[{"data":1,"prerenderedAt":601},["ShallowReactive",2],{"uc-batch-record-and-deviation-review":3,"uc-regulations":376},{"useCase":4,"evidence":169,"blitsAiDeployments":281,"benchmarks":282,"indicative":293,"related":296,"indexability":374,"includeUnpublished":176},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":26,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":39,"indicativeValue":43,"macroEstimates":71,"feasibility":72,"implementation":84,"risk":121,"blitsAi":146,"faq":148,"related":161,"datePublished":164,"dateModified":164,"lastVerified":164,"changelog":165,"slug":168},"AI review of batch records and process data for quality deviations in pharma manufacturing","Batch record and deviation review","AI batch record and deviation review","AI reviews batch records and process data for GMP deviations before release. Recordati traced a yield drop to its cause, cutting cost of goods sold by 2%.","published","AI that reads and correlates batch records, sensor data and quality certificates from a pharmaceutical manufacturing line, compares each new batch against a profile of the best performing past batches, and flags the process parameters and deviations that explain a change in yield or quality, so manufacturing science and quality teams can act on a cause instead of reading records line by line after the fact.",[12,13,14,15,16],"AI batch record review","GMP deviation detection","batch comparison analytics","AI quality control in pharma manufacturing","golden batch analytics",[18],"pharma-and-life-sciences",[20,21],"operations","regulatory-compliance",[23,24,25],"document-processing","anomaly-detection","prediction-and-scoring",[27],"internal-tools","employee-facing","assist","early-adopters","manufacturing and quality","A batch record in pharmaceutical manufacturing documents every step, parameter and sign off\nneeded to prove a batch was made under good manufacturing practice (GMP): temperatures, timings,\noperator initials, in process test results and any deviation from the written procedure.\nReviewing it, and comparing it against sensor and process data from many separate systems, has\ntraditionally been a manual, paper heavy job, which is slow, and which finds a quality problem\nonly after the batch is already made.\n\nThe same fragmentation makes root cause analysis hard when yield or quality drifts. Process\ndata sits in SCADA and historian systems, batch records sit on paper or in a manufacturing\nexecution system, and certificates of analysis sit as PDFs, so tracing a yield drop back to a\ncause, such as one operator's performance or a seasonal change in raw material or utility\ntemperature, can take a specialist team a long stretch of manual correlation, by which time\nseveral more batches may have run the same way.",[],"1. **Bring the data together.** Batch records, sensor and historian data, material attributes\n   and certificates of analysis are consolidated into one platform, including digitizing\n   paper records and PDFs where that is still how a site works.\n2. **Build a reference, or \"golden batch\", profile.** The system learns the parameter footprint\n   of the best performing batches for a product, such as the ideal pressure or temperature\n   curve during a critical step.\n3. **Compare every new batch against it.** Each batch is scored against the reference profile,\n   and process parameters that most strongly correlate with a good or bad outcome are ranked,\n   not just flagged individually.\n4. **Surface deviations and root causes to a person.** A quality or manufacturing science\n   reviewer sees which batches strayed from the profile, on what parameter, and what in the\n   underlying data most likely explains it, instead of a raw list of exceptions.\n5. **Feed fixes back into the batch record.** Confirmed causes, such as a drying time that needs\n   to change or an instruction that needs to be clearer, are turned into revised batch record\n   instructions and monitored in the next campaign.",[36,37,38],"cost-to-serve","risk-reduction","employee-productivity",[40,41,42],"error-reduction","cost-reduction","search-time-reduction",{"referenceOrg":44,"inputs":45,"formula":66,"currency":67,"period":68,"resultLabel":69,"caveat":70},"A manufacturing line producing 100 batches a year",[46,53,60],{"key":47,"label":48,"low":49,"high":50,"unit":51,"note":52},"batchesPerYear","Batches produced per year",60,150,"batches per year","Editorial assumption for a single mid sized production line. Replace with your own batch schedule.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"reviewHoursSavedPerBatch","Quality and manufacturing science hours saved per batch on record review and deviation investigation",1,4,"hours per batch","Editorial assumption, replace with your own time studies; this range is deliberately conservative and does not assume AI removes batch record review, only the manual correlation work behind it.",{"key":61,"label":62,"low":49,"high":63,"unit":64,"note":65},"costPerHour","Fully loaded cost of a quality or manufacturing science specialist",120,"USD per hour","Editorial assumption, replace with your own fully loaded cost.","batchesPerYear * reviewHoursSavedPerBatch * costPerHour","USD","per year","Quality and manufacturing science hours cost avoided","Gross time saved on review and root cause work only. It leaves out the cost of the platform and the data integration work to feed it, and it does not put a number on the separate value of fewer non conforming batches: AstraZeneca reports a whole site result in this direction (a decrease in what it calls non perfect batches), but that figure depends too much on a site's own product mix and margins to generalize into a formula here.",[],{"complexity":73,"complexityNote":74,"dataPrerequisites":75,"integrations":79},"high","The AI itself is a fairly standard correlation and anomaly detection problem. What makes this hard is the data: process data, batch records and quality documents usually sit in different systems, often including paper, and every model used for a GMP critical decision has to be validated, documented and kept under change control like any other GxP system.",[76,77,78],"Structured or digitized batch records for the product and process in scope","Historian or SCADA process data (temperatures, flows, pressures, timings) for the same batches","A validated set of \"good\" reference batches to build the comparison profile from",[80,81,82,83],"Manufacturing execution system (MES) or electronic batch record system","Process historian or SCADA system","Laboratory information management system (LIMS) for certificates of analysis and quality data","Quality management system for deviations and CAPA follow up",{"steps":85,"guardrails":101,"humanInTheLoop":105,"kpisToInstrument":106,"failureModes":111},[86,89,92,95,98],{"title":87,"detail":88},"Start with one line and one known problem","Pick a single product line with a real, recent quality or yield problem rather than building a general purpose system first; a concrete problem gives the model a clear reference set of good and bad batches to learn from.",{"title":90,"detail":91},"Consolidate the data before the model","Integrate batch records, historian data, material attributes and certificates of analysis into one place, digitizing paper records and PDFs with document processing where that is still how the site works, before asking the model to correlate anything.",{"title":93,"detail":94},"Build and validate the reference profile","Define the golden batch profile from a validated set of good batches, and document how the model was trained and tested, since any model used in a GMP critical application needs a change control and validation record like any other system.",{"title":96,"detail":97},"Route findings to a person, not a batch disposition","The model's output is a ranked list of parameters and batches to review, not an automatic release or reject decision; a qualified person makes the disposition call using the model's evidence.",{"title":99,"detail":100},"Feed confirmed causes back into the batch record","When an investigation confirms a cause, update the batch record instructions or process parameters, and track whether the change actually reduces deviations in the following batches.",[102,103,104],"Models used for a GMP critical decision (release, reject, deviation classification) are static and validated, with any dynamic or adaptive model kept to non critical, advisory use only, per PIC/S Annex 22","Every flagged batch shows the reasoning and the underlying data points, never a bare pass or fail score","A qualified person, not the model, makes every batch disposition and deviation classification decision","Quality and manufacturing science teams review every deviation and root cause finding the model surfaces, confirm or reject the model's explanation against their own process knowledge, and are the ones who sign off any change to a batch record or process parameter. The model narrows what a person has to look at; it does not decide.",[107,108,109,110],"Non conforming or out of specification batches per period","Hours spent per batch on record review and deviation investigation","Time from a yield or quality shift to a confirmed root cause","Model prediction confidence against the human reviewer's own conclusion, tracked over time",[112,115,118],{"title":113,"detail":114},"A dynamic model used in a critical decision","An adaptive model that keeps learning after deployment is used to gate batch release or reject a batch, which the draft PIC/S and EU Annex 22 says should not be used in critical GMP applications. Keep any model used in a critical application static and validated, and route dynamic or generative models to advisory, non critical use only.",{"title":116,"detail":117},"Correlation mistaken for cause","The model finds a parameter that correlates with poor yield but is not the actual cause (for example, both drift with the season). Treat every finding as a hypothesis for a person to test, not a conclusion.",{"title":119,"detail":120},"Golden batch profile built on a biased sample","If the \"good\" batches used to build the reference profile share some other flaw, every new batch is compared against the wrong standard. Revalidate the reference profile whenever the process, raw material source or equipment changes.",{"euAiAct":122,"regulations":125,"guidance":128,"controls":141,"incidents":145},{"tier":123,"basis":124},"context-dependent","Reviewing internal manufacturing quality data on its own is not listed in Annex III, and pharmaceutical products are not covered by the AI Act's Annex I product safety legislation the way machinery or medical devices are, so a deployment limited to that stays minimal risk. The tier moves to high risk under Annex III point 4(b), monitoring and evaluating the performance of workers, if the design attributes findings to, or is used to evaluate, a named operator: batch records carry operator initials, and the Recordati evidence on this page shows this kind of root cause analysis can surface \"differences in operator performance\". Keep any per operator finding aggregated or advisory rather than used to evaluate a named person, and treat it as high risk the moment that changes. Good manufacturing practice regulation, not the AI Act, sets the everyday bar for the system itself (see PIC/S Annex 22 below).",[126,127],"eu-ai-act","gdpr",[129,135],{"title":130,"issuer":131,"region":132,"url":133,"note":134},"Annex 22: Artificial Intelligence","Pharmaceutical Inspection Co-operation Scheme (PIC/S)","global","https://picscheme.org/docview/9715","Draft annex to the PIC/S GMP Guide setting out requirements for AI and machine learning used in manufacturing medicinal products and active substances. Criticality turns on direct impact on patient safety, product quality or data integrity, not on who makes the final call. The annex restricts critical GMP applications to static models with deterministic outputs, and in three separate sentences says dynamic models, models with a probabilistic output, and generative AI and large language models each \"should not be used in critical GMP applications.\" Human in the loop is a design element the annex expects within scope, not an exemption from it: sections 3.3 and 10.5 both apply where a model is used \"to give an input to a decision made by a human operator\" (what the annex calls human in the loop, HITL) \"and where the effort to test such model has been diminished\", but each then sets a separate requirement. Section 3.3 requires the intended use description to include the operator's responsibility, and \"the training and consistent performance of the operator should be monitored like any other manual process.\" Section 10.5 requires that \"records should be kept from this process\" and that, \"depending on the criticality of the process and the level of testing of the model, this may imply a consistent review and/or test of every output from the model, according to a procedure.\"",{"title":136,"issuer":137,"region":138,"url":139,"note":140},"Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products","US Food and Drug Administration","north-america","https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological","Draft guidance (January 2025) on a risk based credibility framework for AI models used to produce information that supports a regulatory decision on the quality of a drug.",[142,143,144],"Change control and a documented validation record for every model used in a GMP critical application, per PIC/S Annex 22","Inventory entry for the model with an accountable quality owner","Regular monitoring of model performance and input data drift, with a defined threshold for flagging a prediction as low confidence rather than acting on it",[],{"howToBuild":147},"On Blits.ai this is a **custom function** pipeline: scheduled or triggered REST and SQL calls\nconsolidate batch, historian and quality data, and a **custom function** with a fixed,\nversioned configuration runs the comparison and scoring against the reference batch profile,\nbecause any model used in a GMP critical decision needs to stay static and validated rather\nthan adapt on its own. An **agent** then drafts a plain language explanation of which\nparameters and batches to review, drawing on a **knowledge base** with **hybrid retrieval**\nthat holds the site's own SOPs and past deviation investigations, so the explanation cites the\nsame procedures a human reviewer would.\n\nThe custom function's static, versioned logic carries the critical scoring step; the agent\nlayer stays limited to explaining findings that step has already produced and pointing to\nrelevant SOPs, never deciding a batch disposition or deviation classification itself. The\nagent's **model agnostic** routing and generative capability are used only for this non\ncritical explanation step: PIC/S Annex 22 restricts generative AI and large language models to\nnon critical GMP applications, so the agent's draft stays a reviewer aid, kept as notes and SOP\npointers alongside the custom function's score rather than written into the GMP deviation\nrecord itself. A qualified person writes the deviation record from that score and their own\nreview of the batch. **Guardrails** keep the agent from stating a disposition outcome, and\n**human in the loop approval** gates the workflow before a reviewer acts on its output. **Run\nhistory** and a full **audit trail** on the workflow show what ran and when for every batch\nreviewed. **Test suites** regression test the pipeline whenever a source system or the model\nchanges, and data stays inside the site's chosen **EU or UAE data residency** region.",[149,152,155,158],{"question":150,"answer":151},"Can AI decide whether a batch passes or fails?","No. Under guidance such as PIC/S Annex 22, models used in a GMP critical application like a release or reject decision have to stay static, deterministic and validated; the same guidance says dynamic, probabilistic, and generative or large language models should not be used for that, whether or not a person reviews the output. Separately, under EU GMP (Annex 16), a qualified person, not a model, certifies batch release. AI's role here is to review records and process data faster and point a reviewer at the batches and parameters most likely to explain a deviation.",{"question":153,"answer":154},"How much does AI reduce non conforming batches?","AstraZeneca's Wuxi manufacturing site, recognized by the World Economic Forum's Global Lighthouse Network in 2024, reported that implementing more than 30 digital tools and AI powered solutions, together with developing a digital and agile workforce, decreased what it calls non perfect batches by 80%, alongside gains in output, lead time and productivity. That result covers a whole site's digital and workforce transformation, not one identified AI tool for batch record or deviation review, so treat it as a ceiling on what a broad program can achieve, not a typical result for a first, single tool project.",{"question":156,"answer":157},"Does this replace batch record review, or just speed it up?","It speeds up and focuses the manual work rather than replacing it. Recordati used AI driven root cause analysis on consolidated batch, sensor and process data to trace a yield drop to specific process parameters, and within three months of implementing the solution it reported a 1.5% yield increase and a 2% reduction in cost of goods sold. As with any tool that only advises, a person should still review and confirm every finding before it changes a batch record or process parameter; that is editorial guidance, not something the Recordati case study itself states.",{"question":159,"answer":160},"What data does a batch record and deviation review model need?","Structured or digitized batch records, process historian or SCADA data for the same batches, certificates of analysis and other lab data, and a validated set of good reference batches to compare new ones against.",[162,163],"production-line-quality-inspection","industrial-asset-predictive-maintenance","2026-09-29",[166],{"date":164,"note":167},"First published","batch-record-and-deviation-review",[170,211,235,256],{"title":171,"useCases":172,"organization":174,"vendors":179,"summary":180,"stage":181,"year":182,"channels":183,"languages":184,"metrics":186,"outcomeDisclosed":200,"sources":201,"verification":206,"grade":208,"id":209,"organizationSlug":210},"AstraZeneca: digital and AI program results at the Wuxi and Sodertalje Global Lighthouse sites",[168,173],"clinical-and-regulatory-document-drafting",{"name":175,"anonymized":176,"country":177,"region":178,"industry":18},"AstraZeneca",false,"GB","europe",[],"Two AstraZeneca manufacturing sites, Wuxi in China and Sodertalje in Sweden, were named to the World Economic Forum's Global Lighthouse Network in 2024 for their use of AI and other Fourth Industrial Revolution technologies. At Wuxi, implementing more than 30 digital tools and AI powered solutions, together with developing a digital and agile workforce, decreased what AstraZeneca calls non perfect batches by 80%, alongside gains in output, lead time and productivity. At Sodertalje, more than 50 digital solutions, including AI based digital twins and machine learning, boosted productivity by 56% and cut development lead times for new products by 67%. No single tool inside either site's digital program is named, so these are whole site, whole workforce results, not evidence for one identified AI system that reviews batch records or classifies deviations. In drug development, AstraZeneca says AI and predictive modelling cut the time taken to author some regulatory submission documents by 85%.","scaled",2024,[27],[185],"en",[187,195],{"kpi":40,"value":188,"unit":189,"qualifier":190,"period":191,"claimant":192,"quote":193,"sourceUrl":194},80,"percent","exact","AstraZeneca's Wuxi manufacturing site, as of the 2024 Global Lighthouse Network award","organization","In China, we transformed our manufacturing site in the city of Wuxi by implementing more than 30 digital tools and AI-powered solutions, and by developing a digital and agile workforce. This has helped maintain speed, efficiency and quality in an increasingly complex environment, boosting output by 55%, reducing lead time by 44%, decreasing non-perfect batches by 80%, and improving productivity by 54%.","https://www.astrazeneca.com/media-centre/articles/2024/global-operations-digital-ai-wef-lighthouse.html",{"kpi":196,"value":197,"unit":189,"qualifier":190,"period":198,"claimant":192,"quote":199,"sourceUrl":194},"handling-time-reduction",85,"Some AstraZeneca regulatory submission documents in drug development, as of the 2024 Global Lighthouse Network award","In drug development, we are already using AI technologies and predictive modelling to accelerate regulatory submission filings, reducing the time taken to author some documents by 85%.",true,[202],{"url":194,"title":203,"publisher":175,"date":204,"archivedUrl":205},"Two manufacturing sites join WEF Global Lighthouse Network","2024-10-08","https://web.archive.org/web/2026/https://www.astrazeneca.com/media-centre/articles/2024/global-operations-digital-ai-wef-lighthouse.html",{"level":207,"checkedAt":164},"source-verified","B","astrazeneca-manufacturing-quality-wef-lighthouse",null,{"title":212,"useCases":213,"organization":214,"vendors":217,"summary":221,"stage":222,"year":223,"channels":224,"languages":225,"metrics":226,"outcomeDisclosed":176,"sources":227,"verification":232,"grade":233,"id":234,"organizationSlug":210},"Grifols: unified batch record and process data for AI root cause analysis of yield",[168],{"name":215,"anonymized":176,"country":216,"region":178,"industry":18},"Grifols","ES",[218],{"name":219,"role":220},"Aizon","platform","Grifols, a global plasma derived medicines manufacturer, ran a \"crawl, walk, run\" program spanning several years with Aizon to bring together manual batch record data (phase pH, reactor and phase duration) with SCADA process time series data into one contextualized, batch level view across multiple fractionation and purification sites. AI models then ran multivariate analysis to explain yield and impurity variability and flag the process parameters driving it, such as coordinated pH adjustments during purification, with digitizing the batch records themselves, through Aizon Execute, still in progress. Aizon's case study reports a significant yield increase across three sites and three products from more than 30 process optimizations, without giving a quotable number for the increase or the value captured.","production",2026,[27],[185],[],[228],{"url":229,"title":230,"publisher":219,"archivedUrl":231},"https://www.aizon.ai/success-stories/how-grifols-scaled-yield-optimization-with-unified-data-and-gxp-ai","How Grifols Scaled Yield Optimization With Unified Data and GxP AI","https://web.archive.org/web/20260417190816/https://www.aizon.ai/success-stories/how-grifols-scaled-yield-optimization-with-unified-data-and-gxp-ai",{"level":207,"checkedAt":164},"C","grifols-aizon-batch-yield-analytics",{"title":236,"useCases":237,"organization":238,"vendors":241,"summary":243,"stage":222,"year":244,"channels":245,"languages":246,"metrics":247,"outcomeDisclosed":176,"sources":248,"verification":254,"grade":233,"id":255,"organizationSlug":210},"Curia: automated golden batch comparison to cut yield variability",[168],{"name":239,"anonymized":176,"country":240,"region":138,"industry":18},"Curia","US",[242],{"name":219,"role":220},"Curia, a contract development and manufacturing organization (CDMO), relied on manual batch records and spreadsheets to track four products, which made it hard to consolidate data for global reporting and led to inaccurate top level reports. It adopted Aizon Unify to build \"golden batch\" reference profiles from its best performing batches, automatically compare every new batch against them across hundreds of correlations, and flag when a batch's sensor data strays from the ideal range, so process engineers can trace poor results back to a cause, such as a miscalibrated reactor, instead of comparing one variable in one reactor at a time.",2025,[27],[185],[],[249],{"url":250,"title":251,"publisher":219,"date":252,"archivedUrl":253},"https://www.aizon.ai/success-stories/unify-batch-comparison-analytics-curia-case-study","How Curia, a leading CDMO, reduced yield variability with automated batch comparisons using Aizon Unify","2025-03-19","https://web.archive.org/web/20250319184355/https://www.aizon.ai/success-stories/unify-batch-comparison-analytics-curia-case-study",{"level":207,"checkedAt":164},"curia-batch-comparison-analytics",{"title":257,"useCases":258,"organization":259,"vendors":262,"summary":264,"stage":222,"year":244,"channels":265,"languages":266,"metrics":267,"outcomeDisclosed":200,"sources":274,"verification":279,"grade":233,"id":280,"organizationSlug":210},"Recordati: AI root cause analysis of batch records to fix a yield drop",[168],{"name":260,"anonymized":176,"country":261,"region":178,"industry":18},"Recordati","IT",[263],{"name":219,"role":220},"Recordati faced a sudden yield drop of more than 4% at a single drug manufacturing facility, with data scattered across paper based batch records, third party production systems and quality certificates stored as PDFs. It partnered with Aizon to consolidate process data, material attributes, batch records and sensor data into one GxP cloud platform, then used AI driven root cause analysis and time series clustering to find which process parameters, such as drying time and operator performance, were driving the variability, and to refine batch record instructions so operators work more consistently. Aizon also digitized manual data entry from certificates of analysis with OCR, which the case study says reduces the errors that come from copying data by hand.",[27],[185],[268],{"kpi":41,"value":269,"unit":189,"qualifier":190,"period":270,"claimant":271,"quote":272,"sourceUrl":273},2,"within three months of implementation, single manufacturing facility","vendor","Within three months of implementing the solution, Recordati achieved a 1.5% increase in yield, reducing cost of goods sold (COGS) by 2%.","https://www.aizon.ai/success-stories/how-recordati-improved-yield-by-1-5-in-just-three-months-with-aizons-ai-driven-solutions",[275],{"url":273,"title":276,"publisher":219,"date":277,"archivedUrl":278},"How Recordati Improved Yield by 1.5% in Just Three Months","2025-06-22","https://web.archive.org/web/20250622175516/https://www.aizon.ai/success-stories/how-recordati-improved-yield-by-1-5-in-just-three-months-with-aizons-ai-driven-solutions",{"level":207,"checkedAt":164},"recordati-batch-yield-and-deviation-analytics",0,[283,288],{"kpi":41,"label":284,"unit":189,"aggregate":200,"higherIsBetter":200,"n":56,"nUpTo":281,"median":269,"min":269,"max":269,"byClaimant":285,"vendorOnly":200,"points":286},"Cost reduction",{"organization":281,"vendor":56,"regulator":281,"independent":281},[287],{"evidenceId":280,"organization":260,"value":269,"qualifier":190,"claimant":271,"grade":233,"pooled":200},{"kpi":40,"label":289,"unit":189,"aggregate":200,"higherIsBetter":200,"n":56,"nUpTo":281,"median":188,"min":188,"max":188,"byClaimant":290,"vendorOnly":176,"points":291},"Error reduction",{"organization":56,"vendor":281,"regulator":281,"independent":281},[292],{"evidenceId":209,"organization":175,"value":188,"qualifier":190,"claimant":192,"grade":208,"pooled":200},{"low":294,"high":295},3600,72000,[297,319,337,352],{"slug":162,"title":298,"shortTitle":299,"definition":300,"status":9,"industries":301,"functions":304,"patterns":305,"audience":28,"autonomy":309,"adoptionStage":30,"segment":222,"evidenceCount":310,"publicEvidenceCount":310,"organizations":311,"bestGrade":208,"headline":315,"lastVerified":318,"indexable":200},"AI quality inspection on the production line","Production quality inspection","AI that inspects every unit on a production line, from camera images, sound or machine process data, to find defects, missing parts and wrong variants in real time, and routes the few anomalies it flags to a quality inspector instead of relying on manual sampling at the end of the line.",[302,303],"manufacturing","automotive",[20],[306,24,307,308],"computer-vision","synthetic-data-generation","agentic-workflow","supervised-agent",3,[312,313,314],"Audi","BMW Group","Pegatron",{"kpi":41,"label":284,"unit":189,"n":56,"nUpTo":281,"kind":316,"value":317,"qualifier":190,"claimant":271,"organization":314,"vendorReported":200},"reported",7,"2026-09-27",{"slug":163,"title":320,"shortTitle":321,"definition":322,"status":9,"industries":323,"functions":325,"patterns":327,"audience":28,"autonomy":29,"adoptionStage":30,"segment":328,"evidenceCount":329,"publicEvidenceCount":329,"organizations":330,"bestGrade":208,"headline":210,"lastVerified":318,"indexable":200},"AI predictive maintenance for industrial and energy assets","Industrial predictive maintenance","Machine learning that learns the normal behaviour of industrial and energy equipment from sensor and process data, flags early signs of degradation weeks or months before a failure, and turns them into prioritised maintenance work, so plants and utilities plan repairs instead of reacting to breakdowns.",[324,302],"energy-and-utilities",[20,326],"field-service",[24,25],"asset-management",6,[331,332,333,334,335,336],"Colgate-Palmolive","Duke Energy","DuPont","Georgia-Pacific","Holcim","Shell",{"slug":173,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":342,"patterns":343,"audience":28,"autonomy":346,"adoptionStage":30,"evidenceCount":57,"publicEvidenceCount":57,"organizations":347,"bestGrade":208,"headline":351,"lastVerified":318,"indexable":200},"AI drafting of clinical study reports and regulatory documents","Clinical and regulatory document drafting","Generative AI that drafts clinical study reports and other regulated documents, such as protocols, patient materials and submission modules, from the trial's statistical tables, listings and figures and from approved template text, for medical writers to verify, edit and approve before anything is submitted to a regulator.",[18],[21,20],[344,345,23],"content-generation","rag-knowledge-assistant","copilot",[175,348,349,350],"Bristol Myers Squibb","Merck & Co.","Novo Nordisk",{"kpi":40,"label":289,"unit":189,"n":269,"nUpTo":281,"kind":316,"value":188,"qualifier":190,"claimant":192,"organization":175,"vendorReported":176},{"slug":353,"title":354,"shortTitle":355,"definition":356,"status":9,"industries":357,"functions":363,"patterns":365,"audience":367,"autonomy":309,"adoptionStage":368,"segment":369,"evidenceCount":310,"publicEvidenceCount":310,"organizations":370,"bestGrade":208,"headline":210,"lastVerified":318,"indexable":200},"continuous-controls-testing","AI for continuous controls testing and control self assessment","Continuous controls testing","AI that moves control testing from periodic samples to continuous, full population assurance: it collects evidence from source systems, maps each artefact to the control it supports, tests every transaction or record against the control's rule, flags exceptions for a human to judge and prepares the risk and control self assessment from incident and loss data for the business to review.",[358,359,360,361,362],"cross-industry","banking","insurance","capital-markets","government",[364,21,20],"risk-management",[308,23,24,366],"classification-and-routing","back-office","emerging","second-line",[371,372,373],"Federal Deposit Insurance Corporation","U.S. Department of the Interior","Pension Benefit Guaranty Corporation",{"indexable":200,"reasons":375},[],[377,383,388,395,402,409,415,422,430,437,444,451,457,463,470,477,483,490,496,502,508,515,520,527,532,537,542,548,554,559,567,573,579,585,590,595],{"id":126,"label":378,"issuer":379,"region":178,"url":380,"description":381,"useCases":382,"indexable":200},"EU AI Act","European Union","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.",230,{"id":127,"label":384,"issuer":379,"region":178,"url":385,"description":386,"useCases":387,"indexable":200},"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.",207,{"id":389,"label":390,"issuer":391,"region":132,"url":392,"description":393,"useCases":394,"indexable":200},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":396,"label":397,"issuer":398,"region":138,"url":399,"description":400,"useCases":401,"indexable":200},"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.",92,{"id":403,"label":404,"issuer":405,"region":178,"url":406,"description":407,"useCases":408,"indexable":200},"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.",71,{"id":410,"label":411,"issuer":379,"region":178,"url":412,"description":413,"useCases":414,"indexable":200},"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":416,"label":417,"issuer":418,"region":178,"url":419,"description":420,"useCases":421,"indexable":200},"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.",50,{"id":423,"label":424,"issuer":425,"region":426,"url":427,"description":428,"useCases":429,"indexable":200},"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.",37,{"id":431,"label":432,"issuer":433,"region":426,"url":434,"description":435,"useCases":436,"indexable":200},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":438,"label":439,"issuer":440,"region":138,"url":441,"description":442,"useCases":443,"indexable":200},"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.",22,{"id":445,"label":446,"issuer":447,"region":132,"url":448,"description":449,"useCases":450,"indexable":200},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":452,"label":453,"issuer":379,"region":178,"url":454,"description":455,"useCases":456,"indexable":200},"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.",17,{"id":458,"label":459,"issuer":460,"region":178,"url":461,"description":462,"useCases":456,"indexable":200},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":464,"label":465,"issuer":466,"region":138,"url":467,"description":468,"useCases":469,"indexable":200},"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.",16,{"id":471,"label":472,"issuer":473,"region":132,"url":474,"description":475,"useCases":476,"indexable":200},"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":478,"label":479,"issuer":379,"region":178,"url":480,"description":481,"useCases":482,"indexable":200},"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":484,"label":485,"issuer":486,"region":138,"url":487,"description":488,"useCases":489,"indexable":200},"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":491,"label":492,"issuer":493,"region":138,"url":494,"description":495,"useCases":489,"indexable":200},"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":497,"label":498,"issuer":379,"region":178,"url":499,"description":500,"useCases":501,"indexable":200},"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":503,"label":504,"issuer":505,"region":132,"url":506,"description":507,"useCases":501,"indexable":200},"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":509,"label":510,"issuer":511,"region":138,"url":512,"description":513,"useCases":514,"indexable":200},"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.",11,{"id":516,"label":517,"issuer":379,"region":178,"url":518,"description":519,"useCases":514,"indexable":200},"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.",{"id":521,"label":522,"issuer":523,"region":178,"url":524,"description":525,"useCases":526,"indexable":200},"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.",10,{"id":528,"label":529,"issuer":425,"region":426,"url":530,"description":531,"useCases":526,"indexable":200},"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":533,"label":534,"issuer":379,"region":178,"url":535,"description":536,"useCases":526,"indexable":200},"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":538,"label":539,"issuer":379,"region":178,"url":540,"description":541,"useCases":526,"indexable":200},"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":543,"label":544,"issuer":379,"region":178,"url":545,"description":546,"useCases":547,"indexable":200},"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.",9,{"id":549,"label":550,"issuer":551,"region":138,"url":552,"description":553,"useCases":317,"indexable":200},"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.",{"id":555,"label":556,"issuer":379,"region":178,"url":557,"description":558,"useCases":329,"indexable":200},"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":560,"label":561,"issuer":562,"region":563,"url":564,"description":565,"useCases":566,"indexable":200},"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.",5,{"id":568,"label":569,"issuer":570,"region":178,"url":571,"description":572,"useCases":57,"indexable":200},"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":574,"label":575,"issuer":576,"region":178,"url":577,"description":578,"useCases":57,"indexable":200},"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":580,"label":581,"issuer":582,"region":426,"url":583,"description":584,"useCases":310,"indexable":200},"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":586,"label":587,"issuer":379,"region":178,"url":588,"description":589,"useCases":310,"indexable":200},"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":591,"label":592,"issuer":379,"region":178,"url":593,"description":594,"useCases":310,"indexable":200},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":596,"label":597,"issuer":598,"region":138,"url":599,"description":600,"useCases":310,"indexable":200},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790699628006]