[{"data":1,"prerenderedAt":667},["ShallowReactive",2],{"uc-telecom-field-work-visual-inspection":3,"uc-regulations":440},{"useCase":4,"evidence":196,"blitsAiDeployments":339,"benchmarks":340,"indicative":357,"related":360,"indexability":438,"includeUnpublished":202},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":23,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":45,"valueDrivers":46,"kpis":51,"indicativeValue":56,"macroEstimates":91,"feasibility":92,"implementation":106,"risk":147,"blitsAi":175,"faq":177,"related":187,"datePublished":191,"dateModified":191,"lastVerified":191,"changelog":192,"slug":195},"AI visual inspection of telecom field work and cell sites","Field work visual inspection","AI visual inspection for telecom field work","AI reads technician photos and drone images to catch install and tower defects. IQGeo reports 98% accuracy at Bouygues Telecom; SK Telecom cut tower review time 95%.","published","Telecom field work visual inspection is computer vision that checks technician photos and drone images of fiber installs and cell towers for defects, often replacing sample audits. It flags problems such as an unconnected fiber, a wrong color code or a loose bolt while the technician is still on site or before a reviewer opens the images, and people decide what to repair.",[12,13,14,15,16],"AI installation quality assurance","fiber install photo verification","visual automation for telecom","drone cell tower inspection AI","technician photo checks",[18],"telecommunications",[20,21,22],"field-service","operations","network-operations",[24,25],"computer-vision","classification-and-routing",[27,28],"mobile-app","internal-tools","employee-facing","copilot","early-adopters","network","Operators that build and maintain networks through contractors cannot see most of the work. A\ntechnician connects a cabinet, a distribution point or a modem, takes a few photos and closes the\njob. Someone in a back office may look at a sample later. A defect that slips through shows up as a\nrepeat visit, a degraded fiber asset, a penalty from a wholesale carrier or a customer who leaves.\n\nCell towers have the same problem in another form. They reach up to 75 meters and need periodic\nchecks for corrosion and loose bolts, and the check used to mean climbing the tower or studying many\nimages by eye.\n\nRegulation can add volume. In France, Orange France says ARCEP made photos before and after each\nfiber intervention mandatory in summer 2021, which means analyzing about 20,000 photos a day.\nSampling is not an option when every intervention has to be verified, and a person cannot read that\nmany images.",[35,40],{"statement":36,"sourceTitle":37,"sourceUrl":38,"year":39},"IQGeo's case study says that before AI photo checks, Bouygues Telecom reviewed only 5 to 10% of fiber connections, through manual back office audits that ran after the installation.","Bouygues Telecom case study, NetLux AI","https:\u002F\u002Fwww.iqgeo.com\u002Fhubfs\u002FCustomer%20stories\u002FCustomer%20story\u002FIQGeo-Deepomatic-lens-Bouygues-Telecom-Case-study.pdf?hsLang=en",2026,{"statement":41,"sourceTitle":42,"sourceUrl":43,"year":44},"SK Telecom said in 2023 that safety inspectors previously had to study around 100 drone images to complete the inspection of one cell tower.","SKT Innovates Cell Tower Safety Inspection with AI","https:\u002F\u002Fnews.sktelecom.com\u002Fen\u002F810",2023,"1. **Capture.** The technician photographs the work with the field app: cabinet, distribution\n   point, splice protection, modem or ONT, a connection cable or a nameplate. For towers, a drone\n   flies a set route and takes images of the structure and fasteners.\n2. **Analyze.** An image recognition model compares each photo with the operator's standards for\n   that job type. It flags anomalies such as unconnected fibers, wrong color coding, cable paths\n   that break the rules, missing hardware or loose bolts, and can read text on labels to match it\n   against the job record. Orange France set a target of three seconds so the check runs as the\n   technician is about to leave.\n3. **Feed back on site.** The app marks the problem on the image. The technician fixes it and\n   uploads a new photo, or disagrees with the machine and continues. At Orange France the technician\n   can ignore a recommendation.\n4. **Record.** Photos, verdicts and timestamps become the job record, which supports contractor\n   billing checks, wholesale carrier disputes and a searchable history of the asset.\n5. **Learn.** At Orange France, 50 volunteer technicians tested the first version\n   and their feedback was used to improve the models. Plan the same loop for your own corrections.",[47,48,49,50],"cost-to-serve","compliance","speed","customer-experience",[52,53,54,55],"accuracy","cost-reduction","processing-time-reduction","first-time-fix-rate",{"referenceOrg":57,"inputs":58,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"A fiber operator whose contractors complete 500,000 installations a year",[59,65,72,79],{"key":60,"label":61,"low":62,"high":62,"unit":63,"note":64},"installs","Contractor installations per year",500000,"installations per year","The reference operator.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"defectShare","Share of installations with a defect that would cause a repeat visit or a penalty if unchecked",0.03,0.08,"fraction of installations","Editorial assumption, replace with your own repeat visit and rejection rates. The evidence on this page does not publish a defect rate.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"caughtShare","Share of those defects found on site and fixed before the technician leaves",0.4,0.8,"fraction of defects","Editorial assumption. Bouygues Telecom's vendor reports 98% accuracy on visible defects, but not every defect is visible in a photo and not every flag leads to a fix.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"costPerRepeat","Cost of a repeat visit or penalty for one defective installation",80,160,"USD per defect","Editorial assumption, replace with your own cost per visit.","installs * defectShare * caughtShare * costPerRepeat","USD","per year","Repeat visit and penalty cost avoided","Counts repeat visit and penalty cost only. It leaves out billing checks on contractors (IQGeo reports a 20% cut in fiber unit installation costs at Vodafone Spain, a vendor figure with no baseline period), churn avoided, the review time saved, and the cost of the app, labeling and drone work.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":100},"medium","The build needs a labeled library of correct and defective photos for each equipment type (Orange France had 80,000 photos labeled in three months), a field app that returns a verdict in seconds and works offline, and a clear rule set from network engineering. Towers add drone flights, flight permits and an engineer who signs off on structural findings.",[96,97,98,99],"Photos from past jobs with the later outcome, such as repeat visits, rejected builds and penalties","Written installation standards for each equipment type, turned into checkable rules","Job records with the operator name, location and work order, to match against the photo","For towers, drone image sets with engineer notes on each defect found",[101,102,103,104,105],"Field service management and work order systems","Technician mobile app, with offline capture","Asset and geographic information systems that hold the network records","Contractor billing and quality reporting","Drone flight planning and image storage for tower work",{"steps":107,"guardrails":123,"humanInTheLoop":128,"kpisToInstrument":129,"failureModes":134},[108,111,114,117,120],{"title":109,"detail":110},"Pick the defects that cost money","Start from repeat visits, wholesale penalties and failed activations, and list the visual causes. Choose two or three checks that a photo can settle, such as splice protection or cable color coding, and leave the rest for later.",{"title":112,"detail":113},"Run a proof of concept on live jobs","Vodafone Spain ran four weeks in the field and Bouygues Telecom ran a proof of concept before rollout. Compare the model's verdicts with an auditor's on the same photos and measure missed defects and false alarms separately.",{"title":115,"detail":116},"Put the verdict inside the technician's workflow","Photo taking should already be part of the job. Return the verdict in seconds, show where the issue is on the image and let the technician continue after a disagreement, with the disagreement logged.",{"title":118,"detail":119},"Close the loop with contractors","Share defect rates by contractor and equipment type for coaching and billing checks, with human review before any penalty, and feed model errors back as labeled examples.",{"title":121,"detail":122},"Extend to new job types and sites","Add equipment types, installs at the customer's home and tower inspection one by one. SK Telecom plans to add wind pressure and inclination checks to its drone inspection later.",[124,125,126,127],"A flagged defect is advice to the technician, who can overrule it; the override is logged for review","No penalty, ranking or disciplinary action based on model output alone","Photos of customer premises are limited to what the check needs, with retention and access rules","Structural findings on towers are confirmed by a qualified engineer before any repair decision","Technicians own the work and can disagree with a flag. Quality engineers review disagreements and a sample of passed jobs each week. Engineers sign off on tower findings, and contractor penalties go through a person.",[130,131,132,133],"Share of jobs with a photo check, against the old audit sample rate","Missed defect rate and false alarm rate against an auditor's review of a sample","Repeat visits, rejected builds and wholesale penalties per thousand installations","Time from photo upload to verdict, and technician override rate",[135,138,141,144],{"title":136,"detail":137},"Lighting and angle confuse the model","Orange France says the conditions in which photos are taken can confuse its own orange cables with competitors' red ones, and it reduced the model's performance so it would not contradict what the technician sees. Test in rain, dark cabinets and cramped cabinets.",{"title":139,"detail":140},"Technicians read it as surveillance","If the tool is seen as a way to control people, photos get gamed or skipped. Involve technicians from the start, which Orange France says it did, and have volunteers test the first working version, as its 50 volunteer technicians did.",{"title":142,"detail":143},"Photos pass but the work is wrong","A model that reads one photo can miss a fault that shows only in a test or an inside view. Keep the check next to service activation tests.",{"title":145,"detail":146},"Model drifts as equipment changes","New cabinets, labels or contractors change what photos look like. Keep a labeling process running after launch.",{"euAiAct":148,"regulations":151,"guidance":154,"controls":169,"incidents":174},{"tier":149,"basis":150},"context-dependent","Checking photos of network equipment is normally minimal risk. If the output is used to allocate tasks based on individual behavior or personal traits, or to monitor or evaluate individual technicians or contractor workers, Annex III point 4(b) treats the system as high risk, and under Article 26(7) employers must inform workers' representatives and the affected workers before using it. Keep the model on the work, not the person.",[152,153],"eu-ai-act","gdpr",[155,161,165],{"title":156,"issuer":157,"region":158,"url":159,"note":160},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https:\u002F\u002Fartificialintelligenceact.eu\u002Fannex\u002F3\u002F","Point 4 covers employment and worker management, including task allocation based on individual behavior and monitoring of performance.",{"title":162,"issuer":157,"region":158,"url":163,"note":164},"Article 26, obligations of deployers of high risk AI systems","https:\u002F\u002Fartificialintelligenceact.eu\u002Farticle\u002F26\u002F","Paragraph 7 requires employers to inform workers' representatives and the affected workers before putting a high risk AI system into use at the workplace.",{"title":166,"issuer":157,"region":158,"url":167,"note":168},"General Data Protection Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","Photos of customer premises and of technicians can be personal data, so purpose, minimization and retention rules apply.",[170,171,172,173],"Purpose limits for photo data, agreed with employee representatives where required","Data protection impact assessment for photos taken inside customer premises","Documented comparison of model verdicts with auditors before any contractor consequence","Version control and retraining routine for the image models",[],{"howToBuild":176},"The image model itself is the operator's own or a vision vendor's. On Blits.ai the surrounding\ntechnician experience is an **AI agent** reached through the **REST API channel** inside the\nfield app or through **WhatsApp**. A **flow** with the **receive attachment** block collects the\nphotos, and a **custom function** sends them to the vision service and reads back the verdict and\nthe flagged areas. The agent then explains in plain words which fiber or label to check and asks\nfor a new photo, with **human handover** to a quality engineer when the technician disagrees.\n\n**Agentic workflows** can prepare the contractor feedback and route any proposed penalty to a\nperson through **human in the loop approval**. **Guardrails** check what the agent says against the\noperator's installation standards, **test suites** replay past jobs before every change, and\n**analytics** report on the conversations, while the custom function can log each override so you can track the override rate. **EU data residency** keeps photos\nand job data in the chosen region.",[178,181,184],{"question":179,"answer":180},"How accurate is AI at checking installation photos?","IQGeo says its model reaches 98% accuracy on visible defects at Bouygues Telecom, and that the solution is already analyzing more than 3 million installations. That is a vendor claim and does not say how accuracy was measured. Run your own proof of concept and compare the verdicts with an auditor's on the same photos before you rely on it.",{"question":182,"answer":183},"Why check every installation instead of a sample?","Bouygues Telecom reviewed only 5 to 10% of fiber connections by hand before it adopted AI, and Orange France says French rules require photos for each intervention, about 20,000 photos a day. A model makes full coverage affordable, so the remaining human effort goes to disagreements and structural decisions.",{"question":185,"answer":186},"Does AI replace tower climbers?","It reduces climbing. SK Telecom says its drone and image model lets it minimize the need to go up the towers and cut the review time per tower by 95%, and it describes the system as a complement to existing visual inspection. Engineers still decide what needs repair.",[188,189,190],"field-technician-copilot-and-dispatch","fibre-network-route-and-build-planning","predictive-network-maintenance","2026-10-14",[193],{"date":191,"note":194},"First published","telecom-field-work-visual-inspection",[197,232,255,290,314],{"title":198,"useCases":199,"organization":200,"vendors":206,"summary":209,"stage":210,"year":44,"channels":211,"languages":212,"metrics":215,"outcomeDisclosed":223,"sources":224,"verification":227,"grade":229,"id":230,"organizationSlug":231},"SK Telecom: drone images and AI for cell tower bolt and nut inspection",[195],{"name":201,"anonymized":202,"country":203,"region":204,"industry":18,"wikidata":205},"SK Telecom",false,"KR","asia-pacific","Q493145",[207],{"name":201,"role":208},"in-house","SK Telecom built an image analysis model in house that reads drone photos of cell towers, which reach up to 75 meters, and judges the condition of bolts and nuts. Before, inspectors had to climb the towers or study about 100 images per tower by eye. SKT says the model cut the time for that review by 95% and made the analysis more consistent, and plans to add wind pressure and inclination checks and a link to its safety management system.","production",[28],[213,214],"en","ko",[216],{"kpi":54,"value":217,"unit":218,"qualifier":219,"baseline":220,"claimant":221,"quote":222,"sourceUrl":43},95,"percent","exact","around 100 images studied by a safety inspector per tower","organization","With the adoption of this AI analysis model, SKT was able to reduce the time required for the process by 95%, while increasing the reliability and consistency of the analysis results.",true,[225],{"url":43,"title":42,"publisher":201,"date":226},"2023-01-31",{"level":228,"checkedAt":191},"source-verified","B","sk-telecom-ai-cell-tower-drone-inspection",null,{"title":233,"useCases":234,"organization":235,"vendors":239,"summary":243,"stage":210,"year":244,"channels":245,"languages":246,"metrics":247,"outcomeDisclosed":202,"sources":248,"verification":253,"grade":229,"id":254,"organizationSlug":231},"CityFibre: visual automation of build, maintenance and install quality checks",[195],{"name":236,"anonymized":202,"country":237,"region":158,"industry":18,"wikidata":238},"CityFibre","GB","Q113509733",[240],{"name":241,"role":242},"Deepomatic","platform","In February 2022 CityFibre announced a national rollout of Deepomatic's image recognition across its build, upgrade and maintenance work, to be complete by the end of 2022. Engineers upload photos of their work before they close a job, and the images are analyzed in real time to either validate the work or point out issues to fix before leaving the site. CityFibre says this supports its Right First Time installation rates and builds a searchable visual record of the network. The release reports no measured results.",2022,[27],[213],[],[249],{"url":250,"title":251,"publisher":236,"date":252},"https:\u002F\u002Fcityfibre.com\u002Fnews\u002Fcityfibre-partners-with-deepomatic-for-nationwide-deployment-of-visual-automation-technology","CityFibre partners with Deepomatic for nationwide deployment of Visual Automation Technology","2022-02-16",{"level":228,"checkedAt":191},"cityfibre-deepomatic-visual-automation",{"title":256,"useCases":257,"organization":258,"vendors":262,"summary":265,"stage":266,"year":39,"channels":267,"languages":268,"metrics":270,"outcomeDisclosed":223,"sources":282,"verification":286,"grade":287,"id":288,"organizationSlug":289},"Bouygues Telecom: AI photo checks on fiber installations by contractors",[195],{"name":259,"anonymized":202,"country":260,"region":158,"industry":18,"wikidata":261},"Bouygues Telecom","FR","Q581438",[263],{"name":264,"role":242},"IQGeo (NetLux AI, formerly Deepomatic Lens)","Bouygues Telecom relies on contractors for its fiber rollout and used to check only a small share of installations through manual back office audits. After a proof of concept, technicians now photograph the cabinet, distribution point and modem or ONT with a smartphone, and an image recognition model flags problems such as unconnected fibers, wrong color coding and cable paths that break the rules. IQGeo's case study says the solution is already analyzing more than 3 million installations and reaches 98% accuracy on visible defects. Both figures are IQGeo's own claims.","scaled",[27],[269],"fr",[271,275],{"kpi":52,"value":272,"unit":218,"qualifier":219,"claimant":273,"quote":274,"sourceUrl":38},98,"vendor","With 98% accuracy, the AI consistently detects visible defects, setting a new benchmark for quality assurance.",{"kpi":276,"value":277,"unit":278,"qualifier":279,"period":280,"claimant":273,"quote":281,"sourceUrl":38},"interactions-handled",3000000,"count","at-least","installations the solution is already analyzing, per IQGeo","Already analyzing more than 3 million installations, the solution evolves alongside Bouygues’ needs, supporting new equipment, tasks, and quality standards.",[283],{"url":38,"title":37,"publisher":284,"date":285},"IQGeo","2026-01-16",{"level":228,"checkedAt":191},"C","bouygues-telecom-netlux-ai-installation-photo-checks","bouygues-telecom",{"title":291,"useCases":292,"organization":293,"vendors":297,"summary":299,"stage":266,"year":39,"channels":300,"languages":301,"metrics":303,"outcomeDisclosed":223,"sources":309,"verification":312,"grade":287,"id":313,"organizationSlug":231},"Vodafone Spain: AI photo check of the connection cable on contractor fiber installs",[195],{"name":294,"anonymized":202,"country":295,"region":158,"industry":18,"wikidata":296},"Vodafone Spain","ES","Q7939295",[298],{"name":264,"role":242},"Vodafone Spain used to let contractor technicians type the operator's name into the field service app without proof, so it could not confirm that the work matched what was invoiced. After a four week field proof of concept, it rolled out NetLux AI to all contractors: the technician photographs the connection cable during a fiber installation and the system reads the operator's name from the image and cross checks it against the field service data. IQGeo's case study reports a 20% cut in fiber unit installation costs and a billing process Vodafone Spain can verify.",[27],[302],"es",[304],{"kpi":53,"value":305,"unit":218,"qualifier":219,"baseline":306,"claimant":273,"quote":307,"sourceUrl":308},20,"fiber unit installation costs","Reduced fiber unit installation costs by 20%.","https:\u002F\u002Fwww.iqgeo.com\u002Fhubfs\u002FCustomer%20stories\u002FCustomer%20story\u002FIQGeo-Deepomatic-lens-Vodafone-Spain-Case-study.pdf?hsLang=en",[310],{"url":308,"title":311,"publisher":284,"date":285},"Vodafone Spain case study, NetLux AI",{"level":228,"checkedAt":191},"vodafone-spain-netlux-ai-fibre-installation-photo-qa",{"title":315,"useCases":316,"organization":317,"vendors":320,"summary":324,"stage":266,"year":44,"channels":325,"languages":326,"metrics":327,"outcomeDisclosed":223,"sources":334,"verification":337,"grade":287,"id":338,"organizationSlug":231},"Orange France: image recognition check of fiber technician interventions",[195],{"name":318,"anonymized":202,"country":260,"region":158,"industry":18,"wikidata":319},"Orange France","Q19698200",[321],{"name":322,"role":323},"Artefact","integrator","Since summer 2021 the French regulator ARCEP has required photos before and after each fiber intervention, which Orange France says means analyzing about 20,000 photos a day. Orange and the consultancy Artefact built a computer vision model inside the technician's app that checks, close to real time, whether the work on network equipment is conforming and says which area is not. The technician can overrule it. The first version took nine months, was tested by 50 volunteer technicians and is now used by the 10,000 technicians deployed every day in France.",[27],[269],[328],{"kpi":329,"value":330,"unit":278,"qualifier":219,"period":331,"claimant":273,"quote":332,"sourceUrl":333},"users-served",10000,"technicians using the tool every day in France","It was a real technical and human feat, as the tool has now been adopted by the 10,000 technicians deployed every day in France.","https:\u002F\u002Fwww.artefact.com\u002Fcases\u002Forange-visual-recognition-ai-solution-improves-orange-france-call-out-service-quality\u002F",[335],{"url":333,"title":336,"publisher":322},"Orange France AI solution of visual recognition at the service of Orange France technical intervention quality",{"level":228,"checkedAt":191},"orange-france-artefact-fibre-intervention-photo-check",0,[341,347,352],{"kpi":52,"label":342,"unit":218,"aggregate":223,"higherIsBetter":223,"n":343,"nUpTo":339,"median":272,"min":272,"max":272,"byClaimant":344,"vendorOnly":223,"points":345},"Accuracy",1,{"organization":339,"vendor":343,"regulator":339,"independent":339},[346],{"evidenceId":288,"organization":259,"value":272,"qualifier":219,"claimant":273,"grade":287,"pooled":223},{"kpi":53,"label":348,"unit":218,"aggregate":223,"higherIsBetter":223,"n":343,"nUpTo":339,"median":305,"min":305,"max":305,"byClaimant":349,"vendorOnly":223,"points":350},"Cost reduction",{"organization":339,"vendor":343,"regulator":339,"independent":339},[351],{"evidenceId":313,"organization":294,"value":305,"qualifier":219,"claimant":273,"grade":287,"pooled":223},{"kpi":54,"label":353,"unit":218,"aggregate":223,"higherIsBetter":223,"n":343,"nUpTo":339,"median":217,"min":217,"max":217,"byClaimant":354,"vendorOnly":202,"points":355},"Cycle time reduction",{"organization":343,"vendor":339,"regulator":339,"independent":339},[356],{"evidenceId":230,"organization":201,"value":217,"qualifier":219,"claimant":221,"grade":229,"pooled":223},{"low":358,"high":359},480000,5120000,[361,380,402,422],{"slug":188,"title":362,"shortTitle":363,"definition":364,"status":9,"industries":365,"functions":366,"patterns":368,"audience":29,"autonomy":30,"adoptionStage":372,"segment":32,"evidenceCount":373,"publicEvidenceCount":373,"organizations":374,"bestGrade":229,"headline":231,"lastVerified":379,"indexable":223},"AI copilot for field technicians and dispatch optimization","Field technician copilot and dispatch","A field technician copilot is AI that decides whether a fault needs a site visit at all and predicts what work and parts the job will need. It also 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],[20,21,367],"customer-service",[369,370,371,25],"prediction-and-scoring","rag-knowledge-assistant","conversational-agent","emerging",5,[375,259,376,377,378],"Advanced Info Service (AIS)","nbn","Openreach","Vodafone","2026-09-27",{"slug":189,"title":381,"shortTitle":382,"definition":383,"status":9,"industries":384,"functions":385,"patterns":386,"audience":29,"autonomy":30,"adoptionStage":372,"segment":32,"evidenceCount":387,"publicEvidenceCount":387,"organizations":388,"bestGrade":229,"headline":396,"lastVerified":401,"indexable":223},"AI for fibre network route and build planning","Fibre route and build planning","Fibre network route and build planning is AI that turns geospatial survey data, existing infrastructure records and historical civil works costs into a proposed fibre route and a cost estimate. Planners spend less time drawing routes by hand and get a more consistent, defensible estimate before construction starts.",[18],[22,21],[24,369],8,[389,390,391,377,392,393,394,395],"Bonfire Fiber","Deutsche Telekom","eir","Proximus","Telefónica España","Telia","UPP",{"kpi":54,"label":353,"unit":218,"n":397,"nUpTo":339,"kind":398,"value":399,"qualifier":400,"claimant":221,"organization":389,"vendorReported":202},2,"reported",75,"approximately","2026-09-29",{"slug":190,"title":403,"shortTitle":404,"definition":405,"status":9,"industries":406,"functions":407,"patterns":408,"audience":411,"autonomy":412,"adoptionStage":31,"segment":32,"evidenceCount":413,"publicEvidenceCount":413,"organizations":414,"bestGrade":229,"headline":231,"lastVerified":379,"indexable":223},"AI for predictive network maintenance in telecom","Predictive network maintenance","Predictive network maintenance is machine learning that spots early signs of network failure and triggers a fix before customers lose service, for telecom operators. It watches for degrading cells, faulty customer equipment, aging hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention.",[18],[22,20,21],[409,369,410],"anomaly-detection","agentic-workflow","back-office","supervised-agent",9,[415,416,417,393,418,419,420,421,378],"Grameenphone","KDDI","Orange","Telkomsel","Telstra","TIM Brasil","Verizon",{"slug":423,"title":424,"shortTitle":425,"definition":426,"status":9,"industries":427,"functions":428,"patterns":429,"audience":432,"autonomy":412,"adoptionStage":372,"segment":433,"evidenceCount":434,"publicEvidenceCount":397,"organizations":435,"bestGrade":229,"headline":231,"lastVerified":379,"indexable":223},"network-outage-communication-agent","AI agent for network outage detection and customer communication","Outage communication","AI network outage communication is an agent that turns network alarms into a clear picture of which customers are affected by an outage and why. It proactively tells those customers by message, app or phone, giving a cause and an estimated fix time, answers their questions during the incident, and updates them until service is restored.",[18],[367,22,20],[409,25,430,371,431],"content-generation","voice-agent","customer-facing","front-office",3,[436,437],"AT&T","Comcast",{"indexable":223,"reasons":439},[],[441,446,450,458,466,473,479,486,493,500,506,512,518,524,531,537,543,549,555,562,567,572,578,585,592,597,602,608,614,619,627,633,639,645,651,656,661],{"id":152,"label":442,"issuer":157,"region":158,"url":443,"description":444,"useCases":445,"indexable":223},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",295,{"id":153,"label":447,"issuer":157,"region":158,"url":167,"description":448,"useCases":449,"indexable":223},"GDPR","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",265,{"id":451,"label":452,"issuer":453,"region":454,"url":455,"description":456,"useCases":457,"indexable":223},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",148,{"id":459,"label":460,"issuer":461,"region":462,"url":463,"description":464,"useCases":465,"indexable":223},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",115,{"id":467,"label":468,"issuer":469,"region":158,"url":470,"description":471,"useCases":472,"indexable":223},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",84,{"id":474,"label":475,"issuer":157,"region":158,"url":476,"description":477,"useCases":478,"indexable":223},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",73,{"id":480,"label":481,"issuer":482,"region":158,"url":483,"description":484,"useCases":485,"indexable":223},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",53,{"id":487,"label":488,"issuer":489,"region":204,"url":490,"description":491,"useCases":492,"indexable":223},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":494,"label":495,"issuer":496,"region":204,"url":497,"description":498,"useCases":499,"indexable":223},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":501,"label":502,"issuer":157,"region":158,"url":503,"description":504,"useCases":505,"indexable":223},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":507,"label":508,"issuer":509,"region":454,"url":510,"description":511,"useCases":505,"indexable":223},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":513,"label":514,"issuer":515,"region":462,"url":516,"description":517,"useCases":505,"indexable":223},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":519,"label":520,"issuer":521,"region":462,"url":522,"description":523,"useCases":305,"indexable":223},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",{"id":525,"label":526,"issuer":527,"region":158,"url":528,"description":529,"useCases":530,"indexable":223},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":532,"label":533,"issuer":157,"region":158,"url":534,"description":535,"useCases":536,"indexable":223},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",15,{"id":538,"label":539,"issuer":540,"region":454,"url":541,"description":542,"useCases":536,"indexable":223},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":544,"label":545,"issuer":546,"region":462,"url":547,"description":548,"useCases":536,"indexable":223},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":550,"label":551,"issuer":157,"region":158,"url":552,"description":553,"useCases":554,"indexable":223},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":556,"label":557,"issuer":558,"region":462,"url":559,"description":560,"useCases":561,"indexable":223},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":563,"label":564,"issuer":157,"region":158,"url":565,"description":566,"useCases":561,"indexable":223},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":568,"label":569,"issuer":157,"region":158,"url":570,"description":571,"useCases":561,"indexable":223},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":573,"label":574,"issuer":575,"region":454,"url":576,"description":577,"useCases":561,"indexable":223},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":579,"label":580,"issuer":581,"region":462,"url":582,"description":583,"useCases":584,"indexable":223},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":586,"label":587,"issuer":588,"region":158,"url":589,"description":590,"useCases":591,"indexable":223},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":593,"label":594,"issuer":157,"region":158,"url":595,"description":596,"useCases":591,"indexable":223},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":598,"label":599,"issuer":157,"region":158,"url":600,"description":601,"useCases":591,"indexable":223},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":603,"label":604,"issuer":489,"region":204,"url":605,"description":606,"useCases":607,"indexable":223},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":609,"label":610,"issuer":611,"region":462,"url":612,"description":613,"useCases":387,"indexable":223},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":615,"label":616,"issuer":157,"region":158,"url":617,"description":618,"useCases":387,"indexable":223},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":620,"label":621,"issuer":622,"region":623,"url":624,"description":625,"useCases":626,"indexable":223},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",6,{"id":628,"label":629,"issuer":630,"region":158,"url":631,"description":632,"useCases":373,"indexable":223},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":634,"label":635,"issuer":157,"region":158,"url":636,"description":637,"useCases":638,"indexable":223},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",4,{"id":640,"label":641,"issuer":642,"region":158,"url":643,"description":644,"useCases":638,"indexable":223},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":646,"label":647,"issuer":648,"region":204,"url":649,"description":650,"useCases":434,"indexable":223},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":652,"label":653,"issuer":157,"region":158,"url":654,"description":655,"useCases":434,"indexable":223},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":657,"label":658,"issuer":489,"region":204,"url":659,"description":660,"useCases":434,"indexable":223},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":662,"label":663,"issuer":664,"region":462,"url":665,"description":666,"useCases":434,"indexable":223},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1792049865633]