[{"data":1,"prerenderedAt":658},["ShallowReactive",2],{"uc-rfp-and-proposal-response-drafting":3,"uc-regulations":450},{"useCase":4,"evidence":197,"blitsAiDeployments":323,"benchmarks":324,"indicative":356,"related":359,"indexability":448,"includeUnpublished":203},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":18,"functions":22,"patterns":25,"channels":29,"audience":32,"autonomy":33,"adoptionStage":34,"problem":35,"problemStats":36,"howItWorks":42,"valueDrivers":43,"kpis":48,"indicativeValue":56,"macroEstimates":91,"feasibility":92,"implementation":105,"risk":147,"blitsAi":177,"faq":179,"related":189,"datePublished":192,"dateModified":192,"lastVerified":192,"changelog":193,"slug":196},"AI for RFP, tender and sales proposal response drafting","RFP and proposal drafting","AI for RFP responses and proposal writing","AI finds approved answers and drafts RFP and proposal responses for bid teams to review. Responsive says Microsoft sellers used AI answers 200,000+ times.","published","AI that helps sales and bid teams answer requests for proposal, tenders, security questionnaires and sales proposals: it breaks the request into questions and requirements, retrieves approved answers and past proposals, drafts the response and a compliance matrix, and routes open points to subject matter experts, with a proposal manager reviewing everything before submission.",[12,13,14,15,16,17],"AI RFP response software","AI proposal writing","AI bid writing","tender response automation","security questionnaire automation","RFx response management",[19,20,21],"cross-industry","professional-services","technology",[23,24],"sales","knowledge-management",[26,27,28],"content-generation","rag-knowledge-assistant","document-processing",[30,31],"internal-tools","microsoft-teams","employee-facing","copilot","early-adopters","For many business to business sellers, the formal response is the sale: a public tender, a\nrequest for proposal with hundreds of questions, a due diligence or security questionnaire from a\ncustomer's procurement team. Each one arrives with a deadline, a mandatory format and questions\nthat have mostly been answered before, somewhere, by someone. Proposal teams spend their time\nfinding the latest approved answer, chasing experts for the rest and reformatting, and sellers\nwho are not bid professionals struggle to write a structured proposal at all.\n\nThe volume keeps rising. Loopio's 2026 benchmark of more than 1,500 companies reports that\nresponse teams now submit an average of 166 responses a year. The work does not scale by adding\nwriters, and a wrong or outdated answer can become a contractual commitment.",[37],{"statement":38,"sourceTitle":39,"sourceUrl":40,"year":41},"Loopio's 2026 RFP trends report, based on more than 1,500 companies, finds that response teams submit an average of 166 RFP responses a year.","RFP Report: 2026 Trends & Benchmarks","https://loopio.com/trends-report/",2026,"1. **Read the request.** The AI parses the RFP, tender or questionnaire (PDF, Word, spreadsheet\n   or portal export) into individual questions, mandatory requirements, evaluation criteria and\n   deadlines, and builds a compliance matrix.\n2. **Find approved answers.** For each question it retrieves the best matching answers from a\n   curated answer library and past winning proposals, with the owner and the date each answer was\n   last approved.\n3. **Draft the response.** It drafts answers tailored to the buyer's context and wording, marks\n   which parts come from approved content and which are new, and flags questions it cannot\n   answer with confidence.\n4. **Route to experts.** Security, legal, pricing and technical questions without an approved\n   answer go to the right subject matter expert, and their approved answers flow back into the\n   library.\n5. **Review and submit.** The proposal manager reviews the whole response, checks commitments\n   and pricing, and submits it; the final version and the outcome are stored for the next bid.",[44,45,46,47],"employee-productivity","revenue-growth","speed","compliance",[49,50,51,52,53,54,55],"processing-time-reduction","handling-time-reduction","time-saved-per-task","hours-saved","users-served","interactions-handled","cost-savings",{"referenceOrg":57,"inputs":58,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"A business to business company that submits 150 RFP and questionnaire responses a year",[59,66,73,80],{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"responses","Responses submitted per year",100,200,"responses per year","Around the Loopio 2026 benchmark of 166 responses a year cited on this page. Replace with your own volume.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"hoursPerResponse","Team hours per response",25,40,"hours per response","Editorial assumption covering writers, reviewers and subject matter experts. Replace with a time study of your own bids.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"timeReduction","Share of response time saved",0.3,0.6,"fraction of hours","Conservative against the evidence on this page (GroupeActive reports 75% less drafting time in a pilot, and Microsoft reports in its customer story that Copilot cut ICG's proposal response time by 80%), because both are small firms and review time does not shrink as much.",{"key":81,"label":82,"low":83,"high":62,"unit":84,"note":85},"hourlyCost","Fully loaded cost per hour",60,"USD per hour","Editorial assumption for a blended proposal, sales and expert team.","responses * hoursPerResponse * timeReduction * hourlyCost","USD","per year","Proposal team time released","Time value only. It leaves out the cost of building and curating the answer library, the software, and the upside that matters most: more bids answered and a higher win rate, which none of the organizations on this page has quantified.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":100},"low","The technology is retrieval and drafting over documents the organization already owns. The real work is curating an answer library with owners and review dates, and agreeing who approves security, legal and pricing answers.",[96,97,98,99],"A curated library of approved answers, with an owner and a review date per answer","Past proposals and their outcomes, cleaned of client confidential details where needed","Current product, security, certification and company fact sheets","A list of statements that need legal or pricing approval every time",[101,102,103,104],"Document storage such as SharePoint or Google Drive","CRM for the opportunity, the account and the outcome","Collaboration tools such as Microsoft Teams for expert routing","Procurement portals or email for receiving and submitting responses",{"steps":106,"guardrails":122,"humanInTheLoop":127,"kpisToInstrument":128,"failureModes":134},[107,110,113,116,119],{"title":108,"detail":109},"Curate the answer library before the model","Harvest answers from the last two years of bids, deduplicate them, and give every answer an owner and a review date. Microsoft's library was built as a curated, verified source so the AI output could be trusted.",{"title":111,"detail":112},"Start with questionnaires","Security, due diligence and vendor questionnaires are repetitive and scored on accuracy, so they show value fastest. Move to narrative proposals once the library is trusted.",{"title":114,"detail":115},"Show the source of every answer","Mark each drafted answer as approved content, adapted content or new text, with a link to its source, so reviewers spend their time on what is new.",{"title":117,"detail":118},"Route the gaps to experts","Send unanswered or low confidence questions to named experts with a deadline, and feed their approved answers back into the library so the next bid starts further ahead.",{"title":120,"detail":121},"Close the loop with outcomes","Store the submitted version with the win or loss and the buyer's feedback, and retire answers that keep losing or keep being rewritten.",[123,124,125,126],"Answers drafted only from the approved library and named source documents, with citations","Human approval for every statement about security controls, certifications, pricing, liability and service levels","No client confidential information from past bids reused in a proposal for another client","Disclosure of AI use when a buyer asks for it, as UK central government buyers may under PPN 017","The proposal manager owns the response and reviews every answer before submission. Subject matter experts approve new or changed answers in their area, and legal and pricing sign off commitments. The AI never submits a response.",[129,130,131,132,133],"Hours per response and elapsed days from receipt to submission","Share of answers drafted from approved content without edits","Number of bids answered per quarter and bids declined for lack of capacity","Win rate on comparable bids, before and after","Answers flagged as outdated or wrong in review",[135,138,141,144],{"title":136,"detail":137},"Outdated answers","The AI confidently reuses a security or certification answer that is no longer true, and it becomes a contractual commitment. Give answers review dates and let the draft show them.",{"title":139,"detail":140},"Generic proposals","Drafts read the same for every buyer and lose on evaluation criteria. Make the buyer's own requirements and scoring the structure of the draft.",{"title":142,"detail":143},"Confidentiality leaks between clients","Content from one client's proposal ends up in another's. Separate client specific material from reusable answers in the library.",{"title":145,"detail":146},"Nobody curates the library","Without owners, the library fills with duplicates and the AI surfaces the wrong version. Budget time for knowledge managers, as Microsoft's proposal team does according to Responsive.",{"euAiAct":148,"regulations":151,"guidance":155,"controls":167,"incidents":172},{"tier":149,"basis":150},"limited","Drafting bid responses for staff to review is not listed in Annex III, and the buyer receives the seller's own document rather than interacting with an AI system, so the high risk tier and the Article 50(1) duty towards the buyer do not apply. Staff who chat with the agent must know it is an AI system, which an internal tool labelled as an AI assistant meets by design. Article 50(2) does apply to the drafting itself: the provider of a system that generates text must mark its output in a machine readable format as artificially generated, whether or not a person reviews the draft, unless the system only performs an assistive function for standard editing. A seller that uses a third party drafting tool relies on that tool's provider for the marking; a seller that builds its own agent that generates proposal text, as GroupeActive did with Witivio on Copilot Studio, can be the provider and then carries the duty itself. AI literacy under Article 4 applies in both cases, and the seller remains responsible for every statement in the submitted response.",[152,153,154],"eu-ai-act","gdpr","iso-42001",[156,162],{"title":157,"issuer":158,"region":159,"url":160,"note":161},"Regulation (EU) 2024/1689 (AI Act), Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Official text on EUR-Lex. Article 50(2) requires providers of AI systems that generate text to mark the output as artificially generated in a machine readable format, with an exception for systems that only perform an assistive function for standard editing. Article 50(1) covers systems that interact directly with people, here the staff who use the agent.",{"title":163,"issuer":164,"region":159,"url":165,"note":166},"PPN 017: Improving transparency of AI use in procurement","UK Cabinet Office","https://www.gov.uk/government/publications/ppn-017-improving-transparency-of-ai-use-in-procurement","Published 17 February 2025. Does not prohibit suppliers from using AI to write bids, but lets UK central government departments, their executive agencies and non departmental public bodies ask suppliers to disclose it and do extra due diligence, because AI can introduce misleading statements through hallucination. Other public sector buyers may choose to apply it. For procurements commenced, or contracts awarded, before 24 February 2025, the earlier PPN 02/24 applies.",[168,169,170,171],"An answer library with an owner, an approval status and a review date per answer","Mandatory human approval of security, legal, pricing and service level statements","Access controls that keep client confidential bid content out of other clients' drafts","A record of which answers were AI drafted and who approved them, per submitted bid",[173],{"title":174,"url":175,"note":176},"Incident 1193: Purportedly Taxpayer-Funded Deloitte Report for Australian Government Contains Alleged AI-Generated Citations and Fabricated Legal Quote","https://incidentdatabase.ai/cite/1193/","A consultancy report for Australia's Department of Employment and Workplace Relations contained nonexistent academic references and a misquoted court judgment; the firm acknowledged using generative AI, issued a corrected version and partially refunded the fee. It was a client deliverable rather than a bid, but it shows what unchecked AI drafted content costs when it reaches a client under the firm's name.",{"howToBuild":178},"On Blits.ai the answer library, past proposals and fact sheets go into a **knowledge base**\n(PDF, DOCX, PPTX, XLSX and crawled web pages, with version control) searched with **hybrid\nretrieval**. An **agentic workflow**, triggered by API or from the team's chat, reads the\nincoming RFP, splits it into questions with **structured output**, drafts each answer with\ncitations to its sources, and uses the **file generation** tool to return the draft and a\ncompliance matrix.\n\nA **human in the loop** confirmation on the workflow action that returns or sends the draft lets\na reviewer approve or reject it first, and the draft can be reviewed with the team in\n**Microsoft Teams**. **Custom functions** read the opportunity from the CRM (Salesforce is in the\nintegration catalog, and HubSpot and Dynamics 365 are ready made tools) and write the outcome\nback. An **output guardrail** with an admin written policy checks answers against the approved\ncontent, **test suites** check the agent against known questions with approved answers, and the platform\nis model agnostic, with EU and UAE data residency for bid content that must stay in region.",[180,183,186],{"question":181,"answer":182},"How much time does AI save on RFP responses?","The public figures come mostly from small teams: GroupeActive reports that, in a pilot, its members save an average of 75% of drafting time on sales proposals, and Microsoft reports in its customer story that Copilot cut ICG's proposal response time by 80%. At scale, Responsive reports that Microsoft's sellers save 20 minutes per search for proposal content. Expect less on complex bids, where expert review dominates.",{"question":184,"answer":185},"Can AI write a whole tender response on its own?","It can draft most of it from approved content, but a proposal manager should review everything, and security, legal and pricing statements need an expert's approval because they become commitments. Some public buyers may ask suppliers to disclose AI use in their bids: UK central government guidance (PPN 017) gives buyers example disclosure questions for this.",{"question":187,"answer":188},"What matters more, the AI tool or the content library?","The library. AI retrieval and drafting are only as good as the approved answers behind them, which is why, as Responsive describes it, Microsoft relies on the proposal team's knowledge managers and on technical experts across the company to keep more than 18,000 question and answer pairs current.",[190,191],"business-connectivity-quoting-and-service-assistant","enterprise-knowledge-search","2026-09-27",[194],{"date":192,"note":195},"First published","rfp-and-proposal-response-drafting",[198,226,259,282],{"title":199,"useCases":200,"organization":201,"vendors":206,"summary":210,"stage":211,"year":41,"channels":212,"languages":213,"metrics":215,"outcomeDisclosed":203,"sources":216,"verification":221,"grade":223,"id":224,"organizationSlug":225},"Verdantas: Copilot Studio agent that supports proposal development across a merged firm",[196],{"name":202,"anonymized":203,"country":204,"region":205,"industry":20},"Verdantas",false,"US","north-america",[207],{"name":208,"role":209},"Microsoft","platform","Verdantas, an environmental science, engineering and consulting firm of about 2,500 professionals formed through acquisitions, built a technical resource agent in Microsoft Copilot Studio on data unified in Microsoft Fabric. For proposal teams the agent summarizes RFPs, retrieves previous and similar proposals, surfaces market specific marketing material and finds staff with the right skills and licences. Multi agent orchestration made the agent answer up to twice as fast; no proposal outcome is disclosed.","production",[31],[214],"en",[],[217],{"url":218,"title":219,"publisher":208,"date":220},"https://www.microsoft.com/en/customers/story/26233-verdantas-microsoft-copilot-studio","Verdantas builds agents for proposal development, contract management, and collaboration using Microsoft Copilot Studio","2026-03-19",{"level":222,"checkedAt":192},"source-verified","C","verdantas-copilot-studio-proposal-agent",null,{"title":227,"useCases":228,"organization":229,"vendors":232,"summary":237,"stage":238,"year":239,"channels":240,"languages":241,"metrics":243,"outcomeDisclosed":252,"sources":253,"verification":257,"grade":223,"id":258,"organizationSlug":225},"GroupeActive: GAIA Propale agent drafts sales proposals from meeting notes",[196],{"name":230,"anonymized":203,"country":231,"region":159,"industry":20},"GroupeActive","FR",[233,234],{"name":208,"role":209},{"name":235,"role":236},"Witivio","integrator","GroupeActive, a French SME that supports a network of about 80 independent experts, built the \"GAIA Propale\" agent in Microsoft Copilot Studio with the partner Witivio. The agent turns the expert's customer meeting notes into several chapters of a structured sales proposal, which the expert reviews and a proofreading committee checks. In the pilot a proposal takes about two hours instead of a day, and the time from proposal to signed contract fell by a factor of four.","pilot",2025,[30],[242],"fr",[244],{"kpi":50,"value":245,"unit":246,"qualifier":247,"period":248,"claimant":249,"quote":250,"sourceUrl":251},75,"percent","exact","pilot, average drafting time per sales proposal","organization","Our members save an average of 75% on drafting time.","https://www.microsoft.com/en/customers/story/24754-groupeactive-microsoft-teams",true,[254],{"url":251,"title":255,"publisher":208,"date":256},"GroupeActive and Microsoft reinvent sales proposals with Microsoft Copilot Studio","2025-07-16",{"level":222,"checkedAt":192},"groupeactive-gaia-propale-proposals",{"title":260,"useCases":261,"organization":262,"vendors":264,"summary":266,"stage":211,"year":239,"channels":267,"languages":268,"metrics":269,"outcomeDisclosed":252,"sources":276,"verification":280,"grade":223,"id":281,"organizationSlug":225},"Industrialized Construction Group: Microsoft 365 Copilot for proposal writing",[196],{"name":263,"anonymized":203,"country":204,"region":205,"industry":20},"Industrialized Construction Group",[265],{"name":208,"role":209},"Industrialized Construction Group (ICG), a five person construction consultancy, uses Microsoft 365 Copilot to turn historical proposals into new proposals from a template instead of rewriting them for every customer, alongside marketing, onboarding and reporting tasks. Microsoft reports that Copilot reduced ICG's proposal response time by 80%.",[30],[214],[270],{"kpi":49,"value":271,"unit":246,"qualifier":247,"period":272,"claimant":273,"quote":274,"sourceUrl":275},80,"proposal response time","vendor","Already, Copilot has helped ICG reduce proposal response time by 80%, so the team can focus on customers instead of paperwork.","https://www.microsoft.com/en/customers/story/23620-icg-microsoft-365-copilot",[277],{"url":275,"title":278,"publisher":208,"date":279},"ICG cuts proposal response time by 80% with Microsoft 365 Copilot","2025-04-25",{"level":222,"checkedAt":192},"icg-copilot-proposal-drafting",{"title":283,"useCases":284,"organization":285,"vendors":287,"summary":290,"stage":291,"year":292,"channels":293,"languages":294,"metrics":295,"outcomeDisclosed":252,"sources":317,"verification":321,"grade":223,"id":322,"organizationSlug":225},"Microsoft: AI powered Proposal Resource Library for sellers answering RFPs and questionnaires",[196],{"name":208,"anonymized":203,"country":204,"region":286,"industry":21},"global",[288],{"name":289,"role":209},"Responsive","Microsoft's Proposal Center of Excellence has run a Proposal Resource Library on the Responsive platform since 2020. Sellers and experts across the worldwide sales organization use its AI recommendations to find vetted answers for proposals, RFPs, RFIs and security, legal and compliance assessments, searching more than 18,000 question and answer pairs that the proposal team's knowledge managers and technical experts across the company keep current. The vendor reports 18,000 users and, counted over a wider pool of more than 20,000 resources, more than 200,000 uses of AI answers.","scaled",2024,[30],[214],[296,302,307,312],{"kpi":53,"value":297,"unit":298,"qualifier":247,"period":299,"claimant":273,"quote":300,"sourceUrl":301},18000,"count","authenticated users of the library","18K authenticated users leverage Responsive AI to quickly find proposal content and answers for security questionnaires, legal assessments, and highly technical bids","https://www.responsive.io/customer-stories/microsoft",{"kpi":54,"value":303,"unit":298,"qualifier":304,"period":305,"claimant":273,"quote":306,"sourceUrl":301},200000,"at-least","uses of AI powered answers in proposals and assessments, cumulative","The Field used AI-powered answers — drawn from over 20,000 resources — more than 200,000 times in sales proposals, RFPs, RFIs, and security, legal, and compliance assessments.",{"kpi":51,"value":308,"unit":309,"qualifier":247,"period":310,"claimant":273,"quote":311,"sourceUrl":301},20,"minutes","per search for proposal content","The Field saves 20 minutes per search for proposal content, totaling more than $17M worth of time spent on customer relationships and building pipeline instead of searching for content.",{"kpi":52,"value":313,"unit":314,"qualifier":247,"period":315,"claimant":273,"quote":316,"sourceUrl":301},93000,"hours","cumulative seller hours, period not stated","Sellers gained 93K additional hours to spend on customer relationships and building pipeline, instead of searching for answers and proposal content.",[318],{"url":301,"title":319,"publisher":289,"date":320},"Microsoft - Customer Story | Responsive","2024-11-10",{"level":222,"checkedAt":192},"microsoft-responsive-proposal-resource-library",0,[325,331,336,341,346,351],{"kpi":49,"label":326,"unit":246,"aggregate":252,"higherIsBetter":252,"n":327,"nUpTo":323,"median":271,"min":271,"max":271,"byClaimant":328,"vendorOnly":252,"points":329},"Cycle time reduction",1,{"organization":323,"vendor":327,"regulator":323,"independent":323},[330],{"evidenceId":281,"organization":263,"value":271,"qualifier":247,"claimant":273,"grade":223,"pooled":252},{"kpi":50,"label":332,"unit":246,"aggregate":252,"higherIsBetter":252,"n":327,"nUpTo":323,"median":245,"min":245,"max":245,"byClaimant":333,"vendorOnly":203,"points":334},"Handling time reduction",{"organization":327,"vendor":323,"regulator":323,"independent":323},[335],{"evidenceId":258,"organization":230,"value":245,"qualifier":247,"claimant":249,"grade":223,"pooled":252},{"kpi":52,"label":337,"unit":314,"aggregate":203,"higherIsBetter":252,"n":327,"nUpTo":323,"median":313,"min":313,"max":313,"byClaimant":338,"vendorOnly":252,"points":339},"Hours saved",{"organization":323,"vendor":327,"regulator":323,"independent":323},[340],{"evidenceId":322,"organization":208,"value":313,"qualifier":247,"claimant":273,"grade":223,"pooled":252},{"kpi":54,"label":342,"unit":298,"aggregate":203,"higherIsBetter":252,"n":327,"nUpTo":323,"median":303,"min":303,"max":303,"byClaimant":343,"vendorOnly":252,"points":344},"Interactions handled",{"organization":323,"vendor":327,"regulator":323,"independent":323},[345],{"evidenceId":322,"organization":208,"value":303,"qualifier":304,"claimant":273,"grade":223,"pooled":252},{"kpi":51,"label":347,"unit":309,"aggregate":252,"higherIsBetter":252,"n":327,"nUpTo":323,"median":308,"min":308,"max":308,"byClaimant":348,"vendorOnly":252,"points":349},"Time saved per task",{"organization":323,"vendor":327,"regulator":323,"independent":323},[350],{"evidenceId":322,"organization":208,"value":308,"qualifier":247,"claimant":273,"grade":223,"pooled":252},{"kpi":53,"label":352,"unit":298,"aggregate":203,"higherIsBetter":252,"n":327,"nUpTo":323,"median":297,"min":297,"max":297,"byClaimant":353,"vendorOnly":252,"points":354},"Users served",{"organization":323,"vendor":327,"regulator":323,"independent":323},[355],{"evidenceId":322,"organization":208,"value":297,"qualifier":247,"claimant":273,"grade":223,"pooled":252},{"low":357,"high":358},45000,480000,[360,389,410,429],{"slug":190,"title":361,"shortTitle":362,"definition":363,"status":9,"industries":364,"functions":366,"patterns":369,"audience":373,"autonomy":374,"adoptionStage":34,"segment":375,"evidenceCount":376,"publicEvidenceCount":376,"organizations":377,"bestGrade":383,"headline":384,"lastVerified":192,"indexable":252},"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.",[365],"telecommunications",[23,367,368],"customer-service","product-and-pricing",[370,27,371,372,26],"conversational-agent","agentic-workflow","recommendation-and-personalization","customer-facing","supervised-agent","front-office",5,[378,379,380,381,382],"Lumen Technologies","SoftBank Corp.","Telefónica España","Verizon","Vodafone Business","B",{"kpi":385,"label":386,"unit":246,"n":327,"nUpTo":323,"kind":387,"value":388,"qualifier":247,"claimant":249,"organization":379,"vendorReported":203},"containment-rate","Containment rate","reported",70,{"slug":191,"title":390,"shortTitle":391,"definition":392,"status":9,"industries":393,"functions":398,"patterns":400,"audience":32,"autonomy":402,"adoptionStage":403,"evidenceCount":404,"publicEvidenceCount":404,"organizations":405,"bestGrade":383,"headline":225,"lastVerified":192,"indexable":252},"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.",[19,394,395,396,397,20],"banking","wealth-and-asset-management","insurance","government",[24,399,367],"operations",[27,370,401],"summarization","assist","mainstream",4,[406,407,408,409],"Bank of America","Morgan Stanley","SIGNAL IDUNA","Wells Fargo",{"slug":411,"title":412,"shortTitle":413,"definition":414,"status":9,"industries":415,"functions":416,"patterns":418,"audience":32,"autonomy":33,"adoptionStage":34,"evidenceCount":404,"publicEvidenceCount":404,"organizations":419,"bestGrade":383,"headline":424,"lastVerified":192,"indexable":252},"legal-research-and-drafting-assistant","AI legal research and drafting assistant for lawyers","Legal research and drafting","A generative AI assistant for lawyers in firms, legal departments and public bodies that finds and summarises case law, legislation and internal know how, answers legal questions with citations and drafts first versions of memos, briefings, letters and filings, which a lawyer verifies and signs off.",[20,19,397],[417,24],"legal",[27,26,401,28],[420,421,422,423],"A&O Shearman","Ashurst Perkins Coie","U.S. Department of Justice","U.S. Securities and Exchange Commission",{"kpi":425,"label":426,"unit":246,"n":327,"nUpTo":323,"kind":387,"value":427,"qualifier":428,"claimant":249,"organization":421,"vendorReported":203},"productivity-gain","Productivity gain",45,"approximately",{"slug":430,"title":431,"shortTitle":432,"definition":433,"status":9,"industries":434,"functions":435,"patterns":437,"audience":32,"autonomy":33,"adoptionStage":34,"evidenceCount":439,"publicEvidenceCount":440,"organizations":441,"bestGrade":223,"headline":225,"lastVerified":192,"indexable":252},"outbound-sales-prospecting-agent","AI agent for outbound sales prospecting and personalized outreach","Outbound sales prospecting","An AI agent that researches target accounts and contacts, drafts personalized outbound outreach (emails, LinkedIn messages and call scripts) from the campaign, the prospect's context and the sales goals, and sequences the follow ups, with a sales development rep approving or sending every message.",[19,21,20],[23,436],"marketing",[26,371,372,438],"prediction-and-scoring",9,7,[442,443,444,378,445,446,447],"A-LIGN","ANS","Dun & Bradstreet","Merge","Oyster","Unifonic",{"indexable":252,"reasons":449},[],[451,455,460,466,473,479,486,493,501,507,513,519,526,533,539,544,551,557,563,569,575,581,587,592,597,603,610,615,621,628,634,640,647,652],{"id":152,"label":452,"issuer":158,"region":159,"url":160,"description":453,"useCases":454,"indexable":252},"EU AI Act","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":153,"label":456,"issuer":158,"region":159,"url":457,"description":458,"useCases":459,"indexable":252},"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":154,"label":461,"issuer":462,"region":286,"url":463,"description":464,"useCases":465,"indexable":252},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":467,"label":468,"issuer":469,"region":205,"url":470,"description":471,"useCases":472,"indexable":252},"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":474,"label":475,"issuer":158,"region":159,"url":476,"description":477,"useCases":478,"indexable":252},"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":480,"label":481,"issuer":482,"region":159,"url":483,"description":484,"useCases":485,"indexable":252},"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":487,"label":488,"issuer":489,"region":159,"url":490,"description":491,"useCases":492,"indexable":252},"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":494,"label":495,"issuer":496,"region":497,"url":498,"description":499,"useCases":500,"indexable":252},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","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":502,"label":503,"issuer":504,"region":497,"url":505,"description":506,"useCases":69,"indexable":252},"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":508,"label":509,"issuer":510,"region":286,"url":511,"description":512,"useCases":308,"indexable":252},"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":514,"label":515,"issuer":516,"region":205,"url":517,"description":518,"useCases":308,"indexable":252},"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":520,"label":521,"issuer":522,"region":159,"url":523,"description":524,"useCases":525,"indexable":252},"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":527,"label":528,"issuer":529,"region":286,"url":530,"description":531,"useCases":532,"indexable":252},"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":534,"label":535,"issuer":158,"region":159,"url":536,"description":537,"useCases":538,"indexable":252},"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":540,"label":541,"issuer":158,"region":159,"url":542,"description":543,"useCases":538,"indexable":252},"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":545,"label":546,"issuer":547,"region":205,"url":548,"description":549,"useCases":550,"indexable":252},"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":552,"label":553,"issuer":158,"region":159,"url":554,"description":555,"useCases":556,"indexable":252},"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":558,"label":559,"issuer":560,"region":205,"url":561,"description":562,"useCases":556,"indexable":252},"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":564,"label":565,"issuer":566,"region":286,"url":567,"description":568,"useCases":556,"indexable":252},"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":570,"label":571,"issuer":158,"region":159,"url":572,"description":573,"useCases":574,"indexable":252},"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":576,"label":577,"issuer":578,"region":205,"url":579,"description":580,"useCases":574,"indexable":252},"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":582,"label":583,"issuer":496,"region":497,"url":584,"description":585,"useCases":586,"indexable":252},"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":588,"label":589,"issuer":158,"region":159,"url":590,"description":591,"useCases":586,"indexable":252},"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":593,"label":594,"issuer":158,"region":159,"url":595,"description":596,"useCases":586,"indexable":252},"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":598,"label":599,"issuer":600,"region":159,"url":601,"description":602,"useCases":439,"indexable":252},"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":604,"label":605,"issuer":606,"region":205,"url":607,"description":608,"useCases":609,"indexable":252},"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":611,"label":612,"issuer":158,"region":159,"url":613,"description":614,"useCases":609,"indexable":252},"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":616,"label":617,"issuer":158,"region":159,"url":618,"description":619,"useCases":620,"indexable":252},"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":622,"label":623,"issuer":624,"region":625,"url":626,"description":627,"useCases":376,"indexable":252},"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":629,"label":630,"issuer":631,"region":159,"url":632,"description":633,"useCases":404,"indexable":252},"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":635,"label":636,"issuer":637,"region":159,"url":638,"description":639,"useCases":404,"indexable":252},"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":641,"label":642,"issuer":643,"region":497,"url":644,"description":645,"useCases":646,"indexable":252},"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.",3,{"id":648,"label":649,"issuer":158,"region":159,"url":650,"description":651,"useCases":646,"indexable":252},"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":653,"label":654,"issuer":655,"region":205,"url":656,"description":657,"useCases":646,"indexable":252},"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.",1790598301358]