to say and when to stop. Unlike inbound qualification, the prospect has not asked to be\ncontacted, so consent, privacy and anti spam rules shape the design from the start.",[38],{"statement":39,"sourceTitle":40,"sourceUrl":41,"year":42},"Lumen Technologies' chief revenue officer says it typically takes a seller four hours to do research for customer outreach.","Lumen's strategic leap: How Copilot is redefining productivity and employee engagement","https://customers.microsoft.com/en-us/story/1771760434465986810-lumen-microsoft-copilot-telecommunications-en-united-states",2024,"1. **Pick the accounts.** The agent scores the target account list against the ideal customer\n   profile and live signals (funding, hiring, product launches, leadership changes, website\n   visits) and proposes which accounts each rep should work this week, with the reason.\n2. **Research the account and the people.** For each account it gathers public and first party\n   context (news, filings, job posts, technology used, past CRM activity and calls) and writes a\n   short brief with the likely need, the relevant product and the right contacts.\n3. **Draft the outreach.** From the brief, the campaign and the approved positioning it drafts a\n   first email, a LinkedIn message and a call opener, each citing the signal it is based on,\n   within brand, claims and tone rules.\n4. **Check the rules before anything leaves.** Contact source, consent or legitimate interest\n   basis, suppression and opt out lists, country rules and quiet hours are checked for every\n   contact, and anything that fails is dropped.\n5. **Rep approves and sends.** The rep edits, approves or rejects each draft; approved messages go\n   out from the rep's own mailbox or sequencing tool, never as anonymous bulk mail.\n6. **Sequence and learn.** The agent proposes follow ups based on replies and new signals, stops\n   the sequence on any reply or opt out, logs everything in the CRM and feeds reply and meeting\n   rates back into account scoring and message variants.",[45,46,47],"revenue-growth","employee-productivity","speed",[49,50,51,52,53],"conversion-rate-uplift","productivity-gain","hours-saved","cost-reduction","revenue-uplift",{"referenceOrg":55,"inputs":56,"formula":89,"currency":90,"period":91,"resultLabel":92,"caveat":93},"A B2B software company with 20 sales development reps",[57,62,69,76,82],{"key":58,"label":59,"low":60,"high":60,"unit":58,"note":61},"reps","Sales development reps",20,"The reference company.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"researchHours","Hours per rep per week on account research and writing outreach",8,15,"hours per rep per week","Editorial assumption. Lumen's chief revenue officer puts research for customer outreach at about four hours a week per seller; this range adds writing first messages and follow ups. Replace with a time study of your own reps.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"shareSaved","Share of that time the agent saves",0.3,0.6,"fraction of research and writing time","Conservative against the evidence on this page (Clay reports that Merge's SDRs got 10+ hours back every week; Clay reports an estimate by the person who built Oyster's workflows of about 40 hours per rep per month; Lumen's chief revenue officer says research that took four hours now takes 15 minutes), because vendor case studies select their best results and reps still review every draft.",{"key":77,"label":78,"low":79,"high":80,"unit":77,"note":81},"weeks","Working weeks per year",44,46,"Editorial assumption after holidays and training.",{"key":83,"label":84,"low":85,"high":86,"unit":87,"note":88},"hourlyCost","Fully loaded cost per SDR hour",40,70,"USD per hour","Editorial assumption. Replace with your own fully loaded SDR cost.","reps * researchHours * shareSaved * weeks * hourlyCost","USD","per year","SDR capacity released from research and drafting","Capacity, not cash: the value is only real if reps spend the time on more or better outreach. It leaves out the cost of the agent and data providers, any change in reply or meeting rates, and the revenue those meetings produce.",[],{"complexity":96,"complexityNote":97,"dataPrerequisites":98,"integrations":104},"medium","Drafting an email is easy. The work is in reliable account and contact data, signals that are current, positioning content marketing and sales agree on, CRM and sequencing integration, and contact rules per country that are checked before every send.",[99,100,101,102,103],"A written ideal customer profile and target account list with territories","Approved positioning, value propositions, proof points and claims per segment and product","CRM account, contact and activity history, including previous calls and emails","Contact source records, consent or legitimate interest assessments and suppression lists","Access to signal and enrichment data (news, filings, hiring, technology used) under licence",[105,106,107,108,109],"CRM (accounts, contacts, activities, opportunities)","Sales engagement or sequencing tool and the reps' mailboxes","Enrichment and intent data providers, and web research","Call recording and conversation intelligence for past interactions","Suppression, opt out and consent management systems",{"steps":111,"guardrails":130,"humanInTheLoop":137,"kpisToInstrument":138,"failureModes":144},[112,115,118,121,124,127],{"title":113,"detail":114},"Agree who to target and why","Write down the ideal customer profile, the signals that make an account worth contacting now and the territory rules with sales leadership. The agent can only prioritize as well as these rules.",{"title":116,"detail":117},"Build the account brief first","Start with research and briefs that reps read before writing themselves. Reps will trust drafts only after they trust the research, and the brief shows which sources the agent uses.",{"title":119,"detail":120},"Ground every claim","Load approved positioning, case studies and claims into the knowledge base and make the agent cite the signal behind each personalization. No invented customer names, numbers or compliments about the prospect.",{"title":122,"detail":123},"Put the contact rules in code","Check the lawful basis, opt outs, country rules and send limits for every contact before a draft is created, not after. Log the basis in the CRM.",{"title":125,"detail":126},"Keep the rep in the loop","Every first message and every follow up is approved by the rep and sent from their own account. Autonomous sending comes later, if at all, and only for low risk follow ups.",{"title":128,"detail":129},"Measure against a control","Compare reply, meeting and opportunity rates, and opt outs, for agent drafted outreach against a control group of reps or accounts, not only the volume sent.",[131,132,133,134,135,136],"No message leaves without rep approval, and every message is sent from a named person","Personalization must cite a verifiable signal; invented facts, flattery and fake familiarity are blocked","Lawful basis, suppression list and opt out checks before every draft, per contact and country","Send volume limits per rep and domain, and automatic stop on reply, opt out or complaint","No sensitive personal data (health, family, politics) used for personalization","Protection against prompt injection from web pages and documents the agent reads","Reps approve, edit or reject every message and decide when to stop a sequence. Sales leadership owns targeting rules and positioning; marketing and legal own claims and the contact policy. A weekly review of a sample of drafts, replies and opt outs catches tone problems, wrong facts and accounts that should not have been contacted.",[139,140,141,142,143],"Reply rate and positive reply rate versus a control group","Meetings booked and opportunities created per rep per week","Rep time spent on research and writing, from a time study before and after","Share of drafts approved without major edits, and the reasons for rejection","Opt outs, spam complaints, bounce rate and domain reputation",[145,148,151,154],{"title":146,"detail":147},"Personalization that is wrong or creepy","The agent cites an outdated role, the wrong company news or personal details the prospect never made public. Require a source per fact, freshness limits and a ban on sensitive data.",{"title":149,"detail":150},"More volume instead of better outreach","Teams use the time saved to send many more generic messages, reply rates fall and sending domains get blocked. Cap volume and measure quality, not activity.",{"title":152,"detail":153},"Contacting people you may not contact","Consent and opt out rules differ by country and channel; a contact scraped from the web is not a lawful basis. Check before every draft and keep the evidence.",{"title":155,"detail":156},"Reps stop reading the drafts","Approval turns into a rubber stamp and errors reach prospects. Track edit rates, sample approved messages and keep the rep accountable for what is sent.",{"euAiAct":158,"regulations":161,"guidance":166,"controls":189,"incidents":196},{"tier":159,"basis":160},"context-dependent","Drafting outreach that a rep reviews and sends as their own message is typically minimal risk. If the agent holds conversations with prospects itself, for example by replying to emails or calling, people must be told they are interacting with AI (Article 50, limited risk). It is not an Annex III use case.",[162,163,164,165],"eu-ai-act","gdpr","uk-gdpr","us-tcpa",[167,173,178,184],{"title":168,"issuer":169,"region":170,"url":171,"note":172},"Directive 2002/58/EC on privacy and electronic communications (ePrivacy Directive)","European Union","europe","https://eur-lex.europa.eu/eli/dir/2002/58/oj","Article 13 sets consent rules for unsolicited electronic marketing to individuals; member states set the rules for legal persons, so B2B email rules differ by country.",{"title":174,"issuer":175,"region":170,"url":176,"note":177},"Electronic mail marketing (guide to PECR)","Information Commissioner's Office","https://ico.org.uk/for-organisations/direct-marketing-and-privacy-and-electronic-communications/guide-to-pecr/electronic-and-telephone-marketing/electronic-mail-marketing/","UK rules for marketing emails and texts, including the difference between individuals and businesses and the soft opt in for existing customers.",{"title":179,"issuer":180,"region":181,"url":182,"note":183},"CAN-SPAM Act: a compliance guide for business","Federal Trade Commission","north-america","https://www.ftc.gov/business-guidance/resources/can-spam-act-compliance-guide-business","US rules for commercial email, including honest headers and subject lines, identification, a working opt out and honouring opt outs promptly; the law makes no exception for business to business email.",{"title":185,"issuer":186,"region":181,"url":187,"note":188},"Declaratory ruling on AI generated voices under the TCPA (FCC 24-17)","Federal Communications Commission","https://docs.fcc.gov/public/attachments/FCC-24-17A1.pdf","Calls that use AI generated voices count as artificial or prerecorded voice calls, so outbound calls with an AI voice to US numbers need the called party's prior express consent unless an exemption applies.",[190,191,192,193,194,195],"Documented lawful basis per contact source and country, with a legitimate interest assessment where relied on","Suppression and opt out lists synchronized across CRM, sequencing tool and agent","Record of who approved and sent each message, with the draft and the sources used","Approved claims library and review of new message templates by marketing or legal","Data retention limits for prospect research and deletion on request","AI disclosure whenever the agent converses with prospects directly",[],{"howToBuild":198},"On Blits.ai this is an **agentic workflow** that researches each target account with the\nbuilt in **web search and web page browsing** tools, reads CRM history through the ready made\n**HubSpot or Microsoft Dynamics 365** tools (or **custom functions** for another CRM such as\nSalesforce), and writes the account brief and drafts as **structured output**. Approved\npositioning, case studies and claims sit in a **knowledge base** with hybrid retrieval, so every\ndraft is grounded in content marketing and sales signed off. The workflow can run on a\nschedule for a territory or be triggered through an **API token** from the tools reps already\nuse.\n\n**Human in the loop confirmation**, with its threshold set to cover sending, holds every message\nuntil the rep approves or rejects it, and the **tool execution policy** limits which tools the\nagent may use on its own. A custom function checks suppression and opt out lists before\ndrafting, **PII masking** at the gateway protects contact data, and input and output\n**guardrails** screen for prompt injection attempts. Run history gives a full **audit trail**\nper account, **test suites** check drafts against tone, claims and fact rules before every\nprompt change goes live, and the platform is model agnostic, with EU and UAE data residency.",[200,203,206,209],{"question":201,"answer":202},"How is this different from an inbound AI SDR?","An inbound agent talks to people who came to you and asked for something. An outbound prospecting agent works on people who did not, so its job is research and drafting for a rep, and consent, opt out and anti spam rules decide who may be contacted at all.",{"question":204,"answer":205},"How much time does it save sales development reps?","Vendor case studies report large savings: Clay reports that Merge's SDRs got 10+ hours back every week and response rates climbed 20%, and it reports an estimate by Petra Hajal, who built the workflows in Oyster's marketing operations team (she now runs an agency that serves Oyster), that each rep saves about 40 hours a month. These are vendor selected results; run your own time study and a control group.",{"question":207,"answer":208},"Is AI personalized cold email legal under GDPR?","It can be, but the AI does not change the rules. You need a lawful basis for processing the contact's data, usually a documented legitimate interest for B2B, you must respect ePrivacy or PECR consent rules for electronic marketing, which are stricter for individuals than for companies, and every message needs a working opt out.",{"question":210,"answer":211},"Should the agent send messages on its own?","Start with rep approval for every message. Autonomous sending multiplies any error in facts, tone or targeting across thousands of prospects, and in the United States calls with an AI voice need the called party's prior express consent under the TCPA unless an exemption applies.",[213,214,215,216,217],"inbound-lead-qualification-agent","personalized-marketing-at-scale","sales-call-coaching-and-crm-update","client-briefing-and-call-report-copilot","proactive-outbound-engagement-agent","2026-09-27",[220],{"date":218,"note":221},"First published","outbound-sales-prospecting-agent",[224,252,270,293,313,335,362],{"title":225,"useCases":226,"organization":227,"vendors":231,"summary":235,"stage":236,"year":237,"channels":238,"languages":239,"metrics":241,"outcomeDisclosed":242,"sources":243,"verification":247,"grade":249,"id":250,"organizationSlug":251},"Merge: AI agents that research key accounts and draft tailored outreach for reps",[222],{"name":228,"anonymized":229,"country":230,"region":181,"industry":19},"Merge",false,"US",[232],{"name":233,"role":234},"Clay","platform","Merge, which sells integration products into many B2B industries, used Clay to enrich and categorize more than 50,000 accounts in Salesforce with an industry and a suggested use case, so reps no longer research each account to find the angle. A separate AI agent follows more than 1,500 enterprise accounts every week for launches, partnerships and organizational changes and, when it finds a relevant update, sends the account owner an alert with the context and a drafted email ready for the rep to send. Clay reports that SDRs got more than 10 hours back every week, that response rates climbed 20% (enriched accounts compared with accounts not enriched) and that enterprise meetings booked rose 15%.","production",2026,[30,31],[240],"en",[],true,[244],{"url":245,"title":246,"publisher":233},"https://www.clay.com/customers/merge","How Merge books 15% more enterprise meetings by researching 1,500 key accounts weekly with Clay",{"level":248,"checkedAt":218},"source-verified","C","merge-clay-account-research-outreach",null,{"title":253,"useCases":254,"organization":255,"vendors":257,"summary":259,"stage":236,"year":260,"channels":261,"languages":262,"metrics":263,"outcomeDisclosed":242,"sources":264,"verification":268,"grade":249,"id":269,"organizationSlug":251},"A-LIGN: automated account research for competitive displacement outreach",[222],{"name":256,"anonymized":229,"country":230,"region":181,"industry":20},"A-LIGN",[258],{"name":233,"role":234},"A-LIGN, a security and compliance firm, previously paid a contractor to research 2,000 target accounts by hand over six months, which produced yes or no answers that reps found of little use. It replaced this with AI research workflows in Clay that find which of 15 compliance services each account uses, why it needs them and which competitor provides them, and push the result into Salesforce, so reps open conversations with a specific reason instead of a generic pitch. The workflow went live between March and May 2025 and the vendor reports lower research costs and displacement pipeline tracked through a rep incentive program. The vendor states the cost saving inconsistently: an 83% reduction in the headline and results, but a Clay contract that cost USD 10,000 less than the USD 60,000 manual contract in the body.",2025,[31],[240],[],[265],{"url":266,"title":267,"publisher":233},"https://www.clay.com/customers/a-lign","How A-LIGN cut research costs by 83% and generated millions in pipeline",{"level":248,"checkedAt":218},"a-lign-outbound-account-research",{"title":271,"useCases":272,"organization":273,"vendors":276,"summary":279,"stage":236,"year":260,"channels":280,"languages":281,"metrics":282,"outcomeDisclosed":229,"sources":283,"verification":291,"grade":249,"id":292,"organizationSlug":251},"ANS: agents that gather account context so sellers prioritize the right accounts",[222],{"name":274,"anonymized":229,"country":275,"region":170,"industry":19},"ANS","GB",[277],{"name":278,"role":234},"Microsoft","ANS, a UK cloud, security and digital technology provider and Microsoft partner, uses Microsoft Copilot and agents in its selling process. Sellers ask an agent to gather and summarize information from several data sources, including past customer interactions, so they can decide which accounts and opportunities to focus on. Microsoft reports an expected improvement in closing ratio, which is a forecast and is not recorded as a result.",[31],[240],[],[284,288],{"url":285,"title":286,"publisher":278,"date":287},"https://www.microsoft.com/en-us/microsoft-cloud/blog/2025/07/24/ai-powered-success-with-1000-stories-of-customer-transformation-and-innovation/","AI-powered success, with more than 1,000 stories of customer transformation and innovation","2025-07-24",{"url":289,"title":290,"publisher":274},"https://www.ans.co.uk/","ANS: Leading Cloud, Security & Digital Technology Provider for AI",{"level":248,"checkedAt":218},"ans-copilot-seller-account-agent",{"title":294,"useCases":295,"organization":296,"vendors":300,"summary":302,"stage":236,"year":260,"channels":303,"languages":304,"metrics":305,"outcomeDisclosed":242,"sources":306,"verification":311,"grade":249,"id":312,"organizationSlug":251},"Unifonic: generative AI for sales outreach content and conversation insights",[222],{"name":297,"anonymized":229,"country":298,"region":299,"industry":19},"Unifonic","SA","middle-east",[301],{"name":278,"role":234},"Unifonic, a Saudi customer communications platform, uses Microsoft 365 Copilot in its sales and marketing teams to analyze conversations across platforms, draft proposals, email campaigns and social media content, and summarize the action points of recorded customer meetings so a sales representative can email the customer with next steps right after. Microsoft reports that this led to a 20% increase in total sales outreach volume. The deployment is a general productivity assistant used for outreach, not a dedicated prospecting agent.",[30,31],[],[],[307,308],{"url":285,"title":286,"publisher":278,"date":287},{"url":309,"title":310,"publisher":278},"https://www.microsoft.com/en/customers/story/23690-unifonic-microsoft-365-e5","Unifonic boosts productivity and accelerates customer engagement using Microsoft 365 Copilot",{"level":248,"checkedAt":218},"unifonic-copilot-sales-outreach",{"title":314,"useCases":315,"organization":316,"vendors":318,"summary":322,"stage":236,"year":42,"channels":323,"languages":324,"metrics":325,"outcomeDisclosed":229,"sources":326,"verification":333,"grade":249,"id":334,"organizationSlug":251},"Dun & Bradstreet: generative AI email tool for sellers",[222],{"name":317,"anonymized":229,"country":230,"region":181,"industry":20},"Dun & Bradstreet",[319],{"name":320,"role":321},"Google Cloud","model-provider","Dun & Bradstreet, a business research and intelligence company, built an email generation tool with Google's Gemini models that helps its sellers write tailored, personalized messages to prospects and customers about its research services. No outcome has been published.",[30,31],[240],[],[327,330],{"url":328,"title":329,"publisher":320},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","Real-world gen AI use cases from the world's leading organizations",{"url":331,"title":332,"publisher":317},"https://www.dnb.com/contact-us.html","Contact Dun & Bradstreet for Support",{"level":248,"checkedAt":218},"dun-and-bradstreet-seller-email-generation",{"title":336,"useCases":337,"organization":339,"vendors":342,"summary":344,"stage":236,"year":42,"channels":345,"languages":346,"metrics":347,"outcomeDisclosed":242,"sources":357,"verification":359,"grade":249,"id":361,"organizationSlug":251},"Lumen Technologies: Copilot summaries of past sales interactions and account research",[215,338,222],"business-connectivity-quoting-and-service-assistant",{"name":340,"anonymized":229,"country":230,"region":181,"industry":341},"Lumen Technologies","telecommunications",[343],{"name":278,"role":234},"Lumen's sellers use Microsoft Copilot to summarize past sales interactions, gather recent news, identify business challenges and industry trends, and suggest next steps for an account. Microsoft reports that this work took up to four hours per seller and that Lumen cut it to 15 minutes in 2024. Lumen's projected annual value of USD 50 million is a projection and is not recorded as a result.",[31],[240],[348],{"kpi":349,"value":350,"unit":351,"qualifier":352,"period":353,"baseline":354,"claimant":355,"quote":356,"sourceUrl":285},"time-saved-per-task",225,"minutes","up-to","summarizing past interactions and researching an account, per seller","up to four hours per seller before","vendor","This process traditionally took up to four hours per seller. In 2024, Lumen reduced that time to just 15 minutes, projecting annual time savings worth USD50 million.",[358],{"url":285,"title":286,"publisher":278,"date":287},{"level":248,"checkedAt":360},"2026-09-26","lumen-copilot-sales-account-research",{"title":363,"useCases":364,"organization":365,"vendors":367,"summary":369,"stage":236,"year":370,"channels":371,"languages":372,"metrics":373,"outcomeDisclosed":242,"sources":374,"verification":378,"grade":249,"id":379,"organizationSlug":251},"Oyster: automated research, enrichment and tailored messaging for intent based outbound",[222],{"name":366,"anonymized":229,"country":230,"region":181,"industry":19},"Oyster",[368],{"name":233,"role":234},"Oyster, a global employment platform, ran an intent based outbound program that depended on manual account research and enrichment across G2, Clearbit, Salesforce, HubSpot and other tools. Its marketing operations team automated research, qualification, enrichment and message segmentation in Clay, generating content tailored to each intent signal and syncing accounts to the right BDR and email tool while keeping the CRM as the system of record. BDR leaders wanted the automation to support strategic account selection and personalized outreach, and the workflows were designed to safeguard those internal processes and rules of engagement. The vendor reports that Petra Hajal, who built the workflows, estimates each representative now saves approximately 40 hours per month.",2023,[30,31],[240],[],[375],{"url":376,"title":377,"publisher":233},"https://www.clay.com/customers/oyster","How Oyster uses Clay to run intent-based outbound campaigns, saving 40hrs/month per sales rep",{"level":248,"checkedAt":218},"oyster-intent-based-outbound-automation",1,[],{"low":383,"high":384},84480,579600,[386,413,437,456,472],{"slug":213,"title":387,"shortTitle":388,"definition":389,"status":9,"industries":390,"functions":393,"patterns":394,"audience":398,"autonomy":399,"adoptionStage":35,"evidenceCount":400,"publicEvidenceCount":400,"organizations":401,"bestGrade":249,"headline":406,"lastVerified":218,"indexable":242},"AI agent for inbound lead qualification and meeting booking","Inbound lead qualification","An AI agent that engages inbound prospects the moment they arrive on the website, chat, messaging or the sales phone line, answers their first questions, qualifies them against the organization's criteria, and books a meeting or hands a ready conversation to the right salesperson, with the context written into the CRM.",[18,19,391,392],"automotive","banking",[22,23],[395,396,397,26],"conversational-agent","voice-agent","classification-and-routing","customer-facing","supervised-agent",4,[402,403,404,405],"8x8","CarMax","Rocket Mortgage","SUSE",{"kpi":49,"label":407,"unit":408,"n":380,"nUpTo":409,"kind":410,"value":411,"qualifier":412,"claimant":355,"organization":402,"vendorReported":242},"Conversion uplift","percent",0,"reported",19,"exact",{"slug":214,"title":414,"shortTitle":415,"definition":416,"status":9,"industries":417,"functions":421,"patterns":422,"audience":423,"autonomy":399,"adoptionStage":424,"evidenceCount":65,"publicEvidenceCount":425,"organizations":426,"bestGrade":434,"headline":435,"lastVerified":218,"indexable":242},"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.",[18,418,419,420,392],"travel-and-hospitality","media-and-entertainment","retail-and-ecommerce",[23,22],[27,28,25],"back-office","mainstream",7,[427,428,429,430,431,432,433],"Amazon","Catchtable","Commonwealth Bank of Australia","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages","B",{"kpi":49,"label":407,"unit":408,"n":380,"nUpTo":409,"kind":410,"value":436,"qualifier":412,"claimant":355,"organization":428,"vendorReported":242},30,{"slug":215,"title":438,"shortTitle":439,"definition":440,"status":9,"industries":441,"functions":444,"patterns":445,"audience":33,"autonomy":34,"adoptionStage":35,"evidenceCount":400,"publicEvidenceCount":400,"organizations":448,"bestGrade":249,"headline":452,"lastVerified":218,"indexable":242},"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.",[18,341,442,443],"manufacturing","insurance",[22],[446,447,25],"speech-analytics","summarization",[449,340,450,451],"Hughes Network Systems","Sandvik Coromant","Zurich Insurance Group",{"kpi":349,"label":453,"unit":351,"n":380,"nUpTo":380,"kind":410,"value":454,"qualifier":412,"claimant":455,"organization":450,"vendorReported":229},"Time saved per task",3,"organization",{"slug":216,"title":457,"shortTitle":458,"definition":459,"status":9,"industries":460,"functions":463,"patterns":465,"audience":33,"autonomy":34,"adoptionStage":35,"segment":467,"evidenceCount":454,"publicEvidenceCount":454,"organizations":468,"bestGrade":434,"headline":251,"lastVerified":218,"indexable":242},"AI copilot for corporate client briefings and call reports","Client briefing and call reports","An AI copilot for relationship managers, mainly in corporate and commercial banking, whose main job is preparation: before a client meeting it assembles a briefing pack from filings, news, internal notes, product holdings and upcoming maturities, and afterwards it turns the banker's notes into a structured call report and CRM update. Unlike a meeting notetaker, which centres on capturing the conversation, it centres on the credit and cross sell context around the meeting; wealth advisor tools that also prepare meetings overlap with it. The banker reviews every output.",[392,461,462],"wealth-and-asset-management","capital-markets",[22,464],"knowledge-management",[466,447,25,26],"rag-knowledge-assistant","specialized-businesses",[469,470,471],"Bank of America","Scotiabank","Standard Chartered",{"slug":217,"title":473,"shortTitle":474,"definition":475,"status":9,"industries":476,"functions":478,"patterns":480,"audience":398,"autonomy":399,"adoptionStage":481,"segment":482,"evidenceCount":454,"publicEvidenceCount":454,"organizations":483,"bestGrade":434,"headline":251,"lastVerified":218,"indexable":242},"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.",[392,477],"payments",[23,22,479],"customer-service",[395,396,26,27],"emerging","front-office",[469,484,429],"Capital One",{"indexable":242,"reasons":486},[],[488,493,498,506,513,519,524,531,539,546,552,558,565,571,577,582,589,595,601,607,613,617,623,628,633,640,646,651,657,664,670,676,682,687],{"id":162,"label":489,"issuer":169,"region":170,"url":490,"description":491,"useCases":492,"indexable":242},"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":494,"issuer":169,"region":170,"url":495,"description":496,"useCases":497,"indexable":242},"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":499,"label":500,"issuer":501,"region":502,"url":503,"description":504,"useCases":505,"indexable":242},"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":507,"label":508,"issuer":509,"region":181,"url":510,"description":511,"useCases":512,"indexable":242},"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":514,"label":515,"issuer":169,"region":170,"url":516,"description":517,"useCases":518,"indexable":242},"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":164,"label":520,"issuer":175,"region":170,"url":521,"description":522,"useCases":523,"indexable":242},"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":525,"label":526,"issuer":527,"region":170,"url":528,"description":529,"useCases":530,"indexable":242},"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":532,"label":533,"issuer":534,"region":535,"url":536,"description":537,"useCases":538,"indexable":242},"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":540,"label":541,"issuer":542,"region":535,"url":543,"description":544,"useCases":545,"indexable":242},"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":547,"label":548,"issuer":549,"region":502,"url":550,"description":551,"useCases":60,"indexable":242},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":553,"label":554,"issuer":555,"region":181,"url":556,"description":557,"useCases":60,"indexable":242},"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":559,"label":560,"issuer":561,"region":170,"url":562,"description":563,"useCases":564,"indexable":242},"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":566,"label":567,"issuer":568,"region":502,"url":569,"description":570,"useCases":66,"indexable":242},"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.",{"id":572,"label":573,"issuer":169,"region":170,"url":574,"description":575,"useCases":576,"indexable":242},"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":578,"label":579,"issuer":169,"region":170,"url":580,"description":581,"useCases":576,"indexable":242},"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":583,"label":584,"issuer":585,"region":181,"url":586,"description":587,"useCases":588,"indexable":242},"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":590,"label":591,"issuer":169,"region":170,"url":592,"description":593,"useCases":594,"indexable":242},"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":596,"label":597,"issuer":598,"region":181,"url":599,"description":600,"useCases":594,"indexable":242},"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":602,"label":603,"issuer":604,"region":502,"url":605,"description":606,"useCases":594,"indexable":242},"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":608,"label":609,"issuer":169,"region":170,"url":610,"description":611,"useCases":612,"indexable":242},"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":165,"label":614,"issuer":186,"region":181,"url":615,"description":616,"useCases":612,"indexable":242},"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":618,"label":619,"issuer":534,"region":535,"url":620,"description":621,"useCases":622,"indexable":242},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":624,"label":625,"issuer":169,"region":170,"url":626,"description":627,"useCases":622,"indexable":242},"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":629,"label":630,"issuer":169,"region":170,"url":631,"description":632,"useCases":622,"indexable":242},"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":634,"label":635,"issuer":636,"region":170,"url":637,"description":638,"useCases":639,"indexable":242},"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":641,"label":642,"issuer":643,"region":181,"url":644,"description":645,"useCases":65,"indexable":242},"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":647,"label":648,"issuer":169,"region":170,"url":649,"description":650,"useCases":65,"indexable":242},"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":652,"label":653,"issuer":169,"region":170,"url":654,"description":655,"useCases":656,"indexable":242},"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":658,"label":659,"issuer":660,"region":299,"url":661,"description":662,"useCases":663,"indexable":242},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":665,"label":666,"issuer":667,"region":170,"url":668,"description":669,"useCases":400,"indexable":242},"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":671,"label":672,"issuer":673,"region":170,"url":674,"description":675,"useCases":400,"indexable":242},"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":677,"label":678,"issuer":679,"region":535,"url":680,"description":681,"useCases":454,"indexable":242},"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":683,"label":684,"issuer":169,"region":170,"url":685,"description":686,"useCases":454,"indexable":242},"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":688,"label":689,"issuer":180,"region":181,"url":690,"description":691,"useCases":454,"indexable":242},"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.",1790598295518]