[{"data":1,"prerenderedAt":712},["ShallowReactive",2],{"uc-it-service-desk-resolution-agent":3,"uc-regulations":509},{"useCase":4,"evidence":213,"blitsAiDeployments":378,"benchmarks":379,"indicative":421,"related":424,"indexability":507,"includeUnpublished":219},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":22,"patterns":25,"channels":30,"audience":34,"autonomy":35,"adoptionStage":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":44,"indicativeValue":53,"macroEstimates":87,"feasibility":88,"implementation":102,"risk":148,"blitsAi":190,"faq":192,"related":202,"datePublished":208,"dateModified":208,"lastVerified":208,"changelog":209,"slug":212},"AI agent for IT service desk resolution","IT service desk resolution","AI agents for IT help desk ticket resolution","An AI agent resets passwords, unlocks accounts and routes IT tickets in Teams or Slack. Bank of America more than halved its IT service desk calls.","published","An AI agent in Microsoft Teams, Slack or the intranet that takes the high volume IT support queue, such as password and MFA resets, account unlocks, VPN, device and software requests, and resolves common requests by acting in the identity and IT service management systems, handing the rest to the right resolver group with the context attached.",[12,13,14,15],"IT helpdesk chatbot","virtual IT support agent","employee IT support assistant","ITSM virtual agent",[17,18,19,20,21],"cross-industry","banking","technology","retail-and-ecommerce","healthcare",[23,24],"it-and-engineering","operations",[26,27,28,29],"conversational-agent","agentic-workflow","rag-knowledge-assistant","classification-and-routing",[31,32,33],"microsoft-teams","internal-tools","web-chat","employee-facing","supervised-agent","mainstream","Most organizations run an IT service desk, and much of its volume is the same few requests:\nforgotten passwords, locked accounts, MFA devices, VPN trouble, a new laptop, access to an\napplication. Each one is quick for an analyst, but they arrive in bursts (Monday mornings,\nafter a password policy change, during an outage) and employees wait in a queue for something a\nsystem could have done in seconds. The first line desk spends its day reading and routing tickets\ninstead of fixing the harder problems.\n\nThe first wave of IT chatbots answered with a knowledge article. The employee still had to follow\nthe steps or raise a ticket anyway. The step change is an agent that is connected to the identity\nprovider and the ITSM tool, can reset, unlock, provision and route within strict limits, and\nlogs every action like a human analyst would. In a bank the same agent also has to respect\nentitlement rules and access reviews, because access changes are a control, not just a service.",[],"1. **Understand the request.** The employee writes in their own words in Teams, Slack or the\n   portal (\"my VPN keeps dropping\", \"I need Visio\"). The agent classifies the intent and\n   extracts the details it needs.\n2. **Verify the person proportionally.** The chat session is already signed in through single\n   sign on. Sensitive actions such as a password or MFA reset need a step up check, because\n   the service desk is a known target for social engineering.\n3. **Act through approved tools.** For a small allow list of requests the agent calls the\n   identity provider or ITSM APIs directly: reset, unlock, add to a group, start a software\n   request that runs the normal approval, and confirms the result.\n4. **Answer from the IT knowledge base.** How to questions are answered by retrieval over\n   current, owned IT articles, with a link to the source.\n5. **Route what it cannot finish.** Everything else becomes a ticket with a summary, category\n   and the right assignment group, so no analyst has to read and reroute it.\n6. **Log everything.** Every action is written to the ITSM record with the requester, the\n   action and the result, so audit and access reviews see the same trail as for a human.",[41,42,43],"cost-to-serve","employee-productivity","speed",[45,46,47,48,49,50,51,52],"containment-rate","automation-rate","contact-deflection","accuracy","employee-adoption","users-served","processing-time-reduction","productivity-gain",{"referenceOrg":54,"inputs":55,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"An organization with 20,000 employees",[56,61,68,75],{"key":57,"label":58,"low":59,"high":59,"unit":57,"note":60},"employees","Employees served by the service desk",20000,"The reference organization.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"ticketsPerEmployee","IT support contacts per employee per year",6,10,"contacts per employee per year","Editorial assumption, replace with your own ticket and call volume.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"resolvedShare","Share of contacts the agent resolves without an analyst",0.3,0.6,"fraction of contacts","Conservative against the evidence on this page (IBM reports over 75% of AskIT queries resolved by an assistant that mainly answers from IT support content; Moveworks reports over 74% of issues at Mercari US handled autonomously), because early waves cover fewer intents.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"costPerTicket","Fully loaded cost of an analyst handled contact",15,25,"USD per contact","Editorial assumption for a blended first line desk. Replace with your own cost per ticket.","employees * ticketsPerEmployee * resolvedShare * costPerTicket","USD","per year","Analyst handled contact cost avoided","Gross avoided handling cost only. It leaves out the cost of the platform and integrations, the productivity of employees who get unblocked faster, and any reduction in outsourced desk contracts, which usually only happens at renewal.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":96},"medium","Answering IT questions is easy. The work is in the integrations with the identity provider and the ITSM tool, in safe verification before resets, and in keeping the knowledge base current. Organizations with messy assignment groups need to clean them up before routing works well.",[92,93,94,95],"Ticket history with categories and resolutions, to pick the first intents by volume","Current IT knowledge articles with an owner and review date","A clean list of assignment groups and what each one handles","Entitlement rules for what can be granted without approval",[97,98,99,100,101],"Identity provider (password reset, unlock, MFA, group membership)","ITSM platform such as ServiceNow, Jira Service Management or Freshservice","Collaboration channel (Microsoft Teams, Slack) and the intranet","Device and software management for provisioning requests","HR system for joiner, mover and leaver context",{"steps":103,"guardrails":122,"humanInTheLoop":128,"kpisToInstrument":129,"failureModes":135},[104,107,110,113,116,119],{"title":105,"detail":106},"Pick intents from the ticket data","Export a year of tickets and calls, group them by resolution, and choose the ten to twenty intents that are both frequent and safe to automate. Password, unlock and access requests are usually the top of the list.",{"title":108,"detail":109},"Write the action allow list","For each action, record the API, the verification it needs, the limits (which groups, which software, which approvals) and what the agent says when it cannot proceed.",{"title":111,"detail":112},"Harden the reset flows","Treat password and MFA resets as the highest risk actions. Require step up verification tied to something the attacker does not have, and alert on unusual reset patterns.",{"title":114,"detail":115},"Clean and connect the knowledge base","Retire stale articles, give every article an owner, and make the agent refuse and route when retrieval finds nothing rather than improvising steps.",{"title":117,"detail":118},"Route with context","For requests the agent cannot finish, create the ticket with a summary, category and assignment group. Measure how often analysts reassign it and tune from there.",{"title":120,"detail":121},"Launch where people already ask for help","Put the agent in the chat tool employees already use, announce it with a few concrete examples, and track adoption and repeat contacts per intent.",[123,124,125,126,127],"Actions only through an allow list of API calls, each with its own verification level and limits","Step up verification before any password, MFA or privileged access change","Access requests follow the same approval and least privilege rules as a human request","Answers only from owned, current IT articles, with refusal and routing when nothing matches","Every action written to the ITSM record and an immutable audit log","Analysts own everything outside the allow list, all privileged access, and any request that fails verification. Approvers stay in the loop for software and access that needs approval, and the desk reviews a weekly sample of resolved conversations and reset logs.",[130,131,132,133,134],"Share of contacts resolved by the agent per intent, counting a repeat contact within seven days as not resolved","Automated routing accuracy, measured by reassignment rate","Employee adoption (share of employees who used the agent in the last 30 days)","Median time to resolution for automated versus analyst handled tickets","Reset volume and anomalies, reviewed by security",[136,139,142,145],{"title":137,"detail":138},"The help desk becomes the attack path","Social engineering against resets works on bots as well as people. Tie resets to strong verification, rate limit them and alert security on unusual patterns.",{"title":140,"detail":141},"Deflection instead of resolution","The agent sends an article, the employee gives up and phones the desk. Measure repeat contacts, not just conversations closed.",{"title":143,"detail":144},"Access creep","Convenient self service grants access without the approvals that access reviews assume. Keep approvals and least privilege identical to the manual path.",{"title":146,"detail":147},"Stale knowledge","Articles describe old tools and old screens. Give each article an owner and a review date and retire what is not maintained.",{"euAiAct":149,"regulations":152,"guidance":158,"controls":183,"incidents":189},{"tier":150,"basis":151},"limited","Article 50(1) requires an assistant that talks with people to make clear they are interacting with AI, unless that is obvious from the context. It is not listed in Annex III. The agent does allocate work, but it routes tickets to resolver and assignment groups based on the content of the request, not to individual workers based on their behaviour or personal traits or characteristics, so Annex III point 4(b) does not apply. It also does not decide on recruitment, promotion, credit or access to essential services. Any use that assigns work to individual analysts, or monitors and evaluates them from their behaviour or performance (including through the agent's logs), would need its own assessment.",[153,154,155,156,157],"eu-ai-act","gdpr","dora","iso-42001","nist-ai-rmf",[159,165,171,177],{"title":160,"issuer":161,"region":162,"url":163,"note":164},"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":166,"issuer":167,"region":168,"url":169,"note":170},"Guidelines on Risk Management Practices, Technology Risk","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/regulation/guidelines/technology-risk-management-guidelines","Example of regional expectations on access management, privileged access and logging that an automated service desk has to meet at a financial institution.",{"title":172,"issuer":173,"region":174,"url":175,"note":176},"OWASP Top 10 for LLM Applications","OWASP Gen AI Security Project","global","https://genai.owasp.org/llm-top-10/","Covers prompt injection and excessive agency, the two main risks when an agent can change accounts and access.",{"title":178,"issuer":179,"region":180,"url":181,"note":182},"Scattered Spider, cybersecurity advisory AA23-320A","CISA and FBI","north-america","https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-320a","Describes a criminal group that phones IT help desks to get passwords and MFA tokens reset, the attack path that automated resets must be designed against.",[184,185,186,187,188],"Inventory entry with an accountable owner and a documented action allow list","Same entitlement and approval rules for automated and manual access changes","Immutable log of every automated action, reviewed in periodic access reviews","Security monitoring on reset and unlock volumes","Change control and regression tests for every new intent or action",[],{"howToBuild":191},"On Blits.ai this is an **AI agent** in the **Microsoft Teams** or **Slack** channel, with\n**custom functions** that call the identity provider and ITSM APIs (REST calls with their own\ncredentials and limits). The integration catalog includes systems such as ServiceNow, Jira and\nOkta, and Freshdesk is available as a ready made tool. How to questions are answered from a\n**knowledge base** of IT articles with hybrid retrieval, and a **flow** handles the deterministic steps of a reset, including an\nauthentication step, before the agent is allowed to call the reset function.\n\n**Agentic workflows** can run multi step requests such as provisioning, with **human in the\nloop approval** above a set threshold and a **tool execution policy** that limits which tools\nthe agent may use. Every run has an audit trail. **Guardrails** check input and output,\n**PII masking** keeps personal data out of prompts, and **human handover** passes the\nconversation to an analyst. **Test suites** run automated conversation evaluations of the agent\nbefore changes go live, **monitors** run scheduled health checks, and **analytics** show\nvolumes, recognition rate and satisfaction. Model agnostic routing and EU or UAE data residency\nare available, and administrators sign in to the platform with single sign on through Microsoft\nEntra or Google.",[193,196,199],{"question":194,"answer":195},"What share of IT requests can an AI agent resolve?","Published figures are high, for assistants that act and for those that mainly answer. Moveworks reports that Mercari US's assistant, which resets passwords, edits email groups and provisions software, handles over 74% of issues autonomously. IBM reports that over 75% of queries submitted to AskIT in its first four months were resolved by the assistant, which mainly surfaces answers from IT support content. Start lower: early waves cover fewer intents.",{"question":197,"answer":198},"Does it reduce calls to the service desk?","Bank of America says Erica for Employees, used by over 90% of its employees, has reduced calls into the IT service desk by more than half. Measure it as calls and tickets per employee before and after, not as chatbot conversations.",{"question":200,"answer":201},"Is it safe to let an AI reset passwords?","Only with strong verification. CISA and the FBI have warned that criminal groups phone IT help desks to get passwords and MFA tokens reset, so a reset should need the same or stronger proof of identity than a human analyst would ask for, with rate limits and security alerts on unusual patterns.",[203,204,205,206,207],"hr-and-policy-assistant","aiops-incident-triage","employee-onboarding-assistant","enterprise-knowledge-search","support-knowledge-article-generation","2026-09-27",[210],{"date":208,"note":211},"First published","it-service-desk-resolution-agent",[214,252,280,308,335,356],{"title":215,"useCases":216,"organization":217,"vendors":221,"summary":224,"stage":225,"year":226,"channels":227,"languages":228,"metrics":229,"outcomeDisclosed":242,"sources":243,"verification":246,"grade":249,"id":250,"organizationSlug":251},"IBM: AskIT, the internal IT support assistant",[212],{"name":218,"anonymized":219,"country":220,"region":174,"industry":19},"IBM",false,"US",[222],{"name":218,"role":223},"in-house","IBM's CIO organization receives around 785,000 IT support tickets a year covering device setup, password resets, VPN problems and replacements. It launched AskIT, built on watsonx Assistant after an analysis of over 300,000 support tickets and trained on the 80% of IT issues the company faces most often. It covers more than 200 support topics in more than 40 languages for over 280,000 employees. In its first four months more than 133,000 employees used it.","scaled",2023,[32],[],[230,238],{"kpi":45,"value":231,"unit":232,"qualifier":233,"period":234,"claimant":235,"quote":236,"sourceUrl":237},75,"percent","at-least","first four months after release","organization","Of the queries that were submitted, over 75% were resolved by the new assistant itself.","https://www.ibm.com/case-studies/cio-watsonx-askit",{"kpi":50,"value":239,"unit":240,"qualifier":233,"period":234,"claimant":235,"quote":241,"sourceUrl":237},133000,"count","In the four months since AskIT’s release, over 133,000 IBM employees used the tool at least once.",true,[244],{"url":237,"title":245,"publisher":218},"Using AI to deliver a digital first employee experience",{"level":247,"checkedAt":248},"source-verified","2026-09-26","B","ibm-askit-service-desk-assistant",null,{"title":253,"useCases":254,"organization":255,"vendors":257,"summary":259,"stage":225,"year":260,"channels":261,"languages":262,"metrics":264,"outcomeDisclosed":242,"sources":273,"verification":277,"grade":249,"id":278,"organizationSlug":279},"Bank of America: Erica for Employees, the internal IT and HR assistant",[212,203],{"name":256,"anonymized":219,"country":220,"region":180,"industry":18},"Bank of America",[258],{"name":256,"role":223},"Bank of America launched Erica for Employees in 2020, building on its customer facing assistant, to give staff technology support such as mobile device password resets and device activation. In 2023 it was extended to HR topics such as where to review health benefits and how to find payroll and tax forms. The bank reports that most employees use it and that it has more than halved calls into the IT service desk.",2020,[32],[263],"en",[265,270],{"kpi":49,"value":266,"unit":232,"qualifier":233,"period":267,"claimant":235,"quote":268,"sourceUrl":269},90,"as of April 2025","Today, over 90% of employees use Erica for Employees, with the virtual assistant having reduced calls into the IT service desk by more than 50%.","https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/04/ai-adoption-by-bofa-s-global-workforce-improves-productivity--cl.html",{"kpi":47,"value":271,"unit":232,"qualifier":233,"period":272,"claimant":235,"quote":268,"sourceUrl":269},50,"calls into the IT service desk, as of April 2025",[274],{"url":269,"title":275,"publisher":256,"date":276},"AI Adoption by BofA's Global Workforce Improves Productivity, Client Service","2025-04-08",{"level":247,"checkedAt":248},"bank-of-america-erica-for-employees","bank-of-america",{"title":281,"useCases":282,"organization":283,"vendors":286,"summary":290,"stage":291,"year":292,"channels":293,"languages":294,"metrics":295,"outcomeDisclosed":242,"sources":301,"verification":305,"grade":306,"id":307,"organizationSlug":251},"7-Eleven Vietnam: internal IT support chatbot for employees",[212],{"name":284,"anonymized":219,"country":285,"region":168,"industry":20},"7-Eleven Vietnam","VN",[287],{"name":288,"role":289},"Google Cloud","platform","7-Eleven Vietnam, with 140 stores, built an internal IT support chatbot on Vertex AI Agent Builder and Gemini so employees can resolve technical issues on their own. Google Cloud reports that it halved the time spent fixing IT issues and lightened the IT team's workload.","production",2025,[32],[],[296],{"kpi":52,"value":271,"unit":232,"qualifier":297,"claimant":298,"quote":299,"sourceUrl":300},"exact","vendor","The chatbot reduced time spent fixing IT issues by 50%, lightening the workload for the IT team.","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",[302],{"url":300,"title":303,"publisher":288,"date":304},"Real world gen AI use cases from the world's leading organizations","2024-04-12",{"level":247,"checkedAt":248},"C","7-eleven-vietnam-it-support-chatbot",{"title":309,"useCases":310,"organization":311,"vendors":313,"summary":316,"stage":225,"year":317,"channels":318,"languages":319,"metrics":320,"outcomeDisclosed":242,"sources":330,"verification":333,"grade":306,"id":334,"organizationSlug":251},"Mercari US: agentic AI assistant for IT support",[212],{"name":312,"anonymized":219,"country":220,"region":180,"industry":20},"Mercari US",[314],{"name":315,"role":289},"Moveworks","Mercari US, the US arm of the online marketplace, deployed a Moveworks AI assistant in Slack in July 2021. It resolves IT issues such as password resets, email group changes, device troubleshooting and software provisioning. Moveworks reports that the assistant resolves most issues without the service desk and that most employees now go to the assistant first rather than messaging the IT team.",2021,[32],[263],[321,326],{"kpi":46,"value":322,"unit":232,"qualifier":233,"period":323,"claimant":298,"quote":324,"sourceUrl":325},74,"as reported in the undated case study","Today, the agentic AI Assistant handles over 74% of issues completely autonomously.","https://www.moveworks.com/us/en/customers/mercari-reduced-it-ticket-volume-moveworks-conversational-ai",{"kpi":49,"value":327,"unit":232,"qualifier":297,"period":328,"claimant":298,"quote":329,"sourceUrl":325},94,"employees who go to the assistant first, as reported in the undated case study","As a result, the vast majority of employees — 94% — reach out to the Assistant first when they have questions instead of Slacking the IT team directly.",[331],{"url":325,"title":332,"publisher":315},"Mercari US Reduced IT Tickets By 74% With AI",{"level":247,"checkedAt":248},"mercari-it-support-assistant",{"title":336,"useCases":337,"organization":338,"vendors":340,"summary":342,"stage":225,"year":260,"channels":343,"languages":344,"metrics":345,"outcomeDisclosed":242,"sources":351,"verification":354,"grade":306,"id":355,"organizationSlug":251},"Vituity: AI assistant for IT and HR requests in a physician group",[212,203],{"name":339,"anonymized":219,"country":220,"region":180,"industry":21},"Vituity",[341],{"name":315,"role":289},"Vituity, a healthcare organization owned and led by a partnership of nearly 5,000 physicians, deployed a Moveworks AI assistant called Otto in Microsoft Teams in April 2020, connected to ServiceNow, Okta and internal knowledge. It started with IT workflows such as password resets, account provisioning and software access, then expanded into HR questions and other operational domains. The vendor reports that the average time to close issues fell by one full business day and that first line help desk capacity was freed.",[31],[263],[346],{"kpi":52,"value":347,"unit":232,"qualifier":297,"period":348,"claimant":298,"quote":349,"sourceUrl":350},40,"level 1 help desk capacity","It now absorbs a significant share of routine IT and HR requests — including password resets, software access, HR questions, and account provisioning— freeing up 40% of level 1 help-desk capacity.","https://www.moveworks.com/us/en/customers/vituity-helps-physicians-with-moveworks-proactive-it-support",[352],{"url":350,"title":353,"publisher":315},"Vituity helps physicians with proactive IT support",{"level":247,"checkedAt":248},"vituity-it-and-hr-assistant",{"title":357,"useCases":358,"organization":359,"vendors":361,"summary":363,"stage":225,"year":364,"channels":365,"languages":366,"metrics":367,"outcomeDisclosed":242,"sources":373,"verification":376,"grade":306,"id":377,"organizationSlug":251},"Equinix: E-Bot resolves and routes IT tickets in Microsoft Teams",[212],{"name":360,"anonymized":219,"country":220,"region":174,"industry":19},"Equinix",[362],{"name":315,"role":289},"Equinix launched a Moveworks assistant, known internally as E-Bot, in April 2019. It resolves IT support issues end to end in Microsoft Teams and, for tickets it cannot finish, assigns them to the right one of thousands of IT assignment groups within 30 seconds. Moveworks contrasts this with the five hours a first line desk takes on average to read and route a ticket, and reports that E-Bot routes 82% of Equinix tickets automatically, which cut the average lifespan of all tickets by almost a third.",2019,[31],[263],[368],{"kpi":48,"value":369,"unit":232,"qualifier":297,"period":370,"claimant":298,"quote":371,"sourceUrl":372},96,"routing of tickets the assistant cannot resolve, about ten months after launch","Instead of high-touch tickets requiring time-consuming agent attention, the Moveworks Triage Skill allows E-Bot to assign the tickets it can’t finish resolving to the correct subject matter experts — with the same 96% accuracy rate achieved by help desk agents.","https://www.moveworks.com/us/en/customers/equinix-disappears-it-queue-with-moveworks-triage-ticketing-system",[374],{"url":372,"title":375,"publisher":315},"Equinix Makes IT Queues Disappear With AI Triage",{"level":247,"checkedAt":248},"equinix-it-ticket-triage-assistant",2,[380,389,396,401,406,411,416],{"kpi":49,"label":381,"unit":232,"aggregate":242,"higherIsBetter":242,"n":378,"nUpTo":382,"median":383,"min":266,"max":327,"byClaimant":384,"vendorOnly":219,"points":386},"Employee adoption",0,92,{"organization":385,"vendor":385,"regulator":382,"independent":382},1,[387,388],{"evidenceId":334,"organization":312,"value":327,"qualifier":297,"claimant":298,"grade":306,"pooled":242},{"evidenceId":278,"organization":256,"value":266,"qualifier":233,"claimant":235,"grade":249,"pooled":242},{"kpi":52,"label":390,"unit":232,"aggregate":242,"higherIsBetter":242,"n":378,"nUpTo":382,"median":391,"min":347,"max":271,"byClaimant":392,"vendorOnly":242,"points":393},"Productivity gain",45,{"organization":382,"vendor":378,"regulator":382,"independent":382},[394,395],{"evidenceId":307,"organization":284,"value":271,"qualifier":297,"claimant":298,"grade":306,"pooled":242},{"evidenceId":355,"organization":339,"value":347,"qualifier":297,"claimant":298,"grade":306,"pooled":242},{"kpi":48,"label":397,"unit":232,"aggregate":242,"higherIsBetter":242,"n":385,"nUpTo":382,"median":369,"min":369,"max":369,"byClaimant":398,"vendorOnly":242,"points":399},"Accuracy",{"organization":382,"vendor":385,"regulator":382,"independent":382},[400],{"evidenceId":377,"organization":360,"value":369,"qualifier":297,"claimant":298,"grade":306,"pooled":242},{"kpi":46,"label":402,"unit":232,"aggregate":242,"higherIsBetter":242,"n":385,"nUpTo":382,"median":322,"min":322,"max":322,"byClaimant":403,"vendorOnly":242,"points":404},"Automation rate",{"organization":382,"vendor":385,"regulator":382,"independent":382},[405],{"evidenceId":334,"organization":312,"value":322,"qualifier":233,"claimant":298,"grade":306,"pooled":242},{"kpi":47,"label":407,"unit":232,"aggregate":242,"higherIsBetter":242,"n":385,"nUpTo":382,"median":271,"min":271,"max":271,"byClaimant":408,"vendorOnly":219,"points":409},"Contact deflection",{"organization":385,"vendor":382,"regulator":382,"independent":382},[410],{"evidenceId":278,"organization":256,"value":271,"qualifier":233,"claimant":235,"grade":249,"pooled":242},{"kpi":45,"label":412,"unit":232,"aggregate":242,"higherIsBetter":242,"n":385,"nUpTo":382,"median":231,"min":231,"max":231,"byClaimant":413,"vendorOnly":219,"points":414},"Containment rate",{"organization":385,"vendor":382,"regulator":382,"independent":382},[415],{"evidenceId":250,"organization":218,"value":231,"qualifier":233,"claimant":235,"grade":249,"pooled":242},{"kpi":50,"label":417,"unit":240,"aggregate":219,"higherIsBetter":242,"n":385,"nUpTo":382,"median":239,"min":239,"max":239,"byClaimant":418,"vendorOnly":219,"points":419},"Users served",{"organization":385,"vendor":382,"regulator":382,"independent":382},[420],{"evidenceId":250,"organization":218,"value":239,"qualifier":233,"claimant":235,"grade":249,"pooled":242},{"low":422,"high":423},540000,3000000,[425,442,464,477,492],{"slug":203,"title":426,"shortTitle":427,"definition":428,"status":9,"industries":429,"functions":430,"patterns":433,"audience":34,"autonomy":35,"adoptionStage":434,"evidenceCount":435,"publicEvidenceCount":436,"organizations":437,"bestGrade":249,"headline":439,"lastVerified":208,"indexable":242},"AI assistant for HR and policy questions","HR and policy assistant","An employee self service assistant that answers questions on leave, pay and tax forms, benefits, expenses, travel and conduct policies from the organization's own HR documents, personalized to the employee's country and role, and starts simple HR transactions such as leave requests or employment letters in the HR system.",[17,18,19,21],[431,432],"human-resources","knowledge-management",[28,26,27],"early-adopters",5,4,[256,218,438,339],"Turing",{"kpi":49,"label":381,"unit":232,"n":378,"nUpTo":382,"kind":440,"value":441,"qualifier":297,"claimant":235,"organization":218,"vendorReported":219},"reported",99,{"slug":204,"title":443,"shortTitle":444,"definition":445,"status":9,"industries":446,"functions":449,"patterns":451,"audience":34,"autonomy":454,"adoptionStage":434,"evidenceCount":64,"publicEvidenceCount":435,"organizations":455,"bestGrade":249,"headline":461,"lastVerified":208,"indexable":242},"AI for IT incident triage and root cause analysis (AIOps)","AIOps incident triage","AI that turns a flood of monitoring alerts into one probable incident, routes it to the right team, proposes likely root causes and remediation from runbooks and past incidents, and drafts the stakeholder updates and the post incident review, while an engineer authorizes every change.",[17,18,19,447,448],"telecommunications","payments",[23,24,450],"risk-management",[452,29,453,28,27],"anomaly-detection","summarization","copilot",[456,457,458,459,460],"Google","Meta","Microsoft","Mizuho Financial Group","TD Bank",{"kpi":48,"label":397,"unit":232,"n":462,"nUpTo":382,"kind":463,"value":266,"qualifier":297,"claimant":251,"organization":251,"vendorReported":219},3,"median",{"slug":205,"title":465,"shortTitle":466,"definition":467,"status":9,"industries":468,"functions":471,"patterns":472,"audience":34,"autonomy":35,"adoptionStage":434,"evidenceCount":436,"publicEvidenceCount":462,"organizations":473,"bestGrade":249,"headline":251,"lastVerified":208,"indexable":242},"AI assistant for employee onboarding","Employee onboarding assistant","An assistant that guides each new employee from signed contract through the first months: it answers first week questions in plain language, tracks the personal onboarding checklist, triggers the paperwork, equipment, access and training steps in the systems that own them, and keeps the manager and HR informed of what is still open.",[17,469,470,21],"government","professional-services",[431,432],[26,28,27],[474,475,476],"American Addiction Centers","KPMG","U.S. Department of Agriculture",{"slug":206,"title":478,"shortTitle":479,"definition":480,"status":9,"industries":481,"functions":484,"patterns":486,"audience":34,"autonomy":487,"adoptionStage":36,"evidenceCount":436,"publicEvidenceCount":436,"organizations":488,"bestGrade":249,"headline":251,"lastVerified":208,"indexable":242},"AI enterprise knowledge search for employees","Enterprise knowledge search","An assistant that lets any employee ask a question in plain language and get a synthesized answer from the organization's own policies, procedures, product manuals and research, with citations to the source documents and only from documents the employee is allowed to see.",[17,18,482,483,469,470],"wealth-and-asset-management","insurance",[432,24,485],"customer-service",[28,26,453],"assist",[256,489,490,491],"Morgan Stanley","SIGNAL IDUNA","Wells Fargo",{"slug":207,"title":493,"shortTitle":494,"definition":495,"status":9,"industries":496,"functions":498,"patterns":499,"audience":34,"autonomy":454,"adoptionStage":501,"evidenceCount":436,"publicEvidenceCount":436,"organizations":502,"bestGrade":249,"headline":251,"lastVerified":208,"indexable":242},"AI for support knowledge article generation and maintenance","Knowledge article generation","AI that drafts knowledge base articles from resolved tickets, cases and conversations, detects questions the knowledge base does not answer and articles that are outdated or contradict each other, and proposes new or revised articles for a knowledge owner to review and publish.",[17,469,497],"automotive",[432,485,23],[500,453,29],"content-generation","emerging",[503,504,505,506],"Centers for Disease Control and Prevention","Internal Revenue Service","U.S. National Science Foundation","Rivian",{"indexable":242,"reasons":508},[],[510,515,520,526,532,537,544,551,557,563,570,576,583,589,595,600,607,613,619,625,631,637,642,647,652,659,666,671,676,683,689,695,701,706],{"id":153,"label":511,"issuer":161,"region":162,"url":512,"description":513,"useCases":514,"indexable":242},"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":154,"label":516,"issuer":161,"region":162,"url":517,"description":518,"useCases":519,"indexable":242},"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":156,"label":521,"issuer":522,"region":174,"url":523,"description":524,"useCases":525,"indexable":242},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":157,"label":527,"issuer":528,"region":180,"url":529,"description":530,"useCases":531,"indexable":242},"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":155,"label":533,"issuer":161,"region":162,"url":534,"description":535,"useCases":536,"indexable":242},"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":538,"label":539,"issuer":540,"region":162,"url":541,"description":542,"useCases":543,"indexable":242},"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":545,"label":546,"issuer":547,"region":162,"url":548,"description":549,"useCases":550,"indexable":242},"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":552,"label":553,"issuer":167,"region":168,"url":554,"description":555,"useCases":556,"indexable":242},"mas-ai-risk-management","MAS AI risk management guidelines","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":558,"label":559,"issuer":560,"region":168,"url":561,"description":562,"useCases":79,"indexable":242},"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.",{"id":564,"label":565,"issuer":566,"region":174,"url":567,"description":568,"useCases":569,"indexable":242},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":571,"label":572,"issuer":573,"region":180,"url":574,"description":575,"useCases":569,"indexable":242},"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":577,"label":578,"issuer":579,"region":162,"url":580,"description":581,"useCases":582,"indexable":242},"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":584,"label":585,"issuer":586,"region":174,"url":587,"description":588,"useCases":78,"indexable":242},"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":590,"label":591,"issuer":161,"region":162,"url":592,"description":593,"useCases":594,"indexable":242},"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":596,"label":597,"issuer":161,"region":162,"url":598,"description":599,"useCases":594,"indexable":242},"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":601,"label":602,"issuer":603,"region":180,"url":604,"description":605,"useCases":606,"indexable":242},"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":608,"label":609,"issuer":161,"region":162,"url":610,"description":611,"useCases":612,"indexable":242},"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":614,"label":615,"issuer":616,"region":180,"url":617,"description":618,"useCases":612,"indexable":242},"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":620,"label":621,"issuer":622,"region":174,"url":623,"description":624,"useCases":612,"indexable":242},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":626,"label":627,"issuer":161,"region":162,"url":628,"description":629,"useCases":630,"indexable":242},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":632,"label":633,"issuer":634,"region":180,"url":635,"description":636,"useCases":630,"indexable":242},"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":638,"label":639,"issuer":167,"region":168,"url":640,"description":641,"useCases":65,"indexable":242},"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.",{"id":643,"label":644,"issuer":161,"region":162,"url":645,"description":646,"useCases":65,"indexable":242},"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":648,"label":649,"issuer":161,"region":162,"url":650,"description":651,"useCases":65,"indexable":242},"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":653,"label":654,"issuer":655,"region":162,"url":656,"description":657,"useCases":658,"indexable":242},"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":660,"label":661,"issuer":662,"region":180,"url":663,"description":664,"useCases":665,"indexable":242},"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":667,"label":668,"issuer":161,"region":162,"url":669,"description":670,"useCases":665,"indexable":242},"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":672,"label":673,"issuer":161,"region":162,"url":674,"description":675,"useCases":64,"indexable":242},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":677,"label":678,"issuer":679,"region":680,"url":681,"description":682,"useCases":435,"indexable":242},"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":684,"label":685,"issuer":686,"region":162,"url":687,"description":688,"useCases":436,"indexable":242},"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":690,"label":691,"issuer":692,"region":162,"url":693,"description":694,"useCases":436,"indexable":242},"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":696,"label":697,"issuer":698,"region":168,"url":699,"description":700,"useCases":462,"indexable":242},"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":702,"label":703,"issuer":161,"region":162,"url":704,"description":705,"useCases":462,"indexable":242},"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":707,"label":708,"issuer":709,"region":180,"url":710,"description":711,"useCases":462,"indexable":242},"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.",1790598295253]