[{"data":1,"prerenderedAt":787},["ShallowReactive",2],{"uc-plan-upgrade-and-sales-assistant":3,"uc-regulations":581},{"useCase":4,"evidence":202,"blitsAiDeployments":471,"benchmarks":472,"indicative":493,"related":496,"indexability":579,"includeUnpublished":209},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":23,"channels":29,"audience":35,"autonomy":36,"adoptionStage":37,"segment":38,"problem":39,"problemStats":40,"howItWorks":41,"valueDrivers":42,"kpis":46,"indicativeValue":53,"macroEstimates":87,"feasibility":88,"implementation":102,"risk":145,"blitsAi":178,"faq":180,"related":190,"datePublished":196,"dateModified":196,"lastVerified":197,"changelog":198,"slug":201},"AI assistant for telecom plan upgrades, add ons and sales","Plan upgrade and sales assistant","AI sales assistant for telecom plan upgrades","AI assistants help telecom customers choose and buy plans, add ons and devices. See results from Singtel, Telenet and Orange France, the value math and the rules.","published","An AI assistant that helps existing and prospective customers choose, compare and buy the right mobile, broadband or TV plan, device or extra, in the app, in messaging, on the phone or through a human advisor, using the customer's usage and eligibility and the operator's current offers, and that completes the order or passes a ready quote to a person.",[12,13,14,15,16],"telco sales assistant","upgrade assistant","guided selling for telecom","add on recommendation agent","advisor sales copilot",[18],"telecommunications",[20,21,22],"sales","customer-service","marketing",[24,25,26,27,28],"recommendation-and-personalization","conversational-agent","rag-knowledge-assistant","voice-agent","agentic-workflow",[30,31,32,33,34],"mobile-app","web-chat","whatsapp","voice","agent-desktop","customer-facing","supervised-agent","early-adopters","front-office","Telecom offers are hard to compare. Plans differ by data, speed, contract length, device\ninstalments, bundles and promotions that change often, and eligibility depends on the\ncustomer's current contract, credit and address. Customers who cannot compare offers may pick a\nplan that does not fit, or call to ask, and the answer can depend on how current the advisor's\nknowledge of the promotions is.\n\nOperators already score who is likely to upgrade, but the moment of decision happens in a\nconversation: a question in the app, a chat about roaming before a trip, a call about a slow\nconnection. Rule based chatbots could list plans, not reason about which one fits this customer,\nand advisors lose time during sales calls looking up the current offer and writing up the call.",[],"1. **Understand the need.** The assistant asks about usage, household, devices and budget, or\n   reads the customer's actual usage and contract after authentication.\n2. **Check eligibility and current offers.** It retrieves the offers, promotions and upgrade\n   eligibility that apply to this customer from the product catalogue and decisioning engine,\n   never from memory.\n3. **Recommend and compare.** It proposes a small number of options with the reasons and the\n   total cost, including what changes on the next bill, and compares devices on request.\n4. **Complete or hand over.** For simple purchases (a roaming pass, an extra, a plan change) it\n   completes the order with confirmation; for contracts, devices on credit or complex bundles it\n   prepares the quote and passes it to an advisor or the checkout.\n5. **Assist advisors.** In stores and contact centres the same engine suggests the next best\n   offer and answers product questions for the advisor during the conversation.",[43,44,45],"revenue-growth","customer-experience","employee-productivity",[47,48,49,50,51,52],"conversion-rate-uplift","revenue-uplift","interactions-handled","users-served","handling-time-reduction","customer-satisfaction",{"referenceOrg":54,"inputs":55,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"An operator with 3 million postpaid mobile and broadband customers",[56,61,68,75],{"key":57,"label":58,"low":59,"high":59,"unit":57,"note":60},"customers","Postpaid customers",3000000,"The reference operator.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"engagedShare","Share of customers who have a sales conversation with the assistant or an assisted advisor each year",0.2,0.4,"fraction of customers","Editorial assumption, replace with your own digital and assisted sales reach.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"incrementalConversion","Additional upgrades or extras bought per engaged customer",0.01,0.02,"fraction of engaged customers","Editorial assumption and deliberately low against the benchmark on this page (Pega reports a 75% increase in offer acceptance at Telenet), because that figure is relative, has no stated baseline or control group and comes from one vendor story.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"marginPerSale","Incremental annual margin per upgrade or extra",30,60,"USD per sale per year","Editorial assumption, replace with your own margin per upgrade.","customers * engagedShare * incrementalConversion * marginPerSale","USD","per year","Incremental annual margin from assisted upgrades","Counts only the first year's margin on additional sales. It leaves out lower churn from better fitting plans, the cost of the AI and catalogue integration, cannibalisation of sales that would have happened anyway, and discounts given to close.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":96},"medium","Recommending is easy; recommending correctly is not. The work is in a clean, current product and promotion catalogue, real eligibility rules and an order API. Selling on credit or under long contracts adds pre contract information and consent duties.",[92,93,94,95],"A current product, price and promotion catalogue with start and end dates","Upgrade eligibility and credit rules available by API","Customer usage and contract data for authenticated recommendations","Marketing consent and contact preferences per customer",[97,98,99,100,101],"Product catalogue and pricing engine","Decisioning or next best action engine","Order management and checkout","CRM with contract, usage and consent data","Contact centre and store systems for advisor assistance",{"steps":103,"guardrails":119,"humanInTheLoop":125,"kpisToInstrument":126,"failureModes":132},[104,107,110,113,116],{"title":105,"detail":106},"Fix the catalogue before the conversation","Put every plan, extra and promotion, with its eligibility and end date, into one source the assistant reads through a tool. Stale promotions are the fastest way to lose trust.",{"title":108,"detail":109},"Start with simple, reversible purchases","Roaming passes, data extras and plan changes without a new contract are low risk. Add devices on credit and new contracts later, with the full pre contract information flow.",{"title":111,"detail":112},"Show the total cost","Make the assistant state the monthly and total cost and the change on the next bill for every option, so customers do not feel sold to and complaints stay low.",{"title":114,"detail":115},"Give advisors the same brain","Use the same recommendation and product answers in stores and contact centres, so a customer hears the same offer on every channel.",{"title":117,"detail":118},"Measure against a control group","Hold out a random share of customers or conversations to measure real incremental conversion, not sales that would have happened anyway.",[120,121,122,123,124],"Prices, promotions and eligibility come only from catalogue tools, never from the model","Total cost and contract length stated before any order is confirmed","Marketing consent checked before any proactive or outbound offer","No offers to customers flagged as vulnerable or in financial difficulty without human review","Explicit confirmation before every order, with a record of what the customer saw","Advisors complete contract and device on credit sales from the prepared quote. The commercial team approves every new offer the assistant may present, and a quality team reviews a weekly sample of sales conversations for mis selling and unclear pricing.",[127,128,129,130,131],"Incremental conversion against a holdout group","Order cancellations and returns within the cooling off period","Complaints about sales or pricing that mention the assistant","Advisor handling time on sales calls with and without assistance","Revenue and margin per assisted sale",[133,136,139,142],{"title":134,"detail":135},"Stale or wrong offers","The assistant quotes a promotion that ended. Keep all offers in a tool with end dates and test daily.",{"title":137,"detail":138},"Mis selling","Customers are pushed to bigger plans they do not need. Recommend on actual usage, show the total cost and review samples.",{"title":140,"detail":141},"Ignoring consent","Proactive offers reach customers who opted out of marketing. Check consent in the tool, not in the prompt.",{"title":143,"detail":144},"Credit decisions by the back door","The assistant decides who may buy a device on credit. Keep credit checks in the existing, governed process.",{"euAiAct":146,"regulations":149,"guidance":155,"controls":171,"incidents":177},{"tier":147,"basis":148},"context-dependent","A sales assistant is limited risk with an Article 50 duty to disclose AI. If it assesses the creditworthiness of individuals for devices on credit, that part is high risk under Annex III point 5(b), so keep credit decisions in the existing governed process. Selling that uses manipulative or deceptive techniques, or exploits a customer's age, disability or economic situation, to materially distort a purchase decision in a way likely to cause significant harm is prohibited under Article 5(1)(a) and (b).",[150,151,152,153,154],"eu-ai-act","gdpr","telecom-consumer-rules","eecc","us-tcpa",[156,162,166],{"title":157,"issuer":158,"region":159,"url":160,"note":161},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":163,"issuer":158,"region":159,"url":164,"note":165},"Directive on privacy and electronic communications (Directive 2002/58/EC)","https://eur-lex.europa.eu/eli/dir/2002/58/oj","Sets consent rules for unsolicited electronic marketing, which apply to proactive offers sent by messaging, email or automated calls.",{"title":167,"issuer":168,"region":159,"url":169,"note":170},"Customers to get clearer broadband information","Ofcom","https://www.ofcom.org.uk/phones-and-broadband/bills-and-charges/customers-to-get-clearer-broadband-information","Ofcom guidance says UK broadband providers must describe the underlying network technology in clear terms before a customer agrees to buy, whether online, by phone or in person, so a sales assistant has to give the same information.",[172,173,174,175,176],"AI disclosure at the start of every sales conversation","Offer approval workflow with owners, start and end dates","Consent and vulnerability checks enforced in tools","Record of the offer, price and terms shown before each order","Monthly review of cancellations, returns and sales complaints",[],{"howToBuild":179},"On Blits.ai this is an **AI agent** with **custom functions** that read the product catalogue,\neligibility and the decisioning engine, and that place orders through the order API. A\n**knowledge base** with hybrid retrieval answers product questions from approved content, rich\n**cards and carousels** in the chat widget compare plans and devices, and a **flow** handles the\norder confirmation with the total cost and consent check. Payments for extras can be taken in\nthe conversation with **payment links**.\n\nThe same agent serves **web chat, WhatsApp and voice**, reaches the operator's own app through\nthe **REST and WebSocket API**, and can run for advisors in **Microsoft Teams** or the agent\ndesktop through the same API. **Guardrails** block unapproved\nclaims, **PII masking** protects customer data, **human handover** passes a prepared quote to an\nadvisor, and **test suites** replay sales conversations whenever offers change. The platform is\nmodel agnostic with EU and UAE data residency.",[181,184,187],{"question":182,"answer":183},"Does AI actually increase telecom sales?","There is early evidence, mostly vendor reported. Pega reports that Telenet saw a 75% increase in offer acceptance and a 33% increase in cross sell with AI decisioning, and Singtel reports that customers bought more than 200 roaming add ons independently through its assistant Shirley, among the initial results after launch. Neither states a control group, so measure against a holdout group before you trust the uplift.",{"question":185,"answer":186},"Should the assistant sell to customers or help advisors sell?","Many operators do both. Simple extras and plan changes suit self service, while devices on credit and new contracts often go through advisors, who benefit from the same offer and product information: T-Mobile gives store and call centre staff its PromoGenius app, and Orange France gives 3,000 sales advisors an AI assistant during customer calls.",{"question":188,"answer":189},"What rules apply to AI generated offers?","The usual telecom and consumer rules: clear pre contract information, total cost, cooling off rights and marketing consent for proactive offers. An AI assistant has to follow them in every conversation, which is easier to prove when prices and terms come from tools.",[191,192,193,194,195],"churn-prediction-and-retention-offers","order-to-activation-and-esim-onboarding-assistant","retail-store-and-kiosk-assistant","business-connectivity-quoting-and-service-assistant","bill-explanation-and-billing-dispute-agent","2026-09-27","2026-09-26",[199],{"date":196,"note":200},"First published","plan-upgrade-and-sales-assistant",[203,256,289,312,334,359,396,417,443],{"title":204,"useCases":205,"organization":207,"vendors":212,"summary":216,"stage":217,"year":218,"channels":219,"languages":220,"metrics":222,"outcomeDisclosed":243,"sources":244,"verification":251,"grade":253,"id":254,"organizationSlug":255},"Singtel: agentic AI assistant Shirley for care, roaming and sales",[206,192,201],"device-and-connectivity-troubleshooting-agent",{"name":208,"anonymized":209,"country":210,"region":211,"industry":18},"Singtel",false,"SG","asia-pacific",[213],{"name":214,"role":215},"Sierra","platform","In a partnership announced on 4 March 2026, Singtel upgraded the customer care assistant Shirley of Singtel Singapore with Sierra's agentic AI, starting with a pilot that went live in under ten weeks. Shirley verifies customer details, resolves mobile and home troubleshooting, completes roaming sign ups and understands local expressions including Singlish; customers purchased more than 200 roaming add ons independently. In its first six weeks it handled over 70,000 cases. Singtel Singapore also says it will deploy voice AI agents for outbound sales calls within defined compliance and governance standards.","production",2026,[31,33],[221],"en",[223,232,237],{"kpi":224,"value":225,"unit":226,"qualifier":227,"period":228,"claimant":229,"quote":230,"sourceUrl":231},"containment-rate",73,"percent","exact","mobile and home troubleshooting cases, initial results after launch","organization","73% of mobile and home troubleshooting cases were resolved without requiring a Customer Care officer.","https://sierra.ai/customers/singtel",{"kpi":233,"value":234,"unit":226,"qualifier":227,"period":235,"claimant":229,"quote":236,"sourceUrl":231},"automation-rate",76,"roaming sign up requests, initial results after launch","76% of roaming sign-up requests were completed successfully without requiring a Customer Care officer.",{"kpi":49,"value":238,"unit":239,"qualifier":240,"period":241,"claimant":229,"quote":242,"sourceUrl":231},70000,"count","at-least","first six weeks after launch","Singtel went live in less than 10 weeks, and in the first six weeks since launch, ”Shirley” handled over 70,000 customer cases involving high volume requests in areas such as mobile issues and roaming services.",true,[245,247],{"url":231,"title":246,"publisher":214},"Singtel Group partners with Sierra to transform customer engagement with AI",{"url":248,"title":249,"publisher":208,"date":250},"https://www.singtel.com/about-us/media-centre/news-releases/singtel-group-partners-sierra-to-transform-custome-engagement-with-ai","Singtel Group partners Sierra to transform customer engagement with AI","2026-03-04",{"level":252,"checkedAt":197},"source-verified","B","singtel-shirley-agentic-ai-agent",null,{"title":257,"useCases":258,"organization":259,"vendors":262,"summary":269,"stage":270,"year":271,"channels":272,"languages":273,"metrics":275,"outcomeDisclosed":243,"sources":282,"verification":287,"grade":253,"id":288,"organizationSlug":255},"Orange France: Mon Assistant IA for sales advisors and the Sharlie voice assistant",[201],{"name":260,"anonymized":209,"country":261,"region":159,"industry":18},"Orange France","FR",[263,265,267],{"name":264,"role":215},"Verint",{"name":266,"role":215},"Microsoft",{"name":268,"role":215},"ILLUIN Technology","Orange France deployed Mon Assistant IA (MAIA) with Verint to 3,000 Orange sales advisors, launching it in early December 2025. During calls it understands the conversation, detects customer needs, retrieves relevant information and summarises the exchange to update the customer file; the advisor validates the proposed responses. Orange also announced Sharlie, a speech to speech voice assistant for its digital brand Sosh built with Microsoft and ILLUIN Technology on the ILLUIN Dialogue and Microsoft Foundry platforms, with capacity for over 3 million conversations a year once deployed.","scaled",2025,[34,33],[274],"fr",[276],{"kpi":49,"value":277,"unit":239,"qualifier":278,"period":279,"claimant":229,"quote":280,"sourceUrl":281},1000000,"approximately","conversations supported per month, as of March 2026","Launched in early December 2025, Mon Assistant IA already supports nearly 1 million conversations per month.","https://newsroom.orange.com/orange-france-launches-two-new-artificial-intelligence-services-to-enhance-customer-relations/",[283],{"url":281,"title":284,"publisher":285,"date":286},"Orange France launches two new artificial intelligence services to enhance customer relations","Orange","2026-03-17",{"level":252,"checkedAt":197},"orange-france-maia-advisor-assistant",{"title":290,"useCases":291,"organization":292,"vendors":296,"summary":297,"stage":217,"year":271,"channels":298,"languages":299,"metrics":300,"outcomeDisclosed":209,"sources":301,"verification":309,"grade":253,"id":310,"organizationSlug":311},"Verizon: AI powered Verizon Assistant and Customer Champion service model",[195,201,192],{"name":293,"anonymized":209,"country":294,"region":295,"industry":18},"Verizon","US","north-america",[],"In June 2025 Verizon announced a customer experience overhaul: a Customer Champion who owns complex issues end to end, drawing on Google Cloud AI including Gemini models, 24/7 live chat with human agents, and a new My Verizon app, which includes an AI powered Verizon Assistant, in which customers can become a customer, manage upgrades, add lines and ask billing questions. Verizon describes the assistant as voice enabled for mobile customers. Verizon's chief executive framed the programme as a way to build loyalty and improve retention. No outcome figures were published.",[30,33],[221],[],[302,306],{"url":303,"title":304,"publisher":293,"date":305},"https://www.verizon.com/about/news/verizon-launches-industry-leading-ai-powered-customer-experience","Verizon, America's Most Reliable 5G Network, Launches Industry-Leading, AI Powered Customer Experience Innovations","2025-06-24",{"url":307,"title":308,"publisher":293},"https://www.verizon.com/about/customer-experience","Verizon Customer Experience",{"level":252,"checkedAt":196},"verizon-ai-customer-experience-transformation","verizon",{"title":313,"useCases":314,"organization":315,"vendors":318,"summary":321,"stage":322,"year":271,"channels":323,"languages":324,"metrics":325,"outcomeDisclosed":209,"sources":326,"verification":331,"grade":253,"id":332,"organizationSlug":333},"Virgin Media O2: Lumi AI prompts for care, telesales and retention advisors",[191,201],{"name":316,"anonymized":209,"country":317,"region":159,"industry":18},"Virgin Media O2","GB",[319],{"name":316,"role":320},"in-house","Virgin Media O2 built its own tool, Lumi AI, that analyses a live conversation and prompts the advisor with resolutions that worked for similar customers and with the products and services most likely to interest this customer. In July 2025 it was in pilot with a cohort of advisors in care, telesales and retentions, with a wider rollout planned. Alongside it the operator uses an AI contact centre service from Amazon Web Services that routes callers by their stated reason, software that flags potentially vulnerable customers, and automatic call summaries. The retention effect of Lumi AI has not been published.","pilot",[34,33],[221],[],[327],{"url":328,"title":329,"publisher":316,"date":330},"https://news.virginmediao2.co.uk/virgin-media-o2-launches-new-ai-tools-to-supercharge-customer-services-and-better-help-most-vulnerable-customers/","Virgin Media O2 launches new AI tools to supercharge customer services and better help most vulnerable customers","2025-07-28",{"level":252,"checkedAt":197},"virgin-media-o2-lumi-ai-advisor-assistant","virgin-media-o2",{"title":335,"useCases":336,"organization":337,"vendors":339,"summary":341,"stage":270,"year":271,"channels":342,"languages":345,"metrics":346,"outcomeDisclosed":243,"sources":353,"verification":356,"grade":357,"id":358,"organizationSlug":255},"T-Mobile: PromoGenius app and product agent for retail and care staff",[193,201],{"name":338,"anonymized":209,"country":294,"region":295,"industry":18},"T-Mobile",[340],{"name":266,"role":215},"T-Mobile built PromoGenius on Power Apps to give retail and call centre representatives one place for current promotions, discounts and trade in values, used on iPads on the shop floor. An agent built in Copilot Studio reads more than 20 device makers' websites, answers technical questions in natural language during a customer conversation and builds comparison tables that can be shown to the customer. Microsoft reports over 83,000 unique users and 500,000 launches a month for the app, which supports all T-Mobile retail stores and call centres.",[343,344],"internal-tools","kiosk",[221],[347],{"kpi":50,"value":348,"unit":239,"qualifier":240,"period":349,"claimant":350,"quote":351,"sourceUrl":352},83000,"unique users, retail and call centre staff","vendor","The app, called PromoGenius, is the second most popular app at T-Mobile, supporting all T-Mobile retail outlets and call centers, with over 83,000 unique users and 500,000 launches a month.","https://www.microsoft.com/en/customers/story/23087-t-mobile-usa-microsoft-copilot-studio",[354],{"url":352,"title":355,"publisher":266},"T-Mobile drives more effective customer conversations with Microsoft Power Apps and Copilot Studio",{"level":252,"checkedAt":197},"C","t-mobile-promogenius-retail-agent",{"title":360,"useCases":361,"organization":363,"vendors":365,"summary":367,"stage":270,"year":368,"channels":369,"languages":371,"metrics":373,"outcomeDisclosed":243,"sources":388,"verification":393,"grade":357,"id":394,"organizationSlug":395},"Vodafone: TOBi virtual assistant across markets",[362,206,201],"first-line-contact-centre-agent",{"name":364,"anonymized":209,"country":317,"region":159,"industry":18},"Vodafone",[366],{"name":266,"role":215},"TOBi is Vodafone's digital assistant on the website, the My Vodafone app, messaging and telephony, first launched in Italy and extended to 15 language versions. It handles billing questions, contract updates and simple technical troubleshooting, hands over to a live agent with a summary, and during the pandemic made the same sales offers as human agents, such as data boosts and upgrades. Microsoft reports that TOBi now fully resolves 70% of inquiries arriving through digital channels; an earlier Microsoft story quotes Vodafone on a 12% year on year fall in contacts to call centres after launch.",2024,[31,30,370,33],"social-messaging",[221,372],"it",[374,379,382],{"kpi":224,"value":375,"unit":226,"qualifier":227,"period":376,"claimant":350,"quote":377,"sourceUrl":378},70,"inquiries arriving through digital channels","Currently, TOBi handles nearly 45 million customer calls a month, fully resolving 70% of customer inquiries coming through the company’s digital channels.","https://customers.microsoft.com/en-gb/story/1770174778560829849-vodafone-group-azure-telecommunications-en-united-kingdom",{"kpi":49,"value":380,"unit":239,"qualifier":278,"period":381,"claimant":350,"quote":377,"sourceUrl":378},45000000,"per month",{"kpi":383,"value":384,"unit":226,"qualifier":227,"period":385,"claimant":229,"quote":386,"sourceUrl":387},"contact-deflection",12,"frequency of customer contacts to call centres, year over year after the TOBi launch","Since launching TOBi, we’ve reduced the frequency of customer contacts to call centers by 12 percent year-over-year","https://www.microsoft.com/en/customers/story/838350-vodafone-telecom-azure-cognitive-services",[389,391],{"url":378,"title":390,"publisher":266},"Vodafone amplifies call center innovation, customer service, and employee inclusion with Azure AI",{"url":387,"title":392,"publisher":266},"Vodafone transforms its customer care strategy with digital assistant built on Azure Cognitive Services",{"level":252,"checkedAt":197},"vodafone-tobi-virtual-assistant","vodafone",{"title":397,"useCases":398,"organization":399,"vendors":402,"summary":405,"stage":270,"year":406,"channels":407,"languages":408,"metrics":409,"outcomeDisclosed":243,"sources":410,"verification":415,"grade":357,"id":416,"organizationSlug":255},"Jio: WhatsApp assistant for acquisition, porting, plans and care",[192,201],{"name":400,"anonymized":209,"country":401,"region":211,"industry":18},"Reliance Jio","IN",[403],{"name":404,"role":215},"Haptik","Jio runs a WhatsApp assistant built with Haptik with more than 900 intents. It covers the 5G customer lifecycle end to end, from lead generation and buying a 5G device to porting into Jio, choosing and buying plans and customer care, with separate journeys for prepaid and postpaid, and sends proactive top up and recharge reminders. Haptik reports that the channel acquires 8,000 new Jio Fiber and 5G customers a day.",2023,[32],[],[],[411],{"url":412,"title":413,"publisher":404,"archivedUrl":414},"https://www.haptik.ai/resources/case-study/jio-digital-life","Jio Transforms CX with Haptik","https://web.archive.org/web/20230528064657/https://www.haptik.ai/resources/case-study/jio-digital-life",{"level":252,"checkedAt":197},"jio-whatsapp-customer-lifecycle",{"title":418,"useCases":419,"organization":420,"vendors":423,"summary":426,"stage":270,"year":406,"channels":427,"languages":428,"metrics":429,"outcomeDisclosed":243,"sources":438,"verification":441,"grade":357,"id":442,"organizationSlug":255},"Telenet: AI decisioning for next best action, churn and upgrades",[191,201],{"name":421,"anonymized":209,"country":422,"region":159,"industry":18},"Telenet","BE",[424],{"name":425,"role":215},"Pega","Telenet, a provider of connectivity and entertainment services in Belgium, uses Pega's AI based Customer Decision Hub as a single decisioning system that responds to customer signals in real time, anticipates how behaviour may change and proposes the next best action, such as personalised upgrades and solutions, with the stated goals of reducing churn and raising offer acceptance. Pega reports a 20% reduction in churn, a 75% increase in offer acceptance and a 33% increase in cross sell. These figures cover the whole decisioning programme, including upgrades and cross sell, not retention alone.",[],[],[430,435],{"kpi":431,"value":432,"unit":226,"qualifier":227,"claimant":350,"quote":433,"sourceUrl":434},"churn-reduction",20,"20% reduction in churn","https://www.pega.com/customers/telenet-customer-decision-hub",{"kpi":47,"value":436,"unit":226,"qualifier":227,"claimant":350,"quote":437,"sourceUrl":434},75,"75% increase in offer acceptance",[439],{"url":434,"title":440,"publisher":425},"Anticipating customer needs with AI-powered decisioning",{"level":252,"checkedAt":197},"telenet-next-best-action-decisioning",{"title":444,"useCases":445,"organization":446,"vendors":450,"summary":453,"stage":270,"year":454,"channels":455,"languages":456,"metrics":457,"outcomeDisclosed":243,"sources":465,"verification":469,"grade":357,"id":470,"organizationSlug":255},"Mobily: AI self service agents across eight messaging channels",[195,362,201],{"name":447,"anonymized":209,"country":448,"region":449,"industry":18},"Mobily","SA","middle-east",[451],{"name":452,"role":215},"NiCE Cognigy","Mobily deployed customer facing AI agents on eight channels, including WhatsApp, Twitter and Apple Business Chat, connected to its internal systems. The agents answer billing, balance and data usage questions, change subscriptions, sell add ons, take payments and recharges, and handle feedback and complaints, with a warm handover to a specialist who can take over or hand back. NiCE Cognigy reports that the first response time fell from 20 minutes to about 6 seconds. The deployment was already live in 2022, when the case study described it as conversational AI; the current version presents it as agentic AI.",2022,[32,370],[],[458],{"kpi":459,"value":460,"unit":226,"qualifier":227,"period":461,"baseline":462,"claimant":350,"quote":463,"sourceUrl":464},"response-time-reduction",99.5,"first response time on messaging channels","first response up to 20 minutes before","An AI agent picks up any inquiry in around 6 seconds, reducing first response times significantly from the previous 20 minutes: a 99,5% improvement.","https://www.cognigy.com/en/case-study/mobily",[466],{"url":464,"title":467,"publisher":452,"archivedUrl":468},"Mobily: 99.5% faster response times with Agentic AI","https://web.archive.org/web/20220413151348/https://www.cognigy.com/en/case-study/mobily",{"level":252,"checkedAt":196},"mobily-agentic-ai-self-service",0,[473,483,488],{"kpi":49,"label":474,"unit":239,"aggregate":209,"higherIsBetter":243,"n":475,"nUpTo":471,"median":277,"min":238,"max":380,"byClaimant":476,"vendorOnly":209,"points":479},"Interactions handled",3,{"organization":477,"vendor":478,"regulator":471,"independent":471},2,1,[480,481,482],{"evidenceId":394,"organization":364,"value":380,"qualifier":278,"claimant":350,"grade":357,"pooled":243},{"evidenceId":288,"organization":260,"value":277,"qualifier":278,"claimant":229,"grade":253,"pooled":243},{"evidenceId":254,"organization":208,"value":238,"qualifier":240,"claimant":229,"grade":253,"pooled":243},{"kpi":47,"label":484,"unit":226,"aggregate":243,"higherIsBetter":243,"n":478,"nUpTo":471,"median":436,"min":436,"max":436,"byClaimant":485,"vendorOnly":243,"points":486},"Conversion uplift",{"organization":471,"vendor":478,"regulator":471,"independent":471},[487],{"evidenceId":442,"organization":421,"value":436,"qualifier":227,"claimant":350,"grade":357,"pooled":243},{"kpi":50,"label":489,"unit":239,"aggregate":209,"higherIsBetter":243,"n":478,"nUpTo":471,"median":348,"min":348,"max":348,"byClaimant":490,"vendorOnly":243,"points":491},"Users served",{"organization":471,"vendor":478,"regulator":471,"independent":471},[492],{"evidenceId":358,"organization":338,"value":348,"qualifier":240,"claimant":350,"grade":357,"pooled":243},{"low":494,"high":495},180000,1440000,[497,516,531,549,566],{"slug":191,"title":498,"shortTitle":499,"definition":500,"status":9,"industries":501,"functions":502,"patterns":504,"audience":506,"autonomy":36,"adoptionStage":507,"segment":508,"evidenceCount":509,"publicEvidenceCount":509,"organizations":510,"bestGrade":253,"headline":513,"lastVerified":196,"indexable":243},"AI for telecom churn prediction and retention offers","Churn prediction and retention","AI for telecom operators that scores each subscriber's risk of leaving from usage, service, billing and contact signals, explains the likely reason, and chooses the next best retention action, such as fixing a problem, adjusting a plan or making an offer, delivered through the app, messaging, an agent or an advisor within approved offer budgets.",[18],[22,21,20,503],"analytics-and-reporting",[505,24,25],"prediction-and-scoring","back-office","mainstream","middle-office",4,[511,421,316,512],"Etisalat","Vodafone UK",{"kpi":431,"label":514,"unit":226,"n":477,"nUpTo":471,"kind":515,"value":432,"qualifier":227,"claimant":350,"organization":421,"vendorReported":243},"Churn reduction","reported",{"slug":192,"title":517,"shortTitle":518,"definition":519,"status":9,"industries":520,"functions":521,"patterns":524,"audience":35,"autonomy":36,"adoptionStage":527,"segment":38,"evidenceCount":475,"publicEvidenceCount":475,"organizations":528,"bestGrade":253,"headline":529,"lastVerified":197,"indexable":243},"AI assistant for telecom order to activation and eSIM onboarding","Order to activation and eSIM onboarding","An AI assistant that takes a new or existing customer from order to a working service: it collects and checks the order details, guides number porting, eSIM download or SIM activation and installation appointments, tracks the order and fixes or escalates the step that is stuck, on messaging, app, web or phone.",[18],[20,522,21,523],"onboarding-and-kyc","operations",[25,28,525,526],"classification-and-routing","document-processing","emerging",[400,208,293],{"kpi":233,"label":530,"unit":226,"n":478,"nUpTo":471,"kind":515,"value":234,"qualifier":227,"claimant":229,"organization":208,"vendorReported":209},"Automation rate",{"slug":193,"title":532,"shortTitle":533,"definition":534,"status":9,"industries":535,"functions":536,"patterns":538,"audience":540,"autonomy":541,"adoptionStage":37,"segment":38,"evidenceCount":475,"publicEvidenceCount":475,"organizations":542,"bestGrade":357,"headline":545,"lastVerified":196,"indexable":243},"AI assistant for telecom retail stores, from associate copilot to digital human kiosk","Retail store and kiosk assistant","An AI assistant for telecom shops that gives store associates quick, sourced answers on plans, promotions, devices and the customer's account during the conversation, and that can also greet and serve customers directly on an in store screen or kiosk, sometimes as a digital human, handing them to an associate when they are ready to buy or need help.",[18],[20,21,537],"knowledge-management",[26,539,25,24],"digital-human","employee-facing","assist",[543,544,338],"Bouygues Telecom","Deutsche Telekom",{"kpi":546,"label":547,"unit":226,"n":478,"nUpTo":471,"kind":515,"value":548,"qualifier":227,"claimant":350,"organization":543,"vendorReported":243},"accuracy","Accuracy",95,{"slug":194,"title":550,"shortTitle":551,"definition":552,"status":9,"industries":553,"functions":554,"patterns":556,"audience":35,"autonomy":36,"adoptionStage":37,"segment":38,"evidenceCount":558,"publicEvidenceCount":558,"organizations":559,"bestGrade":253,"headline":564,"lastVerified":196,"indexable":243},"AI assistant for B2B telecom quoting, sales and service","B2B quoting and service","An AI assistant that serves business customers of a telecom operator and the sellers who look after them: it answers product, pricing and contract questions, prepares configurations and quotes for connectivity, mobile fleets and devices, drafts responses to tenders, and handles routine service requests and fault tickets, with a sales or service specialist approving anything binding.",[18],[20,21,555],"product-and-pricing",[25,26,28,24,557],"content-generation",5,[560,561,562,293,563],"Lumen Technologies","SoftBank Corp.","Telefónica España","Vodafone Business",{"kpi":224,"label":565,"unit":226,"n":478,"nUpTo":471,"kind":515,"value":375,"qualifier":227,"claimant":229,"organization":561,"vendorReported":209},"Containment rate",{"slug":195,"title":567,"shortTitle":568,"definition":569,"status":9,"industries":570,"functions":571,"patterns":573,"audience":35,"autonomy":36,"adoptionStage":37,"segment":38,"evidenceCount":509,"publicEvidenceCount":509,"organizations":574,"bestGrade":253,"headline":576,"lastVerified":196,"indexable":243},"AI agent for telecom bill explanation and billing disputes","Bill explanation and disputes","An AI agent that explains a customer's telecom bill line by line, in plain language and on any channel, answers why a charge changed or appeared, corrects clear errors within set limits and opens a billing dispute with the evidence attached when a human has to decide.",[18],[21,572],"case-management",[25,26,28,27],[575,447,293,364],"BT Group",{"kpi":577,"label":578,"unit":226,"n":478,"nUpTo":471,"kind":515,"value":79,"qualifier":227,"claimant":229,"organization":364,"vendorReported":209},"first-contact-resolution","First contact resolution",{"indexable":243,"reasons":580},[],[582,587,592,600,607,613,620,627,634,641,647,653,660,667,673,678,685,690,696,701,706,711,717,722,727,734,741,746,752,758,764,770,776,781],{"id":150,"label":583,"issuer":158,"region":159,"url":584,"description":585,"useCases":586,"indexable":243},"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":151,"label":588,"issuer":158,"region":159,"url":589,"description":590,"useCases":591,"indexable":243},"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":593,"label":594,"issuer":595,"region":596,"url":597,"description":598,"useCases":599,"indexable":243},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":601,"label":602,"issuer":603,"region":295,"url":604,"description":605,"useCases":606,"indexable":243},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":608,"label":609,"issuer":158,"region":159,"url":610,"description":611,"useCases":612,"indexable":243},"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":614,"label":615,"issuer":616,"region":159,"url":617,"description":618,"useCases":619,"indexable":243},"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":621,"label":622,"issuer":623,"region":159,"url":624,"description":625,"useCases":626,"indexable":243},"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":628,"label":629,"issuer":630,"region":211,"url":631,"description":632,"useCases":633,"indexable":243},"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":635,"label":636,"issuer":637,"region":211,"url":638,"description":639,"useCases":640,"indexable":243},"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":642,"label":643,"issuer":644,"region":596,"url":645,"description":646,"useCases":432,"indexable":243},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":648,"label":649,"issuer":650,"region":295,"url":651,"description":652,"useCases":432,"indexable":243},"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":654,"label":655,"issuer":656,"region":159,"url":657,"description":658,"useCases":659,"indexable":243},"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":661,"label":662,"issuer":663,"region":596,"url":664,"description":665,"useCases":666,"indexable":243},"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":668,"label":669,"issuer":158,"region":159,"url":670,"description":671,"useCases":672,"indexable":243},"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":674,"label":675,"issuer":158,"region":159,"url":676,"description":677,"useCases":672,"indexable":243},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":679,"label":680,"issuer":681,"region":295,"url":682,"description":683,"useCases":684,"indexable":243},"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":686,"label":687,"issuer":158,"region":159,"url":688,"description":689,"useCases":384,"indexable":243},"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.",{"id":691,"label":692,"issuer":693,"region":295,"url":694,"description":695,"useCases":384,"indexable":243},"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":152,"label":697,"issuer":698,"region":596,"url":699,"description":700,"useCases":384,"indexable":243},"Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":153,"label":702,"issuer":158,"region":159,"url":703,"description":704,"useCases":705,"indexable":243},"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":154,"label":707,"issuer":708,"region":295,"url":709,"description":710,"useCases":705,"indexable":243},"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":712,"label":713,"issuer":630,"region":211,"url":714,"description":715,"useCases":716,"indexable":243},"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":718,"label":719,"issuer":158,"region":159,"url":720,"description":721,"useCases":716,"indexable":243},"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":723,"label":724,"issuer":158,"region":159,"url":725,"description":726,"useCases":716,"indexable":243},"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":728,"label":729,"issuer":730,"region":159,"url":731,"description":732,"useCases":733,"indexable":243},"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":735,"label":736,"issuer":737,"region":295,"url":738,"description":739,"useCases":740,"indexable":243},"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":742,"label":743,"issuer":158,"region":159,"url":744,"description":745,"useCases":740,"indexable":243},"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":747,"label":748,"issuer":158,"region":159,"url":749,"description":750,"useCases":751,"indexable":243},"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.",6,{"id":753,"label":754,"issuer":755,"region":449,"url":756,"description":757,"useCases":558,"indexable":243},"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":759,"label":760,"issuer":761,"region":159,"url":762,"description":763,"useCases":509,"indexable":243},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":765,"label":766,"issuer":767,"region":159,"url":768,"description":769,"useCases":509,"indexable":243},"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":771,"label":772,"issuer":773,"region":211,"url":774,"description":775,"useCases":475,"indexable":243},"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":777,"label":778,"issuer":158,"region":159,"url":779,"description":780,"useCases":475,"indexable":243},"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":782,"label":783,"issuer":784,"region":295,"url":785,"description":786,"useCases":475,"indexable":243},"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.",1790598297445]