[{"data":1,"prerenderedAt":651},["ShallowReactive",2],{"uc-predictive-network-maintenance":3,"uc-regulations":441},{"useCase":4,"evidence":191,"blitsAiDeployments":345,"benchmarks":346,"indicative":355,"related":358,"indexability":439,"includeUnpublished":197},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":41,"indicativeValue":46,"macroEstimates":75,"feasibility":76,"implementation":91,"risk":133,"blitsAi":168,"faq":170,"related":180,"datePublished":186,"dateModified":186,"lastVerified":186,"changelog":187,"slug":190},"AI for predictive network maintenance in telecom","Predictive network maintenance","AI predictive maintenance for telecom networks","AI spots early signs of network failure so operators fix faults before customers notice. Telstra's SmartFix performed 2.5 million proactive actions in FY25.","published","Machine learning that spots the early signs of network failure, such as degrading cells, faulty customer equipment, ageing hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention before customers lose service.",[12,13,14,15],"predictive maintenance for telecom networks","proactive network assurance","network failure prediction","silent cell detection",[17],"telecommunications",[19,20,21],"network-operations","field-service","operations",[23,24,25],"anomaly-detection","prediction-and-scoring","agentic-workflow",[27,28],"internal-tools","api","back-office","supervised-agent","early-adopters","network","Much network maintenance is reactive or calendar based. Faults are found when an alarm fires\nor when customers call, and field teams replace equipment on a schedule whether it needs it or\nnot. Many failures give warning signs first: a cell whose throughput slowly degrades, a modem\nthat keeps dropping its connection, a router with rising error counts, a battery that no longer\nholds its charge. Those signals sit in performance data that nobody has time to watch.\n\nSome outages have nothing to do with the equipment itself. Verizon notes that every year thousands\nof fiber lines are damaged by accidental cuts during construction and excavation, which can affect\ncustomers' connectivity for anything from a few hours to several days. A fault that reaches the\ncustomer can cost a support call, a technician visit and some goodwill. The opportunity is to act\non the warning signs early enough to fix the problem remotely, during a planned window, or before\nthe digger arrives.",[],"1. **Gather the signals.** Performance counters, alarms, device telemetry from customer equipment,\n   environmental and power data from sites, and external data such as dig requests or weather.\n2. **Score the risk.** Models learn the patterns that preceded past failures and score each cell,\n   line, device or site for the probability of failure or degradation in the coming days.\n3. **Classify the likely cause.** For each at risk element the system proposes the probable root\n   cause (hardware, configuration, interference, power, external damage) so the right fix is chosen.\n4. **Act at the right level.** Low risk fixes such as a remote reset, a configuration rollback or\n   a customer equipment reboot run automatically within limits; hardware swaps and site visits are\n   scheduled as planned work; external risks trigger outreach, such as contacting an excavator.\n5. **Learn from the outcome.** Every prevented and every missed failure is fed back to retrain the\n   models and tune the thresholds.",[37,38,39,40],"customer-experience","cost-to-serve","risk-reduction","speed",[42,43,44,45],"interactions-handled","detection-rate-improvement","cost-savings","processing-time-reduction",{"referenceOrg":47,"inputs":48,"formula":70,"currency":71,"period":72,"resultLabel":73,"caveat":74},"A national fixed and mobile operator with about 40,000 customer affecting network faults a year",[49,56,63],{"key":50,"label":51,"low":52,"high":53,"unit":54,"note":55},"faults","Customer affecting network faults per year",20000,60000,"faults per year","Editorial assumption for a national operator. Replace with your own fault volume.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"preventableShare","Share of faults prevented or fixed before customers are affected",0.1,0.25,"fraction of faults","Editorial assumption, not calibrated by any source on this page. Telstra's 2.5 million SmartFix proactive actions in FY25 show the scale of such programs but say nothing about the share of faults that can be prevented; the share of your own faults that show warning signs is the number to measure first.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"costPerFault","Cost of a customer affecting fault",300,800,"USD per fault","Editorial assumption covering repair, technician visits and customer contacts. Replace with your own fully loaded cost.","faults * preventableShare * costPerFault","USD","per year","Fault handling cost avoided","Direct fault cost only. It leaves out avoided service level penalties, churn and complaint handling, the extra cost of preventive work on false alarms, and the cost of the data platform.",[],{"complexity":77,"complexityNote":78,"dataPrerequisites":79,"integrations":85},"high","Predicting failures needs years of clean history that links alarms and performance data to the faults that followed. Many operators have the data but not the labels, and acting on predictions means changing how field and NOC work is planned.",[80,81,82,83,84],"Historical performance and alarm data per network element, kept for at least a year","Fault and repair records with root cause codes that can be joined to that data","Telemetry from customer premises equipment where fixed access is in scope","Site power, battery and environmental data","External data sources where relevant, such as dig request notifications",[86,87,88,89,90],"Performance and fault management systems","Network inventory and topology","Workforce management and field scheduling","Device management platforms for customer equipment","Trouble ticketing and customer notification systems",{"steps":92,"guardrails":108,"humanInTheLoop":113,"kpisToInstrument":114,"failureModes":120},[93,96,99,102,105],{"title":94,"detail":95},"Pick a failure mode with a clear payoff","Start with one failure that is frequent, costly and preceded by measurable signals, such as degrading cells, unstable customer equipment or fibre damage from digging.",{"title":97,"detail":98},"Build the labelled history","Join past faults to the data that preceded them. This is usually most of the work and the main reason projects stall.",{"title":100,"detail":101},"Decide the action before the model","Agree what happens when the score is high: an automated reset, a planned visit or a call to a third party. A prediction nobody acts on is only a report.",{"title":103,"detail":104},"Automate only the safe fixes","Let the system run reversible remote actions within limits and route everything else to engineers and field planners with the evidence attached.",{"title":106,"detail":107},"Measure prevented and missed failures","Track both, because a model that raises many alarms can look busy while missing the failures that matter.",[109,110,111,112],"Automatic actions limited to reversible, low impact fixes with rollback and rate limits","Hardware swaps and site visits approved by planners, not triggered directly by a score","Maintenance windows and change freezes respected by every automated action","Human review of any action that affects many customers at once","Engineers set the thresholds and the list of automated fixes, planners approve preventive visits, and the NOC can pause automation at any time. A sample of automated actions is reviewed every week against what actually happened to the element afterwards.",[115,116,117,118,119],"Faults prevented, measured against a comparable control group of elements","Precision of predictions, as the share of flagged elements that really degraded","Customer contacts and technician visits per thousand customers","Mean time between failures for the targeted element types","Automated actions that had to be rolled back",[121,124,127,130],{"title":122,"detail":123},"Predictions without actions","The model scores risk accurately but nobody owns the follow up. Tie every score band to a named action and owner.",{"title":125,"detail":126},"Preventive work on healthy equipment","Too many false alarms send technicians to sites that were fine. Track precision and the cost of each preventive visit.",{"title":128,"detail":129},"Automation that causes outages","An automated reset during peak hours takes down more customers than the fault would have. Use windows, rate limits and rollback.",{"title":131,"detail":132},"Drift after network change","New equipment and software releases change what normal looks like. Retrain and revalidate after major upgrades.",{"euAiAct":134,"regulations":137,"guidance":142,"controls":162,"incidents":167},{"tier":135,"basis":136},"context-dependent","Scoring failure risk and planning maintenance is normally minimal risk. Annex III point 2 lists AI systems intended as safety components in the management and operation of critical digital infrastructure as high risk, and public electronic communications networks fall within that infrastructure. Recital 55 limits safety components to systems that directly protect the physical integrity of the infrastructure or the health and safety of persons and property, and excludes components used solely for cybersecurity. An operator whose automated actions meet that test must treat the system as high risk.",[138,139,140,141],"eu-ai-act","nist-ai-rmf","iso-42001","nis2",[143,149,153,157],{"title":144,"issuer":145,"region":146,"url":147,"note":148},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 2 covers AI systems intended as safety components in the management and operation of critical digital infrastructure.",{"title":150,"issuer":145,"region":146,"url":151,"note":152},"Recital 55, safety components of critical infrastructure","https://artificialintelligenceact.eu/recital/55/","Explains which critical digital infrastructure is meant and what counts as a safety component, and excludes components used solely for cybersecurity.",{"title":154,"issuer":145,"region":146,"url":155,"note":156},"Directive (EU) 2022/2557 on the resilience of critical entities","https://eur-lex.europa.eu/eli/dir/2022/2557/oj","Point 8 of the Annex (digital infrastructure) lists providers of public electronic communications networks, the infrastructure that Recital 55 of the AI Act points to for Annex III point 2.",{"title":158,"issuer":159,"region":146,"url":160,"note":161},"NIS2 Directive, securing network and information systems","European Commission","https://digital-strategy.ec.europa.eu/en/policies/nis2-directive","NIS2 covers providers of public electronic communications networks and services, with risk management and incident reporting duties for their network and information systems, which include the automation that acts on the network.",[163,164,165,166],"Documented list of automated actions with owners, limits and rollback procedures","Change control for model thresholds and automation rules","Audit trail linking each prediction to the action taken and the outcome","Periodic validation of model precision and recall on recent failures",[],{"howToBuild":169},"Prediction models usually run in the operator's own data platform; Blits.ai adds the layer that\nacts on them. **Agentic tasks** watch for high risk scores (\"when a cell's risk passes the\nthreshold, check recent changes and propose a fix\") and **agentic workflows** call **custom\nfunctions** against the operator's device management, ticketing and scheduling APIs, with **human\nin the loop approval** above a configurable impact threshold and a **tool execution policy** that\nallows only reversible actions without approval.\n\nEngineers and planners query the results through an **AI agent** with a **SQL knowledge base**\nover prediction tables held in a supported SQL database and a **knowledge base** of runbooks. Where\na fault will affect customers, an agentic workflow sends the notice through the outbound\n**email** channel or a **custom function** that calls the operator's SMS or messaging gateway, and\ncustomers who reply or call reach the same agent on the **SMS**, **WhatsApp**, **email** or\n**voice** channel. **Test suites**, **monitors** and full run audit trails keep the automation\nreviewable, and the platform is model agnostic.",[171,174,177],{"question":172,"answer":173},"What does predictive maintenance look like at scale in a telecom operator?","Telstra reports that its SmartFix system performed 2.5 million proactive actions in FY25, fixing many issues before customers noticed and preventing nearly 1 million support calls. Nokia reported in December 2022 that KDDI monitors its 4G and 5G radio network around the clock with Nokia's AVA PDDR, which detects silent cell degradations that raise no alarm and hands them to KDDI's automatic recovery system.",{"question":175,"answer":176},"Is it only about network equipment?","No. Verizon applies machine learning to more than ten million 811 dig requests a year to identify high risk excavations near its fiber, and takes preventive steps such as extra communication with the excavator. The same approach can target customer premises equipment, site power and batteries.",{"question":178,"answer":179},"Where should an operator start?","With one frequent, costly failure mode that has measurable warning signs and an agreed action, and with the labelled history that links past faults to the data that preceded them.",[181,182,183,184,185],"network-fault-triage-copilot","field-technician-copilot-and-dispatch","autonomous-network-operations","network-outage-communication-agent","device-and-connectivity-troubleshooting-agent","2026-09-27",[188],{"date":186,"note":189},"First published","predictive-network-maintenance",[192,225,249,277,299,321],{"title":193,"useCases":194,"organization":195,"vendors":200,"summary":201,"stage":202,"year":203,"channels":204,"languages":205,"metrics":206,"outcomeDisclosed":215,"sources":216,"verification":220,"grade":222,"id":223,"organizationSlug":224},"Telstra: SmartFix proactive fixes in network operations",[190,183],{"name":196,"anonymized":197,"country":198,"region":199,"industry":17},"Telstra",false,"AU","asia-pacific",[],"Telstra's SmartFix system is embedded in its network operations and automatically fixes many issues before customers notice a problem. Telstra reports the number of proactive actions it performed in FY25 and says they prevented nearly 1 million support calls. The blog post is part of Telstra's description of its wider AI program and gives no detail on the models or the types of fixes.","scaled",2025,[],[],[207],{"kpi":42,"value":208,"unit":209,"qualifier":210,"period":211,"claimant":212,"quote":213,"sourceUrl":214},2500000,"count","exact","FY25, proactive actions","organization","It automatically fixes many issues before customers notice a problem – in FY25 it performed 2.5 million proactive actions, preventing nearly 1 million support calls by resolving issues in advance.","https://www.telstra.com.au/exchange/telstra-s-ai-transformation--strategy--partnerships-and-real-wor",true,[217],{"url":214,"title":218,"publisher":196,"date":219},"Telstra's AI transformation: strategy, partnerships and real-world results","2026-04-20",{"level":221,"checkedAt":186},"source-verified","B","telstra-smartfix-proactive-network-fixes","telstra",{"title":226,"useCases":227,"organization":228,"vendors":231,"summary":235,"stage":236,"year":237,"channels":238,"languages":239,"metrics":240,"outcomeDisclosed":215,"sources":241,"verification":246,"grade":222,"id":247,"organizationSlug":248},"Orange: AI alarm correlation and anomaly detection in the network operations centre",[181,190],{"name":229,"anonymized":197,"country":230,"region":146,"industry":17},"Orange","FR",[232],{"name":233,"role":234},"Augtera Networks","platform","After a two year production trial on the French backbone, Orange Global Network and an SD-WAN network, Orange added the Augtera Network AI platform to its NOC tools. Topology based auto correlation groups alarms so operations experts see far fewer of them, and anomaly detection on metrics and logs flags weak signals so incidents can be handled before customers notice. Orange and Augtera say the correlation will cut the daily number of alarms the NOC has to address by 70%, presented as the expected effect of the rollout rather than a measured result. The integration was due to start in April 2024 in Orange Global Networks, an IP network with thousands of routers in 800 points of presence across 100 countries, with full rollout planned by the end of 2024.","production",2024,[27],[],[],[242],{"url":243,"title":244,"publisher":229,"date":245},"https://newsroom.orange.com/orange-introduces-augtera-network-ai-platform-to-offer-best-in-class-quality-of-service-and-customer-experience/","Orange Introduces Augtera Network AI Platform to offer best-in-class quality of service and customer experience","2024-04-11",{"level":221,"checkedAt":186},"orange-augtera-noc-alarm-correlation",null,{"title":250,"useCases":251,"organization":252,"vendors":256,"summary":260,"stage":236,"year":237,"channels":261,"languages":262,"metrics":263,"outcomeDisclosed":215,"sources":270,"verification":274,"grade":222,"id":275,"organizationSlug":276},"Verizon: machine learning to prevent fiber cuts from excavation",[190],{"name":253,"anonymized":197,"country":254,"region":255,"industry":17},"Verizon","US","north-america",[257],{"name":258,"role":259},"Verizon (proprietary technology)","in-house","Verizon uses artificial intelligence and machine learning on the 811 call before you dig requests it receives to identify the excavations most likely to damage its underground fiber. The model weighs historical and current activity at the location and the past record of the excavator on site, and high risk digs trigger preventive steps such as extra communication with the excavator. The solution is integrated with Verizon's 811 system; Verizon describes the potential benefit but has not published a measured reduction in fiber cuts.",[],[],[264],{"kpi":42,"value":265,"unit":209,"qualifier":266,"period":267,"claimant":212,"quote":268,"sourceUrl":269},10000000,"at-least","per year, 811 dig requests screened","Verizon is utilizing advanced artificial intelligence (AI) and machine learning techniques to sort through over ten million 811 dig requests annually to identify high-risk excavations.","https://www.verizon.com/about/news/verizon-uses-ai-machine-learning-prevent-fiber-cuts",[271],{"url":269,"title":272,"publisher":253,"date":273},"Verizon uses AI & machine learning to prevent fiber cuts","2024-08-07",{"level":221,"checkedAt":186},"verizon-fiber-cut-prevention","verizon",{"title":278,"useCases":279,"organization":281,"vendors":284,"summary":287,"stage":236,"year":203,"channels":288,"languages":289,"metrics":290,"outcomeDisclosed":197,"sources":291,"verification":296,"grade":297,"id":298,"organizationSlug":248},"Telefónica España: big data and AI for network anomaly detection and optimization",[280,190],"network-planning-and-capacity-optimization",{"name":282,"anonymized":197,"country":283,"region":146,"industry":17},"Telefónica España","ES",[285],{"name":286,"role":234},"Microsoft","Telefónica España built a network data platform on Microsoft Azure (Azure Data Explorer, Azure Databricks and Power BI) to store and analyse the large volumes of data its 4G and 5G mobile network produces. The team uses it for anomaly detection, to address issues before they affect customers, and for automated network optimization. Telefónica says the project is live with several use cases deployed and that results have been very positive; Microsoft's summary adds substantial savings in operating costs. No figures are published.",[27],[],[],[292],{"url":293,"title":294,"publisher":286,"date":295},"https://www.microsoft.com/en/customers/story/21150-telefonica-group-spain-azure-ai-and-machine-learning","Telefónica España's transformation with Microsoft Azure: Enhancing network performance through big data and AI","2025-02-26",{"level":221,"checkedAt":186},"C","telefonica-espana-network-analytics-optimization",{"title":300,"useCases":301,"organization":302,"vendors":305,"summary":308,"stage":202,"year":309,"channels":310,"languages":311,"metrics":312,"outcomeDisclosed":197,"sources":313,"verification":319,"grade":297,"id":320,"organizationSlug":248},"KDDI: AI detection and automatic recovery of silent cell degradations",[190,181,183],{"name":303,"anonymized":197,"country":304,"region":199,"industry":17},"KDDI","JP",[306],{"name":307,"role":234},"Nokia","KDDI deployed Nokia's AVA Performance Degradation Detection and Resolution (PDDR) solution nationwide to monitor its 4G and 5G radio network around the clock. The model detects performance degradations that raise no alarm, so called silent cells, classifies the likely root cause, and hands recoverable cases to KDDI's own recovery system, which tries to fix them automatically. Recovered cells feed back into the training data. KDDI started on 4G in 2019 and extended the system to its 5G NSA network in 2021.",2022,[],[],[],[314],{"url":315,"title":316,"publisher":307,"date":317,"archivedUrl":318},"https://www.nokia.com/newsroom/nokia-ava-pddr-solution-deployed-by-kddi-to-boost-network-quality/","Nokia AVA PDDR solution deployed by KDDI to boost network quality","2022-12-08","https://web.archive.org/web/20231206182419/https://www.nokia.com/about-us/news/releases/2022/12/08/nokia-ava-pddr-solution-deployed-by-kddi-to-boost-network-quality/",{"level":221,"checkedAt":186},"kddi-nokia-performance-degradation-detection",{"title":322,"useCases":323,"organization":324,"vendors":327,"summary":331,"stage":236,"year":332,"channels":333,"languages":334,"metrics":335,"outcomeDisclosed":197,"sources":336,"verification":342,"grade":297,"id":343,"organizationSlug":344},"Vodafone: machine learning anomaly detection across its European mobile networks",[181,190],{"name":325,"anonymized":197,"country":326,"region":146,"industry":17},"Vodafone","GB",[328,329],{"name":307,"role":234},{"name":330,"role":234},"Google Cloud","Vodafone and Nokia jointly developed an Anomaly Detection Service, based on Nokia Bell Labs technology and running on Google Cloud, that detects and troubleshoots irregularities such as mobile site congestion, interference and unexpected latency before they affect customers. After an initial deployment on more than 60,000 4G cells in Italy, it was being rolled out across Vodafone's European network in July 2021, with all European markets planned by early 2022 and plans to apply it later to 5G and core networks. Vodafone expected around 80 percent of its anomalous mobile network issues and capacity demands to be detected and addressed automatically; no measured result was published.",2021,[27,28],[],[],[337],{"url":338,"title":339,"publisher":307,"date":340,"archivedUrl":341},"https://www.nokia.com/newsroom/nokia-and-vodafone-harness-machine-learning-on-google-cloud-to-detect-network-anomalies/","Nokia and Vodafone harness machine learning on Google Cloud to detect network anomalies","2021-07-20","https://web.archive.org/web/2026/https://www.nokia.com/newsroom/nokia-and-vodafone-harness-machine-learning-on-google-cloud-to-detect-network-anomalies/",{"level":221,"checkedAt":186},"vodafone-nokia-network-anomaly-detection","vodafone",0,[347],{"kpi":42,"label":348,"unit":209,"aggregate":197,"higherIsBetter":215,"n":349,"nUpTo":345,"median":350,"min":208,"max":265,"byClaimant":351,"vendorOnly":197,"points":352},"Interactions handled",2,6250000,{"organization":349,"vendor":345,"regulator":345,"independent":345},[353,354],{"evidenceId":275,"organization":253,"value":265,"qualifier":266,"claimant":212,"grade":222,"pooled":215},{"evidenceId":223,"organization":196,"value":208,"qualifier":210,"claimant":212,"grade":222,"pooled":215},{"low":356,"high":357},600000,12000000,[359,381,394,407,420],{"slug":181,"title":360,"shortTitle":361,"definition":362,"status":9,"industries":363,"functions":364,"patterns":365,"audience":369,"autonomy":370,"adoptionStage":31,"segment":32,"evidenceCount":371,"publicEvidenceCount":371,"organizations":372,"bestGrade":222,"headline":375,"lastVerified":186,"indexable":215},"AI copilot for network operations centre fault triage","NOC fault triage copilot","AI in the network operations centre (NOC) that correlates alarms and performance data from radio, transport, core and fixed networks into a small number of probable faults, ranks them by customer impact, proposes the likely root cause and fix from runbooks, vendor documentation and past tickets, and routes the ticket to the right team, while an engineer decides what to change.",[17],[19,21],[23,366,367,368,25],"classification-and-routing","rag-knowledge-assistant","summarization","employee-facing","copilot",6,[373,374,303,229,196,325],"Bell Canada","Deutsche Telekom",{"kpi":45,"label":376,"unit":377,"n":378,"nUpTo":345,"kind":379,"value":380,"qualifier":266,"claimant":212,"organization":374,"vendorReported":197},"Cycle time reduction","percent",1,"reported",95,{"slug":182,"title":382,"shortTitle":383,"definition":384,"status":9,"industries":385,"functions":386,"patterns":388,"audience":369,"autonomy":370,"adoptionStage":390,"segment":32,"evidenceCount":349,"publicEvidenceCount":349,"organizations":391,"bestGrade":222,"headline":248,"lastVerified":186,"indexable":215},"AI copilot for field technicians and dispatch optimization","Field technician copilot and dispatch","AI that decides whether a fault needs a site visit at all, predicts what work and parts a job will need, helps plan and update appointments, and gives technicians on site guided diagnosis and instant answers from manuals and past jobs, so more jobs are fixed on the first visit.",[17],[20,21,387],"customer-service",[24,367,389,366],"conversational-agent","emerging",[392,393],"nbn","Openreach",{"slug":183,"title":395,"shortTitle":396,"definition":397,"status":9,"industries":398,"functions":399,"patterns":401,"audience":29,"autonomy":30,"adoptionStage":390,"segment":32,"evidenceCount":371,"publicEvidenceCount":371,"organizations":402,"bestGrade":222,"headline":405,"lastVerified":406,"indexable":215},"Agentic AI for autonomous, intent based network operations","Autonomous network operations","AI agents that run closed loops over a telecom network: they take an intent from the operator (for example a latency or availability target for a service), observe the network, diagnose deviations and execute corrective actions across radio, transport and core, within guardrails set by engineers and with human approval for major changes.",[17],[19,400],"it-and-engineering",[25,23,24,366],[374,403,303,404,196],"du","stc Group",{"kpi":45,"label":376,"unit":377,"n":378,"nUpTo":345,"kind":379,"value":380,"qualifier":266,"claimant":212,"organization":374,"vendorReported":197},"2026-09-26",{"slug":184,"title":408,"shortTitle":409,"definition":410,"status":9,"industries":411,"functions":412,"patterns":413,"audience":416,"autonomy":30,"adoptionStage":390,"segment":417,"evidenceCount":349,"publicEvidenceCount":378,"organizations":418,"bestGrade":222,"headline":248,"lastVerified":186,"indexable":215},"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.",[17],[387,19,20],[23,366,414,389,415],"content-generation","voice-agent","customer-facing","front-office",[419],"Comcast",{"slug":185,"title":421,"shortTitle":422,"definition":423,"status":9,"industries":424,"functions":425,"patterns":426,"audience":416,"autonomy":30,"adoptionStage":31,"segment":417,"evidenceCount":428,"publicEvidenceCount":428,"organizations":429,"bestGrade":222,"headline":433,"lastVerified":406,"indexable":215},"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.",[17],[387,20],[389,415,25,367,427],"computer-vision",4,[430,431,432,325],"Singtel","Virgin Media O2","Vodafone Germany",{"kpi":434,"label":435,"unit":377,"n":436,"nUpTo":345,"kind":437,"value":438,"qualifier":210,"claimant":248,"organization":248,"vendorReported":197},"containment-rate","Containment rate",3,"median",70,{"indexable":215,"reasons":440},[],[442,447,453,460,466,472,479,486,493,500,507,513,520,527,533,537,544,550,556,562,568,574,580,585,590,597,604,609,614,622,628,634,640,645],{"id":138,"label":443,"issuer":145,"region":146,"url":444,"description":445,"useCases":446,"indexable":215},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":448,"label":449,"issuer":145,"region":146,"url":450,"description":451,"useCases":452,"indexable":215},"gdpr","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":140,"label":454,"issuer":455,"region":456,"url":457,"description":458,"useCases":459,"indexable":215},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":139,"label":461,"issuer":462,"region":255,"url":463,"description":464,"useCases":465,"indexable":215},"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":467,"label":468,"issuer":145,"region":146,"url":469,"description":470,"useCases":471,"indexable":215},"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":473,"label":474,"issuer":475,"region":146,"url":476,"description":477,"useCases":478,"indexable":215},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":480,"label":481,"issuer":482,"region":146,"url":483,"description":484,"useCases":485,"indexable":215},"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":487,"label":488,"issuer":489,"region":199,"url":490,"description":491,"useCases":492,"indexable":215},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":494,"label":495,"issuer":496,"region":199,"url":497,"description":498,"useCases":499,"indexable":215},"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":501,"label":502,"issuer":503,"region":456,"url":504,"description":505,"useCases":506,"indexable":215},"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":508,"label":509,"issuer":510,"region":255,"url":511,"description":512,"useCases":506,"indexable":215},"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":514,"label":515,"issuer":516,"region":146,"url":517,"description":518,"useCases":519,"indexable":215},"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":521,"label":522,"issuer":523,"region":456,"url":524,"description":525,"useCases":526,"indexable":215},"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":528,"label":529,"issuer":145,"region":146,"url":530,"description":531,"useCases":532,"indexable":215},"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":141,"label":534,"issuer":145,"region":146,"url":535,"description":536,"useCases":532,"indexable":215},"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":538,"label":539,"issuer":540,"region":255,"url":541,"description":542,"useCases":543,"indexable":215},"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":545,"label":546,"issuer":145,"region":146,"url":547,"description":548,"useCases":549,"indexable":215},"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":551,"label":552,"issuer":553,"region":255,"url":554,"description":555,"useCases":549,"indexable":215},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":557,"label":558,"issuer":559,"region":456,"url":560,"description":561,"useCases":549,"indexable":215},"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":563,"label":564,"issuer":145,"region":146,"url":565,"description":566,"useCases":567,"indexable":215},"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":569,"label":570,"issuer":571,"region":255,"url":572,"description":573,"useCases":567,"indexable":215},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":575,"label":576,"issuer":489,"region":199,"url":577,"description":578,"useCases":579,"indexable":215},"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":581,"label":582,"issuer":145,"region":146,"url":583,"description":584,"useCases":579,"indexable":215},"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":586,"label":587,"issuer":145,"region":146,"url":588,"description":589,"useCases":579,"indexable":215},"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":591,"label":592,"issuer":593,"region":146,"url":594,"description":595,"useCases":596,"indexable":215},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":598,"label":599,"issuer":600,"region":255,"url":601,"description":602,"useCases":603,"indexable":215},"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":145,"region":146,"url":607,"description":608,"useCases":603,"indexable":215},"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":145,"region":146,"url":612,"description":613,"useCases":371,"indexable":215},"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":621,"indexable":215},"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.",5,{"id":623,"label":624,"issuer":625,"region":146,"url":626,"description":627,"useCases":428,"indexable":215},"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":629,"label":630,"issuer":631,"region":146,"url":632,"description":633,"useCases":428,"indexable":215},"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":635,"label":636,"issuer":637,"region":199,"url":638,"description":639,"useCases":436,"indexable":215},"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":641,"label":642,"issuer":145,"region":146,"url":643,"description":644,"useCases":436,"indexable":215},"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":646,"label":647,"issuer":648,"region":255,"url":649,"description":650,"useCases":436,"indexable":215},"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.",1790598301074]