[{"data":1,"prerenderedAt":677},["ShallowReactive",2],{"uc-network-fault-triage-copilot":3,"uc-regulations":468},{"useCase":4,"evidence":196,"blitsAiDeployments":369,"benchmarks":370,"indicative":382,"related":385,"indexability":466,"includeUnpublished":202},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":28,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":48,"macroEstimates":84,"feasibility":85,"implementation":100,"risk":146,"blitsAi":173,"faq":175,"related":185,"datePublished":191,"dateModified":191,"lastVerified":191,"changelog":192,"slug":195},"AI copilot for network operations centre fault triage","NOC fault triage copilot","AI for NOC alarm correlation and fault triage","An AI NOC copilot groups network alarms into probable faults and proposes a fix. Bell Canada ranks issues by customer impact; Orange adopted alarm correlation.","published","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.",[12,13,14,15,16],"NOC copilot","network alarm correlation","telecom network AIOps","network fault management AI","root cause analysis for telecom networks",[18],"telecommunications",[20,21],"network-operations","operations",[23,24,25,26,27],"anomaly-detection","classification-and-routing","rag-knowledge-assistant","summarization","agentic-workflow",[29,30],"internal-tools","api","employee-facing","copilot","early-adopters","network","Operator networks are large and multi vendor: Vodafone's first anomaly detection deployment alone\ncovered more than 60,000 4G cells in Italy, and Orange Global Networks has thousands of IP routers in\n800 points of presence. Each element raises its own alarms, so one broken fibre or a failed power\nsupply produces a storm of alarms across radio, transport and core at the same time. NOC engineers\nspend the first part of every incident working out which alarms belong together, which domain owns\nthe problem and whether customers are affected at all, while the ticket queue keeps growing.\n\nThe knowledge that shortens a fault (vendor manuals, runbooks, the fix that worked last month) is\nspread over many tools that do not talk to each other. Telstra describes the multi vendor problem\ndirectly: disparate vendor platforms cannot talk to each other, which slows fault diagnosis. Some\ndegradations raise no alarm at all: Nokia, describing its work with KDDI, calls these \"silent\ncells\", which do not hurt service quality at once but eventually degrade the end user experience.\nThe result is long mean time to repair, engineers who chase noise, and customers who report faults\nbefore the operator sees them.",[],"1. **Collect and normalise.** Alarms, performance counters, logs, topology and inventory from every\n   vendor platform stream into one data layer, with the network topology as a graph.\n2. **Correlate.** Models group alarms that share a topological or temporal cause into one probable\n   incident, so the NOC sees one problem instead of hundreds of alarms. Anomaly detection adds\n   degradations that raised no alarm.\n3. **Rank by customer impact.** The incident is scored with traffic, affected services and\n   customers, so a small fault on a busy site outranks a large one on an idle site.\n4. **Explain and propose.** A language model assistant summarises the evidence, retrieves matching\n   runbook steps, vendor documentation and similar past tickets, and proposes the likely root\n   cause and the next diagnostic or corrective step, with its sources.\n5. **Route and track.** The ticket goes to the owning team (radio, transport, core, field) with the\n   correlated evidence attached; the engineer approves any change, and the outcome is fed back to\n   improve correlation and ranking.",[39,40,41,42],"employee-productivity","speed","customer-experience","cost-to-serve",[44,45,46,47],"alert-volume-reduction","processing-time-reduction","productivity-gain","interactions-handled",{"referenceOrg":49,"inputs":50,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A mobile and fixed operator with a 24 hour NOC handling 100,000 network incidents a year",[51,58,65,72],{"key":52,"label":53,"low":54,"high":55,"unit":56,"note":57},"incidents","Network incidents and trouble tickets triaged by the NOC per year",60000,150000,"incidents per year","Editorial assumption for a national operator. Replace with your own ticket volume.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"triageMinutes","Engineer minutes spent on correlation and diagnosis per incident",30,60,"minutes per incident","Editorial assumption. Replace with a time study of your own NOC.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"timeSaved","Share of triage time saved by correlation and assisted diagnosis",0.2,0.4,"fraction of triage time","Editorial assumption. Orange expects topology based correlation to cut the daily alarms its NOC has to address by 70%, but fewer alarms do not translate one to one into less engineer time, so the range stays well below that.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"hourlyCost","Fully loaded cost of a NOC engineer hour",50,90,"USD per hour","Editorial assumption. Replace with your own fully loaded cost.","incidents * triageMinutes / 60 * timeSaved * hourlyCost","USD","per year","NOC engineering time released","Engineering time only. It leaves out the larger effect of shorter outages on customer experience, churn and service level penalties, the contacts avoided when faults are fixed before customers call, and the cost of the data platform and integration work.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":94},"high","The model is the easy part. The work is in getting clean, timely alarm, performance, topology and inventory data out of many vendor systems, and in earning the trust of engineers who have seen many correlation tools promise more than they delivered.",[89,90,91,92,93],"Alarm and event streams from every vendor element manager and OSS","Performance counters per cell, link and node at a useful granularity","An accurate network topology and inventory, ideally as a graph","Historical trouble tickets with root cause and resolution codes","Runbooks and vendor documentation in a searchable form",[95,96,97,98,99],"Fault management and OSS platforms per domain (radio, transport, core, fixed access)","Performance management and network data lake","Network inventory and topology systems","Trouble ticketing and workforce management","Collaboration tools used by the NOC for incident communication",{"steps":101,"guardrails":120,"humanInTheLoop":126,"kpisToInstrument":127,"failureModes":133},[102,105,108,111,114,117],{"title":103,"detail":104},"Start with one domain and one pain","Pick the domain with the worst alarm noise (often radio access) and measure today's alarms per real incident, time to diagnose and repeat tickets, so you have a baseline.",{"title":106,"detail":107},"Fix the data before the model","Get topology and inventory right and stream alarms in near real time. Correlation built on a wrong topology produces confident nonsense.",{"title":109,"detail":110},"Run in shadow mode","Let the system group alarms and propose root causes next to the existing process for several weeks, and have senior engineers grade each proposal before anyone relies on it.",{"title":112,"detail":113},"Ground the assistant in your own knowledge","Load runbooks, vendor manuals and resolved tickets into retrieval with owners and review dates, and make the assistant cite its sources and say when it does not know.",{"title":115,"detail":116},"Put suppression under change control","Every rule or model that hides alarms from engineers is a risk. Version it, test it against past major incidents and review it like any network change.",{"title":118,"detail":119},"Widen to cross domain incidents","Once radio works, add transport and core so the system can trace a customer impact to its real cause across domains.",[121,122,123,124,125],"The copilot proposes; engineers approve every configuration change and every ticket closure","Suppressed alarms remain visible on request and are sampled for review every week","Answers cite the runbook, document or past ticket they come from, with a refusal when nothing matches","Read only access to network elements for the assistant, with any write actions routed through existing change management","Major incidents always trigger the normal human escalation path, whatever the model says","NOC engineers own diagnosis and every change to the network. The copilot correlates, ranks and drafts; engineers accept, correct or reject its proposals, and their corrections are the training signal. Senior engineers review suppression rules and a sample of closed incidents every week.",[128,129,130,131,132],"Alarms per actionable incident, before and after correlation","Mean time to detect and mean time to repair per domain","Share of root cause proposals accepted by engineers","Incidents first reported by customers rather than detected by the NOC","Missed incidents, where a suppressed or low ranked alarm turned out to matter",[134,137,140,143],{"title":135,"detail":136},"Suppression that hides a real outage","A correlation rule groups a new failure under an old pattern and it never reaches an engineer. Sample suppressed alarms and replay past major incidents on every model change.",{"title":138,"detail":139},"Stale topology","Correlation depends on knowing what is connected to what. When inventory lags behind the network, root cause proposals point at the wrong element.",{"title":141,"detail":142},"Fluent but wrong diagnosis","A language model explains a fault convincingly from an outdated runbook. Require citations and track acceptance rates per runbook.",{"title":144,"detail":145},"Tool nobody opens","The copilot lives in yet another screen. Put its output inside the ticket and the NOC wall, not beside them.",{"euAiAct":147,"regulations":150,"guidance":156,"controls":167,"incidents":172},{"tier":148,"basis":149},"context-dependent","The main test is Annex III point 2, which lists AI systems intended as safety components in the management and operation of critical digital infrastructure as high risk. A copilot that prepares diagnoses for engineers who decide every change is normally not such a safety component, and is then minimal risk. The tier rises when the system is designed to protect the safe operation of the network, for example by acting on it automatically to prevent or contain outages. Article 6(3) can exempt an Annex III system that only performs a preparatory task to an assessment and poses no significant risk of harm, provided the provider documents that assessment and registers the system.",[151,152,153,154,155],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001","nis2",[157,163],{"title":158,"issuer":159,"region":160,"url":161,"note":162},"Regulation (EU) 2024/1689 (AI Act), Annex III high risk AI systems","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng","Point 2 covers AI systems intended as safety components in the management and operation of critical digital infrastructure.",{"title":164,"issuer":159,"region":160,"url":165,"note":166},"Directive (EU) 2022/2555 (NIS2 Directive)","https://eur-lex.europa.eu/eli/dir/2022/2555/oj/eng","Under Article 2(2), providers of public electronic communications networks and services fall under NIS2 regardless of their size, with risk management and significant incident reporting duties that the NOC tooling supports and must not weaken.",[168,169,170,171],"Inventory entry for the copilot with an accountable owner and documented data sources","Change control and regression tests on correlation and suppression rules","Audit trail of every proposal, the engineer's decision and the outcome","Weekly sampling of suppressed alarms and closed incidents by senior engineers",[],{"howToBuild":174},"On Blits.ai the NOC copilot is an **AI agent** available in the engineers' tools (for example\n**Microsoft Teams** or an internal portal through the **REST API**). A **knowledge base** holds\nrunbooks, vendor manuals and resolved tickets with hybrid retrieval, and a **SQL knowledge base**\nlets the agent query incident and performance tables in plain language. **Custom functions** read\nthe correlated incident, topology and ticket history from the operator's OSS and ticketing APIs.\n\nActions that change anything, such as reassigning or closing a ticket, run as **agentic\nworkflows** with **human in the loop approval**, and a **tool execution policy** keeps the agent\nlimited to read only tools on network systems. Output **guardrails** check answers against a\npolicy that requires a cited source, **test suites** rerun questions built from past incidents on every change,\nscheduled **monitors** check the agent's answers, and the platform is model agnostic with EU and\nUAE data residency options.",[176,179,182],{"question":177,"answer":178},"How much alarm noise can AI correlation remove?","It depends on the network and the quality of topology data. After a two year production trial, Orange said topology based auto correlation from Augtera will cut the daily alarms its NOC has to address by 70%, stated as the expected effect of the rollout rather than a measured result. Measure your own alarms per real incident before and after, rather than relying on that figure.",{"question":180,"answer":181},"Does a generative AI copilot replace the correlation engine?","No. Correlation and anomaly detection remain statistical and topology based. The language model sits on top: it explains the incident, finds the relevant runbook and past tickets, and drafts the next step. Bell's setup follows this pattern: custom AI and machine learning models correlate network data with customer experience to prioritise issues, and Gemini models support incident analysis, historical context retrieval and access to vendor documentation.",{"question":183,"answer":184},"Is a NOC copilot high risk under the EU AI Act?","Usually not while engineers make every change. It can become high risk under Annex III point 2 when the system itself acts as a safety component in operating critical digital infrastructure, so the design choice about autonomy decides the tier.",[186,187,188,189,190],"aiops-incident-triage","autonomous-network-operations","predictive-network-maintenance","network-outage-communication-agent","field-technician-copilot-and-dispatch","2026-09-27",[193],{"date":191,"note":194},"First published","network-fault-triage-copilot",[197,231,259,301,322,346],{"title":198,"useCases":199,"organization":200,"vendors":205,"summary":213,"stage":214,"year":215,"channels":216,"languages":217,"metrics":218,"outcomeDisclosed":219,"sources":220,"verification":225,"grade":228,"id":229,"organizationSlug":230},"Telstra: agentic AI proof of concept for self healing telco cloud operations",[187,195],{"name":201,"anonymized":202,"country":203,"region":204,"industry":18},"Telstra",false,"AU","asia-pacific",[206,209,211],{"name":207,"role":208},"Red Hat","platform",{"name":210,"role":208},"Dell Technologies",{"name":212,"role":208},"Cisco","In a proof of concept in a live telco cloud environment, Telstra showed an agentic AI capability that detected an unplanned infrastructure outage and resolved it autonomously by moving critical network applications to healthy hardware in minutes rather than hours. Using the Model Context Protocol with retrieval augmented generation, AI agents connect data from multiple vendor platforms into one view and give teams context aware recommendations to speed up fault resolution. Telstra presents it as groundwork for self healing, self optimising operations, not yet as a production service.","pilot",2026,[29,30],[],[],true,[221],{"url":222,"title":223,"publisher":201,"date":224},"https://www.telstra.com.au/exchange/telstra-advanced-autonomous-networks-ambition-through-breakthrou","Telstra advanced autonomous networks ambition through breakthrough collaboration with Red Hat, Dell Technologies and Cisco","2026-03-02",{"level":226,"checkedAt":227},"source-verified","2026-09-26","B","telstra-self-healing-network-proof-of-concept","telstra",{"title":232,"useCases":233,"organization":234,"vendors":238,"summary":244,"stage":245,"year":246,"channels":247,"languages":248,"metrics":249,"outcomeDisclosed":219,"sources":250,"verification":256,"grade":228,"id":257,"organizationSlug":258},"Bell Canada: network AI Ops for detecting, ranking and resolving network issues",[195],{"name":235,"anonymized":202,"country":236,"region":237,"industry":18},"Bell Canada","CA","north-america",[239,241],{"name":240,"role":208},"Google Cloud",{"name":242,"role":243},"Google (Gemini models)","model-provider","Bell deployed a network AI Ops solution built on Google Cloud that correlates network data with customer experience to rank issues by their real impact, so a small fault on a busy site gets attention before a larger one on a quiet site. Custom machine learning models handle detection and prioritisation, a graph of network relationships estimates customer impact, and Gemini models support incident analysis, retrieval of historical context and access to vendor documentation. Bell says the approach has significantly improved mean time to resolution but does not give a figure specific to AI Ops.","production",2025,[29],[],[],[251],{"url":252,"title":253,"publisher":254,"date":255},"https://www.bce.ca/news-and-media/newsroom?article=Bell-Canada-launches-AI-powered-network-operations-solution-built-on-Google-Cloud","Bell Canada launches AI-powered network operations solution built on Google Cloud","BCE","2025-02-26",{"level":226,"checkedAt":191},"bell-canada-network-ai-ops",null,{"title":260,"useCases":261,"organization":263,"vendors":266,"summary":269,"stage":245,"year":246,"channels":270,"languages":271,"metrics":272,"outcomeDisclosed":219,"sources":287,"verification":299,"grade":228,"id":300,"organizationSlug":258},"Deutsche Telekom: RAN Guardian and MINDR agents for self healing network operations",[187,195,262],"network-planning-and-capacity-optimization",{"name":264,"anonymized":202,"country":265,"region":160,"industry":18},"Deutsche Telekom","DE",[267,268],{"name":240,"role":208},{"name":242,"role":243},"Deutsche Telekom's RAN Guardian Agent, built with Gemini models on Google Cloud, went live in its German mobile network in November 2025. It is a multi agent system: one agent finds upcoming public events from public sources, another assesses whether nearby cells can carry the expected traffic and monitors them live, and a third executes corrective actions such as reallocating resources or adjusting configuration, documenting every action. It is being extended to the Czech Republic and Croatia. In February 2026 Deutsche Telekom announced MINDR, which applies the same approach end to end across radio, transport and core domains, with first production releases planned for later in 2026.",[29,30],[],[273,282],{"kpi":45,"value":274,"unit":275,"qualifier":276,"period":277,"baseline":278,"claimant":279,"quote":280,"sourceUrl":281},95,"percent","at-least","live operations, major events","Time needed to manage major events before the agent (hours)","organization","And in live operations it has reduced the time needed to manage major events from hours to around a minute, a more than 95% improvement.","https://www.telekom.com/en/newsroom/latest-updates/media-information/2026/2/mindr-ai-agents-in-telekom-network",{"kpi":47,"value":283,"unit":284,"qualifier":276,"period":285,"claimant":279,"quote":286,"sourceUrl":281},100,"count","first month after launch, Christmas market events","Since its launch in November 2025, RAN Guardian Agent has autonomously triggered over 100 remediation actions at Christmas market events during its first month.",[288,291,295],{"url":281,"title":289,"publisher":264,"date":290},"Deutsche Telekom and Google Cloud Collaborate for Superior Network Experience with Agentic AI","2026-02-25",{"url":292,"title":293,"publisher":264,"date":294},"https://www.telekom.com/en/newsroom/latest-updates/media-information/2025/11/deutsche-telekom-ai-agents-for-mobile-network","Deutsche Telekom: AI agents for mobile network","2025-11-11",{"url":296,"title":297,"publisher":264,"date":298},"https://www.telekom.com/en/newsroom/latest-updates/media-information/2025/2/agentic-ai-for-autonomous-networks","Deutsche Telekom and Google Cloud Partner on Agentic AI for Autonomous Networks","2025-02-25",{"level":226,"checkedAt":227},"deutsche-telekom-ran-guardian-and-mindr-agents",{"title":302,"useCases":303,"organization":304,"vendors":307,"summary":310,"stage":245,"year":311,"channels":312,"languages":313,"metrics":314,"outcomeDisclosed":219,"sources":315,"verification":320,"grade":228,"id":321,"organizationSlug":258},"Orange: AI alarm correlation and anomaly detection in the network operations centre",[195,188],{"name":305,"anonymized":202,"country":306,"region":160,"industry":18},"Orange","FR",[308],{"name":309,"role":208},"Augtera Networks","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.",2024,[29],[],[],[316],{"url":317,"title":318,"publisher":305,"date":319},"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":226,"checkedAt":191},"orange-augtera-noc-alarm-correlation",{"title":323,"useCases":324,"organization":325,"vendors":328,"summary":331,"stage":332,"year":333,"channels":334,"languages":335,"metrics":336,"outcomeDisclosed":202,"sources":337,"verification":343,"grade":344,"id":345,"organizationSlug":258},"KDDI: AI detection and automatic recovery of silent cell degradations",[188,195,187],{"name":326,"anonymized":202,"country":327,"region":204,"industry":18},"KDDI","JP",[329],{"name":330,"role":208},"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.","scaled",2022,[],[],[],[338],{"url":339,"title":340,"publisher":330,"date":341,"archivedUrl":342},"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":226,"checkedAt":191},"C","kddi-nokia-performance-degradation-detection",{"title":347,"useCases":348,"organization":349,"vendors":352,"summary":355,"stage":245,"year":356,"channels":357,"languages":358,"metrics":359,"outcomeDisclosed":202,"sources":360,"verification":366,"grade":344,"id":367,"organizationSlug":368},"Vodafone: machine learning anomaly detection across its European mobile networks",[195,188],{"name":350,"anonymized":202,"country":351,"region":160,"industry":18},"Vodafone","GB",[353,354],{"name":330,"role":208},{"name":240,"role":208},"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,[29,30],[],[],[361],{"url":362,"title":363,"publisher":330,"date":364,"archivedUrl":365},"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":226,"checkedAt":191},"vodafone-nokia-network-anomaly-detection","vodafone",0,[371,377],{"kpi":45,"label":372,"unit":275,"aggregate":219,"higherIsBetter":219,"n":373,"nUpTo":369,"median":274,"min":274,"max":274,"byClaimant":374,"vendorOnly":202,"points":375},"Cycle time reduction",1,{"organization":373,"vendor":369,"regulator":369,"independent":369},[376],{"evidenceId":300,"organization":264,"value":274,"qualifier":276,"claimant":279,"grade":228,"pooled":219},{"kpi":47,"label":378,"unit":284,"aggregate":202,"higherIsBetter":219,"n":373,"nUpTo":369,"median":283,"min":283,"max":283,"byClaimant":379,"vendorOnly":202,"points":380},"Interactions handled",{"organization":373,"vendor":369,"regulator":369,"independent":369},[381],{"evidenceId":300,"organization":264,"value":283,"qualifier":276,"claimant":279,"grade":228,"pooled":219},{"low":383,"high":384},300000,5400000,[386,413,429,440,456],{"slug":186,"title":387,"shortTitle":388,"definition":389,"status":9,"industries":390,"functions":395,"patterns":398,"audience":31,"autonomy":32,"adoptionStage":33,"evidenceCount":399,"publicEvidenceCount":400,"organizations":401,"bestGrade":228,"headline":407,"lastVerified":191,"indexable":219},"AI for IT incident triage and root cause analysis (AIOps)","AIOps incident triage","AI that turns a flood of monitoring alerts into one probable incident, routes it to the right team, proposes likely root causes and remediation from runbooks and past incidents, and drafts the stakeholder updates and the post incident review, while an engineer authorizes every change.",[391,392,393,18,394],"cross-industry","banking","technology","payments",[396,21,397],"it-and-engineering","risk-management",[23,24,26,25,27],6,5,[402,403,404,405,406],"Google","Meta","Microsoft","Mizuho Financial Group","TD Bank",{"kpi":408,"label":409,"unit":275,"n":410,"nUpTo":369,"kind":411,"value":76,"qualifier":412,"claimant":258,"organization":258,"vendorReported":202},"accuracy","Accuracy",3,"median","exact",{"slug":187,"title":414,"shortTitle":415,"definition":416,"status":9,"industries":417,"functions":418,"patterns":419,"audience":421,"autonomy":422,"adoptionStage":423,"segment":34,"evidenceCount":399,"publicEvidenceCount":399,"organizations":424,"bestGrade":228,"headline":427,"lastVerified":227,"indexable":219},"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.",[18],[20,396],[27,23,420,24],"prediction-and-scoring","back-office","supervised-agent","emerging",[264,425,326,426,201],"du","stc Group",{"kpi":45,"label":372,"unit":275,"n":373,"nUpTo":369,"kind":428,"value":274,"qualifier":276,"claimant":279,"organization":264,"vendorReported":202},"reported",{"slug":188,"title":430,"shortTitle":431,"definition":432,"status":9,"industries":433,"functions":434,"patterns":436,"audience":421,"autonomy":422,"adoptionStage":33,"segment":34,"evidenceCount":399,"publicEvidenceCount":399,"organizations":437,"bestGrade":228,"headline":258,"lastVerified":191,"indexable":219},"AI for predictive network maintenance in telecom","Predictive network maintenance","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.",[18],[20,435,21],"field-service",[23,420,27],[326,305,438,201,439,350],"Telefónica España","Verizon",{"slug":189,"title":441,"shortTitle":442,"definition":443,"status":9,"industries":444,"functions":445,"patterns":447,"audience":451,"autonomy":422,"adoptionStage":423,"segment":452,"evidenceCount":453,"publicEvidenceCount":373,"organizations":454,"bestGrade":228,"headline":258,"lastVerified":191,"indexable":219},"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.",[18],[446,20,435],"customer-service",[23,24,448,449,450],"content-generation","conversational-agent","voice-agent","customer-facing","front-office",2,[455],"Comcast",{"slug":190,"title":457,"shortTitle":458,"definition":459,"status":9,"industries":460,"functions":461,"patterns":462,"audience":31,"autonomy":32,"adoptionStage":423,"segment":34,"evidenceCount":453,"publicEvidenceCount":453,"organizations":463,"bestGrade":228,"headline":258,"lastVerified":191,"indexable":219},"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.",[18],[435,21,446],[420,25,449,24],[464,465],"nbn","Openreach",{"indexable":219,"reasons":467},[],[469,474,479,486,492,498,505,512,519,526,533,539,546,553,559,563,570,576,582,588,594,600,606,611,616,623,630,635,640,647,654,660,666,671],{"id":151,"label":470,"issuer":159,"region":160,"url":471,"description":472,"useCases":473,"indexable":219},"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":152,"label":475,"issuer":159,"region":160,"url":476,"description":477,"useCases":478,"indexable":219},"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":154,"label":480,"issuer":481,"region":482,"url":483,"description":484,"useCases":485,"indexable":219},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":153,"label":487,"issuer":488,"region":237,"url":489,"description":490,"useCases":491,"indexable":219},"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":493,"label":494,"issuer":159,"region":160,"url":495,"description":496,"useCases":497,"indexable":219},"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":499,"label":500,"issuer":501,"region":160,"url":502,"description":503,"useCases":504,"indexable":219},"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":506,"label":507,"issuer":508,"region":160,"url":509,"description":510,"useCases":511,"indexable":219},"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":513,"label":514,"issuer":515,"region":204,"url":516,"description":517,"useCases":518,"indexable":219},"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":520,"label":521,"issuer":522,"region":204,"url":523,"description":524,"useCases":525,"indexable":219},"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":527,"label":528,"issuer":529,"region":482,"url":530,"description":531,"useCases":532,"indexable":219},"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":534,"label":535,"issuer":536,"region":237,"url":537,"description":538,"useCases":532,"indexable":219},"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":540,"label":541,"issuer":542,"region":160,"url":543,"description":544,"useCases":545,"indexable":219},"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":547,"label":548,"issuer":549,"region":482,"url":550,"description":551,"useCases":552,"indexable":219},"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":554,"label":555,"issuer":159,"region":160,"url":556,"description":557,"useCases":558,"indexable":219},"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":155,"label":560,"issuer":159,"region":160,"url":561,"description":562,"useCases":558,"indexable":219},"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":564,"label":565,"issuer":566,"region":237,"url":567,"description":568,"useCases":569,"indexable":219},"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":571,"label":572,"issuer":159,"region":160,"url":573,"description":574,"useCases":575,"indexable":219},"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":577,"label":578,"issuer":579,"region":237,"url":580,"description":581,"useCases":575,"indexable":219},"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":583,"label":584,"issuer":585,"region":482,"url":586,"description":587,"useCases":575,"indexable":219},"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":589,"label":590,"issuer":159,"region":160,"url":591,"description":592,"useCases":593,"indexable":219},"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":595,"label":596,"issuer":597,"region":237,"url":598,"description":599,"useCases":593,"indexable":219},"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":601,"label":602,"issuer":515,"region":204,"url":603,"description":604,"useCases":605,"indexable":219},"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":607,"label":608,"issuer":159,"region":160,"url":609,"description":610,"useCases":605,"indexable":219},"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":612,"label":613,"issuer":159,"region":160,"url":614,"description":615,"useCases":605,"indexable":219},"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":617,"label":618,"issuer":619,"region":160,"url":620,"description":621,"useCases":622,"indexable":219},"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":624,"label":625,"issuer":626,"region":237,"url":627,"description":628,"useCases":629,"indexable":219},"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":631,"label":632,"issuer":159,"region":160,"url":633,"description":634,"useCases":629,"indexable":219},"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":636,"label":637,"issuer":159,"region":160,"url":638,"description":639,"useCases":399,"indexable":219},"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":641,"label":642,"issuer":643,"region":644,"url":645,"description":646,"useCases":400,"indexable":219},"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":648,"label":649,"issuer":650,"region":160,"url":651,"description":652,"useCases":653,"indexable":219},"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.",4,{"id":655,"label":656,"issuer":657,"region":160,"url":658,"description":659,"useCases":653,"indexable":219},"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":661,"label":662,"issuer":663,"region":204,"url":664,"description":665,"useCases":410,"indexable":219},"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":667,"label":668,"issuer":159,"region":160,"url":669,"description":670,"useCases":410,"indexable":219},"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":672,"label":673,"issuer":674,"region":237,"url":675,"description":676,"useCases":410,"indexable":219},"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.",1790598298212]