[{"data":1,"prerenderedAt":680},["ShallowReactive",2],{"uc-email-and-ticket-reply-drafting":3,"uc-regulations":471},{"useCase":4,"evidence":194,"blitsAiDeployments":357,"benchmarks":358,"indicative":367,"related":370,"indexability":469,"includeUnpublished":200},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":23,"patterns":26,"channels":31,"audience":36,"autonomy":37,"adoptionStage":38,"problem":39,"problemStats":40,"howItWorks":41,"valueDrivers":42,"kpis":47,"indicativeValue":52,"macroEstimates":87,"feasibility":88,"implementation":102,"risk":148,"blitsAi":171,"faq":173,"related":183,"datePublished":189,"dateModified":189,"lastVerified":189,"changelog":190,"slug":193},"AI reply drafting for customer email and support tickets","Email and ticket reply drafting","AI reply drafting for support emails and tickets","AI drafts replies to customer emails and tickets for agents to check and send. Google Cloud reports that Turing cut its HR ticket processing time by a third.","published","A copilot for asynchronous service work that drafts the reply to an incoming customer email, message or ticket once it has reached an agent: it summarizes the request, pulls the relevant customer data and approved knowledge, and drafts a reply in the organization's tone and the customer's language for the agent to check, edit and send. Live calls and chats, and the sorting of the inbox itself, are separate use cases.",[12,13,14,15,16],"AI email response assistant","ticket reply suggestions","suggested replies for service agents","AI drafted customer replies","written channel agent assist",[18,19,20,21,22],"cross-industry","government","banking","telecommunications","technology",[24,25],"customer-service","operations",[27,28,29,30],"content-generation","summarization","rag-knowledge-assistant","classification-and-routing",[32,33,34,35],"email","agent-desktop","social-messaging","whatsapp","employee-facing","copilot","mainstream","Written service channels are slow in a way phone is not. An email or ticket waits in a queue, an\nagent opens it, reads the history, looks up the account, searches the knowledge base, writes a\nreply and often has to ask a colleague. Much of that effort is repeated for questions that have\nbeen answered many times before, and the reply quality depends on who happens to pick the ticket\nup. Backlogs build after product changes and incidents, and response time targets slip.\n\nFully automated replies are tempting but risky for anything beyond simple, well understood\nrequests: a confident wrong answer in writing is a record the customer can forward. A common\nmiddle ground is a drafted reply that the agent owns. The AI does the reading, the lookup and the\nfirst draft; the agent brings judgment, corrects and sends. Real time assist during calls and\nchats is covered on its own page; this page is about the asynchronous queue.",[],"1. **Read and summarize.** When a message or ticket arrives, the AI summarizes the request and\n   the thread, detects the intent, language and urgency, and labels it for routing.\n2. **Gather context.** It retrieves the customer's relevant data (orders, account status, open\n   cases) through approved system calls and the relevant passages from the approved knowledge base.\n3. **Draft the reply.** It writes a response in the house tone and the customer's language that\n   addresses every point raised, cites the knowledge it used and leaves placeholders where it lacks\n   information.\n4. **Agent reviews and sends.** The agent edits, completes and sends. Drafts for regulated\n   topics, complaints or vulnerable customers are marked for extra care.\n5. **Automate only the safe tail.** For a narrow set of simple, low risk intents, the organization\n   may send replies automatically, with disclosure and sampling.\n6. **Learn from edits.** The difference between draft and sent reply is logged to improve prompts\n   and to find missing knowledge.",[43,44,45,46],"employee-productivity","cost-to-serve","customer-experience","speed",[48,49,50,51],"handling-time-reduction","processing-time-reduction","response-time-reduction","customer-satisfaction",{"referenceOrg":53,"inputs":54,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"A service team that answers 400,000 emails and tickets a year",[55,61,68,75],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"tickets","Emails and tickets answered per year",400000,"tickets per year","The reference organization.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"minutesPerTicket","Agent handling time per ticket today",6,10,"minutes per ticket","Editorial assumption for written service. Replace with your own handling time.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"reduction","Reduction in handling time with drafted replies",0.2,0.33,"fraction of handling time","Editorial assumption, replace with your own. No source on this page measures agent handling time per email or ticket: Google Cloud reports Turing's HR ticket processing time down by 33%, without defining what that processing time covers, and Microsoft reports HYPE's agents resolving WhatsApp chat conversations in half the time. The range is set at or below both.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"costPerMinute","Fully loaded agent cost per minute",0.6,0.9,"USD per minute","Editorial assumption, replace with your own.","tickets * minutesPerTicket * reduction * costPerMinute","USD","per year","Agent handling cost released","Counts only agent time. It leaves out faster responses and more consistent quality, the cost of the platform, and any effect of automating simple replies end to end. Savings usually show as backlog cleared and capacity redeployed rather than immediately lower cost.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":96},"medium","Drafting from a knowledge base is quick to prototype. The work is in reading customer data through proper integrations, keeping knowledge current, handling many languages and designing which topics may never be answered without extra review.",[92,93,94,95],"Approved, current knowledge articles and reply templates with owners","A contact reason taxonomy with volumes for email and ticket channels","Historical tickets with the replies that resolved them, for testing","Customer and order data reachable through APIs",[97,98,99,100,101],"Ticketing or case management platform (for example Zendesk, Salesforce, Freshdesk, ServiceNow)","Email and messaging channels","CRM, order and account systems for context","Knowledge base","Quality assurance tooling for sampling sent replies",{"steps":103,"guardrails":122,"humanInTheLoop":128,"kpisToInstrument":129,"failureModes":135},[104,107,110,113,116,119],{"title":105,"detail":106},"Pick intents by volume and risk","From the contact reason report, choose high volume topics where the answer is in approved knowledge. Mark complaints, legal and regulated topics for extra review from day one.",{"title":108,"detail":109},"Ground every draft","Draft only from approved knowledge and data from system calls, show the agent the sources, and leave explicit gaps instead of guessing when information is missing.",{"title":111,"detail":112},"Put the draft where agents work","Embed summaries and drafts in the existing ticket view. A separate tool that needs copy and paste loses most of the gain.",{"title":114,"detail":115},"Measure edits, not only speed","Track how much agents change each draft and why. Heavy edits point at missing knowledge or a bad prompt; no edits on complex topics may point at over reliance.",{"title":117,"detail":118},"Test in every language you serve","Build a test set of real tickets per language and intent, and run it on every prompt, model or knowledge change.",{"title":120,"detail":121},"Automate the safe tail last","Only after months of low edit rates on an intent consider sending automatically, with AI disclosure, sampling and an easy route to a person.",[123,124,125,126,127],"The agent sends; no reply leaves without human action unless the intent is on an approved automation list","Drafts use only approved knowledge and data from authorized system calls, with sources shown","Complaints, legal threats and signs of vulnerability are flagged and routed, not just answered","Personal and payment data masked in prompts and logs","Regulated statements (fees, rights, deadlines) come from approved templates, not free generation","Agents review and own every reply they send. Team leads sample sent replies weekly for accuracy and tone, knowledge owners fix the gaps that heavy edits reveal, and any move to automatic sending for an intent needs sign off from the service owner and compliance.",[130,131,132,133,134],"Handling time per ticket by intent, before and after, on the same case mix","Draft acceptance rate and edit distance per intent","Reopen and repeat contact rate within seven days","Quality assurance score of sent replies","Time to first response and backlog age",[136,139,142,145],{"title":137,"detail":138},"Rubber stamping","Agents send drafts unread under time pressure. Sample sent replies and watch for near zero edit rates on complex topics.",{"title":140,"detail":141},"Fluent but wrong","A plausible reply built on outdated knowledge. Show sources, refuse when retrieval finds nothing and keep knowledge owned and current.",{"title":143,"detail":144},"Missed complaint","A complaint is answered as a routine question and never logged. Detect complaint language and route it to the complaints process.",{"title":146,"detail":147},"Data from the wrong customer","Context pulled for a similar name or a shared email address. Match on verified identifiers only and show the agent what was used.",{"euAiAct":149,"regulations":152,"guidance":156,"controls":163,"incidents":170},{"tier":150,"basis":151},"limited","A drafting copilot whose output an agent reviews and sends falls under the transparency tier at most. When replies are sent without human review, customers interact with the AI system directly, and Article 50(1) requires that they are informed unless this is obvious from the context. Article 50(2) separately requires the provider of a system that generates text to mark its output in a machine readable format as artificially generated, whether or not a person reviews the draft. It becomes high risk only if it is used for a purpose listed in Annex III, such as evaluating eligibility for public benefits or creditworthiness (point 5), or evaluating the performance of the agents who use it (point 4).",[153,154,155],"eu-ai-act","gdpr","uk-consumer-duty",[157],{"title":158,"issuer":159,"region":160,"url":161,"note":162},"Regulation (EU) 2024/1689 (AI Act), Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Official text on EUR-Lex. Article 50(1) is relevant when replies are sent automatically, so the customer interacts with the AI system directly; Article 50(2) covers machine readable marking of generated text by the provider.",[164,165,166,167,168,169],"Documented list of intents eligible for drafting and, separately, for automatic sending","Logging of draft, edits and sent version for quality review and disputes","Weekly quality sampling with feedback into knowledge and prompts","Personal data masking and retention limits on drafts and logs","Complaint detection linked to the complaints handling process","For UK retail financial services firms, drafted replies checked against the consumer understanding outcome of the FCA Consumer Duty (PRIN 2A.5) before they are sent",[],{"howToBuild":172},"On Blits.ai the **email channel** (inbound and outbound) or a **REST API** call from the ticketing\nplatform sends each message to an **AI agent** that summarizes it, classifies the intent with\n**structured output** and drafts a reply from the **knowledge base** with hybrid retrieval.\n**Custom functions** read order, account or case data from the systems of record (the integration\ncatalog includes Zendesk, Salesforce and ServiceNow, and Freshdesk is a ready made tool), and\n**PII masking** at the gateway keeps personal and card data out of prompts.\n\nWhen the ticketing platform calls the agent through the API, the draft returns to the ticket for\nthe agent to review and send. The email channel path suits the intents you approve for\nautomation: an **agentic workflow** sends the reply, with **human in the loop** approval above a\nthreshold you set. **Guardrails** check every draft for tone and policy, **multi language**\nsupport answers in the customer's language, and **test suites** replay real tickets before each\nchange goes live. The platform is\nmodel agnostic and can run in the EU or UAE region.",[174,177,180],{"question":175,"answer":176},"How much time does AI reply drafting save?","The figures on this page come from vendors and measure different things. Google Cloud reports that Turing cut its HR ticket processing time by 33% with a model that drafts replies, and Microsoft reports that HYPE's human agents resolve WhatsApp conversations in half the time with Copilot case and conversation summaries and email assistance. It is unclear whether either figure measures agent handling time per email or ticket, so measure your own baseline before relying on either.",{"question":178,"answer":179},"Should AI replies be sent automatically?","Only for a narrow set of simple, low risk intents after a period of drafts that agents barely change, and with AI disclosure. Everything else is better drafted by the AI and sent by an agent. TSA's AskTSA service, for example, uses AI to summarize inquiries and recommend replies to the human agents who answer them.",{"question":181,"answer":182},"How is this different from live agent assist?","Live agent assist works in real time during a call or chat. Reply drafting works on the asynchronous queue of emails, tickets and messages, where the agent has time to review and the draft is the main output.",[184,185,186,187,188],"correspondence-triage-and-routing","live-agent-assist","support-knowledge-article-generation","complaints-handling-agent","civil-servant-drafting-copilot","2026-09-27",[191],{"date":189,"note":192},"First published","email-and-ticket-reply-drafting",[195,228,251,281,311,330],{"title":196,"useCases":197,"organization":198,"vendors":203,"summary":207,"stage":208,"year":209,"channels":210,"languages":212,"metrics":214,"outcomeDisclosed":200,"sources":215,"verification":223,"grade":225,"id":226,"organizationSlug":227},"US Centers for Disease Control and Prevention: SmartFind knowledge bot for partner mailbox replies",[186,193],{"name":199,"anonymized":200,"country":201,"region":202,"industry":19},"Centers for Disease Control and Prevention",false,"US","north-america",[204],{"name":205,"role":206},"Microsoft","platform","CDC's National Center for Immunization and Respiratory Diseases runs SmartFind, an internal knowledge bot with a SharePoint component that helps program staff manage partner emails and lets mailbox managers use a shared knowledge base. The agency lists partner mailbox email management and knowledge base maintenance as the purpose. The bot matches free text questions to agency cleared answers and flags complex or unanswerable queries for manual review. Earlier public facing versions gave agency cleared answers to public and partner questions during the COVID-19 pandemic. No outcome figures are published.","production",2024,[32,211],"internal-tools",[213],"en",[],[216,220],{"url":217,"title":218,"publisher":219},"https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory","2025 Federal Agency AI Use Case Inventory","Office of Management and Budget (GitHub)",{"url":221,"title":222,"publisher":219},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 individually reported AI use cases (HHS entry, NCIRD SmartFind ChatBots, Public and Internal)",{"level":224,"checkedAt":189},"source-verified","B","cdc-smartfind-knowledge-bot",null,{"title":229,"useCases":230,"organization":231,"vendors":233,"summary":236,"stage":208,"year":209,"channels":237,"languages":239,"metrics":240,"outcomeDisclosed":200,"sources":241,"verification":248,"grade":225,"id":250,"organizationSlug":227},"US Transportation Security Administration: AI summaries and recommended replies for AskTSA agents",[193],{"name":232,"anonymized":200,"country":201,"region":202,"industry":19},"Transportation Security Administration",[234],{"name":235,"role":206},"Sprinklr","AskTSA answers traveller questions in writing through X, Facebook Messenger, Apple Messages and text message. Since December 2024 TSA has used generative AI in the AskTSA customer service process to summarize why a traveller is getting in touch, recommend replies tailored to that summary, categorize incoming inquiries and report on where the virtual assistant falls short. The recommended replies assist the human agents; the entry does not describe automatic sending. The department classifies it as not high impact; no outcome figures are published.",[34,238,33],"sms",[213],[],[242,243,245],{"url":217,"title":218,"publisher":219},{"url":221,"title":244,"publisher":219},"2025 individually reported AI use cases (entry DHS-2604, AskTSA)",{"url":246,"title":247,"publisher":232},"https://www.tsa.gov/contact/customer-service","Customer Service",{"level":224,"checkedAt":249},"2026-09-26","tsa-asktsa-response-assist",{"title":252,"useCases":253,"organization":254,"vendors":257,"summary":259,"stage":208,"year":260,"channels":261,"languages":262,"metrics":264,"outcomeDisclosed":273,"sources":274,"verification":278,"grade":279,"id":280,"organizationSlug":227},"HYPE: AI email handling and reply assistance for neobank customer service",[193],{"name":255,"anonymized":200,"country":256,"region":160,"industry":20},"HYPE","IT",[258],{"name":205,"role":206},"Italian neobank HYPE handled nearly 45% of its customer contacts by email and 40% by chat and WhatsApp when it moved to a new CRM. It built an agent that reads customer emails, segments them by subject and triggers an automatic reply, plus a voice agent that handles calls about common issues. Its human agents use Copilot in Dynamics 365 Customer Service for case and conversation summaries and email assistance. Microsoft reports that agents resolve WhatsApp conversations in half the time and that the custom agents cut human customer service intervention by 70% over a year.",2025,[32,35,33],[263],"it",[265],{"kpi":49,"value":266,"unit":267,"qualifier":268,"period":269,"claimant":270,"quote":271,"sourceUrl":272},50,"percent","approximately","WhatsApp chat conversations resolved by human agents with Copilot summaries and email assistance (chat, not email or tickets)","vendor","Copilot in Customer Service provides automated, AI-powered case and conversation summaries and email assistance, helping human agents resolve WhatsApp conversations in half the time.","https://www.microsoft.com/en/customers/story/19684-hype-dynamics-365-customer-service",true,[275],{"url":272,"title":276,"publisher":277},"HYPE makes finance easier with Dynamics 365","Microsoft Customer Stories",{"level":224,"checkedAt":189},"C","hype-customer-service-email-and-chat-assist",{"title":282,"useCases":283,"organization":285,"vendors":287,"summary":290,"stage":208,"year":260,"channels":291,"languages":292,"metrics":293,"outcomeDisclosed":273,"sources":299,"verification":309,"grade":279,"id":310,"organizationSlug":227},"Turing: AI drafted replies to HR support tickets",[284,193],"hr-and-policy-assistant",{"name":286,"anonymized":200,"country":201,"region":202,"industry":22},"Turing",[288],{"name":289,"role":206},"Google","Turing (listed by Google Cloud as Turing Enterprises), an AI company headquartered in San Francisco, built a custom AI model trained on its internal knowledge that drafts replies to HR support tickets. Google Cloud reports a one third cut in ticket processing time after two days of development. A plan to automate 60% of its 52,000 annual HR tickets with Gemini Gems is a target, not a result.",[211],[213],[294],{"kpi":49,"value":295,"unit":267,"qualifier":296,"claimant":270,"quote":297,"sourceUrl":298},33,"exact","Turing also built a custom AI model trained on internal knowledge to draft replies to HR support tickets, reducing ticket processing time by 33% after two days of development.","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",[300,303,306],{"url":298,"title":301,"publisher":302},"Real world gen AI use cases from the world's leading organizations","Google Cloud",{"url":304,"title":305,"publisher":286},"https://www.turing.com/company","About Turing",{"url":307,"title":308,"publisher":286},"https://www.turing.com/terms-of-service","Terms of Service",{"level":224,"checkedAt":189},"turing-hr-ticket-reply-drafting",{"title":312,"useCases":313,"organization":314,"vendors":316,"summary":318,"stage":208,"year":209,"channels":319,"languages":320,"metrics":321,"outcomeDisclosed":200,"sources":322,"verification":328,"grade":279,"id":329,"organizationSlug":227},"Nomad eSIM: generative AI help for support agents answering trouble tickets",[193],{"name":315,"anonymized":200,"country":201,"region":202,"industry":21},"Nomad eSIM",[317],{"name":289,"role":206},"Nomad eSIM, a LotusFlare brand used by international travellers, gives its customer support agents Gemini in Google Workspace to respond to trouble tickets more efficiently. Google Cloud reports higher customer satisfaction from faster support responses, but gives no figure.",[211],[213],[],[323,324],{"url":298,"title":301,"publisher":302},{"url":325,"title":326,"publisher":327},"https://lotusflare.com/contact-us/","Contact us (headquarters of LotusFlare, Inc.)","LotusFlare",{"level":224,"checkedAt":189},"nomad-esim-support-ticket-replies",{"title":331,"useCases":332,"organization":333,"vendors":337,"summary":339,"stage":340,"year":341,"channels":342,"languages":343,"metrics":344,"outcomeDisclosed":273,"sources":352,"verification":355,"grade":279,"id":356,"organizationSlug":227},"First National Bank: Copilot for Sales for commercial bankers",[193],{"name":334,"anonymized":200,"country":335,"region":336,"industry":20},"First National Bank","ZA","africa",[338],{"name":205,"role":206},"First National Bank, a division of FirstRand, rolled out Microsoft Copilot for Sales to its bankers in December 2023, on top of a Dynamics 365 CRM that nearly all of its more than 3,500 bankers already used. The customer story focuses on drafting replies to commercial clients in Outlook that address each point in the client's message, which the banker reviews, edits and sends. Because bankers worried the drafts would sound robotic, the bank paired the rollout with a training programme with modules for each type of task.","scaled",2023,[32,211],[213],[345],{"kpi":346,"value":347,"unit":267,"qualifier":348,"period":349,"claimant":270,"quote":350,"sourceUrl":351},"employee-adoption",94,"at-least","commercial bankers","More than 94% of commercial bankers now use Copilot to help them craft richer communications with their customers.","https://www.microsoft.com/en/customers/story/1761931588230983875-first-national-bank-dynamics-365-sales-banking-and-capital-markets-en-south-Africa",[353],{"url":351,"title":354,"publisher":277},"First National Bank enhances customer communications with Microsoft Copilot for Sales",{"level":224,"checkedAt":189},"first-national-bank-copilot-for-sales",0,[359],{"kpi":49,"label":360,"unit":267,"aggregate":273,"higherIsBetter":273,"n":361,"nUpTo":357,"median":362,"min":295,"max":266,"byClaimant":363,"vendorOnly":273,"points":364},"Cycle time reduction",2,41.5,{"organization":357,"vendor":361,"regulator":357,"independent":357},[365,366],{"evidenceId":280,"organization":255,"value":266,"qualifier":268,"claimant":270,"grade":279,"pooled":273},{"evidenceId":310,"organization":286,"value":295,"qualifier":296,"claimant":270,"grade":279,"pooled":273},{"low":368,"high":369},288000,1188000,[371,396,419,435,455],{"slug":184,"title":372,"shortTitle":373,"definition":374,"status":9,"industries":375,"functions":377,"patterns":379,"audience":381,"autonomy":382,"adoptionStage":38,"segment":381,"evidenceCount":64,"publicEvidenceCount":64,"organizations":383,"bestGrade":225,"headline":390,"lastVerified":189,"indexable":273},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[18,20,376,19],"insurance",[25,24,378],"case-management",[30,380,28],"document-processing","back-office","supervised-agent",[384,385,386,387,388,389],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":391,"label":392,"unit":267,"n":393,"nUpTo":357,"kind":394,"value":395,"qualifier":296,"claimant":270,"organization":388,"vendorReported":273},"accuracy","Accuracy",1,"reported",91,{"slug":185,"title":397,"shortTitle":398,"definition":399,"status":9,"industries":400,"functions":403,"patterns":404,"audience":36,"autonomy":406,"adoptionStage":38,"evidenceCount":407,"publicEvidenceCount":408,"organizations":409,"bestGrade":225,"headline":415,"lastVerified":189,"indexable":273},"Real time AI assist for contact centre agents","Live agent assist","A real time copilot for human contact centre agents during a live call or chat: it transcribes the conversation as it happens, surfaces the relevant knowledge and next step, drafts responses, and writes the after call summary and CRM notes, while the agent stays in control of what is said and done.",[18,20,376,21,401,402,22],"healthcare","retail-and-ecommerce",[24,25],[405,29,28,27],"speech-analytics","assist",7,5,[410,411,412,413,414],"DBS Bank","Definity","Oportun","SEB","SIGNAL IDUNA",{"kpi":416,"label":417,"unit":267,"n":361,"nUpTo":357,"kind":394,"value":418,"qualifier":296,"claimant":270,"organization":411,"vendorReported":273},"productivity-gain","Productivity gain",15,{"slug":186,"title":420,"shortTitle":421,"definition":422,"status":9,"industries":423,"functions":425,"patterns":428,"audience":36,"autonomy":37,"adoptionStage":429,"evidenceCount":430,"publicEvidenceCount":430,"organizations":431,"bestGrade":225,"headline":227,"lastVerified":189,"indexable":273},"AI for support knowledge article generation and maintenance","Knowledge article generation","AI that drafts knowledge base articles from resolved tickets, cases and conversations, detects questions the knowledge base does not answer and articles that are outdated or contradict each other, and proposes new or revised articles for a knowledge owner to review and publish.",[18,19,424],"automotive",[426,24,427],"knowledge-management","it-and-engineering",[27,28,30],"emerging",4,[199,432,433,434],"Internal Revenue Service","U.S. National Science Foundation","Rivian",{"slug":187,"title":436,"shortTitle":437,"definition":438,"status":9,"industries":439,"functions":441,"patterns":443,"audience":36,"autonomy":37,"adoptionStage":445,"segment":446,"evidenceCount":361,"publicEvidenceCount":361,"organizations":447,"bestGrade":225,"headline":450,"lastVerified":189,"indexable":273},"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.",[18,20,440,376,21],"payments",[378,24,442],"regulatory-compliance",[30,28,27,444,29],"agentic-workflow","early-adopters","middle-office",[448,449],"Lloyds Banking Group","NatWest Group",{"kpi":451,"label":452,"unit":453,"n":393,"nUpTo":357,"kind":394,"value":408,"qualifier":268,"claimant":454,"organization":448,"vendorReported":200},"time-saved-per-task","Time saved per task","minutes","organization",{"slug":188,"title":456,"shortTitle":457,"definition":458,"status":9,"industries":459,"functions":460,"patterns":462,"audience":36,"autonomy":37,"adoptionStage":429,"evidenceCount":408,"publicEvidenceCount":408,"organizations":463,"bestGrade":225,"headline":227,"lastVerified":189,"indexable":273},"AI drafting copilot for civil servants for correspondence, briefings and ministerial replies","Civil servant drafting copilot","A generative AI assistant that drafts replies to correspondence from the public and elected representatives, briefings, submissions and summaries for civil servants, grounded in the department's approved lines, policy documents and case data, with the official editing and approving every word before it is sent or cleared.",[19],[461,426,378],"citizen-services",[27,29,28],[464,465,466,467,468],"Cabinet Office (Government Communication Service)","Crown Prosecution Service","Department for Education","Department for Science, Innovation and Technology (Incubator for Artificial Intelligence)","Government Digital Service",{"indexable":273,"reasons":470},[],[472,476,481,489,496,502,509,515,523,530,537,543,550,556,562,567,574,580,586,592,598,604,609,614,619,626,633,638,643,650,656,662,669,674],{"id":153,"label":473,"issuer":159,"region":160,"url":161,"description":474,"useCases":475,"indexable":273},"EU AI Act","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":477,"issuer":159,"region":160,"url":478,"description":479,"useCases":480,"indexable":273},"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":482,"label":483,"issuer":484,"region":485,"url":486,"description":487,"useCases":488,"indexable":273},"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":490,"label":491,"issuer":492,"region":202,"url":493,"description":494,"useCases":495,"indexable":273},"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":497,"label":498,"issuer":159,"region":160,"url":499,"description":500,"useCases":501,"indexable":273},"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":503,"label":504,"issuer":505,"region":160,"url":506,"description":507,"useCases":508,"indexable":273},"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":155,"label":510,"issuer":511,"region":160,"url":512,"description":513,"useCases":514,"indexable":273},"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":516,"label":517,"issuer":518,"region":519,"url":520,"description":521,"useCases":522,"indexable":273},"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":524,"label":525,"issuer":526,"region":519,"url":527,"description":528,"useCases":529,"indexable":273},"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":531,"label":532,"issuer":533,"region":485,"url":534,"description":535,"useCases":536,"indexable":273},"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":538,"label":539,"issuer":540,"region":202,"url":541,"description":542,"useCases":536,"indexable":273},"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":544,"label":545,"issuer":546,"region":160,"url":547,"description":548,"useCases":549,"indexable":273},"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":551,"label":552,"issuer":553,"region":485,"url":554,"description":555,"useCases":418,"indexable":273},"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":557,"label":558,"issuer":159,"region":160,"url":559,"description":560,"useCases":561,"indexable":273},"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":563,"label":564,"issuer":159,"region":160,"url":565,"description":566,"useCases":561,"indexable":273},"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":568,"label":569,"issuer":570,"region":202,"url":571,"description":572,"useCases":573,"indexable":273},"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":575,"label":576,"issuer":159,"region":160,"url":577,"description":578,"useCases":579,"indexable":273},"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":581,"label":582,"issuer":583,"region":202,"url":584,"description":585,"useCases":579,"indexable":273},"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":587,"label":588,"issuer":589,"region":485,"url":590,"description":591,"useCases":579,"indexable":273},"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":593,"label":594,"issuer":159,"region":160,"url":595,"description":596,"useCases":597,"indexable":273},"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":599,"label":600,"issuer":601,"region":202,"url":602,"description":603,"useCases":597,"indexable":273},"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":605,"label":606,"issuer":518,"region":519,"url":607,"description":608,"useCases":65,"indexable":273},"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":610,"label":611,"issuer":159,"region":160,"url":612,"description":613,"useCases":65,"indexable":273},"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":615,"label":616,"issuer":159,"region":160,"url":617,"description":618,"useCases":65,"indexable":273},"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":620,"label":621,"issuer":622,"region":160,"url":623,"description":624,"useCases":625,"indexable":273},"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":627,"label":628,"issuer":629,"region":202,"url":630,"description":631,"useCases":632,"indexable":273},"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":634,"label":635,"issuer":159,"region":160,"url":636,"description":637,"useCases":632,"indexable":273},"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":639,"label":640,"issuer":159,"region":160,"url":641,"description":642,"useCases":64,"indexable":273},"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":644,"label":645,"issuer":646,"region":647,"url":648,"description":649,"useCases":408,"indexable":273},"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":651,"label":652,"issuer":653,"region":160,"url":654,"description":655,"useCases":430,"indexable":273},"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":657,"label":658,"issuer":659,"region":160,"url":660,"description":661,"useCases":430,"indexable":273},"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":663,"label":664,"issuer":665,"region":519,"url":666,"description":667,"useCases":668,"indexable":273},"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.",3,{"id":670,"label":671,"issuer":159,"region":160,"url":672,"description":673,"useCases":668,"indexable":273},"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":675,"label":676,"issuer":677,"region":202,"url":678,"description":679,"useCases":668,"indexable":273},"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.",1790598303597]