Pharma AI Problem Library

Browse real pharma, biotech, and CRO business problems — from clinical trial enrollment to pharmacovigilance workload — and see which AI vendors address each one.

• Authoring periodic safety reports is a heavy lift ( Pharmacovigilance & Safety) — Aggregate reports like PSURs and DSURs pull data from many sources on fixed schedules, and compiling and writing them consumes scarce specialist time.
• Biomedical data is growing faster than teams can read it ( Drug Discovery) — Papers, patents, and public datasets accumulate faster than any team can review, so relevant evidence is missed and work is unknowingly duplicated.
• Clinical and molecular data live in separate worlds ( Data & AI Infrastructure) — Genomic results sit in one system, clinical outcomes in another — so the questions precision medicine depends on, linking molecular profiles to real outcomes, go unanswered at scale.
• Clinical data cleaning and coding is slow and error-prone ( Clinical Trials) — Manual data review, query management, and medical coding consume staff time and delay database lock.
• Competitor and reference label changes go unnoticed ( Regulatory Affairs) — Safety and claims changes on comparable products can have implications for your own label — but manual monitoring rarely keeps up.
• Control arms are hard to fill and hard on patients ( Clinical Trials) — Randomizing patients to control slows enrollment, and in serious diseases patients are reluctant to risk assignment to placebo or standard-of-care arms.
• Critical patient data is trapped in unstructured documents ( Clinical Trials) — Eligibility details, outcomes, and safety-relevant history live in free-text notes, PDFs, and scanned records that structured queries can't reach.
• Demand forecasts miss, causing stockouts and overstock ( Manufacturing & Supply Chain) — When forecasts diverge from real demand, the result is either stockouts and shortages or costly excess inventory and write-offs.
• Developability problems surface too late ( Drug Discovery) — Solubility, stability, formulation, and manufacturability issues often appear after a molecule is chosen, forcing costly rework or restarts.
• Deviation and CAPA investigations pile up ( Manufacturing & Supply Chain) — Investigations, root-cause analysis, and CAPA follow-through accumulate faster than teams can close them, delaying batches and drawing inspection scrutiny.
• Dossier compilation and publishing rework delays filings ( Regulatory Affairs) — Formatting, hyperlinking, and validation issues surface late in eCTD compilation, forcing rework at the worst possible time.
• Drug shortages and supply chain disruptions ( Manufacturing & Supply Chain) — Demand volatility and fragile supply chains cause shortages, excess inventory, and missed delivery commitments.
• Duplicate and low-quality case reports clog intake ( Pharmacovigilance & Safety) — The same event arrives through several channels and many reports come in incomplete, so teams waste effort on duplicates and chasing missing details.
• General-purpose LLMs stumble on biomedical language ( Generative AI & LLMs) — Off-the-shelf language models mishandle the terminology, abbreviations, and conventions of biomedical text — and the gap shows up exactly where precision matters most.
• Global label changes are slow to track and implement ( Regulatory Affairs) — One core label change fans out into dozens of country labels, each with local requirements, translations, and timelines.
• Hallucination risk keeps LLMs out of regulated documents ( Generative AI & LLMs) — General-purpose language models can produce fluent text containing fabricated numbers, citations, or claims — a failure mode regulated pharma documents cannot absorb.
• Health-authority questions arrive faster than answers ( Regulatory Affairs) — Agency information requests carry short, immovable deadlines, and each response means finding evidence across years of documents.
• Identifying novel, validated drug targets is hard ( Drug Discovery) — Target selection is slow and uncertain, and weak target validation propagates risk through the entire pipeline.
• Institutional knowledge is locked in unstructured documents ( Generative AI & LLMs) — Decades of protocols, reports, correspondence, and site documents sit in PDFs and file shares that can't be searched by meaning — so teams re-create knowledge the organization already owns.
• LLM pilots stall before validated production use ( Generative AI & LLMs) — Generative AI experiments multiply across pharma organizations, but few cross the gap into validated, governed, routine production use — pilots impress and then stall.
• Launch targeting relies on guesswork, not data ( Commercialization) — Launch planning often leans on intuition instead of data about where eligible patients and treating clinicians are.
• Lead optimization cycles are too slow ( Drug Discovery) — Design-make-test-analyze loops drag on, with each iteration costly in chemistry time and assay resources.
• Linking patient data across sources without compromising privacy ( Data & AI Infrastructure) — The analyses that need connected patient data are exactly the ones that raise privacy stakes — and getting linkage wrong risks patients, partnerships, and regulatory standing all at once.
• Manual inspection and batch-record review strain teams ( Manufacturing & Supply Chain) — Line-by-line record review and manual visual inspection are slow, repetitive, and hard to staff consistently, yet they gate release and compliance.
• Manufacturing deviations and variability hurt yield ( Manufacturing & Supply Chain) — Process deviations, manual batch review, and variability drive scrap, delays, and compliance risk in GxP manufacturing.
• Market access strategy lacks real-world evidence ( Commercialization) — Payer and HTA negotiations stall without credible real-world evidence of value and the right patient segments.
• Medical coding backlogs slow database lock ( eClinical Systems) — Coding adverse events and medications to standard dictionaries is repetitive expert work that piles up ahead of every interim and final lock.
• Medical writing can't keep pace with submission timelines ( Generative AI & LLMs) — Clinical study reports, protocols, and summary documents are assembled by hand from source tables and prior documents — and writing capacity, not data availability, often sets the submission timeline.
• Monitoring effort is spread evenly instead of where the risk is ( eClinical Systems) — Traditional on-site monitoring spends similar effort on every site, whether or not the data warrants it — while real signals hide in the aggregate.
• Monitoring the literature for safety information is relentless ( Pharmacovigilance & Safety) — Safety-relevant information hides across a growing stream of published articles and other sources, and screening it all by hand is slow and easy to fall behind on.
• New uses for existing drugs are going unnoticed ( Drug Discovery) — Approved and shelved compounds may treat other conditions, but the connecting signals are scattered across disconnected data and easy to miss.
• Patient journeys vanish between care settings ( Data & AI Infrastructure) — A patient's path runs through primary care, specialists, hospitals, and pharmacies — but each system only sees its own segment, so the moments that matter most often happen in nobody's data.
• Patient recruitment is taking too long ( Clinical Trials) — Enrollment timelines keep slipping, delaying readouts and burning budget while sites struggle to find eligible patients.
• Payer coverage policies are fragmented and shifting ( Commercialization) — Coverage and medical policies vary across many payers and change constantly, making the access landscape hard to track.
• Protocol amendments keep inflating trial cost ( Clinical Trials) — Avoidable mid-study amendments add cost and delay; many trace back to inclusion/exclusion criteria set without enough evidence.
• Protocol amendments trigger painful mid-study database changes ( eClinical Systems) — Every amendment fans out into form, edit-check, and integration updates on a live database — with migration and re-validation risk on each change.
• Quality reviews and batch release are slow and manual ( Manufacturing & Supply Chain) — Manual quality review, deviation handling, and CAPA management delay batch release and strain GxP compliance.
• Rare disease diagnosis delays hide eligible patients ( Commercialization) — Long diagnostic odysseys leave rare disease patients undiagnosed and invisible to the therapies that could help.
• Raw real-world data isn't research-ready ( Data & AI Infrastructure) — Real-world data arrives messy — inconsistent coding, missing values, duplicate records, free-text fields — and the curation needed to make it analyzable consumes the time that was meant for analysis.
• Real-world data is fragmented across sources that don't connect ( Data & AI Infrastructure) — Claims, EHR, lab, and registry data each describe a piece of the patient — but they sit in separate systems with separate formats, so no single source answers a research question end to end.
• Real-world evidence isn't built to decision grade ( Data & AI Infrastructure) — Analyses that inform internal strategy get by on convenience data — but evidence meant for payers, HTA bodies, or regulators must survive methodological scrutiny it was often never designed for.
• Regulatory reporting deadlines are hard to hit ( Pharmacovigilance & Safety) — Expedited reporting clocks start the moment a case arrives, and rising volume makes late or missed submissions a constant compliance risk.
• Regulatory submission authoring is a bottleneck ( Regulatory Affairs) — Compiling IND/NDA/BLA/MAA dossiers is document-heavy and time-pressured, with authoring effort gating filing timelines.
• Running trials closer to patients is hard ( Clinical Trials) — Decentralized and hybrid designs promise better access and retention, but ePRO, wearables, and remote monitoring are hard to operationalize.
• Safety monitoring during trials is too reactive ( Clinical Trials) — Emerging safety signals surface late because medical monitoring relies on periodic, manual review of trial data.
• Safety narratives are drafted one case at a time ( Generative AI & LLMs) — Patient safety narratives are written by hand from structured case data — thousands of times per program — making narrative drafting one of the most repetitive writing tasks in pharma.
• Safety signal detection is manual and slow ( Pharmacovigilance & Safety) — Detecting and triaging safety signals across growing data sources strains manual pharmacovigilance workflows.
• Screening produces too many low-quality hits ( Drug Discovery) — Screening campaigns return long hit lists thick with artifacts and dead-end chemistry, and triaging them by hand is slow and expertise-heavy.
• Study database builds sit on the critical path ( eClinical Systems) — Building and validating the data-capture database for a new trial takes weeks of specification, configuration, and testing — and enrollment can't start without it.
• Submission content is rewritten instead of reused ( Regulatory Affairs) — The same product facts are re-authored across modules, markets, and responses — multiplying effort and inconsistency risk.
• The adverse-event case backlog is growing ( Pharmacovigilance & Safety) — Rising case volume outpaces manual intake and processing capacity, putting compliance timelines at risk.
• Too many candidates fail late on ADMET and toxicity ( Drug Discovery) — Compounds that look promising on potency fail on absorption, metabolism, or toxicity after significant investment.
• Trial data is scattered across systems that don't talk ( eClinical Systems) — Data capture, trial management, document, supply, lab, and patient-reported systems each hold a slice of the trial, and keeping them reconciled consumes data-management time.
• Trial sites underperform on enrollment ( Clinical Trials) — Site selection decisions made on outdated feasibility data leave trials carrying sites that enroll slowly or not at all.
• Value dossiers and HTA submissions take too long ( Commercialization) — Assembling evidence and drafting value dossiers and HTA submissions across markets is slow and repetitive.
• We can't find undiagnosed or eligible patients ( Commercialization) — Patients who could benefit go unidentified, limiting reach for rare and specialty therapies.
• ePRO and diary compliance decays as trials run ( eClinical Systems) — Patient-reported entries arrive late, incomplete, or not at all once novelty wears off — and a missed diary window is data lost for good.