[{"data":1,"prerenderedAt":679},["ShallowReactive",2],{"uc-business-connectivity-quoting-and-service-assistant":3,"uc-regulations":474},{"useCase":4,"evidence":195,"blitsAiDeployments":353,"benchmarks":354,"indicative":381,"related":384,"indexability":472,"includeUnpublished":201},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":23,"channels":29,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"problem":38,"problemStats":39,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":52,"macroEstimates":87,"feasibility":88,"implementation":102,"risk":145,"blitsAi":172,"faq":174,"related":184,"datePublished":190,"dateModified":190,"lastVerified":190,"changelog":191,"slug":194},"AI assistant for B2B telecom quoting, sales and service","B2B quoting and service","AI assistant for B2B telecom quoting and service","An AI assistant answers telecom business customers' product and price questions and drafts quotes for sellers to approve. SoftBank reports 70% self resolution.","published","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.",[12,13,14,15,16],"B2B telco sales assistant","enterprise quoting assistant","CPQ assistant for telecom","business customer service agent","RFP response assistant",[18],"telecommunications",[20,21,22],"sales","customer-service","product-and-pricing",[24,25,26,27,28],"conversational-agent","rag-knowledge-assistant","agentic-workflow","recommendation-and-personalization","content-generation",[30,31,32,33],"web-chat","agent-desktop","email","internal-tools","customer-facing","supervised-agent","early-adopters","front-office","Business customers buy complex bundles: fibre and dedicated lines at several sites, mobile fleets,\ndevices, security and cloud services, each with its own availability, pricing rules and service\nlevels. Small businesses want quick answers and quotes without waiting for a sales call; large\naccounts issue tenders with long questionnaires. Sellers spend hours researching accounts,\nchecking availability and assembling quotes, and service teams handle routine requests that the\ncustomer could have done alone.\n\nThe knowledge needed lives in product sheets, price books, contract templates and past proposals\nspread across systems. Configure, price and quote tools help specialists, but they do not answer a\nsmall business owner's question at night or draft the first version of a tender response.",[],"1. **Answer and qualify.** On the business website or portal, the assistant answers questions\n   about products, prices and contracts, checks basic eligibility such as fibre availability at an\n   address, and qualifies the need.\n2. **Configure and quote.** Using the product catalogue and configure, price and quote tools, it\n   prepares a configuration and an indicative quote within standard price rules.\n3. **Hand over to a seller.** Anything non standard (discounts, multi site designs, tenders) goes\n   to a seller with the configuration, the conversation and the account context.\n4. **Assist sellers.** For sellers the assistant researches the account, suggests the next best\n   offer, and drafts tender answers and proposals from approved content.\n5. **Serve after the sale.** Business customers raise and track orders and fault tickets, and the\n   assistant resolves routine requests, keeping them informed during service interruptions.",[42,43,44,45],"revenue-growth","employee-productivity","cost-to-serve","speed",[47,48,49,50,51],"containment-rate","conversion-rate-uplift","time-saved-per-task","handling-time-reduction","interactions-handled",{"referenceOrg":53,"inputs":54,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"A telecom operator's business unit with 50,000 small and medium business customers",[55,61,68,75],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"businessCustomers","Business customers",50000,"customers","The reference business unit.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"inquiriesPerCustomer","Routine sales and service inquiries per customer per year",2,4,"inquiries per customer per year","Editorial assumption, replace with your own business contact volumes.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"resolvedShare","Share of routine inquiries the assistant resolves",0.3,0.6,"fraction of inquiries","Conservative against the benchmark on this page (SoftBank reports a self resolution rate of 70% for its business website agent).",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"costPerInquiry","Cost of an inquiry handled by a seller or business service agent",15,30,"USD per inquiry","Editorial assumption; business inquiries take longer and are handled by more expensive staff. Replace with your own cost.","businessCustomers * inquiriesPerCustomer * resolvedShare * costPerInquiry","USD","per year","Sales and service handling cost avoided","Counts only routine inquiries handled without a person. It leaves out additional sales from faster quotes and better prepared sellers, faster tender responses, the cost of the AI and the catalogue and quoting integrations, and discounts that still need approval.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":96},"medium","Answering product questions is easy once content is clean. Quoting needs the product catalogue, availability checks and price rules behind APIs, and binding offers must stay in the approved configure, price and quote process.",[92,93,94,95],"Current business product catalogue, price books and discount rules","Address level availability for fixed products","Approved contract templates, service levels and past tender answers","Account data in the CRM, including installed base and contract dates",[97,98,99,100,101],"CRM and account management","Configure, price and quote system","Availability and serviceability checks","Service management and ticketing for business customers","Document and proposal repositories",{"steps":103,"guardrails":119,"humanInTheLoop":125,"kpisToInstrument":126,"failureModes":132},[104,107,110,113,116],{"title":105,"detail":106},"Start with questions and qualification","Put an assistant on the business website that answers product, price and contract questions from approved content and routes qualified leads, as SoftBank did first because it had the most customer touchpoints.",{"title":108,"detail":109},"Connect availability and indicative quotes","Add address availability and standard price quotes through tools, and keep every binding quote and discount in the existing approval process.",{"title":111,"detail":112},"Give sellers an account and tender assistant","Let sellers ask for account research, next best offers and draft tender answers from an approved library, and measure time saved per proposal.",{"title":114,"detail":115},"Bring service into the same assistant","Let business customers raise and track orders and tickets and get status during service interruptions, so the relationship does not end at the sale.",{"title":117,"detail":118},"Improve from unresolved questions","Review the questions the assistant could not answer each week and add the missing content. SoftBank credits this kind of iterative improvement for raising its self resolution rate from about 50% at launch to 70%.",[120,121,122,123,124],"Prices, discounts and availability only from tools, never from the model","Binding quotes and non standard discounts approved by a seller","Tender answers drawn only from an approved answer library, with a seller reviewing every submission","Customer contract data visible only to authorised users of that account","Handover to a named seller or service agent on request","Sellers approve every binding quote, discount and tender response, and service specialists handle complex faults and escalations. Product and pricing teams own the content the assistant uses and review unresolved questions weekly.",[127,128,129,130,131],"Self resolution rate of business inquiries, checked against repeat contacts","Time from inquiry to quote","Win rate of assisted quotes versus unassisted ones","Seller hours spent on research and tender responses","Quote errors found at approval",[133,136,139,142],{"title":134,"detail":135},"Quotes the operator cannot honour","The assistant quotes a price or service that is not available at the address. Check availability and price in tools and label quotes as indicative.",{"title":137,"detail":138},"Tender answers that overpromise","A generated answer commits to a service level the operator does not offer. Use an approved library and seller review.",{"title":140,"detail":141},"Leaking account data","A user sees another company's contract details. Enforce account level access in the tools.",{"title":143,"detail":144},"Leads lost in handover","Qualified leads sit in an inbox. Route them to named sellers with service levels for follow up.",{"euAiAct":146,"regulations":149,"guidance":154,"controls":165,"incidents":171},{"tier":147,"basis":148},"limited","Article 50(1): people must be informed that they are interacting with an AI system, unless that is obvious from the context. Quoting, sales support and service for business customers are not listed in Annex III. The use would become high risk under Annex III point 5(b) only if the assistant itself evaluated the creditworthiness of a natural person, such as a sole trader, to decide whether to offer a contract.",[150,151,152,153],"eu-ai-act","gdpr","telecom-consumer-rules","eecc",[155,161],{"title":156,"issuer":157,"region":158,"url":159,"note":160},"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":162,"issuer":157,"region":158,"url":163,"note":164},"European Electronic Communications Code (Directive (EU) 2018/1972)","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Several end user protections, such as contract information, also apply to microenterprises and small businesses unless they waive them, so a B2B assistant cannot treat every business customer as a large enterprise.",[166,167,168,169,170],"AI disclosure on the business website and portal","Price and discount approval workflow outside the model","Approved answer library with owners for tender content","Account level access control and audit logging","Weekly review of unresolved questions and quote errors",[],{"howToBuild":173},"On Blits.ai this is an **AI agent** grounded in a **knowledge base** of product sheets, price\nrules and approved tender answers with hybrid retrieval, and connected through **custom\nfunctions** or ready made connectors to the CRM, quoting and availability systems (for example\nSalesforce, Microsoft Dynamics 365, HubSpot or ServiceNow from the integration catalogue).\n**SQL knowledge bases** let sellers ask questions about their installed base in plain language.\n\nCustomers use it in **web chat** on the business site and by **email**; sellers use it in\n**Microsoft Teams**. **Agentic workflows** with **human in the loop approval** draft quotes and\ntender answers for a seller to approve, **human handover** routes qualified leads, **guardrails**\nkeep prices and commitments inside approved content, and **test suites** replay product\nquestions whenever the catalogue changes. The platform is model agnostic with EU and UAE data\nresidency.",[175,178,181],{"question":176,"answer":177},"Can business customers really self serve with an AI agent?","For routine questions, yes. SoftBank reports that the self resolution rate of its website agent for small and medium business customers rose from about 50% at launch to 70%. Sierra, the platform vendor, measures that rate with its own AI monitoring and puts the volume at about 100 inquiries a day. Complex designs and discounts still go to sellers.",{"question":179,"answer":180},"What does AI do for B2B sellers?","Mostly research and preparation. Microsoft reports that Lumen cut account research that took a seller up to four hours to 15 minutes with Copilot, and Pega reports a 15% improvement in win rate from Verizon's AI guided selling engine.",{"question":182,"answer":183},"Should the assistant produce binding quotes?","Indicative quotes, yes; binding ones, no. Keep discounts and binding offers in the approved quoting process, with a seller signing off.",[185,186,187,188,189],"plan-upgrade-and-sales-assistant","corporate-client-servicing-assistant","network-outage-communication-agent","order-to-activation-and-esim-onboarding-assistant","inbound-lead-qualification-agent","2026-09-27",[192],{"date":190,"note":193},"First published","business-connectivity-quoting-and-service-assistant",[196,225,258,294,325],{"title":197,"useCases":198,"organization":199,"vendors":203,"summary":207,"stage":208,"year":209,"channels":210,"languages":211,"metrics":213,"outcomeDisclosed":201,"sources":214,"verification":219,"grade":222,"id":223,"organizationSlug":224},"Telefónica: GenIA assistant in a device marketplace for businesses and public bodies",[194],{"name":200,"anonymized":201,"country":202,"region":158,"industry":18},"Telefónica España",false,"ES",[204],{"name":205,"role":206},"Telefónica","in-house","In June 2026 Telefónica España launched a marketplace for companies and government agencies to select and buy workplace devices from a catalogue of 160 models. Its GenIA virtual assistant lets buyers describe needs in natural language, compare alternatives, get personalised recommendations and resolve technical questions in real time, alongside a productivity calculator. No outcome figures were published.","production",2026,[30],[212],"es",[],[215],{"url":216,"title":217,"publisher":205,"date":218},"https://www.telefonica.com/en/communication-room/press-room/telefonica-is-using-ai-to-optimize-equipment-selection-for-businesses-and-government-agencies/","Telefónica is using AI to optimize equipment selection for businesses and government agencies","2026-06-17",{"level":220,"checkedAt":221},"source-verified","2026-09-26","B","telefonica-genia-b2b-device-marketplace",null,{"title":226,"useCases":227,"organization":228,"vendors":231,"summary":235,"stage":208,"year":236,"channels":237,"languages":239,"metrics":241,"outcomeDisclosed":250,"sources":251,"verification":256,"grade":222,"id":257,"organizationSlug":224},"Vodafone Business: AI powered service management with ServiceNow",[194],{"name":229,"anonymized":201,"country":230,"region":158,"industry":18},"Vodafone Business","GB",[232],{"name":233,"role":234},"ServiceNow","platform","Vodafone Business and ServiceNow agreed a five year collaboration to run service management for business customers on ServiceNow's telecom service assurance products with agentic AI. Vodafone says it will give a single view of each customer's networks and applications, and that it can detect and fix service anomalies within minutes rather than hours, using AI and machine learning to predict, minimise and manage service interruptions, and to route customers to the right department first time across online, email and phone. Vodafone reports that an initial deployment in Ireland raised digital channel use by 45%, and says satisfaction levels rose fourfold there.",2025,[30,32,238,33],"voice",[240],"en",[242],{"kpi":243,"value":65,"unit":244,"qualifier":245,"period":246,"claimant":247,"quote":248,"sourceUrl":249},"customer-satisfaction-uplift","multiplier","exact","initial deployment in Ireland","organization","An initial deployment in Ireland led to a 45% rise in customers using digital channels, boosting satisfaction levels by 4x.","https://www.vodafone.com/news/newsroom/technology/vodafone-business-and-service-now-collaborate-to-enhance-the-customer-experience-with-ai-powered-service-automation",true,[252],{"url":249,"title":253,"publisher":254,"date":255},"Vodafone Business and ServiceNow collaborate to enhance the customer experience with AI-powered service automation","Vodafone","2025-04-24",{"level":220,"checkedAt":221},"vodafone-business-servicenow-service-automation",{"title":259,"useCases":260,"organization":261,"vendors":265,"summary":268,"stage":208,"year":209,"channels":269,"languages":270,"metrics":272,"outcomeDisclosed":250,"sources":287,"verification":291,"grade":292,"id":293,"organizationSlug":224},"SoftBank: AI agent for small and medium business sales inquiries",[194],{"name":262,"anonymized":201,"country":263,"region":264,"industry":18},"SoftBank Corp.","JP","asia-pacific",[266],{"name":267,"role":234},"Sierra","SoftBank's Customer Growth Division, which sells to Japan's small and medium sized businesses, put a conversational AI agent on its corporate website that answers questions about products, services, pricing and contracts. What it cannot resolve goes through an inquiry form to a sales representative. SoftBank says the self resolution rate, measured by the platform's own AI assessment, rose from about 50% at launch to 70%; Sierra puts the volume at about 100 inquiries a day.",[30],[271],"ja",[273,280],{"kpi":47,"value":274,"unit":275,"qualifier":245,"period":276,"baseline":277,"claimant":247,"quote":278,"sourceUrl":279},70,"percent","website inquiries from business customers","about 50% at launch","SoftBank's corporate business covers a wide range of services, and the self-resolution rate* — which started at around 50% at launch — has now risen to 70%, thanks to iterative improvements to help the AI agent better understand customer intent and the relevant service.","https://sierra.ai/customers/softbank",{"kpi":51,"value":281,"unit":282,"qualifier":283,"period":284,"claimant":285,"quote":286,"sourceUrl":279},100,"count","approximately","inquiries per day","vendor","Currently, the AI agent handles approximately 100 inquiries per day, with 70% resulting in customers finding the information they need and resolving their inquiries on their own.",[288],{"url":279,"title":289,"publisher":267,"date":290},"How SoftBank's Customer Growth Division delivers fast, high-quality customer service with AI","2026-07-14",{"level":220,"checkedAt":221},"C","softbank-smb-sales-ai-agent",{"title":295,"useCases":296,"organization":299,"vendors":303,"summary":306,"stage":208,"year":307,"channels":308,"languages":309,"metrics":310,"outcomeDisclosed":250,"sources":319,"verification":323,"grade":292,"id":324,"organizationSlug":224},"Lumen Technologies: Copilot summaries of past sales interactions and account research",[297,194,298],"sales-call-coaching-and-crm-update","outbound-sales-prospecting-agent",{"name":300,"anonymized":201,"country":301,"region":302,"industry":18},"Lumen Technologies","US","north-america",[304],{"name":305,"role":234},"Microsoft","Lumen's sellers use Microsoft Copilot to summarize past sales interactions, gather recent news, identify business challenges and industry trends, and suggest next steps for an account. Microsoft reports that this work took up to four hours per seller and that Lumen cut it to 15 minutes in 2024. Lumen's projected annual value of USD 50 million is a projection and is not recorded as a result.",2024,[33],[240],[311],{"kpi":49,"value":312,"unit":313,"qualifier":314,"period":315,"baseline":316,"claimant":285,"quote":317,"sourceUrl":318},225,"minutes","up-to","summarizing past interactions and researching an account, per seller","up to four hours per seller before","This process traditionally took up to four hours per seller. In 2024, Lumen reduced that time to just 15 minutes, projecting annual time savings worth USD50 million.","https://www.microsoft.com/en-us/microsoft-cloud/blog/2025/07/24/ai-powered-success-with-1000-stories-of-customer-transformation-and-innovation/",[320],{"url":318,"title":321,"publisher":305,"date":322},"AI-powered success, with more than 1,000 stories of customer transformation and innovation","2025-07-24",{"level":220,"checkedAt":221},"lumen-copilot-sales-account-research",{"title":326,"useCases":327,"organization":328,"vendors":330,"summary":333,"stage":334,"year":335,"channels":336,"languages":337,"metrics":338,"outcomeDisclosed":250,"sources":346,"verification":350,"grade":292,"id":351,"organizationSlug":352},"Verizon Business: AI guided selling engine for large accounts and SMBs",[194],{"name":329,"anonymized":201,"country":301,"region":302,"industry":18},"Verizon",[331],{"name":332,"role":234},"Pega","Verizon's business unit built a single Next Best X decisioning engine on Pega across five lines of business and more than 200 products and offers. It brings AI guided selling into the tools that front line sellers and business customers already use, across sales cycles that range from six month account based programmes to real time transactional sales for small businesses. Pega reports a 15% improvement in win rate, a doubling of the attach rate for value added services and a 10 to 20% reduction in handling time.","scaled",2022,[31,33],[240],[339,343],{"kpi":48,"value":78,"unit":275,"qualifier":245,"period":340,"claimant":285,"quote":341,"sourceUrl":342},"sales win rate","15% improvement in win rate","https://www.pega.com/customers/verizon-customer-decision-hub",{"kpi":50,"value":344,"unit":275,"qualifier":314,"claimant":285,"quote":345,"sourceUrl":342},20,"10-20% improvement in handling time reduction",[347],{"url":342,"title":348,"publisher":332,"date":349},"Verizon builds B2B-grade customer engagement platform","2022-08-12",{"level":220,"checkedAt":221},"verizon-business-next-best-x-engine","verizon",0,[355,361,366,371,376],{"kpi":47,"label":356,"unit":275,"aggregate":250,"higherIsBetter":250,"n":357,"nUpTo":353,"median":274,"min":274,"max":274,"byClaimant":358,"vendorOnly":201,"points":359},"Containment rate",1,{"organization":357,"vendor":353,"regulator":353,"independent":353},[360],{"evidenceId":293,"organization":262,"value":274,"qualifier":245,"claimant":247,"grade":292,"pooled":250},{"kpi":48,"label":362,"unit":275,"aggregate":250,"higherIsBetter":250,"n":357,"nUpTo":353,"median":78,"min":78,"max":78,"byClaimant":363,"vendorOnly":250,"points":364},"Conversion uplift",{"organization":353,"vendor":357,"regulator":353,"independent":353},[365],{"evidenceId":351,"organization":329,"value":78,"qualifier":245,"claimant":285,"grade":292,"pooled":250},{"kpi":51,"label":367,"unit":282,"aggregate":201,"higherIsBetter":250,"n":357,"nUpTo":353,"median":281,"min":281,"max":281,"byClaimant":368,"vendorOnly":250,"points":369},"Interactions handled",{"organization":353,"vendor":357,"regulator":353,"independent":353},[370],{"evidenceId":293,"organization":262,"value":281,"qualifier":283,"claimant":285,"grade":292,"pooled":250},{"kpi":50,"label":372,"unit":275,"aggregate":250,"higherIsBetter":250,"n":353,"nUpTo":357,"median":224,"min":224,"max":224,"byClaimant":373,"vendorOnly":201,"points":374},"Handling time reduction",{"organization":353,"vendor":353,"regulator":353,"independent":353},[375],{"evidenceId":351,"organization":329,"value":344,"qualifier":314,"claimant":285,"grade":292,"pooled":201},{"kpi":49,"label":377,"unit":313,"aggregate":250,"higherIsBetter":250,"n":353,"nUpTo":357,"median":224,"min":224,"max":224,"byClaimant":378,"vendorOnly":201,"points":379},"Time saved per task",{"organization":353,"vendor":353,"regulator":353,"independent":353},[380],{"evidenceId":324,"organization":300,"value":312,"qualifier":314,"claimant":285,"grade":292,"pooled":201},{"low":382,"high":383},450000,3600000,[385,406,426,440,455],{"slug":185,"title":386,"shortTitle":387,"definition":388,"status":9,"industries":389,"functions":390,"patterns":392,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"evidenceCount":394,"publicEvidenceCount":394,"organizations":395,"bestGrade":222,"headline":403,"lastVerified":221,"indexable":250},"AI assistant for telecom plan upgrades, add ons and sales","Plan upgrade and sales assistant","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.",[18],[20,21,391],"marketing",[27,24,25,393,26],"voice-agent",9,[396,397,398,399,400,401,329,402,254],"Reliance Jio","Mobily","Orange France","Singtel","T-Mobile","Telenet","Virgin Media O2",{"kpi":48,"label":362,"unit":275,"n":357,"nUpTo":353,"kind":404,"value":405,"qualifier":245,"claimant":285,"organization":401,"vendorReported":250},"reported",75,{"slug":186,"title":407,"shortTitle":408,"definition":409,"status":9,"industries":410,"functions":413,"patterns":415,"audience":34,"autonomy":35,"adoptionStage":36,"segment":417,"evidenceCount":418,"publicEvidenceCount":64,"organizations":419,"bestGrade":222,"headline":422,"lastVerified":190,"indexable":250},"AI assistant for corporate and commercial client servicing","Corporate client servicing","A conversational assistant inside the corporate banking portal, app and messaging channels that answers finance and treasury teams' servicing questions, such as payment status, balances, cut off times, fees and how to submit an instruction, resolves routine requests end to end and hands the rest to a service specialist who has an AI copilot.",[411,412],"banking","payments",[21,414],"operations",[24,25,26,416],"summarization","specialized-businesses",5,[420,421],"Bank of America","DBS Bank",{"kpi":423,"label":424,"unit":275,"n":357,"nUpTo":353,"kind":404,"value":425,"qualifier":245,"claimant":247,"organization":420,"vendorReported":201},"contact-deflection","Contact deflection",16,{"slug":187,"title":427,"shortTitle":428,"definition":429,"status":9,"industries":430,"functions":431,"patterns":434,"audience":34,"autonomy":35,"adoptionStage":437,"segment":37,"evidenceCount":64,"publicEvidenceCount":357,"organizations":438,"bestGrade":222,"headline":224,"lastVerified":190,"indexable":250},"AI agent for network outage detection and customer communication","Outage communication","An AI agent that turns network alarms into a clear picture of which customers are affected by an outage and why, tells them proactively by message, app or phone with a cause and an estimated fix time, answers their questions during the incident, and updates them until service is restored.",[18],[21,432,433],"network-operations","field-service",[435,436,28,24,393],"anomaly-detection","classification-and-routing","emerging",[439],"Comcast",{"slug":188,"title":441,"shortTitle":442,"definition":443,"status":9,"industries":444,"functions":445,"patterns":447,"audience":34,"autonomy":35,"adoptionStage":437,"segment":37,"evidenceCount":449,"publicEvidenceCount":449,"organizations":450,"bestGrade":222,"headline":451,"lastVerified":221,"indexable":250},"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],[20,446,21,414],"onboarding-and-kyc",[24,26,436,448],"document-processing",3,[396,399,329],{"kpi":452,"label":453,"unit":275,"n":357,"nUpTo":353,"kind":404,"value":454,"qualifier":245,"claimant":247,"organization":399,"vendorReported":201},"automation-rate","Automation rate",76,{"slug":189,"title":456,"shortTitle":457,"definition":458,"status":9,"industries":459,"functions":463,"patterns":464,"audience":34,"autonomy":35,"adoptionStage":36,"evidenceCount":65,"publicEvidenceCount":65,"organizations":465,"bestGrade":292,"headline":470,"lastVerified":190,"indexable":250},"AI agent for inbound lead qualification and meeting booking","Inbound lead qualification","An AI agent that engages inbound prospects the moment they arrive on the website, chat, messaging or the sales phone line, answers their first questions, qualifies them against the organization's criteria, and books a meeting or hands a ready conversation to the right salesperson, with the context written into the CRM.",[460,461,462,411],"cross-industry","technology","automotive",[20,391],[24,393,436,26],[466,467,468,469],"8x8","CarMax","Rocket Mortgage","SUSE",{"kpi":48,"label":362,"unit":275,"n":357,"nUpTo":353,"kind":404,"value":471,"qualifier":245,"claimant":285,"organization":466,"vendorReported":250},19,{"indexable":250,"reasons":473},[],[475,480,485,493,500,506,513,520,527,534,540,546,552,558,564,569,576,582,588,593,597,603,609,614,619,625,632,637,643,650,656,662,668,673],{"id":150,"label":476,"issuer":157,"region":158,"url":477,"description":478,"useCases":479,"indexable":250},"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":481,"issuer":157,"region":158,"url":482,"description":483,"useCases":484,"indexable":250},"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":486,"label":487,"issuer":488,"region":489,"url":490,"description":491,"useCases":492,"indexable":250},"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":494,"label":495,"issuer":496,"region":302,"url":497,"description":498,"useCases":499,"indexable":250},"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":501,"label":502,"issuer":157,"region":158,"url":503,"description":504,"useCases":505,"indexable":250},"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":507,"label":508,"issuer":509,"region":158,"url":510,"description":511,"useCases":512,"indexable":250},"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":514,"label":515,"issuer":516,"region":158,"url":517,"description":518,"useCases":519,"indexable":250},"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":521,"label":522,"issuer":523,"region":264,"url":524,"description":525,"useCases":526,"indexable":250},"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":528,"label":529,"issuer":530,"region":264,"url":531,"description":532,"useCases":533,"indexable":250},"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":535,"label":536,"issuer":537,"region":489,"url":538,"description":539,"useCases":344,"indexable":250},"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":541,"label":542,"issuer":543,"region":302,"url":544,"description":545,"useCases":344,"indexable":250},"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":158,"url":550,"description":551,"useCases":425,"indexable":250},"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.",{"id":553,"label":554,"issuer":555,"region":489,"url":556,"description":557,"useCases":78,"indexable":250},"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.",{"id":559,"label":560,"issuer":157,"region":158,"url":561,"description":562,"useCases":563,"indexable":250},"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":565,"label":566,"issuer":157,"region":158,"url":567,"description":568,"useCases":563,"indexable":250},"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":570,"label":571,"issuer":572,"region":302,"url":573,"description":574,"useCases":575,"indexable":250},"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":577,"label":578,"issuer":157,"region":158,"url":579,"description":580,"useCases":581,"indexable":250},"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":583,"label":584,"issuer":585,"region":302,"url":586,"description":587,"useCases":581,"indexable":250},"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":589,"issuer":590,"region":489,"url":591,"description":592,"useCases":581,"indexable":250},"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":594,"issuer":157,"region":158,"url":163,"description":595,"useCases":596,"indexable":250},"European Electronic Communications Code","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":598,"label":599,"issuer":600,"region":302,"url":601,"description":602,"useCases":596,"indexable":250},"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":604,"label":605,"issuer":523,"region":264,"url":606,"description":607,"useCases":608,"indexable":250},"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":610,"label":611,"issuer":157,"region":158,"url":612,"description":613,"useCases":608,"indexable":250},"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":615,"label":616,"issuer":157,"region":158,"url":617,"description":618,"useCases":608,"indexable":250},"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":620,"label":621,"issuer":622,"region":158,"url":623,"description":624,"useCases":394,"indexable":250},"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.",{"id":626,"label":627,"issuer":628,"region":302,"url":629,"description":630,"useCases":631,"indexable":250},"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":633,"label":634,"issuer":157,"region":158,"url":635,"description":636,"useCases":631,"indexable":250},"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":638,"label":639,"issuer":157,"region":158,"url":640,"description":641,"useCases":642,"indexable":250},"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":644,"label":645,"issuer":646,"region":647,"url":648,"description":649,"useCases":418,"indexable":250},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":651,"label":652,"issuer":653,"region":158,"url":654,"description":655,"useCases":65,"indexable":250},"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":657,"label":658,"issuer":659,"region":158,"url":660,"description":661,"useCases":65,"indexable":250},"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":663,"label":664,"issuer":665,"region":264,"url":666,"description":667,"useCases":449,"indexable":250},"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":669,"label":670,"issuer":157,"region":158,"url":671,"description":672,"useCases":449,"indexable":250},"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":674,"label":675,"issuer":676,"region":302,"url":677,"description":678,"useCases":449,"indexable":250},"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.",1790598296407]