[{"data":1,"prerenderedAt":1074},["ShallowReactive",2],{"uc-reg-uk-gdpr":3},{"regulation":4,"includeUnpublished":11,"indexable":12,"useCases":13},{"id":5,"label":6,"issuer":7,"region":8,"url":9,"description":10},"uk-gdpr","UK GDPR","Information Commissioner's Office","europe","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",false,true,[14,40,62,85,103,131,153,174,194,216,236,255,272,293,312,327,342,353,368,386,405,419,438,460,483,500,513,529,543,558,579,595,608,621,638,652,665,679,695,707,720,734,750,765,780,795,811,832,851,862,877,891,906,924,941,954,973,986,999,1012,1024,1036,1049,1061],{"slug":15,"title":16,"shortTitle":17,"definition":18,"status":19,"industries":20,"functions":22,"patterns":24,"audience":27,"autonomy":28,"adoptionStage":29,"evidenceCount":30,"publicEvidenceCount":30,"organizations":31,"bestGrade":35,"headline":36,"lastVerified":37,"indexable":12,"euAiActTier":38,"euAiActBasis":39},"academic-advising-assistant","AI academic advising assistant for course selection and degree requirements","Academic advising assistant","An AI assistant that answers students' questions about degree requirements, course selection, prerequisites and majors, grounded in the institution's own catalog and advising documents, so students get quick answers to routine questions and are directed to a human advisor for anything that needs judgment, is time sensitive, or falls outside what the assistant can see.","published",[21],"education",[23],"customer-service",[25,26],"conversational-agent","rag-knowledge-assistant","customer-facing","assist","emerging",3,[32,33,34],"Elon University","Harvard College","University of Utah","B",null,"2026-09-28","limited","An assistant that answers informational questions about courses and requirements, without deciding admission, assigning students to an institution, or evaluating learning outcomes, falls under the transparency duty of Article 50: students must be told they are talking to AI. It would move toward Annex III point 3 (education and vocational training) if it were used to determine access or admission to an institution or programme (point 3(a)), or to evaluate learning outcomes, including when those outcomes are used to steer the learning process (point 3(b)).",{"slug":41,"title":42,"shortTitle":43,"definition":44,"status":19,"industries":45,"functions":47,"patterns":50,"audience":27,"autonomy":53,"adoptionStage":54,"evidenceCount":30,"publicEvidenceCount":30,"organizations":55,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":61},"apartment-leasing-and-resident-service-agent","AI agent for apartment leasing inquiries and resident service","Leasing and resident service agent","An AI agent that answers rental prospects and residents by chat, text, email and phone for a property manager: it answers questions about apartments and policies, books tours, takes maintenance requests, sends renewal and payment reminders, and hands anything that needs judgment to leasing or service staff.",[46],"real-estate",[23,48,49],"sales","operations",[25,51,52,26],"voice-agent","agentic-workflow","supervised-agent","early-adopters",[56,57,58],"Asset Living","AvalonBay Communities","Equity Residential","2026-09-27","context-dependent","An agent that answers questions, books tours and takes requests falls under the transparency duty of Article 50. It becomes high risk under Annex III point 5(b) if it evaluates the creditworthiness of applicants, for example in tenant screening, and under point 5(a) if a public body uses it to decide eligibility for social housing or other public assistance.",{"slug":63,"title":64,"shortTitle":65,"definition":66,"status":19,"industries":67,"functions":69,"patterns":70,"audience":27,"autonomy":53,"adoptionStage":29,"segment":71,"evidenceCount":72,"publicEvidenceCount":72,"organizations":73,"bestGrade":35,"headline":75,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":84},"atm-and-self-service-device-assistance","AI agent for ATM and self service device assistance","ATM and device assistance","An AI agent that helps customers with problems at or around ATMs and other self service devices, such as a withdrawal that did not pay out, a retained card, a blocked PIN or finding a working machine with cash, over the app, chat or phone, and that opens and tracks the claim or hands it to a person when it cannot be resolved.",[68],"banking",[23,49],[25,51,52],"front-office",1,[74],"NatWest Group",{"kpi":76,"label":77,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":81,"qualifier":82,"claimant":83,"organization":74,"vendorReported":11},"customer-satisfaction-uplift","Satisfaction uplift","percent",0,"reported",150,"exact","organization","A customer facing assistant must tell people they are interacting with an AI system unless that is obvious (Article 50(1)). It does not evaluate creditworthiness (Annex III point 5(b)) or eligibility for public assistance benefits (point 5(a)), so it is not high risk; biometric verification whose sole purpose is to confirm identity is excluded from Annex III point 1(a).",{"slug":86,"title":87,"shortTitle":88,"definition":89,"status":19,"industries":90,"functions":92,"patterns":94,"audience":27,"autonomy":53,"adoptionStage":54,"segment":71,"evidenceCount":97,"publicEvidenceCount":30,"organizations":98,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":102},"card-dispute-and-chargeback-intake","AI agent for card dispute intake","Card dispute intake","A customer facing AI agent that handles the \"I do not recognise this charge\" moment: it finds the transaction, separates suspected fraud from merchant disputes and simple confusion, explains the customer's rights and timelines, collects the details and evidence the rules require, and opens a correctly classified dispute case for the operations team.",[68,91],"payments",[23,93,49],"fraud-prevention",[25,51,95,96,52],"classification-and-routing","document-processing",4,[99,100,101],"Commonwealth Bank of Australia","Klarna","Visa","A customer facing assistant must tell people they are interacting with an AI system (Article 50(1)). It triages and opens cases but does not evaluate creditworthiness (Annex III point 5(b), which in any case excludes systems used to detect financial fraud) or decide access to an essential service, so it is not high risk under Annex III.",{"slug":104,"title":105,"shortTitle":106,"definition":107,"status":19,"industries":108,"functions":112,"patterns":115,"audience":118,"autonomy":119,"adoptionStage":54,"segment":120,"evidenceCount":121,"publicEvidenceCount":121,"organizations":122,"bestGrade":35,"headline":124,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":130},"complaints-handling-agent","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.",[109,68,91,110,111],"cross-industry","insurance","telecommunications",[113,23,114],"case-management","regulatory-compliance",[95,116,117,52,26],"summarization","content-generation","employee-facing","copilot","middle-office",2,[123,74],"Lloyds Banking Group",{"kpi":125,"label":126,"unit":127,"n":72,"nUpTo":79,"kind":80,"value":128,"qualifier":129,"claimant":83,"organization":123,"vendorReported":11},"time-saved-per-task","Time saved per task","minutes",5,"approximately","Complaint handling is not listed in Annex III, so internal classification and drafting for a handler who decides is minimal risk. Where the agent talks to customers to take the complaint, Article 50(1) requires telling them they are dealing with AI. Only a system that also assessed creditworthiness or priced life and health insurance (Annex III point 5(b) or 5(c)) would be high risk for that part.",{"slug":132,"title":133,"shortTitle":134,"definition":135,"status":19,"industries":136,"functions":137,"patterns":139,"audience":27,"autonomy":53,"adoptionStage":54,"segment":71,"evidenceCount":97,"publicEvidenceCount":97,"organizations":141,"bestGrade":35,"headline":146,"lastVerified":151,"indexable":12,"euAiActTier":38,"euAiActBasis":152},"device-and-connectivity-troubleshooting-agent","AI agent for device and connectivity troubleshooting on voice and chat","Device and connectivity troubleshooting","An AI agent that diagnoses and fixes a customer's broadband, mobile, TV or device problem by conversation on the phone or in chat, running line tests and remote resets through the operator's systems, guiding the customer step by step, and booking an engineer or handing over to a technician when the fault needs a person.",[111],[23,138],"field-service",[25,51,52,26,140],"computer-vision",[142,143,144,145],"Singtel","Virgin Media O2","Vodafone Germany","Vodafone",{"kpi":147,"label":148,"unit":78,"n":30,"nUpTo":79,"kind":149,"value":150,"qualifier":82,"claimant":36,"organization":36,"vendorReported":11},"containment-rate","Containment rate","median",70,"2026-09-26","A customer facing troubleshooting agent must tell people they are interacting with AI unless that is obvious (Article 50). It is not high risk as long as it is not used as a safety component in the management and operation of critical digital infrastructure (Annex III, point 2); running line tests and resets for one customer's service does not make it one.",{"slug":154,"title":155,"shortTitle":156,"definition":157,"status":19,"industries":158,"functions":160,"patterns":162,"audience":118,"autonomy":119,"adoptionStage":54,"evidenceCount":121,"publicEvidenceCount":121,"organizations":163,"bestGrade":166,"headline":167,"lastVerified":37,"indexable":12,"euAiActTier":172,"euAiActBasis":173},"performance-review-drafting-agent","AI agent for drafting employee performance reviews","Performance review drafting","An assistant that gathers an employee's work history, goals and peer feedback from the systems a manager already uses, and drafts a first version of the performance review for the manager to edit, rewrite or reject, so the manager starts from a grounded summary instead of a blank form and a stack of six months of context to recall from memory.",[109,159],"technology",[161],"human-resources",[117,116,52],[164,165],"Case Status","Rho","C",{"kpi":168,"label":169,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":170,"qualifier":82,"claimant":171,"organization":164,"vendorReported":12},"processing-time-reduction","Cycle time reduction",84,"vendor","high","Annex III point 4(b) lists AI systems intended to monitor and evaluate the performance and behaviour of workers as high risk. Synthesising an employee's work history and feedback into a performance evaluation is very plausibly profiling of a natural person under GDPR Article 4(4), which expressly covers analysing or predicting a person's \"performance at work\". Article 6(3)'s last subparagraph makes an Annex III system high risk regardless of the derogations whenever it performs such profiling, so a tool built this way is high risk by default however much the manager edits the output. The derogations in Article 6(3), including a narrow procedural task or improving the result of a previously completed human activity, do not fit drafting an evaluation from scratch; the closest is point (d), a preparatory task ahead of a human assessment, which only has a chance of applying to a design that avoids profiling altogether, for example one that only surfaces raw facts without synthesising a judgement. Where that derogation is argued, the documentation duty under Article 6(4) falls on the provider of the system, and only on the deploying organization when it builds the tool itself. Because the tool is high risk by default, Article 26(7) requires informing affected workers and their representatives before it is put into use in the workplace, whatever the tool's output is used for; using the same system's output directly in pay, promotion or termination decisions removes any doubt and triggers the full high risk regime. Annex III's high risk obligations apply from 2 December 2027.",{"slug":175,"title":176,"shortTitle":177,"definition":178,"status":19,"industries":179,"functions":180,"patterns":182,"audience":27,"autonomy":53,"adoptionStage":54,"segment":181,"evidenceCount":128,"publicEvidenceCount":128,"organizations":183,"bestGrade":35,"headline":189,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":193},"claims-first-notice-of-loss-agent","AI agent for first notice of loss claims intake","First notice of loss agent","An AI agent that takes the first notice of loss from a policyholder by phone, chat or app, identifies the policy, collects the facts of the incident and the evidence the claim type needs, opens the claim in the claims system and tells the customer what happens next, handing complex, injured or vulnerable claimants to a human handler.",[110],[181,23],"claims",[25,51,52,96],[184,185,186,187,188],"DOMCURA","Hippo","Lemonade","Progressive","Travelers",{"kpi":190,"label":191,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":192,"qualifier":82,"claimant":171,"organization":184,"vendorReported":12},"accuracy","Accuracy",90,"A customer facing intake agent must be designed so that people know they are interacting with AI (Article 50(1)). Claims intake and claims handling are not listed in Annex III: point 5(c) covers risk assessment and pricing in life and health insurance, not claims. One design choice changes this: detecting distress by inferring emotions from the caller's voice is emotion recognition based on biometric data, which is high risk under Annex III point 1(c) and needs disclosure under Article 50(3). Detecting vulnerability from what the caller says does not. The limited tier assumes that design: every handover signal on this page (injury, distress, anger, vulnerability) is detected from the words of the conversation, and inferring emotions from the voice itself is out of scope.",{"slug":195,"title":196,"shortTitle":197,"definition":198,"status":19,"industries":199,"functions":201,"patterns":202,"audience":27,"autonomy":53,"adoptionStage":54,"evidenceCount":203,"publicEvidenceCount":203,"organizations":204,"bestGrade":35,"headline":211,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":215},"flight-disruption-and-rebooking-agent","AI agent for flight disruption and rebooking","Flight disruption and rebooking","An AI agent that tells passengers proactively when their flight is delayed, cancelled or misconnected, explains why, and lets them rebook, request a refund or voucher, or claim care such as meals and hotels in one conversation on app, messaging, web or phone, within the airline's reaccommodation rules and passenger rights, handing complex itineraries and upset customers to a human with the context attached.",[200],"travel-and-hospitality",[23,49],[25,51,52,117],6,[205,206,207,208,209,210],"Air India","Delta Air Lines","JetBlue","Lufthansa Group","Pegasus Airlines","United Airlines",{"kpi":212,"label":213,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":214,"qualifier":82,"claimant":171,"organization":205,"vendorReported":12},"automation-rate","Automation rate",97,"A customer facing assistant must tell people they are interacting with AI (Article 50). It is not a high risk use under Annex III: it applies the airline's reaccommodation rules and does not decide on access to an essential public service or on creditworthiness.",{"slug":217,"title":218,"shortTitle":219,"definition":220,"status":19,"industries":221,"functions":223,"patterns":225,"audience":27,"autonomy":53,"adoptionStage":54,"evidenceCount":97,"publicEvidenceCount":97,"organizations":226,"bestGrade":166,"headline":231,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":235},"inbound-lead-qualification-agent","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.",[109,159,222,68],"automotive",[48,224],"marketing",[25,51,95,52],[227,228,229,230],"8x8","CarMax","Rocket Mortgage","SUSE",{"kpi":232,"label":233,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":234,"qualifier":82,"claimant":171,"organization":227,"vendorReported":12},"conversion-rate-uplift","Conversion uplift",19,"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.",{"slug":237,"title":238,"shortTitle":239,"definition":240,"status":19,"industries":241,"functions":242,"patterns":243,"audience":27,"autonomy":53,"adoptionStage":54,"segment":244,"evidenceCount":203,"publicEvidenceCount":203,"organizations":245,"bestGrade":35,"headline":251,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":254},"insurance-policy-servicing-agent","AI agent for insurance policy servicing","Policy servicing agent","An AI agent that answers policyholders' coverage questions from their own policy documents and completes routine policy changes and document requests (address and vehicle changes, adding a named driver or item, payment method updates, certificates and proof of cover) across chat, messaging and phone, and hands anything complex or sensitive to a human with the context.",[110],[23,49],[25,51,26,52],"policy-administration",[246,186,247,248,249,250],"LAQO","Nsure.com","Sun Life","Waterdrop","Zurich Insurance (Hong Kong)",{"kpi":147,"label":148,"unit":78,"n":121,"nUpTo":79,"kind":80,"value":252,"qualifier":253,"claimant":83,"organization":186,"vendorReported":11},50,"at-least","A customer facing assistant must be designed so that people know they are interacting with AI (Article 50(1), applicable from 2 August 2026). It is not high risk as long as it does not carry out risk assessment and pricing in relation to natural persons in life and health insurance (Annex III point 5(c)).",{"slug":256,"title":257,"shortTitle":258,"definition":259,"status":19,"industries":260,"functions":263,"patterns":264,"audience":27,"autonomy":53,"adoptionStage":54,"evidenceCount":128,"publicEvidenceCount":97,"organizations":266,"bestGrade":35,"headline":36,"lastVerified":151,"indexable":12,"euAiActTier":60,"euAiActBasis":271},"outbound-reminder-and-confirmation-agent","AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","An AI agent that contacts customers about something they already booked or ordered (an appointment, a delivery, a reservation or a service visit) to remind them, confirm attendance and let them cancel or move it in the same conversation, by phone, SMS, WhatsApp or email. It is operational service outreach, not marketing: nothing is sold, and success is measured in kept appointments and reused slots, not in conversion.",[109,261,262],"healthcare","government",[23,49],[51,25,52,265],"prediction-and-scoring",[267,268,269,270],"Sheffield Children's NHS Foundation Trust","University Hospitals Coventry and Warwickshire NHS Trust","U.S. Department of Veterans Affairs","WellSpan Health","People must be told they are interacting with an AI system, and synthetic voice or text must be identifiable as such (Article 50). Reminding people of existing bookings and disclosure alone are limited risk. A missed appointment score used by or for a public authority to grant, reduce, revoke or reclaim access to healthcare or other essential public assistance and services, for example deciding who is offered funded transport, can fall within Annex III point 5(a), and profiling of natural persons within Annex III rules out the Article 6(3) exemption. Using the score only to decide who gets extra reminders or support does not by itself place it outside Annex III when that support is itself the assistance being granted.",{"slug":273,"title":274,"shortTitle":275,"definition":276,"status":19,"industries":277,"functions":279,"patterns":280,"audience":118,"autonomy":119,"adoptionStage":54,"evidenceCount":282,"publicEvidenceCount":283,"organizations":284,"bestGrade":166,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":292},"outbound-sales-prospecting-agent","AI agent for outbound sales prospecting and personalized outreach","Outbound sales prospecting","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.",[109,159,278],"professional-services",[48,224],[117,52,281,265],"recommendation-and-personalization",9,7,[285,286,287,288,289,290,291],"A-LIGN","ANS","Dun & Bradstreet","Lumen Technologies","Merge","Oyster","Unifonic","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.",{"slug":294,"title":295,"shortTitle":296,"definition":297,"status":19,"industries":298,"functions":300,"patterns":301,"audience":27,"autonomy":53,"adoptionStage":54,"evidenceCount":203,"publicEvidenceCount":128,"organizations":302,"bestGrade":35,"headline":308,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":311},"parcel-tracking-and-delivery-exception-agent","AI agent for parcel tracking and delivery exceptions","Parcel tracking and delivery exceptions","An AI agent that answers \"where is my parcel\" and resolves delivery exceptions for parcel carriers and postal operators, such as missed deliveries, redelivery or a change of address or pickup point, delays, customs holds and lost or damaged parcel claims, on chat, messaging and phone, and hands disputes and claims above set limits to a human with the tracking history attached.",[299],"logistics-and-transportation",[23,49],[25,51,52,95],[303,304,305,306,307],"Chronopost","DPD Deutschland","DPD UK","Evri","PostNL",{"kpi":309,"label":310,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":252,"qualifier":82,"claimant":83,"organization":306,"vendorReported":11},"contact-deflection","Contact deflection","Article 50(1): an assistant that talks directly with recipients must be designed so they know they are interacting with an AI system, unless that is obvious from the context. It is not high risk: explaining tracking, changing a delivery and taking in a parcel claim fall under none of the Annex III areas (it does not decide on public assistance benefits, creditworthiness, insurance pricing or employment), and it involves no practice prohibited by Article 5.",{"slug":313,"title":314,"shortTitle":315,"definition":316,"status":19,"industries":317,"functions":318,"patterns":319,"audience":27,"autonomy":53,"adoptionStage":54,"evidenceCount":282,"publicEvidenceCount":203,"organizations":320,"bestGrade":35,"headline":324,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":326},"patient-appointment-scheduling-and-reminders-agent","AI agent for patient appointment scheduling, reminders and no show reduction","Patient scheduling and reminders","An AI agent that books, moves and cancels patient appointments by phone and messaging while following the provider's scheduling rules (referral, triage level, clinician and visit type, preparation), confirms and reminds patients in two way conversations, predicts who is likely to miss an appointment, and offers freed slots to patients on the waiting list. Unlike a general branch and appointment booking agent, it writes into the electronic health record and must respect clinical constraints, so anything clinical goes to staff.",[261],[23,49],[51,25,265,52],[321,322,323,267,268,270],"Audibel","Howard Brown Health","Mid and South Essex NHS Foundation Trust",{"kpi":147,"label":148,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":325,"qualifier":82,"claimant":171,"organization":322,"vendorReported":12},30,"Booking, rescheduling and reminders carry transparency duties: patients must be told they are dealing with AI (Article 50(1)). It becomes high risk if a public authority, or a provider acting on its behalf, uses it to evaluate eligibility for healthcare services (Annex III point 5(a)), or if it acts as an emergency healthcare patient triage system (Annex III point 5(d)). Clinical triage may also make it a medical device, which is high risk under Article 6(1) when the device needs a notified body assessment. Keep the agent to scheduling and use risk scores only to offer support.",{"slug":328,"title":329,"shortTitle":330,"definition":331,"status":19,"industries":332,"functions":333,"patterns":334,"audience":27,"autonomy":335,"adoptionStage":336,"segment":71,"evidenceCount":337,"publicEvidenceCount":30,"organizations":338,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":341},"offers-and-rewards-agent","AI agent for personalized offers and rewards","Offers and rewards","A customer facing AI agent for banks and card issuers that picks the offer, reward or loyalty action most relevant to each customer at each moment from their transactions and context, delivers it in the app, in messaging or through a colleague, and helps the customer understand, track and redeem rewards in conversation. Unlike campaign personalization, it works inside the customer's own account and loyalty relationship, one moment at a time.",[68,91],[224,48,23],[281,265,25],"autonomous","mainstream",8,[339,99,340],"Bank of America","DBS Bank","Ranking offers is generally minimal risk and the conversational part carries the Article 50 transparency duty. Using AI to evaluate creditworthiness for a credit offer is high risk (Annex III point 5(b)), and Article 5 prohibits techniques that exploit vulnerabilities due to a person's social or economic situation to distort their behaviour in a harmful way.",{"slug":343,"title":344,"shortTitle":345,"definition":346,"status":19,"industries":347,"functions":348,"patterns":349,"audience":27,"autonomy":53,"adoptionStage":29,"segment":71,"evidenceCount":30,"publicEvidenceCount":30,"organizations":350,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":352},"proactive-outbound-engagement-agent","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.",[68,91],[224,48,23],[25,51,52,281],[339,351,99],"Capital One","A customer facing agent must disclose that it is AI (Article 50(1)). It stays out of Annex III as long as eligibility for credit offers is decided upstream by the bank's own, separately governed credit processes; if the agent itself assessed creditworthiness it would be high risk under point 5(b).",{"slug":354,"title":355,"shortTitle":356,"definition":357,"status":19,"industries":358,"functions":359,"patterns":360,"audience":27,"autonomy":53,"adoptionStage":29,"segment":181,"evidenceCount":30,"publicEvidenceCount":121,"organizations":362,"bestGrade":35,"headline":365,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":367},"travel-insurance-claims-and-assistance-agent","AI agent for travel insurance claims and assistance","Travel insurance claims and assistance","An AI agent that helps insured travellers around the clock and in their own language: it answers cover questions, takes claims for delays, cancellations, lost baggage and medical costs, reads the receipts and certificates they upload, settles simple claims within set limits, and connects medical emergencies and complex cases to the assistance team at once.",[110,200],[181,23],[25,51,96,52,361],"translation",[363,364],"Allianz Partners","General Insurance Association of Singapore",{"kpi":212,"label":213,"unit":78,"n":79,"nUpTo":72,"kind":80,"value":150,"qualifier":366,"claimant":83,"organization":363,"vendorReported":11},"up-to","A customer facing agent must disclose that it is AI (Article 50), unless this is obvious from the context. Travel insurance claims handling is not listed in Annex III; point 5(c) covers risk assessment and pricing in life and health insurance, not the handling of claims. Handing a traveller who reports a medical emergency to the assistance team is not the classification of emergency calls or the patient triage in point 5(d), as long as the agent only hands over and does not set medical priorities. Claim decisions based solely on automated processing are subject to GDPR Article 22 (and its UK equivalent), and medical data is special category data under Article 9.",{"slug":369,"title":370,"shortTitle":371,"definition":372,"status":19,"industries":373,"functions":375,"patterns":376,"audience":27,"autonomy":53,"adoptionStage":54,"evidenceCount":203,"publicEvidenceCount":128,"organizations":377,"bestGrade":35,"headline":383,"lastVerified":151,"indexable":12,"euAiActTier":60,"euAiActBasis":385},"utility-billing-and-move-agent","AI agent for utility billing, payments, meter readings and move in or move out","Utility billing and home moves","An AI agent for energy and water customers that explains bills and tariffs, takes meter readings, sets up or changes payments, and handles move in and move out (final reads, closing one account and opening the next), across phone, messaging, email and the app, while anyone in payment difficulty, in a vulnerable situation or with a complaint is handed to a person.",[374],"energy-and-utilities",[23,49],[25,51,52,26],[378,379,380,381,382],"Aydem Energy","Dubai Electricity and Water Authority","EDF","Octopus Energy","Pacific Gas and Electric Company",{"kpi":147,"label":148,"unit":78,"n":121,"nUpTo":79,"kind":80,"value":384,"qualifier":82,"claimant":83,"organization":378,"vendorReported":11},75,"A customer service agent for bills, readings and moves falls under the transparency duty for systems that interact with people (Article 50(1)): customers must be told they are talking to AI. If the agent assesses creditworthiness, for example to set a deposit when a new customer moves in, that part falls under Annex III point 5(b) and is high risk; keep credit decisions in separately governed systems. The agent is not a safety component in the operation of the gas, water or electricity supply (Annex III point 2), so safety reports such as a gas smell go straight to the emergency line rather than being handled by the agent.",{"slug":387,"title":388,"shortTitle":389,"definition":390,"status":19,"industries":391,"functions":392,"patterns":394,"audience":118,"autonomy":119,"adoptionStage":336,"evidenceCount":30,"publicEvidenceCount":30,"organizations":396,"bestGrade":35,"headline":400,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":404},"ambient-clinical-documentation","AI ambient scribe for clinical documentation","Ambient clinical documentation","An AI scribe that listens, with the patient's consent, to the conversation between a clinician and a patient and drafts the clinical note, and often the letter or after visit summary, for the clinician to review, edit and sign in the health record. It documents; it does not diagnose or decide on treatment.",[261],[49,393],"knowledge-management",[395,116,117],"speech-analytics",[397,398,399],"Great Ormond Street Hospital for Children NHS Foundation Trust","Kaiser Permanente","US Department of Veterans Affairs, Veterans Health Administration",{"kpi":401,"label":402,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":403,"qualifier":82,"claimant":83,"organization":397,"vendorReported":11},"handling-time-reduction","Handling time reduction",8.2,"A scribe that only transcribes and summarises for a clinician to review is not listed in Annex III and is usually minimal risk, although the provider of a system that generates text can still owe the Article 50(2) duty to mark output as AI generated, unless an exception such as an assistive function for standard editing applies. If the product qualifies as medical device software under the EU Medical Device Regulation and needs a notified body assessment, for example because it suggests diagnoses or treatment, it becomes high risk under Article 6(1) and Annex I. Health data in audio and notes falls under GDPR Article 9 in every case.",{"slug":406,"title":407,"shortTitle":408,"definition":409,"status":19,"industries":410,"functions":412,"patterns":413,"audience":27,"autonomy":335,"adoptionStage":54,"evidenceCount":30,"publicEvidenceCount":30,"organizations":414,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":418},"publisher-archive-answer-engine","AI answer engine for readers built on a publisher's own journalism","Publisher archive answer engine","A generative AI search and answer tool on a publisher's own site or app that answers readers' questions only from that publisher's published journalism and archive, cites the articles it used, and declines to answer when its own reporting does not cover the question.",[411],"media-and-entertainment",[23],[26,25,116],[415,416,417],"Financial Times","TIME","The Washington Post","Article 50(1) requires that readers know they are interacting with AI. Article 50(4) requires deployers to disclose AI generated text published to inform the public on matters of public interest, unless the content has undergone human review or editorial control and a person holds editorial responsibility. Whether answers generated on demand for a single reader count as text published to inform the public is open to interpretation, but they are rarely reviewed before readers see them, so the conservative choice is to label them.",{"slug":420,"title":421,"shortTitle":422,"definition":423,"status":19,"industries":424,"functions":425,"patterns":427,"audience":27,"autonomy":119,"adoptionStage":54,"evidenceCount":283,"publicEvidenceCount":283,"organizations":428,"bestGrade":35,"headline":436,"lastVerified":151,"indexable":12,"euAiActTier":60,"euAiActBasis":437},"benefits-eligibility-and-application-assistant","AI assistant for benefits eligibility questions and applications","Benefits eligibility and application assistant","An AI assistant that helps people understand which public benefits and grants may apply to them, explains the rules and documents in plain language, guides them through the application and checks it for completeness, while the eligibility decision stays with the agency's rules and caseworkers.",[262],[426,113,23],"citizen-services",[25,26,51,96],[429,430,431,432,433,434,435],"Department for Work and Pensions","Federal Student Aid (U.S. Department of Education)","Federal Emergency Management Agency","Gemeente Nissewaard","Leeds City Council","Région Provence-Alpes-Côte d'Azur (Région Sud)","YoungWilliams",{"kpi":190,"label":191,"unit":78,"n":121,"nUpTo":79,"kind":80,"value":214,"qualifier":82,"claimant":83,"organization":429,"vendorReported":11},"Annex III point 5(a) makes AI high risk when it is used by or on behalf of public authorities to evaluate the eligibility of natural persons for essential public assistance benefits and services, or to grant, reduce, revoke or reclaim them. An assistant that only explains rules and guides applications carries the Article 50 transparency duties (limited risk); one that screens or scores eligibility falls under point 5(a), and a public body deploying it must carry out a fundamental rights impact assessment first (Article 27).",{"slug":439,"title":440,"shortTitle":441,"definition":442,"status":19,"industries":443,"functions":444,"patterns":445,"audience":27,"autonomy":53,"adoptionStage":336,"evidenceCount":446,"publicEvidenceCount":282,"organizations":447,"bestGrade":35,"headline":457,"lastVerified":59,"indexable":12,"euAiActTier":38,"euAiActBasis":459},"citizen-information-assistant","AI assistant for citizen information and government services","Citizen information assistant","An AI assistant that answers residents' and businesses' questions about government services in plain language, grounded only in official guidance with links to the source, points them to the right online service or office, and hands anything personal, urgent or outside its content to a human with the context attached.",[262],[426,23,393],[26,25,51,95],11,[448,449,450,451,452,453,454,455,456],"Abu Dhabi Government (TAMM)","Driver and Vehicle Licensing Agency","Estonian Information System Authority (RIA)","Foreign, Commonwealth and Development Office","Gemeente Tilburg","Government Digital Service","Government of the City of Buenos Aires","Madrid Destino","Montgomery County Government",{"kpi":190,"label":191,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":458,"qualifier":253,"claimant":83,"organization":451,"vendorReported":11},76,"An information assistant must tell people they are interacting with AI (Article 50). It is not high risk as long as it does not evaluate eligibility for public assistance benefits or services (Annex III point 5(a)); an assistant that starts to pre assess eligibility should be reassessed.",{"slug":461,"title":462,"shortTitle":463,"definition":464,"status":19,"industries":465,"functions":468,"patterns":472,"audience":118,"autonomy":119,"adoptionStage":54,"segment":71,"evidenceCount":128,"publicEvidenceCount":97,"organizations":473,"bestGrade":35,"headline":478,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":482},"deal-sourcing-and-due-diligence-assistant","AI assistant for deal sourcing and M&A due diligence","Deal sourcing and due diligence","An AI assistant that screens the market for acquisition or investment targets, builds company profiles, and speeds up due diligence by reading data room documents, extracting key terms and risks and drafting the investment or diligence memo, for the deal team to verify and decide.",[466,467,278],"capital-markets","wealth-and-asset-management",[469,470,471],"analytics-and-reporting","legal","risk-management",[96,116,26,52,265],[474,475,476,477],"Datasite","EQT","Freshfields","Rogo",{"kpi":479,"label":480,"unit":78,"n":79,"nUpTo":72,"kind":80,"value":481,"qualifier":366,"claimant":171,"organization":474,"vendorReported":12},"productivity-gain","Productivity gain",80,"Decision support for professional investors and advisers about companies is not a use listed in Annex III and is not a practice prohibited by Article 5. The users are deal professionals who know they are working with an AI tool, and no consumer interacts with it, so the Article 50(1) duty to disclose an AI interaction has little practical effect. Article 50(2) is different: a firm that builds the assistant itself, including on a platform such as Blits.ai and putting it into service under its own name, is the provider of that system and must mark generated text in a machine readable format, unless the system only performs an assistive function for standard editing or does not substantially alter the input data or its semantics, which may cover extraction and redaction. A firm that instead licenses a vendor product, such as Datasite or Rogo, should confirm that the vendor meets this duty. Obligations are otherwise general: AI literacy for the deal team under Article 4 and, where personal data in the data room is processed, the GDPR.",{"slug":484,"title":485,"shortTitle":486,"definition":487,"status":19,"industries":488,"functions":489,"patterns":490,"audience":118,"autonomy":53,"adoptionStage":54,"evidenceCount":128,"publicEvidenceCount":97,"organizations":491,"bestGrade":35,"headline":495,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":499},"hr-and-policy-assistant","AI assistant for HR and policy questions","HR and policy assistant","An employee self service assistant that answers questions on leave, pay and tax forms, benefits, expenses, travel and conduct policies from the organization's own HR documents, personalized to the employee's country and role, and starts simple HR transactions such as leave requests or employment letters in the HR system.",[109,68,159,261],[161,393],[26,25,52],[339,492,493,494],"IBM","Turing","Vituity",{"kpi":496,"label":497,"unit":78,"n":121,"nUpTo":79,"kind":80,"value":498,"qualifier":82,"claimant":83,"organization":492,"vendorReported":11},"employee-adoption","Employee adoption",99,"Answering policy questions and starting routine requests is limited risk, with the Article 50 duty to disclose AI. It becomes high risk under Annex III point 4 if it is used to make or support decisions on recruitment, promotion, termination, allocating tasks based on individual behaviour or personal traits, or the monitoring and evaluation of workers; an employer deploying it then must also inform workers' representatives and the affected workers before use (Article 26(7)). Sensitive topic detection should work on what the employee writes: inferring emotions of people in the workplace from biometric data such as voice or facial expressions is prohibited under Article 5(1)(f), except for medical or safety reasons.",{"slug":501,"title":502,"shortTitle":503,"definition":504,"status":19,"industries":505,"functions":506,"patterns":507,"audience":27,"autonomy":53,"adoptionStage":54,"evidenceCount":30,"publicEvidenceCount":30,"organizations":508,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":512},"student-enrollment-and-services-assistant","AI assistant for student enrollment and student services","Student enrollment assistant","An AI assistant that answers admitted and current students' questions about admissions, financial aid, registration, housing and deadlines by text message and web chat, sends timely reminders for the tasks each student still has to complete, and hands personal or complex cases to staff.",[21],[23,49],[25,26,95],[509,510,511],"Adelphi University","Austin Peay State University","Georgia State University","An assistant that answers questions and sends reminders falls under the transparency duty of Article 50. It becomes high risk under Annex III point 3(a) if it is used to determine access or admission or to assign students to institutions, and under point 3(c) if it assesses the level of education a student will receive. Keep admission and placement decisions with staff.",{"slug":514,"title":515,"shortTitle":516,"definition":517,"status":19,"industries":518,"functions":519,"patterns":521,"audience":27,"autonomy":53,"adoptionStage":336,"evidenceCount":97,"publicEvidenceCount":97,"organizations":522,"bestGrade":35,"headline":526,"lastVerified":151,"indexable":12,"euAiActTier":38,"euAiActBasis":528},"tax-questions-and-filing-assistant","AI assistant for tax questions and filing support","Tax questions and filing assistant","An AI assistant that answers taxpayers' questions about taxes, deadlines, refunds and payments, lets authenticated taxpayers check their status or set up a payment plan within set rules, and guides them through filing, while assessments, penalties and disputes stay with the tax authority's staff and systems.",[262],[426,23,520],"finance-and-accounting",[25,51,26,95],[523,524,525],"ClearTax","HM Revenue and Customs","Internal Revenue Service",{"kpi":190,"label":191,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":527,"qualifier":82,"claimant":83,"organization":524,"vendorReported":11},83.03,"A taxpayer assistant must tell people they are interacting with an AI system (Article 50). It is not listed in Annex III as long as it only informs and applies fixed rules. It becomes high risk under Annex III point 5(a) if it evaluates eligibility for, or grants, reduces, revokes or reclaims, public assistance benefits (which can include benefits paid through the tax system). Recital 59 says systems used for administrative proceedings by tax and customs authorities are not high risk law enforcement systems; audit selection and risk scoring are covered on a separate page.",{"slug":530,"title":531,"shortTitle":532,"definition":533,"status":19,"industries":534,"functions":535,"patterns":537,"audience":118,"autonomy":119,"adoptionStage":29,"segment":538,"evidenceCount":121,"publicEvidenceCount":121,"organizations":539,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":542},"adverse-action-explanations","AI drafted explanations for credit declines and adverse actions","Adverse action explanations","An assistant that turns the reason codes of a credit model into an accurate, specific and readable explanation of a decline, reduced limit or repricing for the customer, and a matching internal rationale for the file, without adding any reason the model did not produce.",[68,91],[536,114,23],"lending-and-credit",[117,26,25],"lending",[540,541],"Discover Financial Services","Wells Fargo","The drafting assistant does not assess creditworthiness, so on its own it is not the Annex III point 5(b) credit scoring system. It helps the lender meet the Article 86 right of affected people to a clear and meaningful explanation of decisions based on such a high risk system. If it is built into the scoring system it shares that system's high risk obligations; as a separate drafting tool its tier depends on its design and on how its output is reviewed. The follow up chat assistant must tell customers they are dealing with an AI system (Article 50).",{"slug":544,"title":545,"shortTitle":546,"definition":547,"status":19,"industries":548,"functions":549,"patterns":550,"audience":118,"autonomy":119,"adoptionStage":29,"evidenceCount":128,"publicEvidenceCount":128,"organizations":551,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":556,"euAiActBasis":557},"civil-servant-drafting-copilot","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.",[262],[426,393,113],[117,26,116],[552,553,554,555,453],"Cabinet Office (Government Communication Service)","Crown Prosecution Service","Department for Education","Department for Science, Innovation and Technology (Incubator for Artificial Intelligence)","minimal","An internal drafting assistant that an official reviews is not listed in Annex III. Article 50(4) requires disclosure of AI generated text published to inform the public on matters of public interest, unless it has undergone human review and a person holds editorial responsibility, which this design provides. If the tool is used to evaluate eligibility for public assistance benefits or services rather than to draft, Annex III point 5(a) can apply.",{"slug":559,"title":560,"shortTitle":561,"definition":562,"status":19,"industries":563,"functions":564,"patterns":565,"audience":567,"autonomy":28,"adoptionStage":54,"evidenceCount":128,"publicEvidenceCount":128,"organizations":568,"bestGrade":35,"headline":573,"lastVerified":59,"indexable":12,"euAiActTier":172,"euAiActBasis":578},"benefit-fraud-and-error-detection","AI for benefit fraud and error detection in social security","Benefit fraud and error detection","Risk models that help a social security or benefits agency decide which claims, payments and recipients to check for fraud or error, so that caseworkers verify the riskiest cases first, while every decision on entitlement stays with a person and the model is tested for fairness before and during use.",[262],[93,426,113],[265,566],"anomaly-detection","back-office",[569,429,570,571,572],"Centers for Medicare and Medicaid Services","Gemeente Rotterdam","U.S. Department of the Treasury, Bureau of the Fiscal Service","Uitvoeringsinstituut Werknemersverzekeringen (UWV)",{"kpi":574,"label":575,"unit":576,"n":72,"nUpTo":79,"kind":80,"value":577,"qualifier":82,"claimant":83,"organization":429,"vendorReported":11},"detection-rate-improvement","Detection improvement","multiplier",2.5,"Annex III point 5(a): AI systems used by or on behalf of public authorities to evaluate the eligibility of natural persons for essential public assistance benefits and services, or to grant, reduce, revoke or reclaim them. A fundamental rights impact assessment (Article 27) is required before a public body deploys it. A design that scores people over time on their social behaviour or personal characteristics and leads to unrelated or disproportionate detrimental treatment would fall under the Article 5(1)(c) prohibition on social scoring.",{"slug":580,"title":581,"shortTitle":582,"definition":583,"status":19,"industries":584,"functions":585,"patterns":586,"audience":567,"autonomy":53,"adoptionStage":54,"segment":181,"evidenceCount":282,"publicEvidenceCount":283,"organizations":587,"bestGrade":35,"headline":592,"lastVerified":151,"indexable":12,"euAiActTier":60,"euAiActBasis":594},"claims-triage-and-straight-through-processing","AI for claims triage and straight through processing","Claims triage and STP","AI that reads each new insurance claim and its documents, scores its complexity, cover questions, fraud and recovery signals, sends it to the right handling path and handler, and settles simple, low risk claims end to end within set limits without a person touching them.",[110],[181,49],[95,265,96,52,116],[588,363,589,186,590,591,188],"Admiral Seguros","Hiscox","Sedgwick","Tokio Marine & Nichido Fire Insurance",{"kpi":212,"label":213,"unit":78,"n":72,"nUpTo":72,"kind":80,"value":593,"qualifier":129,"claimant":83,"organization":186,"vendorReported":11},55,"Claims handling as such is not listed in Annex III. The same system becomes high risk when it is also used for risk assessment and pricing of natural persons in life and health insurance (point 5(c)), or when it is used by or on behalf of a public authority to grant, reduce, revoke or reclaim essential public assistance benefits and services, including healthcare services (point 5(a)). Otherwise the tier is minimal, so the design and the operator decide. Decisions on claims based solely on automated processing are also subject to Article 22 of the GDPR and the UK GDPR.",{"slug":596,"title":597,"shortTitle":598,"definition":599,"status":19,"industries":600,"functions":601,"patterns":602,"audience":567,"autonomy":119,"adoptionStage":29,"segment":603,"evidenceCount":30,"publicEvidenceCount":30,"organizations":604,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":556,"euAiActBasis":607},"complaints-root-cause-analysis","AI for complaints root cause and systemic issue analysis","Complaints root cause analysis","AI that reads the free text of complaints across all channels, clusters them into themes, separates systemic causes from one off events, links each theme to the product, process or control behind it and routes the insight to the owner who can fix it, with a human validating every root cause and every remediation.",[109,68,110,91,111,262],[114,23,469],[95,116,52,26],"second-line",[569,605,606],"Board of Governors of the Federal Reserve System","Federal Trade Commission","Analysing complaints in aggregate to find causes is not listed in Annex III, is not a practice prohibited by Article 5 and does not decide on individuals. It does not interact with the public, so the disclosure duty in Article 50(1) does not apply; the machine readable marking of generated text in Article 50(2) is a duty of the provider of the generative model or system that writes the summaries. If the same system decided individual complaint outcomes or redress, or its themes were used to evaluate the performance of individual complaint handlers (Annex III point 4), that design would need its own assessment.",{"slug":609,"title":610,"shortTitle":611,"definition":612,"status":19,"industries":613,"functions":614,"patterns":615,"audience":118,"autonomy":119,"adoptionStage":54,"evidenceCount":97,"publicEvidenceCount":97,"organizations":616,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":620},"court-and-case-file-summarization","AI for court and case file summarization","Case file summarization","AI that condenses court filings, case files, evidence recordings and earlier decisions into structured summaries, chronologies and draft case reports with references to the source pages, so that judges, prosecutors, tribunal staff and government lawyers find what matters faster, while the person responsible reads the underlying material and makes every legal judgment.",[262],[470,113],[116,96,26],[553,617,618,619],"U.S. Department of Justice","Gemeente Amsterdam","Supremo Tribunal Federal","Annex III point 8(a) makes AI high risk when it is intended to assist a judicial authority in researching and interpreting facts and the law and in applying the law to a concrete set of facts. Tools for prosecutors fall under point 6(c) if they evaluate the reliability of evidence, and tools that assist the examination of asylum, visa or residence applications fall under point 7(c). Under Article 6(3) a system that only performs a narrow procedural task or a preparatory task, such as organising a file or transcribing and summarising it for the person who decides, may not be high risk, but the provider must document that assessment (Article 6(4)). Summaries of internal legal advice for government lawyers, as Amsterdam plans, are generally outside Annex III.",{"slug":622,"title":623,"shortTitle":624,"definition":625,"status":19,"industries":626,"functions":627,"patterns":629,"audience":118,"autonomy":119,"adoptionStage":54,"segment":567,"evidenceCount":128,"publicEvidenceCount":128,"organizations":630,"bestGrade":35,"headline":634,"lastVerified":151,"indexable":12,"euAiActTier":38,"euAiActBasis":637},"outbound-notice-drafting","AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[109,68,110,262,261,467],[49,23,628,114,181],"collections-and-recovery",[117,26,361],[631,589,632,633],"Acentra Health","Health Resources and Services Administration","SS&C Technologies",{"kpi":168,"label":169,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":635,"qualifier":82,"claimant":171,"organization":636,"vendorReported":12},25,"SS&C GIDS and RS","Drafting letters for human approval is not listed in Annex III. The decision the letter communicates may come from a separate high risk system, such as credit scoring (Annex III point 5(b)) or a public body's eligibility decision on benefits (point 5(a)); the drafting tool does not make that decision. Article 50(2) requires the provider of an AI system that generates text to mark the output as artificially generated, which puts this on the limited risk (transparency) tier; this includes an organization that builds its own drafting tool. Article 50(2) does not apply where the AI has only an assistive function for standard editing and does not substantially alter the input data or the semantics of the output.",{"slug":639,"title":640,"shortTitle":641,"definition":642,"status":19,"industries":643,"functions":644,"patterns":645,"audience":118,"autonomy":119,"adoptionStage":336,"evidenceCount":97,"publicEvidenceCount":97,"organizations":646,"bestGrade":35,"headline":649,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":651},"ediscovery-and-disclosure-document-review","AI for eDiscovery and disclosure document review","eDiscovery document review","AI that sorts, prioritises and codes large collections of emails, chats and files for relevance, issues and legal privilege in litigation, investigations and regulatory requests, so that lawyers review the documents most likely to matter and can show the court how the rest were handled.",[109,278,262],[470,113],[95,96,116],[617,606,647,648],"Purpose Legal","Serious Fraud Office",{"kpi":168,"label":169,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":650,"qualifier":82,"claimant":171,"organization":647,"vendorReported":12},85,"Document review for a party in civil litigation or an internal investigation is not listed in Annex III, so it is usually minimal risk. It becomes high risk where a law enforcement authority uses AI to evaluate the reliability of evidence in the investigation or prosecution of criminal offences (Annex III point 6(c)), or where a judicial authority uses it to research and interpret facts and law (point 8(a)). Prosecutors and investigators should classify each use against those points.",{"slug":653,"title":654,"shortTitle":655,"definition":656,"status":19,"industries":657,"functions":658,"patterns":659,"audience":118,"autonomy":119,"adoptionStage":54,"evidenceCount":97,"publicEvidenceCount":97,"organizations":660,"bestGrade":35,"headline":36,"lastVerified":151,"indexable":12,"euAiActTier":556,"euAiActBasis":664},"freedom-of-information-request-processing","AI for freedom of information request processing","Freedom of information requests","AI that helps a public body handle freedom of information and open government requests: logging and clarifying requests, spotting duplicates, searching and deduplicating the records in scope, proposing redactions with the exemption that applies, and drafting the response letter, with an FOI officer deciding what is released.",[262],[426,470,113],[96,95,117],[617,661,662,663],"U.S. Food and Drug Administration, Center for Drug Evaluation and Research","Provincie Noord-Holland","U.S. Department of the Interior","Tools that support staff in searching, deduplicating and proposing redactions are not listed in Annex III (point 5(a) covers eligibility for public assistance benefits and services, not access to documents), and every release decision stays with an officer. A public facing request assistant that talks to requesters would carry the Article 50(1) transparency duty.",{"slug":666,"title":667,"shortTitle":668,"definition":669,"status":19,"industries":670,"functions":671,"patterns":672,"audience":27,"autonomy":119,"adoptionStage":54,"evidenceCount":97,"publicEvidenceCount":97,"organizations":673,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":678},"immigration-and-visa-application-assistant","AI for immigration and visa applications, from applicant questions to case preparation","Immigration and visa application assistant","AI that helps applicants understand immigration and visa requirements and submit complete applications, and helps immigration staff prepare cases by extracting form data, classifying evidence, routing applications and supporting interviews, while every grant or refusal is decided by an officer against the immigration rules.",[262],[426,113,49],[25,96,95,361],[674,675,676,677],"Home Office (Visa, Status and Information Services)","U.S. Department of State (Bureau of Consular Affairs)","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Annex III point 7(c) makes AI high risk when it assists public authorities in examining applications for asylum, visas or residence permits, including assessing the reliability of evidence. Applicant facing information assistants that give general guidance fall under the Article 50 transparency duties (limited risk). Evidence classification, routing and interview support used in the examination are likely high risk, unless the provider documents under Article 6(3) that a component only performs a narrow procedural or preparatory task. That exception never applies to a system that profiles natural persons, which matters for routing on personal attributes or risk profiles.",{"slug":680,"title":681,"shortTitle":682,"definition":683,"status":19,"industries":684,"functions":685,"patterns":686,"audience":567,"autonomy":53,"adoptionStage":336,"segment":567,"evidenceCount":203,"publicEvidenceCount":203,"organizations":687,"bestGrade":35,"headline":692,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":694},"correspondence-triage-and-routing","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.",[109,68,110,262],[49,23,113],[95,96,116],[688,689,690,691,188,269],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan",{"kpi":190,"label":191,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":693,"qualifier":82,"claimant":171,"organization":188,"vendorReported":12},91,"It depends on where the system runs. Classifying and routing a bank's or insurer's correspondence is not a use listed in Annex III, so it is minimal risk: the AI literacy duty of Article 4 applies, and the Article 50 duty to tell people they are dealing with AI does not, because the system does not interact with the sender. Used by or for a public authority in a benefits process covered by Annex III point 5(a), the provider can treat it as not high risk only while it performs a narrow procedural or preparatory task under Article 6(3); the provider must then document that assessment before it goes live (Article 6(4)) and register the system in the EU database (Article 49(2)). If the system evaluates eligibility for benefits or profiles the people who write in, it is high risk, so those judgements stay with people.",{"slug":696,"title":697,"shortTitle":698,"definition":699,"status":19,"industries":700,"functions":701,"patterns":702,"audience":567,"autonomy":28,"adoptionStage":336,"segment":181,"evidenceCount":128,"publicEvidenceCount":128,"organizations":703,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":706},"claims-fraud-detection","AI for insurance claims fraud detection","Claims fraud detection","AI that scores every insurance claim for fraud from first notice of loss onwards, combining claim, policy, document, image and network data to find suspicious claims, organised rings and inflated losses, and sends each alert with its reasons to a claims handler or special investigations unit for review.",[110],[181,93],[566,265,96,140,95],[704,705,364,186,591],"Assurant","AXA Switzerland","Claims fraud detection by an insurer is not listed in Annex III, and point 5(b) explicitly excludes AI systems used to detect financial fraud from the credit scoring category. Point 5(c) covers only risk assessment and pricing in life and health insurance, so a fraud model becomes high risk when it also feeds those decisions, or when it is used by or on behalf of a public authority to grant, reduce, revoke or reclaim public assistance benefits (point 5(a)). Profiling and automated decisions remain subject to GDPR, including Article 22 where a claim is refused on a decision based solely on automated processing.",{"slug":708,"title":709,"shortTitle":710,"definition":711,"status":19,"industries":712,"functions":713,"patterns":715,"audience":567,"autonomy":119,"adoptionStage":29,"segment":716,"evidenceCount":30,"publicEvidenceCount":30,"organizations":717,"bestGrade":35,"headline":36,"lastVerified":151,"indexable":12,"euAiActTier":60,"euAiActBasis":719},"insurance-renewal-and-retention","AI for insurance renewal processing and customer retention","Renewal and retention","AI that prepares and runs the renewal cycle: it digitizes renewal submissions and changes in risk for underwriters, flags policies at risk of lapsing or leaving, prepares the renewal conversation and answers customers' renewal questions, while renewal prices stay governed by the insurer's pricing rules and fair value obligations.",[110],[714,23,48],"underwriting",[265,96,25,281],"distribution",[589,247,718],"Zurich Insurance Group","Renewal intake for commercial lines and outreach are not listed in Annex III. Renewal risk assessment or pricing for life or health insurance of natural persons is high risk under point 5(c), and so is a lapse score that feeds those decisions; a lapse score used only to decide who gets a service call is not listed. Customer facing renewal assistants carry the Article 50(1) duty to tell people they are interacting with an AI system, unless that is obvious from the context.",{"slug":721,"title":722,"shortTitle":723,"definition":724,"status":19,"industries":725,"functions":726,"patterns":728,"audience":567,"autonomy":119,"adoptionStage":54,"segment":120,"evidenceCount":30,"publicEvidenceCount":30,"organizations":729,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":556,"euAiActBasis":733},"mule-network-detection","AI for money mule account and network detection","Mule network detection","Graph and behavioural machine learning that finds money mule accounts and the networks around them, such as circular flows, layering chains and clusters of newly linked accounts, and supports investigators in tracing scam proceeds and restricting accounts before the money is gone.",[68,91],[93,727],"financial-crime-compliance",[566,265,52,116],[730,731,732],"BigPay","ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Reserve Bank Innovation Hub (Reserve Bank of India)","Detecting mule accounts is fraud and AML detection by a private firm, which Annex III does not list; point 5(b) explicitly excludes systems used to detect financial fraud from the credit scoring category. Restricting an account based solely on an automated score can be a decision with similarly significant effects under GDPR Article 22, so keep a human decision and a route to challenge.",{"slug":735,"title":736,"shortTitle":737,"definition":738,"status":19,"industries":739,"functions":740,"patterns":741,"audience":27,"autonomy":53,"adoptionStage":54,"evidenceCount":283,"publicEvidenceCount":283,"organizations":742,"bestGrade":35,"headline":748,"lastVerified":151,"indexable":12,"euAiActTier":38,"euAiActBasis":749},"non-emergency-service-request-routing","AI for non emergency service requests and 311 routing","Non emergency service request routing","An AI agent on a city's 311 style phone, chat and messaging channels that answers routine municipal questions, takes service requests such as potholes, missed collections or broken street lights with the right location and details, creates the case in the work order system and routes anything urgent or complex to the right team.",[262],[426,23,113],[25,51,95,52],[448,743,744,745,456,746,747],"London Borough of Barnet","City of Kelowna","Galt Police Department","Newcastle City Council","Rio de Janeiro City Data Office (Escritório de Dados)",{"kpi":190,"label":191,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":481,"qualifier":82,"claimant":171,"organization":744,"vendorReported":12},"A 311 assistant must disclose that it is AI (Article 50). It is not high risk while it only informs and creates service cases. If it evaluates or classifies emergency calls or sets dispatch priority for police, fire or medical services, it falls under Annex III point 5(d) and becomes high risk.",{"slug":751,"title":752,"shortTitle":753,"definition":754,"status":19,"industries":755,"functions":756,"patterns":757,"audience":118,"autonomy":119,"adoptionStage":29,"evidenceCount":128,"publicEvidenceCount":128,"organizations":758,"bestGrade":35,"headline":763,"lastVerified":151,"indexable":12,"euAiActTier":60,"euAiActBasis":764},"permit-and-licence-application-processing","AI for permit and licence application processing","Permit and licence application processing","AI that helps applicants submit complete permit and licence applications and helps officers process them, by answering questions about requirements, checking applications for missing or inconsistent information, pulling the relevant policies, history and constraints, and drafting reports, while the grant or refusal stays with a named officer or a published rule.",[262],[426,113,114],[96,52,26,25],[759,433,760,761,762],"Intellectual Property Office","U.S. Fish and Wildlife Service","U.S. Department of Agriculture","West Berkshire Council",{"kpi":190,"label":191,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":650,"qualifier":253,"claimant":83,"organization":433,"vendorReported":11},"Permit and licence decisions are not listed as such in Annex III, so officer decision support is usually minimal risk, and an assistant that talks to applicants carries the Article 50 transparency duty. The exceptions are permits in an Annex III area: examining applications for visas and residence permits (point 7) and evaluating eligibility for essential public assistance benefits and services (point 5(a)) are high risk. Solely automated decisions with legal or similarly significant effects on a person fall under GDPR Article 22 whatever the tier.",{"slug":766,"title":767,"shortTitle":768,"definition":769,"status":19,"industries":770,"functions":771,"patterns":772,"audience":567,"autonomy":119,"adoptionStage":54,"evidenceCount":128,"publicEvidenceCount":128,"organizations":773,"bestGrade":35,"headline":777,"lastVerified":59,"indexable":12,"euAiActTier":556,"euAiActBasis":779},"public-consultation-response-analysis","AI for public consultation response analysis","Consultation response analysis","AI that reads every free text response to a public consultation or rulemaking comment period, proposes themes, maps each response to the themes that officials have validated, flags duplicates, campaign letters and responses that need special attention, and produces counts and summaries for the analysts who write the government's response.",[262],[426,469],[116,95,117],[774,605,775,555,776],"Centers for Disease Control and Prevention","Department for Transport","U.S. Department of Transportation, Office of the Secretary",{"kpi":190,"label":191,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":778,"qualifier":253,"claimant":83,"organization":775,"vendorReported":11},92,"Organising and summarising consultation responses for analysts does not decide on individuals and is not listed in Annex III, so no high risk obligations apply. If AI generated text is published to inform the public on matters of public interest without human review and editorial responsibility, Article 50(4) requires disclosure.",{"slug":781,"title":782,"shortTitle":783,"definition":784,"status":19,"industries":785,"functions":786,"patterns":787,"audience":27,"autonomy":119,"adoptionStage":54,"evidenceCount":128,"publicEvidenceCount":128,"organizations":788,"bestGrade":35,"headline":793,"lastVerified":59,"indexable":12,"euAiActTier":172,"euAiActBasis":794},"recruitment-screening-and-interview-scheduling","AI for recruitment screening and interview scheduling","Recruitment screening and scheduling","AI that answers candidates' questions, collects applications in conversation, schedules interviews and, where the organization chooses, assesses applications against the job requirements for a recruiter, who makes every selection decision. In the EU, the screening part is a high risk AI system under Annex III point 4 of the AI Act.",[109,262,200,278],[161],[25,95,265,52],[789,790,676,791,792],"Chipotle Mexican Grill","Gojob","Mastercard","Trace3",{"kpi":168,"label":169,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":192,"qualifier":129,"claimant":83,"organization":791,"vendorReported":11},"Annex III point 4(a) lists AI systems intended to be used for the recruitment or selection of natural persons, in particular to place targeted job advertisements, to analyse and filter job applications and to evaluate candidates. Screening, ranking and scoring applications is therefore high risk. A component limited to a narrow procedural task, such as booking interview slots or answering process questions, can fall outside the high risk category under Article 6(3), but only if it does not materially influence the outcome and does not profile people, and that assessment must be documented (Article 6(4)). Deployers of the high risk part must follow the instructions for use, assign competent human oversight, keep logs, inform workers' representatives and inform candidates that a high risk system is used (Article 26). An organization that builds its own screening system becomes its provider, with conformity assessment duties. The chatbot part also carries the Article 50 disclosure duty.",{"slug":796,"title":797,"shortTitle":798,"definition":799,"status":19,"industries":800,"functions":801,"patterns":802,"audience":118,"autonomy":28,"adoptionStage":54,"evidenceCount":203,"publicEvidenceCount":203,"organizations":803,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":810},"inspection-prioritization","AI for risk based inspection prioritization in food safety, workplace and environmental regulation","Inspection prioritization","Models that predict which premises, operators or activities are most likely to be non compliant, so that inspectors in food safety, workplace safety, environmental and other regulation spend their visits where the risk is highest, ideally with inspectors choosing the visits and random inspections testing the model.",[262],[471,113,114],[265,566],[804,805,806,807,808,809],"Care Quality Commission","Driver and Vehicle Standards Agency","U.S. Environmental Protection Agency, Office of Enforcement and Compliance Assurance","Food Standards Agency","Nederlandse Arbeidsinspectie","Nederlandse Voedsel- en Warenautoriteit (NVWA)","Prioritizing inspections of businesses and premises is not a use listed in Annex III, so such a system is usually not high risk. The assessment changes when it scores natural persons, such as individual licensed professionals or sole traders, and the inspectorate acts as a law enforcement authority: assessing the risk that a person offends, or profiling persons in the detection or investigation of criminal offences, is high risk under Annex III point 6 (d) and (e), and predicting that a person will commit a criminal offence based solely on profiling is prohibited by Article 5(1)(d). GDPR applies wherever sole traders, home based businesses or named professionals are scored.",{"slug":812,"title":813,"shortTitle":814,"definition":815,"status":19,"industries":816,"functions":817,"patterns":818,"audience":567,"autonomy":53,"adoptionStage":54,"segment":120,"evidenceCount":283,"publicEvidenceCount":283,"organizations":819,"bestGrade":35,"headline":827,"lastVerified":151,"indexable":12,"euAiActTier":556,"euAiActBasis":831},"sanctions-screening-adjudication","AI for sanctions screening alert adjudication","Sanctions screening adjudication","AI that works the alerts raised when customer, counterparty or payment names match sanctions and watchlists: it resolves fuzzy matches across transliterations, aliases and naming conventions, clears clear non matches with a documented reason, and escalates true or uncertain hits with the evidence attached.",[68,91],[727],[95,265,52],[820,821,822,823,824,825,826],"AJ Bell","First National Bank of Omaha (FNBO)","HSBC","Mashreq","Ratepay","Standard Chartered","United Overseas Bank (UOB)",{"kpi":828,"label":829,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":830,"qualifier":82,"claimant":83,"organization":826,"vendorReported":11},"false-positive-reduction","False positive reduction",60,"Sanctions screening by banks and payment firms is not listed in Annex III: point 5 covers credit scoring and life and health insurance pricing, and point 6 covers AI used by or on behalf of law enforcement authorities. It is not a prohibited practice under Article 5, and as an internal tool it carries no Article 50 transparency duty. It still processes personal data at scale, so GDPR applies, and decisions that block a payment or freeze assets remain human decisions.",{"slug":833,"title":834,"shortTitle":835,"definition":836,"status":19,"industries":837,"functions":838,"patterns":840,"audience":567,"autonomy":53,"adoptionStage":54,"evidenceCount":337,"publicEvidenceCount":283,"organizations":842,"bestGrade":35,"headline":849,"lastVerified":151,"indexable":12,"euAiActTier":60,"euAiActBasis":850},"synthetic-test-data-generation","AI for synthetic test data generation","Synthetic test data generation","AI that generates realistic synthetic datasets, such as customers, transactions, documents and conversations, which keep the structure and statistical properties of production data without containing real personal data, so teams can test software, train and validate models and run demos safely.",[109,68,261,110],[839,469],"it-and-engineering",[841,117],"synthetic-data-generation",[843,844,525,845,846,847,848],"Boomi","Financial Conduct Authority","JPMorgan Chase","Kin Insurance","Merkur Versicherung AG","Patterson Dental",{"kpi":168,"label":169,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":384,"qualifier":82,"claimant":171,"organization":848,"vendorReported":12},"A generator of synthetic tabular test data is not listed in Annex III and does not interact with people, so it is minimal risk with only the AI literacy duty of Article 4. When the system generates synthetic text, images, audio or video, such as documents or conversation transcripts, Article 50(2) requires its provider to mark the output in a machine readable format as artificially generated. When synthetic data is used to train, validate or test a high risk system, such as credit scoring, it falls under that system's data governance duties in Article 10.",{"slug":852,"title":853,"shortTitle":854,"definition":855,"status":19,"industries":856,"functions":857,"patterns":858,"audience":567,"autonomy":28,"adoptionStage":54,"evidenceCount":30,"publicEvidenceCount":30,"organizations":859,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":861},"tax-compliance-risk-scoring","AI for tax compliance risk scoring and audit selection","Tax compliance risk scoring","Models that score tax returns, taxpayers and transactions for the risk of error, underreporting or fraud, so that a tax administration spends its audit and compliance capacity where the risk is highest, with an officer deciding every compliance action and the selection itself monitored for fairness.",[262],[471,113,93],[265,566],[860,524,525],"Belastingdienst","Risk selection for administrative tax audits is not listed in Annex III, and Recital 59 says systems used by tax and customs authorities in administrative proceedings should not be treated as high risk law enforcement systems. Use in criminal tax investigations (Annex III point 6, law enforcement), or evaluating the eligibility of natural persons for public assistance benefits run through the tax system (Annex III point 5(a)), can make it high risk. When individuals are scored in administrative tax work, the GDPR applies, including its profiling rules (Member States may restrict some rights for taxation matters under Article 23). Article 22 applies when a decision with legal or similarly significant effect is taken solely by the model. Criminal investigations fall outside the GDPR and under the Law Enforcement Directive (EU) 2016/680 instead.",{"slug":863,"title":864,"shortTitle":865,"definition":866,"status":19,"industries":867,"functions":868,"patterns":871,"audience":567,"autonomy":53,"adoptionStage":54,"segment":872,"evidenceCount":97,"publicEvidenceCount":97,"organizations":873,"bestGrade":35,"headline":875,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":876},"telecom-fraud-detection","AI for telecom fraud detection (SIM swap, IRSF and Wangiri)","Telecom fraud detection","AI that protects the operator's own network, revenue and numbers from fraud: it watches call, messaging, roaming and account activity to detect SIM swap and port out takeovers, international revenue share fraud (IRSF) and Wangiri one ring scams, blocks or flags them in real time, and shares risk signals with banks and other businesses that rely on the phone number for security. Scam calls aimed at subscribers are handled by call blocking.",[111],[93,869,870],"network-operations","security-operations",[566,265,95],"customer-protection",[874,145],"Telstra",{"kpi":574,"label":575,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":325,"qualifier":82,"claimant":83,"organization":145,"vendorReported":11},"Fraud detection is not listed as high risk in Annex III, and point 5(b) explicitly excludes systems used to detect financial fraud from the creditworthiness category. Blocking fraud traffic is not normally a safety component of critical digital infrastructure (point 2). The tier can change if the same scores are reused for an Annex III purpose: eligibility for essential public assistance benefits and services (point 5(a)), creditworthiness or credit scoring of natural persons (point 5(b)), or risk assessment and pricing for life and health insurance (point 5(c)). A voice or chat agent that takes fraud reports from customers also carries the Article 50(1) duty to tell people they are dealing with an AI system.",{"slug":878,"title":879,"shortTitle":880,"definition":881,"status":19,"industries":882,"functions":884,"patterns":885,"audience":118,"autonomy":28,"adoptionStage":54,"evidenceCount":97,"publicEvidenceCount":97,"organizations":886,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":890},"internal-talent-marketplace-matching","AI internal talent marketplace for matching employees to projects, roles and mentors","Internal talent marketplace","An internal platform that uses AI to infer employees' skills and interests and recommend short term projects, open roles, mentors and learning to them, while showing managers which employees fit an opportunity, so that work is staffed from inside before hiring or contracting externally.",[109,883,91,262],"manufacturing",[161],[281,265],[887,791,888,889],"Federal Bureau of Prisons","Schneider Electric","Unilever","Annex III point 4 lists AI used for the recruitment or selection of natural persons (4(a)) and AI used to make decisions affecting promotion, or to allocate tasks based on individual behaviour, personal traits or characteristics (4(b)). A marketplace that ranks employees for internal roles or allocates projects on the basis of inferred traits is therefore high risk. Recommending learning content or mentors to an employee who chooses freely is usually not. Deployers of the high risk part must inform workers' representatives and the affected employees before use (Article 26).",{"slug":892,"title":893,"shortTitle":894,"definition":895,"status":19,"industries":896,"functions":897,"patterns":898,"audience":118,"autonomy":119,"adoptionStage":54,"evidenceCount":97,"publicEvidenceCount":97,"organizations":899,"bestGrade":35,"headline":903,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":905},"legal-research-and-drafting-assistant","AI legal research and drafting assistant for lawyers","Legal research and drafting","A generative AI assistant for lawyers in firms, legal departments and public bodies that finds and summarises case law, legislation and internal know how, answers legal questions with citations and drafts first versions of memos, briefings, letters and filings, which a lawyer verifies and signs off.",[278,109,262],[470,393],[26,117,116,96],[900,901,617,902],"A&O Shearman","Ashurst Perkins Coie","U.S. Securities and Exchange Commission",{"kpi":479,"label":480,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":904,"qualifier":129,"claimant":83,"organization":901,"vendorReported":11},45,"Research and drafting support for lawyers in firms and companies is not listed in Annex III, so it is normally minimal risk with AI literacy duties. Annex III point 8(a) makes it high risk when a judicial authority, or someone on its behalf, uses AI to research and interpret facts and the law and to apply the law to a concrete set of facts, or when it is used in a similar way in alternative dispute resolution, so a deployment for courts, tribunals or arbitration needs its own classification.",{"slug":907,"title":908,"shortTitle":909,"definition":910,"status":19,"industries":911,"functions":913,"patterns":914,"audience":567,"autonomy":53,"adoptionStage":336,"evidenceCount":337,"publicEvidenceCount":283,"organizations":915,"bestGrade":35,"headline":922,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":923},"personalized-marketing-at-scale","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.",[109,200,411,912,68],"retail-and-ecommerce",[224,48],[281,265,117],[916,917,99,918,919,920,921],"Amazon","Catchtable","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":232,"label":233,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":325,"qualifier":82,"claimant":171,"organization":917,"vendorReported":12},"Most personalization and content generation is minimal risk. Providers of systems that generate synthetic audio, image, video or text content must mark the output as artificially generated, and deployers must disclose deep fakes (Article 50(2) and 50(4)). Personalization that deploys manipulative or deceptive techniques, or exploits vulnerabilities due to age, disability or a specific social or economic situation, in a way that causes or is reasonably likely to cause significant harm, is prohibited under Article 5(1)(a) and (b). Using AI to assess creditworthiness or to price life and health insurance is high risk under Annex III point 5(b) and 5(c) and belongs on its own page. Outside the AI Act, the FCA Consumer Duty applies only to FCA regulated firms (the financial services slice of this use case), and the Telephone Consumer Protection Act applies only to campaigns delivered by call or text message in the US.",{"slug":925,"title":926,"shortTitle":927,"definition":928,"status":19,"industries":929,"functions":930,"patterns":931,"audience":118,"autonomy":119,"adoptionStage":336,"segment":71,"evidenceCount":203,"publicEvidenceCount":203,"organizations":932,"bestGrade":35,"headline":938,"lastVerified":59,"indexable":12,"euAiActTier":556,"euAiActBasis":940},"client-meeting-notes-and-crm-update","AI meeting notes and CRM update for wealth advisors","Advisor meeting notes","An AI notetaker for wealth advisors that turns a client advice meeting, recorded with the client's consent, into the file note, follow up message and CRM record the firm needs to evidence its advice; unlike a general meeting summarizer, its output becomes part of the regulated client record. It drafts a structured note with the client's goals, circumstances, decisions and action items, and writes it into the CRM once the advisor has approved it.",[467,68],[48,114,49],[116,395,52,117],[339,933,934,935,936,937],"Commerzbank","Morgan Stanley","Quilter","SEB","UniSuper",{"kpi":479,"label":480,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":939,"qualifier":82,"claimant":171,"organization":936,"vendorReported":12},15,"Transcribing and summarizing meetings for an employee is not a use listed in Annex III, and the advisor reviews every note before it is filed or sent. The tier would change if the tool inferred emotions: emotion recognition is high risk under Annex III point 1(c), and inferring the emotions of employees at work is prohibited under Article 5(1)(f). Both stay out of scope.",{"slug":942,"title":943,"shortTitle":944,"definition":945,"status":19,"industries":946,"functions":947,"patterns":948,"audience":118,"autonomy":119,"adoptionStage":336,"evidenceCount":128,"publicEvidenceCount":128,"organizations":949,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":953},"meeting-summarization-and-action-items","AI meeting summarization and action items","Meeting summaries and action items","AI that summarizes internal and operational meetings, such as team, project, board and case meetings: it transcribes an online or in person meeting with the participants' knowledge and produces a summary, decisions and action items with owners and dates for the organizer to check and share. It is the general purpose tool; client advice meetings and sales calls, which feed a regulated record or a sales pipeline, have their own pages.",[109,262,159,278],[393,49],[116,395],[950,951,952,792,453],"U.S. Department of Labor","Ministry of Justice","Softcat","Transcribing and summarizing meetings for the participants is minimal risk. It becomes high risk under Annex III point 4(b) if transcripts are analysed to monitor or evaluate individual workers' performance or behaviour, and inferring participants' emotions from their voices or faces at work is prohibited by Article 5(1)(f). Recording and transcription also need a lawful basis and clear information to participants under GDPR.",{"slug":955,"title":956,"shortTitle":957,"definition":958,"status":19,"industries":959,"functions":960,"patterns":961,"audience":567,"autonomy":53,"adoptionStage":54,"evidenceCount":128,"publicEvidenceCount":128,"organizations":962,"bestGrade":166,"headline":968,"lastVerified":59,"indexable":12,"euAiActTier":172,"euAiActBasis":972},"call-quality-and-compliance-monitoring","AI quality and compliance monitoring of every customer interaction","Call quality and compliance","Automated quality assurance that transcribes and scores every customer interaction, voice and chat, against the organization's own rubric, checking required disclosures and script adherence, flagging conduct and mis selling risk, and surfacing coaching opportunities, instead of the small sample a human QA team can review.",[109,68,110,374,111,912],[23,114,49],[395,95,116],[963,964,965,966,967],"British Gas","Central Bank","DoorDash","Oportun","VitalityHealth",{"kpi":969,"label":970,"unit":78,"n":72,"nUpTo":79,"kind":80,"value":971,"qualifier":129,"claimant":171,"organization":963,"vendorReported":12},"quality-score-uplift","Quality score uplift",10,"Scoring individual agents' interactions to monitor and evaluate their performance and behaviour falls under Annex III point 4(b), employment and worker management. The Article 6(3) exception does not apply where the system profiles natural persons. Inferring agents' emotions is prohibited under Article 5(1)(f), except for medical or safety reasons. Inferring customers' emotions from their voice is emotion recognition on biometric data: high risk under Annex III point 1(c), and Article 50(3) requires informing the people exposed to it. Analytics that only aggregate interaction themes without evaluating individuals can fall outside the high risk category.",{"slug":974,"title":975,"shortTitle":976,"definition":977,"status":19,"industries":978,"functions":979,"patterns":980,"audience":567,"autonomy":335,"adoptionStage":336,"segment":981,"evidenceCount":121,"publicEvidenceCount":121,"organizations":982,"bestGrade":35,"headline":36,"lastVerified":37,"indexable":12,"euAiActTier":60,"euAiActBasis":985},"content-recommendation-and-personalization","AI recommendation and personalization engine for streaming and media","Content recommendation and personalization","A recommendation system that decides, for each individual viewer or listener, what to show next on a home page, in search or in a personalized playlist, learned from that person's own viewing or listening history, ratings and context, and continuously updated as new content is added and behavior changes. It ranks the catalog's own content; it is not the marketing engine that decides which offers or campaigns to send, which is a separate use case in this library.",[411],[224,469],[281,265],"content discovery",[983,984],"Netflix, Inc.","Spotify","Recommendation and personalization systems are not listed in Annex III, so most deployments are minimal risk under the EU AI Act. They become a compliance question elsewhere: manipulative or deceptive techniques that materially distort a person's behavior in a way that causes significant harm, or that exploit vulnerabilities linked to age, disability or a specific social or economic situation, are a prohibited practice under Article 5(1)(a) and (b), which is relevant to recommendation systems that target children. A decision based solely on automated processing, including profiling, that produces legal or similarly significant effects on a person falls under GDPR Article 22, though routine content ranking rarely meets that bar on its own.",{"slug":987,"title":988,"shortTitle":989,"definition":990,"status":19,"industries":991,"functions":992,"patterns":993,"audience":118,"autonomy":119,"adoptionStage":54,"evidenceCount":97,"publicEvidenceCount":97,"organizations":994,"bestGrade":166,"headline":997,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":998},"sales-call-coaching-and-crm-update","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.",[109,111,883,110],[48],[395,116,117],[995,288,996,718],"Hughes Network Systems","Sandvik Coromant",{"kpi":125,"label":126,"unit":127,"n":72,"nUpTo":72,"kind":80,"value":30,"qualifier":82,"claimant":83,"organization":996,"vendorReported":11},"Summaries, CRM suggestions and follow up drafts that the seller reviews are not an Annex III use and are minimal risk. Using call analysis to monitor and evaluate the performance and behaviour of individual sellers, or to allocate leads to sellers based on their behaviour or personal traits, is high risk under Annex III point 4(b). Inferring sellers' emotions from their voice is prohibited in the workplace by Article 5(1)(f). Emotion recognition applied to customers' voices is high risk under Annex III point 1(c), and Article 50(3) requires deployers to inform the people exposed to it.",{"slug":1000,"title":1001,"shortTitle":1002,"definition":1003,"status":19,"industries":1004,"functions":1005,"patterns":1006,"audience":567,"autonomy":53,"adoptionStage":336,"evidenceCount":30,"publicEvidenceCount":30,"organizations":1007,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":172,"euAiActBasis":1011},"automated-scoring-of-written-responses","AI scoring of essays and written answers in assessments","Essay and written answer scoring","AI that scores students' essays and short written answers against a rubric, trained on responses scored by human raters, with human raters rescoring a sample of responses and every response the engine is unsure about. In hybrid programmes such as Texas, a human score is the score of record whenever a human scores a response.",[21,262],[49],[95,265],[1008,1009,1010],"ETS","Massachusetts Department of Elementary and Secondary Education","Texas Education Agency","Annex III point 3(b): AI systems intended to be used to evaluate learning outcomes in educational and vocational training institutions at all levels are high risk. Scoring that determines access to an institution or the level of education a student will receive is also covered by points 3(a) and 3(c). Schools and exam bodies that use such a system have the deployer obligations of Article 26.",{"slug":1013,"title":1014,"shortTitle":1015,"definition":1016,"status":19,"industries":1017,"functions":1018,"patterns":1019,"audience":27,"autonomy":335,"adoptionStage":336,"segment":872,"evidenceCount":128,"publicEvidenceCount":128,"organizations":1020,"bestGrade":35,"headline":36,"lastVerified":151,"indexable":12,"euAiActTier":556,"euAiActBasis":1023},"spam-and-scam-call-blocking","AI spam and scam call blocking for mobile and landline subscribers","Spam and scam call blocking","AI in the operator's network that protects subscribers from unwanted calls: it analyses incoming calls in real time, blocks known fraudulent calls, and labels suspected scam, spam and spoofed calls on the customer's screen before they answer, so subscribers can decide whether to pick up. Fraud against the operator itself, such as SIM swap or revenue share fraud, is a separate use case.",[111],[93,23],[566,95,265],[1021,1022,874,143],"Bell Canada","BT Group","Scoring, blocking and labelling calls is not listed in Annex III, is not a prohibited practice under Article 5, and the system does not interact with people or generate content, so Article 50 does not apply. A conversational scambaiting agent such as O2's Daisy talks to callers with a synthetic voice, which raises separate Article 50 transparency questions and should be assessed on its own.",{"slug":1025,"title":1026,"shortTitle":1027,"definition":1028,"status":19,"industries":1029,"functions":1030,"patterns":1031,"audience":118,"autonomy":28,"adoptionStage":54,"evidenceCount":30,"publicEvidenceCount":30,"organizations":1032,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":172,"euAiActBasis":1035},"emergency-call-triage-support","AI support for emergency call triage (112 and 911)","Emergency call triage support","AI that supports emergency call takers and dispatchers during 112 and 911 calls, with live transcription, translation, summaries, location cues and alerts for critical conditions such as cardiac arrest, while the call taker keeps every triage and dispatch decision.",[262,261],[426,49],[395,361,116,265],[1033,1034,745],"Baltimore City 911 (Emergency Communications)","Copenhagen Emergency Medical Services","Annex III point 5(d): AI systems intended to evaluate and classify emergency calls or to dispatch or set priority for emergency first response services (police, fire, medical aid) are high risk. Pure transcription that performs a narrow procedural or preparatory task may fall outside it under the Article 6(3) exceptions, but alerts that influence triage are in scope. An AI agent that speaks with callers directly, for example on a non emergency line, must also tell them they are interacting with AI (Article 50).",{"slug":1037,"title":1038,"shortTitle":1039,"definition":1040,"status":19,"industries":1041,"functions":1042,"patterns":1043,"audience":118,"autonomy":53,"adoptionStage":336,"segment":538,"evidenceCount":30,"publicEvidenceCount":30,"organizations":1044,"bestGrade":35,"headline":36,"lastVerified":37,"indexable":12,"euAiActTier":60,"euAiActBasis":1048},"property-valuation-support","AI support for property valuation and appraisal","Property valuation support","AI, most often an automated valuation model, that estimates a property's market value from comparable sales, property characteristics and location data, and either offers to replace a full appraisal within set limits or gives a professional valuer a first pass estimate, the closest comparable sales and a reliability score, so the valuer's time goes to the properties that need a person's judgment.",[46,68,262],[536,113,49],[265,95],[1045,1046,1047],"Fannie Mae","Riverside County Assessor-County Clerk-Recorder","Valuation Office Agency","An automated valuation model values the collateral, not the person, so it is not itself listed in Annex III; the EU Mortgage Credit Directive treats property valuation (Article 19) and the creditworthiness assessment of the borrower (Article 18) as separate steps, and Article 18(3) says the creditworthiness assessment must not be based predominantly on the value of the property exceeding the amount of credit, or on an assumption that the property's value will increase. The valuation becomes relevant to Annex III point 5(b), creditworthiness assessment of natural persons, only where its output is built into a separate system that evaluates the borrower's creditworthiness, and whether that happens depends on how the lender designs the credit decision, not on the valuation model itself.",{"slug":1050,"title":1051,"shortTitle":1052,"definition":1053,"status":19,"industries":1054,"functions":1055,"patterns":1056,"audience":27,"autonomy":119,"adoptionStage":54,"evidenceCount":337,"publicEvidenceCount":337,"organizations":1057,"bestGrade":35,"headline":36,"lastVerified":151,"indexable":12,"euAiActTier":60,"euAiActBasis":1060},"public-service-translation","AI translation and interpretation for multilingual public services","Public service translation","AI that translates government content, documents and conversations between officials and the public, in writing and in real time speech, so people can use public services in their own language, with human translators and interpreters reviewing what carries legal or safety weight.",[262],[426,23,49],[361,25,395,96],[1033,1058,1059,431,525,455,456,675],"Delaware County","European Commission","Assistants that talk with residents must tell people they are interacting with AI (Article 50(1)), and AI generated text published to inform the public on matters of public interest must be disclosed unless it has had human review under editorial responsibility (Article 50(4)). Internal translation that neither talks with people nor is published carries no specific obligation. Translation can also sit inside an Annex III process, such as examining asylum, visa or residence permit applications (point 7(c)) or evaluating emergency calls and dispatching emergency services (point 5(d)). Whether the translation component is itself high risk depends on its intended purpose (Article 6(3) exempts systems that only perform a narrow procedural task); either way it should be governed with that high risk process.",{"slug":1062,"title":1063,"shortTitle":1064,"definition":1065,"status":19,"industries":1066,"functions":1067,"patterns":1068,"audience":27,"autonomy":28,"adoptionStage":29,"evidenceCount":30,"publicEvidenceCount":30,"organizations":1069,"bestGrade":35,"headline":36,"lastVerified":59,"indexable":12,"euAiActTier":60,"euAiActBasis":1073},"ai-tutor-for-students","AI tutor that coaches students through problems","AI tutor for students","An AI tutor that works with a student on course material in a conversation, asking questions and giving hints instead of handing over answers, grounded in the course content and set up by the school or teacher, with limits on use and a clear route to a human teacher.",[21],[23],[25,26],[1070,1071,1072],"Hamilton County Schools","Harvard University","World Bank","A tutor that only converses with students falls under the transparency duty of Article 50. It becomes high risk under Annex III point 3(b) when it evaluates learning outcomes, including when those outcomes are used to steer a student's learning process, and under point 3(c) when it assesses the level of education a student should receive. Inferring students' emotions is prohibited in education institutions under Article 5(1)(f).",1790598319232]