[{"data":1,"prerenderedAt":681},["ShallowReactive",2],{"uc-autonomous-network-operations":3,"uc-regulations":473},{"useCase":4,"evidence":198,"blitsAiDeployments":364,"benchmarks":365,"indicative":380,"related":383,"indexability":471,"includeUnpublished":204},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":42,"indicativeValue":47,"macroEstimates":75,"feasibility":81,"implementation":95,"risk":141,"blitsAi":174,"faq":176,"related":186,"datePublished":192,"dateModified":192,"lastVerified":193,"changelog":194,"slug":197},"Agentic AI for autonomous, intent based network operations","Autonomous network operations","Autonomous network operations with agentic AI","AI agents run closed loops over telecom networks within guardrails set by engineers. Deutsche Telekom cut the time to manage major events by more than 95%.","published","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.",[12,13,14,15,16],"autonomous networks","intent based networking","self healing networks","zero touch network operations","agentic network operations",[18],"telecommunications",[20,21],"network-operations","it-and-engineering",[23,24,25,26],"agentic-workflow","anomaly-detection","prediction-and-scoring","classification-and-routing",[28,29],"api","internal-tools","back-office","supervised-agent","emerging","network","Telecom networks have become too complex to run by hand. A 5G network mixes several radio\nlayers, cloud native core functions, transport, many vendors and new services such as network\nslices with their own performance promises. Vodafone notes that manual tuning and step by step\nautomation scripts are no longer enough as networks grow more complex, and Deutsche Telekom puts it\nplainly: traditional rule based automation falls short in addressing real time challenges.\n\nThe result is that engineers spend their time on repeated manual checks, tuning and firefighting.\nWhen a large event fills an area or a server fails, the right fix is often known, but applying it\ndepends on someone noticing and acting across several vendor tools that do not share data, a\nbarrier Telstra describes in its own network. Vodafone, Google Cloud and TM Forum describe the goal\nas a shift from manual, reactive operations to intent based autonomy, where operators state the\noutcome and the network works out and executes the actions.",[],"1. **Express the intent.** Operators set target outcomes (availability, latency, throughput,\n   energy) per service, area or customer, instead of individual configuration steps.\n2. **Observe.** Agents continuously collect performance, alarm, inventory and external data, such\n   as public event listings, and build a live view of each service across domains.\n3. **Analyse and decide.** Specialised agents detect deviations or predict them, diagnose the likely\n   cause, and select actions, often testing them first in a digital twin.\n4. **Act within guardrails.** Low risk, reversible actions (reallocating resources, adjusting\n   parameters, moving workloads to healthy hardware) run automatically; major changes go to an\n   engineer for approval.\n5. **Document and learn.** Every action and its outcome is logged, explained and used to improve\n   future decisions, and the closed loop checks that the intent is met again.",[38,39,40,41],"speed","customer-experience","cost-to-serve","employee-productivity",[43,44,45,46],"processing-time-reduction","automation-rate","interactions-handled","cost-reduction",{"referenceOrg":48,"inputs":49,"formula":70,"currency":71,"period":72,"resultLabel":73,"caveat":74},"A mobile operator with 300 staff in network operations and optimisation",[50,56,63],{"key":51,"label":52,"low":53,"high":53,"unit":54,"note":55},"staff","Staff in network operations and optimisation",300,"full time employees","The reference operator.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"costPerFte","Fully loaded cost per employee",80000,120000,"USD per year","Editorial assumption. Replace with your own cost.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"automatedShare","Share of routine operations work taken over by closed loop automation",0.05,0.15,"fraction of working time","Conservative. Deutsche Telekom reports a more than 95% reduction in the time to manage major events, but that covers one task type, not all operations work.","staff * costPerFte * automatedShare","USD","per year","Operations capacity released","Staff capacity only. It leaves out faster recovery from incidents, better service level performance, revenue from assured services such as network slices, energy savings, and the substantial investment in data, cloud platforms and integration that autonomy requires.",[76],{"statement":77,"sourceTitle":78,"sourceUrl":79,"year":80},"Vodafone, Google Cloud and TM Forum cite an STL Partners estimate that the potential upside of autonomous networks is circa USD 800 million per operator annually.","Vodafone, Google Cloud and TM Forum Unveil Framework for Self-Optimising Autonomous Networks","https://www.vodafone.com/news/newsroom/technology/vodafone-google-cloud-tm-forum-unveil-framework-for-self-optimising-autonomous-networks",2026,{"complexity":82,"complexityNote":83,"dataPrerequisites":84,"integrations":89},"high","Autonomy needs a unified data foundation across vendors and domains, programmable network interfaces, a reliable inventory, a way to test actions safely and a governance model that engineers trust. Most operators get there one closed loop at a time.",[85,86,87,88],"Real time performance, alarm and configuration data across radio, transport and core","Accurate inventory and service topology","History of past incidents, actions and outcomes","External context such as event calendars, weather and planned works",[90,91,92,93,94],"Network controllers, SON platforms and orchestration systems per domain","Network data lake or data fabric","Digital twin or simulation environment","Trouble ticketing and change management","Service and slice management systems",{"steps":96,"guardrails":115,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":128},[97,100,103,106,109,112],{"title":98,"detail":99},"Pick closed loops with bounded risk","Start with loops where the action is reversible and the benefit is clear, such as capacity adjustments for planned events or moving workloads away from failed hardware.",{"title":101,"detail":102},"Build the shared data foundation","Agents need one view across domains and vendors. Invest in the data layer and inventory before adding more agents.",{"title":104,"detail":105},"Define intents and guardrails together","Write down the target outcome, the allowed actions, the limits and the approval rules for each loop, with the engineers who will be accountable.",{"title":107,"detail":108},"Prove it in shadow and in a twin","Run agents in recommendation mode and test their actions in a digital twin before letting them act on the live network.",{"title":110,"detail":111},"Raise autonomy step by step","Move each loop from recommend to act with approval to act within limits as measured accuracy allows, and keep an easy way to switch back.",{"title":113,"detail":114},"Coordinate the agents","As loops multiply, add coordination so agents in different domains do not work against each other, and keep one audit trail.",[116,117,118,119,120],"Explicit allow list of actions per agent, with limits and rollback","Human in the loop approval for major network changes and for any action outside the limits","Automatic stop and rollback when service indicators degrade after an action","Every action logged with its reason, data and outcome, and explainable to engineers","Change freezes and maintenance windows enforced for autonomous actions","Engineers define intents, allowed actions and limits, approve major changes and review the audit trail. Autonomy levels are set per loop and raised only on evidence. Operations leaders can pause any agent instantly.",[123,124,125,126,127],"Time to detect and resolve deviations from intent, per loop","Share of events handled end to end without human action","Actions rolled back and incidents caused by automation","Service level attainment for assured services","Engineer time spent on routine tasks",[129,132,135,138],{"title":130,"detail":131},"Agents that fight each other","A radio agent and an energy agent undo each other's changes. Coordinate loops and give them shared intents.",{"title":133,"detail":134},"Autonomy without a trusted data foundation","Agents act on stale inventory or partial data. Fix data and topology first.",{"title":136,"detail":137},"Unexplained actions","Engineers cannot see why an agent acted and stop trusting it. Require explanations and full logs.",{"title":139,"detail":140},"A small error at network scale","One wrong automated change is repeated across thousands of elements. Use canaries, rate limits and automatic rollback.",{"euAiAct":142,"regulations":145,"guidance":151,"controls":167,"incidents":173},{"tier":143,"basis":144},"context-dependent","Annex III point 2 lists AI systems intended as safety components in the management and operation of critical digital infrastructure as high risk; Recital 55 ties this to the digital infrastructure in the Annex to Directive (EU) 2022/2557, which includes providers of public electronic communications networks. Recital 55 defines such safety components as 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. Loops that only optimise performance or capacity are usually not safety components, but a loop that protects physical integrity or life safety services can be, so operators should assess each closed loop and document the outcome.",[146,147,148,149,150],"eu-ai-act","nist-ai-rmf","iso-42001","nis2","eecc",[152,158,162],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"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":159,"issuer":154,"region":155,"url":160,"note":161},"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":163,"issuer":164,"region":155,"url":165,"note":166},"NIS2 Directive, securing network and information systems","European Commission","https://digital-strategy.ec.europa.eu/en/policies/nis2-directive","Risk management and incident duties for telecom operators that also apply to automated operations.",[168,169,170,171,172],"Inventory of autonomous loops with owners, intents, allowed actions and autonomy levels","Pre deployment testing in a digital twin or staging network","Complete, tamper evident audit trail of agent actions","Kill switch and rollback procedures tested regularly","Periodic review of autonomy levels against measured accuracy",[],{"howToBuild":175},"Closed loops that touch the network run in the operator's orchestration and domain controllers.\nBlits.ai provides the agent layer around them: **agentic workflows** with an agent loop that\ncalls tools, **custom functions** and **MCP servers** that expose controller and ticketing APIs,\na **tool execution policy** that allow lists what each agent may do, and **human in the loop\napproval** for actions above a configurable threshold. **Agentic tasks** implement \"do X when Y\"\nloops with scheduled rechecks and dry run mode.\n\nEngineers talk to the agents through an **AI agent** in **Microsoft Teams** or the **web chat\nwidget** on an internal portal, grounded in a **knowledge base** of runbooks and a **SQL knowledge\nbase** over operational data held in PostgreSQL. Every workflow run has a full audit trail, **test suites** replay scenarios before changes go live, **monitors**\ncheck agent behaviour, and the platform is model agnostic with EU and UAE data residency.",[177,180,183],{"question":178,"answer":179},"Are autonomous networks real or still a vision?","Parts are live. Deutsche Telekom's RAN Guardian agent went live in Germany in November 2025 and reduced the time to manage major events from hours to around a minute. Telstra showed a self healing proof of concept that moved network applications away from failed hardware in minutes. Full end to end autonomy across all domains is still ahead.",{"question":181,"answer":182},"What does intent based mean?","The operator states the outcome, such as a latency target for a 5G service, and the system works out and executes the actions to achieve and keep it. du and Nokia describe autonomous network slicing that adjusts radio policies to keep premium service levels.",{"question":184,"answer":185},"Who is accountable when an agent changes the network?","The operator. Vodafone's framework with Google Cloud and TM Forum stresses policies, explicit guardrails and human in the loop approval for major network changes. Every agent needs an owner, an allow list and an audit trail.",[187,188,189,190,191],"network-fault-triage-copilot","predictive-network-maintenance","ran-energy-optimization","network-planning-and-capacity-optimization","aiops-incident-triage","2026-09-27","2026-09-26",[195],{"date":192,"note":196},"First published","autonomous-network-operations",[199,231,278,300,323,343],{"title":200,"useCases":201,"organization":202,"vendors":207,"summary":215,"stage":216,"year":80,"channels":217,"languages":218,"metrics":219,"outcomeDisclosed":220,"sources":221,"verification":226,"grade":228,"id":229,"organizationSlug":230},"Telstra: agentic AI proof of concept for self healing telco cloud operations",[197,187],{"name":203,"anonymized":204,"country":205,"region":206,"industry":18},"Telstra",false,"AU","asia-pacific",[208,211,213],{"name":209,"role":210},"Red Hat","platform",{"name":212,"role":210},"Dell Technologies",{"name":214,"role":210},"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",[29,28],[],[],true,[222],{"url":223,"title":224,"publisher":203,"date":225},"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":227,"checkedAt":193},"source-verified","B","telstra-self-healing-network-proof-of-concept","telstra",{"title":232,"useCases":233,"organization":234,"vendors":237,"summary":243,"stage":244,"year":245,"channels":246,"languages":247,"metrics":248,"outcomeDisclosed":220,"sources":263,"verification":275,"grade":228,"id":276,"organizationSlug":277},"Deutsche Telekom: RAN Guardian and MINDR agents for self healing network operations",[197,187,190],{"name":235,"anonymized":204,"country":236,"region":155,"industry":18},"Deutsche Telekom","DE",[238,240],{"name":239,"role":210},"Google Cloud",{"name":241,"role":242},"Google (Gemini models)","model-provider","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.","production",2025,[29,28],[],[249,258],{"kpi":43,"value":250,"unit":251,"qualifier":252,"period":253,"baseline":254,"claimant":255,"quote":256,"sourceUrl":257},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":45,"value":259,"unit":260,"qualifier":252,"period":261,"claimant":255,"quote":262,"sourceUrl":257},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.",[264,267,271],{"url":257,"title":265,"publisher":235,"date":266},"Deutsche Telekom and Google Cloud Collaborate for Superior Network Experience with Agentic AI","2026-02-25",{"url":268,"title":269,"publisher":235,"date":270},"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":272,"title":273,"publisher":235,"date":274},"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":227,"checkedAt":193},"deutsche-telekom-ran-guardian-and-mindr-agents",null,{"title":279,"useCases":280,"organization":281,"vendors":282,"summary":283,"stage":284,"year":245,"channels":285,"languages":286,"metrics":287,"outcomeDisclosed":220,"sources":294,"verification":298,"grade":228,"id":299,"organizationSlug":230},"Telstra: SmartFix proactive fixes in network operations",[188,197],{"name":203,"anonymized":204,"country":205,"region":206,"industry":18},[],"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",[],[],[288],{"kpi":45,"value":289,"unit":260,"qualifier":290,"period":291,"claimant":255,"quote":292,"sourceUrl":293},2500000,"exact","FY25, proactive actions","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",[295],{"url":293,"title":296,"publisher":203,"date":297},"Telstra's AI transformation: strategy, partnerships and real-world results","2026-04-20",{"level":227,"checkedAt":192},"telstra-smartfix-proactive-network-fixes",{"title":301,"useCases":302,"organization":303,"vendors":307,"summary":310,"stage":216,"year":245,"channels":311,"languages":312,"metrics":313,"outcomeDisclosed":204,"sources":314,"verification":320,"grade":321,"id":322,"organizationSlug":277},"du: intent based autonomous network slicing on 5G Advanced",[197],{"name":304,"anonymized":204,"country":305,"region":306,"industry":18},"du","AE","middle-east",[308],{"name":309,"role":210},"Nokia","du and Nokia implemented a 5G Advanced autonomous network slicing solution that continuously measures slice performance and adjusts radio policies by itself, so du can guarantee capacity or low latency for enterprise, event, gaming and broadcasting customers. Machine learning keeps an enterprise customer's capacity intent in all network conditions and enforces low latency slice policies for premium gamers in crowded areas. The companies present it as an industry first implementation; no operational results are published.",[28],[],[],[315],{"url":316,"title":317,"publisher":309,"date":318,"archivedUrl":319},"https://www.nokia.com/newsroom/nokia-and-du-set-new-benchmark-in-5g-innovation-with-autonomous-network-slicing-in-industry-first/","Nokia and du set new benchmark in 5G innovation with autonomous network slicing in industry first","2025-12-03","https://web.archive.org/web/20260105214502/https://www.nokia.com/newsroom/nokia-and-du-set-new-benchmark-in-5g-innovation-with-autonomous-network-slicing-in-industry-first/",{"level":227,"checkedAt":193},"C","du-nokia-autonomous-network-slicing",{"title":324,"useCases":325,"organization":326,"vendors":329,"summary":331,"stage":244,"year":332,"channels":333,"languages":334,"metrics":335,"outcomeDisclosed":220,"sources":336,"verification":341,"grade":321,"id":342,"organizationSlug":277},"stc: AI powered cognitive SON for autonomous radio optimisation",[190,197],{"name":327,"anonymized":204,"country":328,"region":306,"industry":18},"stc Group","SA",[330],{"name":309,"role":210},"Nokia deployed its MantaRay Cognitive SON, an AI powered feature of its self organizing network platform, in stc's commercial network in Saudi Arabia for the first time. The system optimises radio parameters autonomously. Nokia reports that during a period of high traffic it processed more than 10,000 actions, raised the utilisation rate of loaded cells by about 30 percent and average user throughput by 10 percent while traffic rose 40 percent, and that it reduced manual work. The results are stated by the vendor.",2024,[28],[],[],[337],{"url":338,"title":339,"publisher":309,"date":340},"https://www.nokia.com/newsroom/nokia-and-stc-group-optimize-network-with-ai-powered-mantaray-cognitive-son-solution-in-saudi-arabia/","Nokia and stc Group optimize network with AI-powered MantaRay Cognitive SON solution in Saudi Arabia","2024-07-02",{"level":227,"checkedAt":192},"stc-nokia-cognitive-son",{"title":344,"useCases":345,"organization":346,"vendors":349,"summary":351,"stage":284,"year":352,"channels":353,"languages":354,"metrics":355,"outcomeDisclosed":204,"sources":356,"verification":362,"grade":321,"id":363,"organizationSlug":277},"KDDI: AI detection and automatic recovery of silent cell degradations",[188,187,197],{"name":347,"anonymized":204,"country":348,"region":206,"industry":18},"KDDI","JP",[350],{"name":309,"role":210},"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,[],[],[],[357],{"url":358,"title":359,"publisher":309,"date":360,"archivedUrl":361},"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":227,"checkedAt":192},"kddi-nokia-performance-degradation-detection",0,[366,374],{"kpi":45,"label":367,"unit":260,"aggregate":204,"higherIsBetter":220,"n":368,"nUpTo":364,"median":369,"min":259,"max":289,"byClaimant":370,"vendorOnly":204,"points":371},"Interactions handled",2,1250050,{"organization":368,"vendor":364,"regulator":364,"independent":364},[372,373],{"evidenceId":299,"organization":203,"value":289,"qualifier":290,"claimant":255,"grade":228,"pooled":220},{"evidenceId":276,"organization":235,"value":259,"qualifier":252,"claimant":255,"grade":228,"pooled":220},{"kpi":43,"label":375,"unit":251,"aggregate":220,"higherIsBetter":220,"n":376,"nUpTo":364,"median":250,"min":250,"max":250,"byClaimant":377,"vendorOnly":204,"points":378},"Cycle time reduction",1,{"organization":376,"vendor":364,"regulator":364,"independent":364},[379],{"evidenceId":276,"organization":235,"value":250,"qualifier":252,"claimant":255,"grade":228,"pooled":220},{"low":381,"high":382},1200000,5400000,[384,404,415,435,447],{"slug":187,"title":385,"shortTitle":386,"definition":387,"status":9,"industries":388,"functions":389,"patterns":391,"audience":394,"autonomy":395,"adoptionStage":396,"segment":33,"evidenceCount":397,"publicEvidenceCount":397,"organizations":398,"bestGrade":228,"headline":402,"lastVerified":192,"indexable":220},"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.",[18],[20,390],"operations",[24,26,392,393,23],"rag-knowledge-assistant","summarization","employee-facing","copilot","early-adopters",6,[399,235,347,400,203,401],"Bell Canada","Orange","Vodafone",{"kpi":43,"label":375,"unit":251,"n":376,"nUpTo":364,"kind":403,"value":250,"qualifier":252,"claimant":255,"organization":235,"vendorReported":204},"reported",{"slug":188,"title":405,"shortTitle":406,"definition":407,"status":9,"industries":408,"functions":409,"patterns":411,"audience":30,"autonomy":31,"adoptionStage":396,"segment":33,"evidenceCount":397,"publicEvidenceCount":397,"organizations":412,"bestGrade":228,"headline":277,"lastVerified":192,"indexable":220},"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],[20,410,390],"field-service",[24,25,23],[347,400,413,203,414,401],"Telefónica España","Verizon",{"slug":189,"title":416,"shortTitle":417,"definition":418,"status":9,"industries":419,"functions":420,"patterns":421,"audience":30,"autonomy":422,"adoptionStage":396,"segment":33,"evidenceCount":423,"publicEvidenceCount":423,"organizations":424,"bestGrade":228,"headline":430,"lastVerified":192,"indexable":220},"AI for radio access network energy optimization","RAN energy optimization","Machine learning that predicts traffic per cell and puts radio carriers, cells and hardware components into sleep modes when demand is low, then wakes them before users notice, so a mobile network uses less electricity without losing coverage or quality.",[18],[20],[25],"autonomous",5,[425,426,427,428,429],"BT Group","Indosat Ooredoo Hutchison","O2 Telefónica Germany","Safaricom","Telefónica",{"kpi":431,"label":432,"unit":251,"n":364,"nUpTo":376,"kind":403,"value":433,"qualifier":434,"claimant":255,"organization":429,"vendorReported":204},"energy-savings","Energy savings",8,"up-to",{"slug":190,"title":436,"shortTitle":437,"definition":438,"status":9,"industries":439,"functions":440,"patterns":442,"audience":394,"autonomy":31,"adoptionStage":396,"segment":33,"evidenceCount":423,"publicEvidenceCount":423,"organizations":444,"bestGrade":228,"headline":446,"lastVerified":192,"indexable":220},"AI for mobile network planning and capacity optimization","Network planning and capacity","Machine learning that forecasts where and when a mobile network will run out of capacity, recommends where to add cells, spectrum or hardware, and continuously tunes radio parameters so existing capacity carries more traffic, with planners approving investments and major changes.",[18],[20,441],"analytics-and-reporting",[25,443,24,23],"recommendation-and-personalization",[235,445,327,413,401],"NTT DOCOMO",{"kpi":43,"label":375,"unit":251,"n":376,"nUpTo":364,"kind":403,"value":250,"qualifier":252,"claimant":255,"organization":235,"vendorReported":204},{"slug":191,"title":448,"shortTitle":449,"definition":450,"status":9,"industries":451,"functions":456,"patterns":458,"audience":394,"autonomy":395,"adoptionStage":396,"evidenceCount":397,"publicEvidenceCount":423,"organizations":459,"bestGrade":228,"headline":465,"lastVerified":192,"indexable":220},"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.",[452,453,454,18,455],"cross-industry","banking","technology","payments",[21,390,457],"risk-management",[24,26,393,392,23],[460,461,462,463,464],"Google","Meta","Microsoft","Mizuho Financial Group","TD Bank",{"kpi":466,"label":467,"unit":251,"n":468,"nUpTo":364,"kind":469,"value":470,"qualifier":290,"claimant":277,"organization":277,"vendorReported":204},"accuracy","Accuracy",3,"median",90,{"indexable":220,"reasons":472},[],[474,479,485,492,499,505,512,519,526,533,540,546,553,560,566,570,577,583,589,595,600,606,612,617,622,629,635,640,645,651,658,664,670,675],{"id":146,"label":475,"issuer":154,"region":155,"url":476,"description":477,"useCases":478,"indexable":220},"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":480,"label":481,"issuer":154,"region":155,"url":482,"description":483,"useCases":484,"indexable":220},"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":148,"label":486,"issuer":487,"region":488,"url":489,"description":490,"useCases":491,"indexable":220},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":147,"label":493,"issuer":494,"region":495,"url":496,"description":497,"useCases":498,"indexable":220},"NIST AI Risk Management Framework","NIST","north-america","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":500,"label":501,"issuer":154,"region":155,"url":502,"description":503,"useCases":504,"indexable":220},"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":506,"label":507,"issuer":508,"region":155,"url":509,"description":510,"useCases":511,"indexable":220},"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":513,"label":514,"issuer":515,"region":155,"url":516,"description":517,"useCases":518,"indexable":220},"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":520,"label":521,"issuer":522,"region":206,"url":523,"description":524,"useCases":525,"indexable":220},"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":527,"label":528,"issuer":529,"region":206,"url":530,"description":531,"useCases":532,"indexable":220},"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":534,"label":535,"issuer":536,"region":488,"url":537,"description":538,"useCases":539,"indexable":220},"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":541,"label":542,"issuer":543,"region":495,"url":544,"description":545,"useCases":539,"indexable":220},"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":547,"label":548,"issuer":549,"region":155,"url":550,"description":551,"useCases":552,"indexable":220},"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":554,"label":555,"issuer":556,"region":488,"url":557,"description":558,"useCases":559,"indexable":220},"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":561,"label":562,"issuer":154,"region":155,"url":563,"description":564,"useCases":565,"indexable":220},"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":149,"label":567,"issuer":154,"region":155,"url":568,"description":569,"useCases":565,"indexable":220},"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":571,"label":572,"issuer":573,"region":495,"url":574,"description":575,"useCases":576,"indexable":220},"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":578,"label":579,"issuer":154,"region":155,"url":580,"description":581,"useCases":582,"indexable":220},"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":584,"label":585,"issuer":586,"region":495,"url":587,"description":588,"useCases":582,"indexable":220},"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":590,"label":591,"issuer":592,"region":488,"url":593,"description":594,"useCases":582,"indexable":220},"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":150,"label":596,"issuer":154,"region":155,"url":597,"description":598,"useCases":599,"indexable":220},"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":601,"label":602,"issuer":603,"region":495,"url":604,"description":605,"useCases":599,"indexable":220},"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":607,"label":608,"issuer":522,"region":206,"url":609,"description":610,"useCases":611,"indexable":220},"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":613,"label":614,"issuer":154,"region":155,"url":615,"description":616,"useCases":611,"indexable":220},"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":618,"label":619,"issuer":154,"region":155,"url":620,"description":621,"useCases":611,"indexable":220},"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":623,"label":624,"issuer":625,"region":155,"url":626,"description":627,"useCases":628,"indexable":220},"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":630,"label":631,"issuer":632,"region":495,"url":633,"description":634,"useCases":433,"indexable":220},"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.",{"id":636,"label":637,"issuer":154,"region":155,"url":638,"description":639,"useCases":433,"indexable":220},"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":641,"label":642,"issuer":154,"region":155,"url":643,"description":644,"useCases":397,"indexable":220},"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":646,"label":647,"issuer":648,"region":306,"url":649,"description":650,"useCases":423,"indexable":220},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":652,"label":653,"issuer":654,"region":155,"url":655,"description":656,"useCases":657,"indexable":220},"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":659,"label":660,"issuer":661,"region":155,"url":662,"description":663,"useCases":657,"indexable":220},"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":665,"label":666,"issuer":667,"region":206,"url":668,"description":669,"useCases":468,"indexable":220},"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":671,"label":672,"issuer":154,"region":155,"url":673,"description":674,"useCases":468,"indexable":220},"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":676,"label":677,"issuer":678,"region":495,"url":679,"description":680,"useCases":468,"indexable":220},"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.",1790598293836]