[{"data":1,"prerenderedAt":542},["ShallowReactive",2],{"uc-hub-function-analytics-and-reporting":3},{"type":4,"typeLabel":5,"term":6,"includeUnpublished":10,"indexable":11,"stats":12,"useCases":24,"benchmarks":436,"topIndustries":476,"topFunctions":493,"topPatterns":494,"stageMix":513,"regionMix":526,"organizations":541,"other":74},"function","Business function",{"id":7,"label":8,"description":9},"analytics-and-reporting","Analytics and reporting","Turning data into insight, dashboards and management information.",false,true,{"useCases":13,"publicDeployments":14,"blitsAiDeployments":15,"organizations":16,"countries":17,"outcomeDisclosureRate":18,"gradeMix":19},23,81,3,74,18,51,{"A":20,"B":21,"C":22,"D":23},0,42,38,1,[25,58,75,105,130,148,163,182,202,216,235,255,272,292,312,329,341,355,370,381,395,408,424],{"slug":26,"title":27,"shortTitle":28,"definition":29,"status":30,"industries":31,"functions":35,"patterns":37,"audience":41,"autonomy":42,"adoptionStage":43,"evidenceCount":44,"publicEvidenceCount":44,"organizations":45,"bestGrade":48,"headline":49,"lastVerified":57,"indexable":11},"data-quality-monitoring-agent","AI agent for data quality monitoring and observability","Data quality monitoring agent","An AI agent that watches data pipelines and tables continuously, uses machine learning to learn the normal pattern of freshness, volume, schema and distribution for each one, flags anomalies before they reach a dashboard or a downstream model, and traces the lineage back to the change that caused them so an engineer can fix the source, not just the symptom.","published",[32,33,34],"cross-industry","technology","retail-and-ecommerce",[36,7],"it-and-engineering",[38,39,40],"anomaly-detection","classification-and-routing","summarization","employee-facing","assist","early-adopters",2,[46,47],"Contentsquare","SeatGeek","C",{"kpi":50,"label":51,"unit":52,"n":23,"nUpTo":20,"kind":53,"value":54,"qualifier":55,"claimant":56,"organization":47,"vendorReported":11},"productivity-gain","Productivity gain","percent","reported",50,"exact","vendor","2026-09-28",{"slug":59,"title":60,"shortTitle":61,"definition":62,"status":30,"industries":63,"functions":65,"patterns":67,"audience":69,"autonomy":42,"adoptionStage":43,"segment":70,"evidenceCount":44,"publicEvidenceCount":44,"organizations":71,"bestGrade":48,"headline":74,"lastVerified":57,"indexable":11},"smart-meter-analytics","AI analytics for smart meter and AMI data","Smart meter analytics","AI that turns the flood of readings from smart electricity, gas and water meters into usable information: it monitors meter and network health at scale, estimates which appliances drive a household's usage from the meter signal alone, flags unusual consumption, and targets efficiency and electrification programmes at the customers who will benefit most, instead of a utility treating every meter and every customer the same way.",[64],"energy-and-utilities",[66,7],"operations",[38,68],"prediction-and-scoring","back-office","metering-and-billing",[72,73],"Consolidated Edison (Con Edison)","Southern California Gas Company (SoCalGas)",null,{"slug":76,"title":77,"shortTitle":78,"definition":79,"status":30,"industries":80,"functions":84,"patterns":87,"audience":41,"autonomy":91,"adoptionStage":43,"segment":92,"evidenceCount":93,"publicEvidenceCount":94,"organizations":95,"bestGrade":100,"headline":101,"lastVerified":104,"indexable":11},"deal-sourcing-and-due-diligence-assistant","AI assistant for deal sourcing and M&A due diligence","Deal sourcing and due diligence","An AI assistant that screens the market for acquisition or investment targets, builds company profiles, and speeds up due diligence by reading data room documents, extracting key terms and risks and drafting the investment or diligence memo, for the deal team to verify and decide.",[81,82,83],"capital-markets","wealth-and-asset-management","professional-services",[7,85,86],"legal","risk-management",[88,40,89,90,68],"document-processing","rag-knowledge-assistant","agentic-workflow","copilot","front-office",5,4,[96,97,98,99],"Datasite","EQT","Freshfields","Rogo","B",{"kpi":50,"label":51,"unit":52,"n":20,"nUpTo":23,"kind":53,"value":102,"qualifier":103,"claimant":56,"organization":96,"vendorReported":11},80,"up-to","2026-09-27",{"slug":106,"title":107,"shortTitle":108,"definition":109,"status":30,"industries":110,"functions":114,"patterns":117,"audience":41,"autonomy":42,"adoptionStage":43,"segment":119,"evidenceCount":93,"publicEvidenceCount":93,"organizations":120,"bestGrade":100,"headline":126,"lastVerified":104,"indexable":11},"treasury-cash-flow-forecasting","AI cash flow forecasting for corporate treasury","Treasury cash forecasting","Machine learning and conversational analytics, offered by some banks inside their cash management platforms, that categorise a company's cash flows, forecast positions across accounts and currencies, and answer treasurers' questions in plain language, so the treasury team decides on funding and idle balances with better information and less spreadsheet work.",[111,32,112,34,113],"banking","logistics-and-transportation","manufacturing",[115,116,7],"treasury","finance-and-accounting",[68,39,118,90],"conversational-agent","specialized-businesses",[121,122,123,124,125],"Amtrak","Bank of America","Domino's Pizza","JPMorgan Chase","Prysmian",{"kpi":50,"label":51,"unit":52,"n":44,"nUpTo":23,"kind":53,"value":127,"qualifier":128,"claimant":129,"organization":124,"vendorReported":10},90,"approximately","organization",{"slug":131,"title":132,"shortTitle":133,"definition":134,"status":30,"industries":135,"functions":138,"patterns":139,"audience":41,"autonomy":42,"adoptionStage":43,"evidenceCount":15,"publicEvidenceCount":15,"organizations":140,"bestGrade":100,"headline":144,"lastVerified":104,"indexable":11},"clinical-trial-patient-matching","AI clinical trial patient matching and prescreening","Clinical trial patient matching","AI that reads structured data and clinical notes in the health record, compares each patient with the inclusion and exclusion criteria of open clinical trials, and gives research staff and treating clinicians a ranked list of likely eligible patients with the evidence for each criterion, so that people confirm eligibility and invite the patient.",[136,137],"healthcare","pharma-and-life-sciences",[66,7],[88,39],[141,142,143],"Cleveland Clinic","Mount Sinai Health System","Yale Cancer Center",{"kpi":145,"label":146,"unit":52,"n":23,"nUpTo":20,"kind":53,"value":147,"qualifier":55,"claimant":129,"organization":141,"vendorReported":10},"accuracy","Accuracy",100,{"slug":149,"title":150,"shortTitle":151,"definition":152,"status":30,"industries":153,"functions":154,"patterns":155,"audience":41,"autonomy":42,"adoptionStage":43,"segment":156,"evidenceCount":44,"publicEvidenceCount":44,"organizations":157,"bestGrade":100,"headline":160,"lastVerified":57,"indexable":11},"hospital-bed-and-staff-capacity-command-center","AI command center for hospital bed and staff capacity planning","Hospital capacity command center","An AI powered operations center that predicts patient admissions, discharges and transfers across a hospital or health system, and helps a team of coordinators sitting in one room sequence real time bed assignments, staffing levels and patient moves, so patients get into the right bed faster and existing capacity is used fully without adding beds.",[136],[66,7],[68,39,38],"hospital operations",[158,159],"Humber River Health","Johns Hopkins Medicine",{"kpi":161,"label":162,"unit":52,"n":44,"nUpTo":20,"kind":53,"value":22,"qualifier":55,"claimant":129,"organization":159,"vendorReported":10},"processing-time-reduction","Cycle time reduction",{"slug":164,"title":165,"shortTitle":166,"definition":167,"status":30,"industries":168,"functions":170,"patterns":172,"audience":41,"autonomy":173,"adoptionStage":43,"segment":174,"evidenceCount":44,"publicEvidenceCount":44,"organizations":175,"bestGrade":48,"headline":178,"lastVerified":57,"indexable":11},"hotel-revenue-management-copilot","AI copilot for hotel revenue management","Hotel revenue management copilot","An employee facing AI system that forecasts demand for a hotel or portfolio by date, room type and segment, recommends or automatically adjusts room prices and availability controls within limits a revenue manager sets, and scores group and event enquiries for true profitability, so a small revenue team can run pricing that used to need daily manual adjustment in the property management system.",[169],"travel-and-hospitality",[171,7],"product-and-pricing",[68,90],"supervised-agent","revenue-management",[176,177],"Hôtel Swexan","RIMC Hotels & Resorts Group",{"kpi":179,"label":180,"unit":52,"n":44,"nUpTo":20,"kind":53,"value":181,"qualifier":55,"claimant":56,"organization":176,"vendorReported":11},"revenue-uplift","Revenue uplift",33,{"slug":183,"title":184,"shortTitle":185,"definition":186,"status":30,"industries":187,"functions":189,"patterns":190,"audience":41,"autonomy":91,"adoptionStage":43,"segment":192,"evidenceCount":93,"publicEvidenceCount":93,"organizations":193,"bestGrade":100,"headline":199,"lastVerified":201,"indexable":11},"insurance-pricing-and-actuarial-copilot","AI copilot for insurance pricing and actuarial analysis","Pricing and actuarial copilot","AI that speeds up the work of pricing and actuarial teams, from automated, transparent risk and demand model building to natural language analysis of rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.",[188],"insurance",[171,86,7],[68,191,90,40],"code-generation","pricing",[194,195,196,197,198],"Accelerant Holdings","Europ Assistance","Generali France","Kinsale Capital Group","MAIF",{"kpi":50,"label":51,"unit":200,"n":23,"nUpTo":20,"kind":53,"value":93,"qualifier":55,"claimant":129,"organization":196,"vendorReported":10},"multiplier","2026-09-26",{"slug":203,"title":204,"shortTitle":205,"definition":206,"status":30,"industries":207,"functions":208,"patterns":209,"audience":69,"autonomy":173,"adoptionStage":210,"evidenceCount":94,"publicEvidenceCount":94,"organizations":211,"bestGrade":100,"headline":74,"lastVerified":104,"indexable":11},"retail-demand-forecasting-and-replenishment","AI demand forecasting and automated replenishment for retail","Demand forecasting and replenishment","Machine learning that forecasts demand for every item in every store or fulfillment center, day by day, from sales history, promotions, prices, weather and local events, and turns the forecast into automatic store and warehouse orders within limits set by planners, who handle the exceptions.",[34],[66,7],[68,38],"mainstream",[212,213,214,215],"Albert Heijn","Morrisons","One Stop","Walmart",{"slug":217,"title":218,"shortTitle":219,"definition":220,"status":30,"industries":221,"functions":225,"patterns":228,"audience":69,"autonomy":91,"adoptionStage":229,"segment":230,"evidenceCount":15,"publicEvidenceCount":15,"organizations":231,"bestGrade":100,"headline":74,"lastVerified":104,"indexable":11},"complaints-root-cause-analysis","AI for complaints root cause and systemic issue analysis","Complaints root cause analysis","AI that reads the free text of complaints across all channels, clusters them into themes, separates systemic causes from one off events, links each theme to the product, process or control behind it and routes the insight to the owner who can fix it, with a human validating every root cause and every remediation.",[32,111,188,222,223,224],"payments","telecommunications","government",[226,227,7],"regulatory-compliance","customer-service",[39,40,90,89],"emerging","second-line",[232,233,234],"Centers for Medicare and Medicaid Services","Board of Governors of the Federal Reserve System","Federal Trade Commission",{"slug":236,"title":237,"shortTitle":238,"definition":239,"status":30,"industries":240,"functions":241,"patterns":243,"audience":41,"autonomy":173,"adoptionStage":43,"segment":245,"evidenceCount":93,"publicEvidenceCount":93,"organizations":246,"bestGrade":100,"headline":252,"lastVerified":104,"indexable":11},"network-planning-and-capacity-optimization","AI for mobile network planning and capacity optimization","Network planning and capacity","Machine learning that forecasts where and when a mobile network will run out of capacity, recommends where to add cells, spectrum or hardware, and continuously tunes radio parameters so existing capacity carries more traffic, with planners approving investments and major changes.",[223],[242,7],"network-operations",[68,244,38,90],"recommendation-and-personalization","network",[247,248,249,250,251],"Deutsche Telekom","NTT DOCOMO","stc Group","Telefónica España","Vodafone",{"kpi":161,"label":162,"unit":52,"n":23,"nUpTo":20,"kind":53,"value":253,"qualifier":254,"claimant":129,"organization":247,"vendorReported":10},95,"at-least",{"slug":256,"title":257,"shortTitle":258,"definition":259,"status":30,"industries":260,"functions":261,"patterns":263,"audience":69,"autonomy":91,"adoptionStage":43,"evidenceCount":93,"publicEvidenceCount":93,"organizations":265,"bestGrade":100,"headline":270,"lastVerified":104,"indexable":11},"public-consultation-response-analysis","AI for public consultation response analysis","Consultation response analysis","AI that reads every free text response to a public consultation or rulemaking comment period, proposes themes, maps each response to the themes that officials have validated, flags duplicates, campaign letters and responses that need special attention, and produces counts and summaries for the analysts who write the government's response.",[224],[262,7],"citizen-services",[40,39,264],"content-generation",[266,233,267,268,269],"Centers for Disease Control and Prevention","Department for Transport","Department for Science, Innovation and Technology (Incubator for Artificial Intelligence)","U.S. Department of Transportation, Office of the Secretary",{"kpi":145,"label":146,"unit":52,"n":23,"nUpTo":20,"kind":53,"value":271,"qualifier":254,"claimant":129,"organization":267,"vendorReported":10},92,{"slug":273,"title":274,"shortTitle":275,"definition":276,"status":30,"industries":277,"functions":278,"patterns":279,"audience":69,"autonomy":173,"adoptionStage":43,"evidenceCount":281,"publicEvidenceCount":282,"organizations":283,"bestGrade":100,"headline":290,"lastVerified":201,"indexable":11},"synthetic-test-data-generation","AI for synthetic test data generation","Synthetic test data generation","AI that generates realistic synthetic datasets, such as customers, transactions, documents and conversations, which keep the structure and statistical properties of production data without containing real personal data, so teams can test software, train and validate models and run demos safely.",[32,111,136,188],[36,7],[280,264],"synthetic-data-generation",8,7,[284,285,286,124,287,288,289],"Boomi","Financial Conduct Authority","Internal Revenue Service","Kin Insurance","Merkur Versicherung AG","Patterson Dental",{"kpi":161,"label":162,"unit":52,"n":23,"nUpTo":20,"kind":53,"value":291,"qualifier":55,"claimant":56,"organization":289,"vendorReported":11},75,{"slug":293,"title":294,"shortTitle":295,"definition":296,"status":30,"industries":297,"functions":298,"patterns":301,"audience":69,"autonomy":173,"adoptionStage":210,"segment":302,"evidenceCount":94,"publicEvidenceCount":94,"organizations":303,"bestGrade":100,"headline":308,"lastVerified":104,"indexable":11},"churn-prediction-and-retention-offers","AI for telecom churn prediction and retention offers","Churn prediction and retention","AI for telecom operators that scores each subscriber's risk of leaving from usage, service, billing and contact signals, explains the likely reason, and chooses the next best retention action, such as fixing a problem, adjusting a plan or making an offer, delivered through the app, messaging, an agent or an advisor within approved offer budgets.",[223],[299,227,300,7],"marketing","sales",[68,244,118],"middle-office",[304,305,306,307],"Etisalat","Telenet","Virgin Media O2","Vodafone UK",{"kpi":309,"label":310,"unit":52,"n":44,"nUpTo":20,"kind":53,"value":311,"qualifier":55,"claimant":56,"organization":305,"vendorReported":11},"churn-reduction","Churn reduction",20,{"slug":313,"title":314,"shortTitle":315,"definition":316,"status":30,"industries":317,"functions":318,"patterns":319,"audience":69,"autonomy":91,"adoptionStage":210,"evidenceCount":93,"publicEvidenceCount":93,"organizations":321,"bestGrade":100,"headline":327,"lastVerified":104,"indexable":11},"customer-feedback-analysis","AI for voice of the customer and feedback analysis","Customer feedback analysis","AI that reads every piece of free text customer feedback, such as survey verbatims, NPS comments, reviews, social posts, chat and call transcripts, and turns it into themes, sentiment, drivers and suggested actions that a named owner can act on, so the organization hears all of its customers instead of a sample.",[32,34,224,113],[227,299,7],[39,40,320],"speech-analytics",[322,323,324,325,326],"U.S. Department of Housing and Urban Development","Majid Al Futtaim Retail","Mattel","SBF Group","U.S. Social Security Administration",{"kpi":145,"label":146,"unit":52,"n":23,"nUpTo":20,"kind":53,"value":328,"qualifier":55,"claimant":56,"organization":325,"vendorReported":11},84,{"slug":330,"title":331,"shortTitle":332,"definition":333,"status":30,"industries":334,"functions":335,"patterns":336,"audience":69,"autonomy":91,"adoptionStage":43,"segment":302,"evidenceCount":15,"publicEvidenceCount":44,"organizations":338,"bestGrade":48,"headline":74,"lastVerified":104,"indexable":11},"portfolio-reporting-and-commentary","AI generated client portfolio reports and commentary","Portfolio commentary","AI that drafts each client's periodic portfolio commentary and report narrative (performance, attribution, what drove returns, positioning and outlook) in plain language and in the client's language, where every figure comes from the portfolio system of record and a reviewer approves the text before delivery.",[82,111],[7,227,66],[264,40,337],"translation",[339,340],"Morgan Stanley","Quilter",{"slug":342,"title":343,"shortTitle":344,"definition":345,"status":30,"industries":346,"functions":347,"patterns":348,"audience":41,"autonomy":91,"adoptionStage":43,"segment":349,"evidenceCount":44,"publicEvidenceCount":44,"organizations":350,"bestGrade":100,"headline":353,"lastVerified":57,"indexable":11},"ai-drug-discovery-platform","AI native platform for drug target discovery and molecule design","AI drug discovery platform","An AI native research platform that prioritizes disease targets from biological data, generates and optimizes candidate drug molecules computationally, and predicts their properties before a chemist synthesizes and tests them, so a pharmaceutical or biotech company reaches a validated preclinical candidate with far fewer molecules made and tested than a conventional medicinal chemistry program.",[137],[66,7],[68,264],"drug discovery",[351,352],"Insilico Medicine","Recursion Pharmaceuticals",{"kpi":161,"label":162,"unit":52,"n":44,"nUpTo":20,"kind":53,"value":354,"qualifier":128,"claimant":129,"organization":351,"vendorReported":10},60,{"slug":356,"title":357,"shortTitle":358,"definition":359,"status":30,"industries":360,"functions":361,"patterns":362,"audience":41,"autonomy":42,"adoptionStage":43,"segment":92,"evidenceCount":93,"publicEvidenceCount":93,"organizations":363,"bestGrade":100,"headline":367,"lastVerified":104,"indexable":11},"next-best-action-for-advisors","AI next best action prompts for wealth advisors","Advisor next best action","An AI engine for wealth advisors, not customers, that scans an advisor's whole book and surfaces a short, ranked list of client specific prompts, such as idle cash, a maturing deposit, a concentration to review, a life event or an early sign of attrition, each with the reasoning and data behind it, for the advisor to act on or dismiss.",[82,111],[300,299,7],[244,68,264],[364,365,124,339,366],"CIMB Niaga","Citi","UBS",{"kpi":368,"label":369,"unit":52,"n":23,"nUpTo":20,"kind":53,"value":102,"qualifier":55,"claimant":129,"organization":366,"vendorReported":10},"employee-adoption","Employee adoption",{"slug":371,"title":372,"shortTitle":373,"definition":374,"status":30,"industries":375,"functions":376,"patterns":377,"audience":69,"autonomy":91,"adoptionStage":229,"segment":302,"evidenceCount":94,"publicEvidenceCount":15,"organizations":378,"bestGrade":100,"headline":74,"lastVerified":104,"indexable":11},"portfolio-drift-monitoring-and-rebalancing","AI portfolio drift monitoring and rebalancing proposals","Drift and rebalancing","Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.",[82,111],[66,86,7],[38,90,68,264],[339,379,380],"SimCorp","Vanguard",{"slug":382,"title":383,"shortTitle":384,"definition":385,"status":30,"industries":386,"functions":388,"patterns":389,"audience":69,"autonomy":390,"adoptionStage":210,"segment":391,"evidenceCount":44,"publicEvidenceCount":44,"organizations":392,"bestGrade":100,"headline":74,"lastVerified":57,"indexable":11},"content-recommendation-and-personalization","AI recommendation and personalization engine for streaming and media","Content recommendation and personalization","A recommendation system that decides, for each individual viewer or listener, what to show next on a home page, in search or in a personalized playlist, learned from that person's own viewing or listening history, ratings and context, and continuously updated as new content is added and behavior changes. It ranks the catalog's own content; it is not the marketing engine that decides which offers or campaigns to send, which is a separate use case in this library.",[387],"media-and-entertainment",[299,7],[244,68],"autonomous","content discovery",[393,394],"Netflix, Inc.","Spotify",{"slug":396,"title":397,"shortTitle":398,"definition":399,"status":30,"industries":400,"functions":401,"patterns":403,"audience":69,"autonomy":173,"adoptionStage":43,"evidenceCount":94,"publicEvidenceCount":94,"organizations":404,"bestGrade":100,"headline":74,"lastVerified":104,"indexable":11},"procurement-spend-classification","AI spend classification and spend analytics for procurement","Spend classification","AI that reads purchase orders, invoices, card transactions and contracts and assigns each line of spend to a category in the organization's taxonomy, and to the right supplier, so that procurement can see what is bought, from whom and where to consolidate or negotiate.",[32,224,113,136],[402,116,7],"procurement",[39,88,40],[405,286,406,407],"U.S. General Services Administration","U.S. Department of Agriculture","Veterans Health Administration",{"slug":409,"title":410,"shortTitle":411,"definition":412,"status":30,"industries":413,"functions":414,"patterns":416,"audience":41,"autonomy":91,"adoptionStage":43,"segment":92,"evidenceCount":94,"publicEvidenceCount":94,"organizations":417,"bestGrade":100,"headline":419,"lastVerified":104,"indexable":11},"investment-research-summarization","AI summaries of investment research and the house view","Research summaries","An AI assistant that condenses long research reports, overnight market moves and the house view into short, sourced briefings for advisors and analysts, answers \"what is our view on X\" on demand, and adapts approved research for different client segments and languages, with every figure traced to the original research.",[82,81,111],[7,300,415],"knowledge-management",[40,89,264,337],[365,418,339,366],"Deutsche Bank",{"kpi":420,"label":421,"unit":422,"n":20,"nUpTo":23,"kind":53,"value":423,"qualifier":103,"claimant":129,"organization":418,"vendorReported":10},"time-saved-per-task","Time saved per task","minutes",120,{"slug":425,"title":426,"shortTitle":427,"definition":428,"status":30,"industries":429,"functions":430,"patterns":431,"audience":41,"autonomy":42,"adoptionStage":43,"evidenceCount":15,"publicEvidenceCount":15,"organizations":432,"bestGrade":100,"headline":74,"lastVerified":104,"indexable":11},"governed-text-to-sql-analytics","Governed text to SQL analytics assistant","Governed SQL analytics","An assistant that turns a business user's plain language question into a query against governed data, runs it under that user's own data permissions and returns the table or chart together with the SQL and the tables used, so routine ad hoc questions no longer queue for the data team.",[32,111,188,34,33,137],[7,36],[118,191,89],[433,434,435],"Bayer","LinkedIn","Uber 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