[{"data":1,"prerenderedAt":650},["ShallowReactive",2],{"uc-outbound-reminder-and-confirmation-agent":3,"uc-regulations":443},{"useCase":4,"evidence":210,"blitsAiDeployments":323,"benchmarks":324,"indicative":331,"related":334,"indexability":441,"includeUnpublished":216},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":24,"channels":29,"audience":34,"autonomy":35,"adoptionStage":36,"problem":37,"problemStats":38,"howItWorks":49,"valueDrivers":50,"kpis":55,"indicativeValue":60,"macroEstimates":94,"feasibility":95,"implementation":109,"risk":152,"blitsAi":182,"faq":184,"related":197,"datePublished":204,"dateModified":204,"lastVerified":205,"changelog":206,"slug":209},"AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","AI appointment reminder and confirmation agents","AI reminder agents let customers confirm, cancel or move a booking by text or phone. An NHS pilot linked AI texts to more attended appointments.","published","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.",[12,13,14,15,16],"AI appointment reminder calls","automated confirmation calls","two way reminder messaging","service visit reminder agent","reservation confirmation agent",[18,19,20],"cross-industry","healthcare","government",[22,23],"customer-service","operations",[25,26,27,28],"voice-agent","conversational-agent","agentic-workflow","prediction-and-scoring",[30,31,32,33],"voice","sms","whatsapp","email","customer-facing","supervised-agent","early-adopters","Every business that books time with customers loses some of it. Patients miss appointments,\nengineers arrive at empty houses, delivery drivers carry parcels back to the depot and restaurant\ntables stay empty. The cost is not only the lost slot: it is the other customer who could have had\nit, and the repeat visit that now has to be arranged.\n\nMany organizations already send reminders, but classic reminders are one way broadcasts at a fixed\nmoment. They cannot answer \"can I come an hour later\", they do not know which customers are likely\nto miss, and a cancellation made the evening before usually leaves the slot empty. Human\nconfirmation calls allow a real conversation but are costly to make for every booking.",[39,44],{"statement":40,"sourceTitle":41,"sourceUrl":42,"year":43},"The UK government says sending reminders has been shown to reduce missed appointments by up to 80%, and that NHS trusts report better results when communication with the patient is two way.","Power to patients as government sets out plan to cut waiting lists","https://www.gov.uk/government/news/power-to-patient-as-government-sets-out-plan-to-cut-waiting-lists",2025,{"statement":45,"sourceTitle":46,"sourceUrl":47,"year":48},"NHS England reports that eight million (6.4%) of 124.5 million outpatient appointments in England in the previous year were not attended.","NHS AI expansion to help tackle missed appointments and improve waiting times","https://www.england.nhs.uk/2024/03/nhs-ai-expansion-to-help-tackle-missed-appointments-and-improve-waiting-times/",2024,"1. **The booking system triggers the contact.** Reminders and confirmations start from an existing\n   booking, order or reservation, at times set per service. The agent does not choose who to\n   contact for commercial reasons.\n2. **Prioritise where it matters.** A risk score can decide who gets an extra reminder, a call\n   instead of a text, or an offer of help, as Sheffield Children's did in its AI Predictor pilot.\n3. **Open with who and why.** The agent names the organization, says it is an automated assistant\n   and states the booking it is about, without revealing sensitive details before the person is\n   verified.\n4. **Let the customer act.** The customer can confirm, cancel, move to another offered slot or ask a\n   practical question (address, parking, preparation, delivery window) in the same conversation.\n5. **Write back and reuse.** Confirmations, cancellations and new times go straight back to the\n   booking system, and freed slots are offered to the next customer on the waiting list.\n6. **Escalate what is not routine.** Complaints, distress, clinical questions and customers who ask\n   for a person are handed to staff with the conversation attached.",[51,52,53,54],"cost-to-serve","customer-experience","speed","inclusion-and-access",[56,57,58,59],"interactions-handled","containment-rate","customer-satisfaction","cost-reduction",{"referenceOrg":61,"inputs":62,"formula":89,"currency":90,"period":91,"resultLabel":92,"caveat":93},"A service organization with 200,000 booked appointments or visits a year",[63,69,76,82],{"key":64,"label":65,"low":66,"high":66,"unit":67,"note":68},"bookings","Booked appointments or visits per year",200000,"bookings per year","The reference organization.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"missedRate","Share of bookings missed without notice today",0.05,0.1,"fraction of bookings","Editorial assumption. NHS England reports 6.4% for outpatient appointments; replace with your own rate.",{"key":77,"label":78,"low":73,"high":79,"unit":80,"note":81},"reduction","Share of missed bookings avoided or refilled because of the agent",0.2,"fraction of missed bookings","Editorial assumption, set at or below the NHS pilots on this page. Sheffield Children's expected 8,581 missed appointments against a benchmark rate, not a control group, and recorded just under 6,500, about a 24% reduction. UHCW's move from 10% to 4% came from reminder timing found through process mining, not from an agent. Measure against a control group.",{"key":83,"label":84,"low":85,"high":86,"unit":87,"note":88},"valuePerBooking","Value of a booking that is kept or refilled",40,120,"EUR per booking","Editorial assumption covering the margin or cost of a wasted slot or visit. Replace with your own figure.","bookings * missedRate * reduction * valuePerBooking","EUR","per year","Value of bookings kept or refilled","Counts kept and refilled bookings only. It leaves out staff time saved on manual confirmation calls, the cost of messages, calls and the AI, and any annoyance from contact that customers did not want.",[],{"complexity":96,"complexityNote":97,"dataPrerequisites":98,"integrations":103},"medium","The dialogue is short. The effort goes into a clean trigger from the booking system, a reliable write back of confirmations and changes, contact rules per channel and market, and verifying the person before details are shared.",[99,100,101,102],"Bookings with time, location, service type and contact details","Available alternative slots and a waiting list, if freed slots are to be reused","Channel preferences, consent and opt outs per customer","Historic attendance data if contacts are prioritised by risk",[104,105,106,107,108],"Booking, scheduling or order management system (read and write)","Telephony and messaging (voice, SMS, WhatsApp, email)","Waiting list or capacity planning","CRM or case management for follow up","Contact centre for handover",{"steps":110,"guardrails":126,"humanInTheLoop":132,"kpisToInstrument":133,"failureModes":139},[111,114,117,120,123],{"title":112,"detail":113},"Start with the reminders you already send","Make the existing reminder two way before adding new ones: let the customer confirm, cancel or move it by replying. This alone turns late cancellations into slots you can reuse.",{"title":115,"detail":116},"Test the timing","Timing matters as much as wording. University Hospitals Coventry and Warwickshire found a spike in last minute cancellations after two text reminders, and found that reminders 14 days and four days ahead worked best because patients cancelled early enough for the slot to be rebooked.",{"title":118,"detail":119},"Add risk based escalation","Use a missed booking score to decide who gets an extra message, a call or practical help, and keep a control group so you can see the effect.",{"title":121,"detail":122},"Connect cancellations to the waiting list","A reminder that produces a cancellation is only valuable if someone else gets the slot. Automate the offer to the next suitable customer.",{"title":124,"detail":125},"Keep marketing out","Do not add offers or upsell to service reminders. It changes the legal basis for the contact and the trust customers place in the channel.",[127,128,129,130,131],"Contact only about an existing booking, order or reservation, never for sales","Disclose that the caller or sender is an automated assistant and name the organization","Verify the person before sharing sensitive details such as a clinic or diagnosis","Honour opt outs and quiet hours on every channel","Never ask for passwords, card details or payment in a reminder","Service owners approve every reminder script, timing and channel. Staff take over complaints, distress, clinical or safety questions and anyone who asks for a person, and review a sample of conversations and outcomes each month, with attention to groups that miss the most bookings.",[134,135,136,137,138],"Missed booking rate against a control group","Share of cancellations made early enough to refill, and refill rate","Confirmation, cancellation and rebooking rates per reminder","Opt out and complaint rate per reminder type","Reach and outcomes by language, age and deprivation band",[140,143,146,149],{"title":141,"detail":142},"Reminders that look like scams","Unexpected calls and texts are what fraudsters imitate. Name the organization, never ask for secrets, and point to known channels.",{"title":144,"detail":145},"Late cancellations with no refill","The reminder works but the slot stays empty. Time reminders so cancellations come early and connect them to a waiting list.",{"title":147,"detail":148},"Privacy leaks in the message","A reminder names a sensitive clinic or service to whoever reads the phone. Keep content minimal until the person is verified.",{"title":150,"detail":151},"Service outreach turning into marketing","Offers creep into reminders and the contact now needs marketing consent. Keep scripts service only and review them.",{"euAiAct":153,"regulations":156,"guidance":162,"controls":175,"incidents":181},{"tier":154,"basis":155},"context-dependent","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.",[157,158,159,160,161],"eu-ai-act","gdpr","uk-gdpr","hipaa","us-tcpa",[163,169],{"title":164,"issuer":165,"region":166,"url":167,"note":168},"Declaratory ruling on AI generated voices under the TCPA (FCC 24-17)","Federal Communications Commission","north-america","https://docs.fcc.gov/public/attachments/FCC-24-17A1.pdf","AI generated voices count as artificial or prerecorded voice under the TCPA, so US reminder calls made with AI need the consent the TCPA requires for such calls.",{"title":170,"issuer":171,"region":172,"url":173,"note":174},"Guide to PECR: electronic and telephone marketing","Information Commissioner's Office","europe","https://ico.org.uk/for-organisations/direct-marketing-and-privacy-and-electronic-communications/guide-to-pecr/electronic-and-telephone-marketing/","UK rules on marketing by phone, text and email. Routine customer service messages about a current contract or past purchase, such as delivery arrangements, do not count as direct marketing; unsolicited marketing often needs specific consent.",[176,177,178,179,180],"Documented purpose and legal basis for each reminder type","Opt out, quiet hour and frequency checks logged per contact","Approved scripts per reminder type with an accountable owner","Audit trail of every contact and every change written back to the booking system","Monitoring of complaints, opt outs and outcomes by group",[],{"howToBuild":183},"On Blits.ai each reminder type is an **agentic workflow** started on a schedule or by the booking\nsystem through an API token, with the booking passed in as data. The workflow sends the reminder\nthrough the outbound **email** channel or a **custom function** that calls the messaging provider,\nand an **agentic task** can recheck later and follow up when a booking is still unconfirmed.\nReplies reach an **AI agent** on the **SMS**, **WhatsApp** or **email** channel, where **custom\nfunctions** confirm, cancel or move the booking and offer freed slots to the waiting list.\nCustomers who call back reach the same agent on the **voice** channel.\n\nA **flow** fixes the opening (who is contacting, why, and how to opt out) and any verification step\nbefore details are shared, and the **GDPR toolkit** handles consent and data removal. **Guardrails**\nkeep offers and payment requests out of reminders, **human handover** routes complaints and\nclinical questions to staff, and **analytics** with the workflow run history show confirmations,\ncancellations and refills per reminder type.",[185,188,191,194],{"question":186,"answer":187},"How is this different from proactive outreach and activation?","Proactive outreach contacts customers about something they have not done yet, such as activating a card or accepting an offer, and often needs marketing consent. Reminder and confirmation outreach is about something the customer already booked or ordered and exists to make it happen as planned.",{"question":189,"answer":190},"Do AI targeted reminders reduce no shows?","NHS pilots suggest so. In the first 12 months of a pilot at Sheffield Children's, an AI Predictor sent 53,800 extra text reminders to families at high risk of missing appointments, and NHS England reports almost 200 more attended appointments a month against the benchmark. Results without a control group should be read with care.",{"question":192,"answer":193},"Should reminders be calls or messages?","Messages for most people, because they are cheap and let the customer reply when it suits them. Calls suit high risk bookings and people who do not use messaging. WellSpan Health first used its AI voice agent to call patients about colorectal cancer screening, and has announced outreach to patients who missed imaging appointments as a next workflow.",{"question":195,"answer":196},"Are AI reminder calls legal?","It depends on the market and the purpose. In the US, AI generated voices fall under the TCPA's rules for artificial voices; in the UK, PECR does not count routine customer service messages as direct marketing. Keep reminders strictly about the booking and record the legal basis.",[198,199,200,201,202,203],"patient-appointment-scheduling-and-reminders-agent","branch-and-appointment-booking-agent","proactive-outbound-engagement-agent","parcel-tracking-and-delivery-exception-agent","network-outage-communication-agent","utility-billing-and-move-agent","2026-09-27","2026-09-26",[207],{"date":204,"note":208},"First published","outbound-reminder-and-confirmation-agent",[211,243,278,295],{"title":212,"useCases":213,"organization":214,"vendors":218,"summary":225,"stage":226,"year":48,"channels":227,"languages":229,"metrics":231,"outcomeDisclosed":232,"sources":233,"verification":238,"grade":240,"id":241,"organizationSlug":242},"University Hospitals Coventry and Warwickshire: AI process mining to time appointment reminders",[198,209],{"name":215,"anonymized":216,"country":217,"region":172,"industry":19},"University Hospitals Coventry and Warwickshire NHS Trust",false,"GB",[219,222],{"name":220,"role":221},"IBM","integrator",{"name":223,"role":224},"Celonis","platform","University Hospitals Coventry and Warwickshire used AI based process mining with IBM and Celonis to study missed appointments, which were more common among patients with high deprivation scores. It found a spike in last minute cancellations after two SMS reminders and moved to a reminder 14 days before the appointment with a second one four days before, so patients could cancel early and the slot could be rebooked. NHS England reports that missed appointments in this subset of patients fell from 10% to 4%. The AI analysed the process; the reminders themselves are standard text messages.","pilot",[31,228],"internal-tools",[230],"en",[],true,[234],{"url":47,"title":46,"publisher":235,"date":236,"archivedUrl":237},"NHS England","2024-03-14","https://web.archive.org/web/20250125233954/https://www.england.nhs.uk/2024/03/nhs-ai-expansion-to-help-tackle-missed-appointments-and-improve-waiting-times/",{"level":239,"checkedAt":204},"source-verified","B","uhcw-process-mining-appointment-reminders",null,{"title":244,"useCases":245,"organization":246,"vendors":249,"summary":252,"stage":253,"year":48,"channels":254,"languages":256,"metrics":258,"outcomeDisclosed":232,"sources":267,"verification":276,"grade":240,"id":277,"organizationSlug":242},"WellSpan Health: Ana, a generative AI voice agent for patient calls, scheduling and outreach",[198,209],{"name":247,"anonymized":216,"country":248,"region":166,"industry":19},"WellSpan Health","US",[250],{"name":251,"role":224},"Hippocratic AI","WellSpan Health started with a Hippocratic AI voice agent that phones patients to close gaps in colorectal cancer screening, in English and Spanish, and supports low risk patients before and after a scheduled colonoscopy, with transcripts sent to clinicians and live transfer to a human where needed. By 2026 the agent, Ana, answered inbound calls and scheduled primary care appointments, and WellSpan announced an expanded partnership whose first new workflows call patients who missed imaging appointments. WellSpan reports that Ana manages more than 160,000 patient calls a month.","scaled",[30,255],"web-chat",[230,257],"es",[259],{"kpi":56,"value":260,"unit":261,"qualifier":262,"period":263,"claimant":264,"quote":265,"sourceUrl":266},160000,"count","at-least","per month, patient calls","organization","Ana, WellSpan’s generative AI agent developed in partnership with Hippocratic AI, currently manages more than 160,000 patient calls per month, engaging in more than 7,000 hours of conversation.","https://www.wellspan.org/articles/2026/07/30/13/05/web---hippocractic-ai-partnership-expansion",[268,271],{"url":266,"title":269,"publisher":247,"date":270},"WellSpan expands Hippocratic AI partnership to enhance operations and patient experience","2026-07-30",{"url":272,"title":273,"publisher":274,"date":275},"https://www.globenewswire.com/en/news-release/2024/09/26/2953949/0/en/WellSpan-One-of-the-First-Major-Health-Systems-in-the-World-to-Launch-Hippocratic-AI-s-Generative-AI-Healthcare-Agent.html","WellSpan One of the First Major Health Systems in the World to Launch Hippocratic AI's Generative AI Healthcare Agent","WellSpan Health (GlobeNewswire)","2024-09-26",{"level":239,"checkedAt":204},"wellspan-hippocratic-ai-patient-voice-agent",{"title":279,"useCases":280,"organization":281,"vendors":283,"summary":286,"stage":226,"year":287,"channels":288,"languages":289,"metrics":290,"outcomeDisclosed":232,"sources":291,"verification":293,"grade":240,"id":294,"organizationSlug":242},"Sheffield Children's NHS Foundation Trust: AI predictor that targets extra reminders and transport support",[198,209],{"name":282,"anonymized":216,"country":217,"region":172,"industry":19},"Sheffield Children's NHS Foundation Trust",[284],{"name":285,"role":224},"Alder Hey Innovation","Sheffield Children's piloted an AI Predictor developed by Alder Hey Innovation that estimates which children are likely to miss (\"was not brought\") an appointment, using markers that include health inequalities. Families with a predicted risk of 50% or more received an extra text reminder with an offer of support the day before; families at 85% or more were contacted and offered funded transport or a rebooking. NHS England reports that 53,800 texts were sent in the first 12 months, that recorded non attendance came in well below the expected benchmark (almost 200 more attended appointments a month), and that in a 13 week period 152 families had transport arranged and 129 appointments were rebooked.",2023,[31],[230],[],[292],{"url":47,"title":46,"publisher":235,"date":236,"archivedUrl":237},{"level":239,"checkedAt":204},"sheffield-childrens-ai-attendance-predictor",{"title":296,"useCases":297,"organization":298,"vendors":300,"summary":303,"stage":304,"year":305,"channels":306,"languages":307,"metrics":308,"outcomeDisclosed":216,"sources":309,"verification":320,"grade":240,"id":321,"organizationSlug":322},"US Department of Veterans Affairs: e-VA digital assistant for appointment reminders and rescheduling (retired)",[209],{"name":299,"anonymized":216,"country":248,"region":166,"industry":20},"US Department of Veterans Affairs, Veterans Benefits Administration",[301],{"name":302,"role":224},"SaraWorks","The Veteran Readiness and Employment service of the Veterans Benefits Administration used e-VA, a digital assistant acquired from SaraWorks, to take routine follow up off vocational rehabilitation counselors. It created reminders for appointments, grades and receipts and sent routine messages by text or email; participants could reply to schedule and reschedule appointments, respond to reminders and send documents. The 2024 federal AI inventory lists it in operation since June 2020 and classifies it as both rights and safety impacting; the 2025 inventory lists it as retired. No results were published.","paused",2020,[31,33],[230],[],[310,314,317],{"url":311,"title":312,"publisher":313},"https://github.com/ombegov/2024-Federal-AI-Use-Case-Inventory","2024 Federal AI Use Case Inventory","Office of Management and Budget (GitHub)",{"url":315,"title":316,"publisher":313},"https://raw.githubusercontent.com/ombegov/2024-Federal-AI-Use-Case-Inventory/main/data/2024_consolidated_ai_inventory_raw_v2.csv","2024 consolidated AI inventory (VA entry Electronic Virtual Assistant (e-VA))",{"url":318,"title":319,"publisher":313},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 individually reported AI use cases (VA entry electronic Virtual Assistant (e-VA), VA-24-3824)",{"level":239,"checkedAt":205},"va-evirtual-assistant-appointment-reminders","u-s-department-of-veterans-affairs",0,[325],{"kpi":56,"label":326,"unit":261,"aggregate":216,"higherIsBetter":232,"n":327,"nUpTo":323,"median":260,"min":260,"max":260,"byClaimant":328,"vendorOnly":216,"points":329},"Interactions handled",1,{"organization":327,"vendor":323,"regulator":323,"independent":323},[330],{"evidenceId":277,"organization":247,"value":260,"qualifier":262,"claimant":264,"grade":240,"pooled":232},{"low":332,"high":333},40000,480000,[335,355,374,390,410,425],{"slug":198,"title":336,"shortTitle":337,"definition":338,"status":9,"industries":339,"functions":340,"patterns":341,"audience":34,"autonomy":35,"adoptionStage":36,"evidenceCount":342,"publicEvidenceCount":343,"organizations":344,"bestGrade":240,"headline":348,"lastVerified":204,"indexable":232},"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.",[19],[22,23],[25,26,28,27],9,6,[345,346,347,282,215,247],"Audibel","Howard Brown Health","Mid and South Essex NHS Foundation Trust",{"kpi":57,"label":349,"unit":350,"n":327,"nUpTo":323,"kind":351,"value":352,"qualifier":353,"claimant":354,"organization":346,"vendorReported":232},"Containment rate","percent","reported",30,"exact","vendor",{"slug":199,"title":356,"shortTitle":357,"definition":358,"status":9,"industries":359,"functions":363,"patterns":365,"audience":34,"autonomy":367,"adoptionStage":36,"evidenceCount":368,"publicEvidenceCount":368,"organizations":369,"bestGrade":240,"headline":242,"lastVerified":204,"indexable":232},"AI agent for branch finding and appointment booking","Branch and appointment booking","A conversational agent that finds the nearest suitable location, checks opening hours and which services it offers, books an in person or video appointment with the right specialist, and records the reason for the visit so staff are prepared. In banking it answers \"where is my nearest branch\" and books the mortgage or business banker; the same job exists in retail, healthcare and property.",[18,360,361,19,362],"banking","retail-and-ecommerce","real-estate",[22,364],"sales",[26,27,366,25],"rag-knowledge-assistant","autonomous",4,[370,371,372,373],"Bank of America","Best Buy","Hemominas","MOGUL.sg",{"slug":200,"title":375,"shortTitle":376,"definition":377,"status":9,"industries":378,"functions":380,"patterns":382,"audience":34,"autonomy":35,"adoptionStage":384,"segment":385,"evidenceCount":386,"publicEvidenceCount":386,"organizations":387,"bestGrade":240,"headline":242,"lastVerified":204,"indexable":232},"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.",[360,379],"payments",[381,364,22],"marketing",[26,25,27,383],"recommendation-and-personalization","emerging","front-office",3,[370,388,389],"Capital One","Commonwealth Bank of Australia",{"slug":201,"title":391,"shortTitle":392,"definition":393,"status":9,"industries":394,"functions":396,"patterns":397,"audience":34,"autonomy":35,"adoptionStage":36,"evidenceCount":343,"publicEvidenceCount":399,"organizations":400,"bestGrade":240,"headline":406,"lastVerified":204,"indexable":232},"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.",[395],"logistics-and-transportation",[22,23],[26,25,27,398],"classification-and-routing",5,[401,402,403,404,405],"Chronopost","DPD Deutschland","DPD UK","Evri","PostNL",{"kpi":407,"label":408,"unit":350,"n":327,"nUpTo":323,"kind":351,"value":409,"qualifier":353,"claimant":264,"organization":404,"vendorReported":216},"contact-deflection","Contact deflection",50,{"slug":202,"title":411,"shortTitle":412,"definition":413,"status":9,"industries":414,"functions":416,"patterns":419,"audience":34,"autonomy":35,"adoptionStage":384,"segment":385,"evidenceCount":422,"publicEvidenceCount":327,"organizations":423,"bestGrade":240,"headline":242,"lastVerified":204,"indexable":232},"AI agent for network outage detection and customer communication","Outage communication","An AI agent that turns network alarms into a clear picture of which customers are affected by an outage and why, tells them proactively by message, app or phone with a cause and an estimated fix time, answers their questions during the incident, and updates them until service is restored.",[415],"telecommunications",[22,417,418],"network-operations","field-service",[420,398,421,26,25],"anomaly-detection","content-generation",2,[424],"Comcast",{"slug":203,"title":426,"shortTitle":427,"definition":428,"status":9,"industries":429,"functions":431,"patterns":432,"audience":34,"autonomy":35,"adoptionStage":36,"evidenceCount":343,"publicEvidenceCount":399,"organizations":433,"bestGrade":240,"headline":439,"lastVerified":205,"indexable":232},"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.",[430],"energy-and-utilities",[22,23],[26,25,27,366],[434,435,436,437,438],"Aydem Energy","Dubai Electricity and Water Authority","EDF","Octopus Energy","Pacific Gas and Electric Company",{"kpi":57,"label":349,"unit":350,"n":422,"nUpTo":323,"kind":351,"value":440,"qualifier":353,"claimant":264,"organization":434,"vendorReported":216},75,{"indexable":232,"reasons":442},[],[444,450,455,463,470,476,481,488,496,503,510,516,523,530,536,541,548,554,559,565,571,575,581,586,591,597,604,609,614,621,627,633,639,644],{"id":157,"label":445,"issuer":446,"region":172,"url":447,"description":448,"useCases":449,"indexable":232},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":158,"label":451,"issuer":446,"region":172,"url":452,"description":453,"useCases":454,"indexable":232},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":456,"label":457,"issuer":458,"region":459,"url":460,"description":461,"useCases":462,"indexable":232},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":464,"label":465,"issuer":466,"region":166,"url":467,"description":468,"useCases":469,"indexable":232},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":471,"label":472,"issuer":446,"region":172,"url":473,"description":474,"useCases":475,"indexable":232},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":159,"label":477,"issuer":171,"region":172,"url":478,"description":479,"useCases":480,"indexable":232},"UK GDPR","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":482,"label":483,"issuer":484,"region":172,"url":485,"description":486,"useCases":487,"indexable":232},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":489,"label":490,"issuer":491,"region":492,"url":493,"description":494,"useCases":495,"indexable":232},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":497,"label":498,"issuer":499,"region":492,"url":500,"description":501,"useCases":502,"indexable":232},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":504,"label":505,"issuer":506,"region":459,"url":507,"description":508,"useCases":509,"indexable":232},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":511,"label":512,"issuer":513,"region":166,"url":514,"description":515,"useCases":509,"indexable":232},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":517,"label":518,"issuer":519,"region":172,"url":520,"description":521,"useCases":522,"indexable":232},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":524,"label":525,"issuer":526,"region":459,"url":527,"description":528,"useCases":529,"indexable":232},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":531,"label":532,"issuer":446,"region":172,"url":533,"description":534,"useCases":535,"indexable":232},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":537,"label":538,"issuer":446,"region":172,"url":539,"description":540,"useCases":535,"indexable":232},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":542,"label":543,"issuer":544,"region":166,"url":545,"description":546,"useCases":547,"indexable":232},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":549,"label":550,"issuer":446,"region":172,"url":551,"description":552,"useCases":553,"indexable":232},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":160,"label":555,"issuer":556,"region":166,"url":557,"description":558,"useCases":553,"indexable":232},"HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":560,"label":561,"issuer":562,"region":459,"url":563,"description":564,"useCases":553,"indexable":232},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":566,"label":567,"issuer":446,"region":172,"url":568,"description":569,"useCases":570,"indexable":232},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":161,"label":572,"issuer":165,"region":166,"url":573,"description":574,"useCases":570,"indexable":232},"Telephone Consumer Protection Act","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":576,"label":577,"issuer":491,"region":492,"url":578,"description":579,"useCases":580,"indexable":232},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":582,"label":583,"issuer":446,"region":172,"url":584,"description":585,"useCases":580,"indexable":232},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":587,"label":588,"issuer":446,"region":172,"url":589,"description":590,"useCases":580,"indexable":232},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":592,"label":593,"issuer":594,"region":172,"url":595,"description":596,"useCases":342,"indexable":232},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",{"id":598,"label":599,"issuer":600,"region":166,"url":601,"description":602,"useCases":603,"indexable":232},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":605,"label":606,"issuer":446,"region":172,"url":607,"description":608,"useCases":603,"indexable":232},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":610,"label":611,"issuer":446,"region":172,"url":612,"description":613,"useCases":343,"indexable":232},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":615,"label":616,"issuer":617,"region":618,"url":619,"description":620,"useCases":399,"indexable":232},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":622,"label":623,"issuer":624,"region":172,"url":625,"description":626,"useCases":368,"indexable":232},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":628,"label":629,"issuer":630,"region":172,"url":631,"description":632,"useCases":368,"indexable":232},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":634,"label":635,"issuer":636,"region":492,"url":637,"description":638,"useCases":386,"indexable":232},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":640,"label":641,"issuer":446,"region":172,"url":642,"description":643,"useCases":386,"indexable":232},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":645,"label":646,"issuer":647,"region":166,"url":648,"description":649,"useCases":386,"indexable":232},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598295437]