AI use cases in biopharma: Mapping high-value opportunities across the operating model

Biopharma is well-suited to AI because much of its work already depends on structured data, regulated documents, expert decisions, and repeatable workflows. From early research and clinical development to regulatory submission, manufacturing, safety monitoring and market access, each stage generates information that must be reviewed, interpreted, validated and acted upon. This makes biopharma a strong environment for AI systems that can connect data, summarize complex evidence, support decision-making and improve workflow efficiency while maintaining oversight and compliance.

As the market grows, slow handoffs become more costly across each stage. Global medicine spending was projected to reach US$1.6 trillion by 2025 [1], and estimates suggest strategic AI use in life sciences could unlock $5 billion to $7 billion in value [2]. That opportunity is not limited to drafting text. Predictive models provide clinical teams with earlier enrollment signals, while anomaly detection helps quality reviewers focus on the deviations that most need attention, thereby shortening review queues and improving decision quality.

The value, however, does not come from putting a generic chatbot beside regulated work and hoping teams adapt. It comes from embedding AI inside the workflow where a role already makes a documented judgment. In clinical operations, a study startup manager can view enrollment risk scores and site-priority recommendations during trial planning, so the study lead reviews a focused set of choices rather than rebuilding the analysis manually. In regulatory affairs, an AI assistant can prepare a first-pass response narrative from approved source documents, but the regulatory reviewer confirms the language before it is entered into a submission. In quality, anomaly detection can rank deviation reports for review, which gives a quality assurance reviewer a clearer way to prioritize follow-up without weakening control accountability.

Because the useful work sits inside existing steps, AI opportunities should be mapped at the function, process, and sub-process level before platforms or model features are selected. A function view shows where work belongs, but the sub-process view is where the build becomes real: the team can see which system holds the record, which artifact is being updated, who owns the review, and which control must be satisfied. This matters in biopharma because a recommendation that looks attractive in isolation may be hard to implement if the source data is incomplete, the audit trail is weak, or the reviewer is outside the normal approval path. Mapping at that level provides technology teams and business process owners with a practical way to prioritize use cases based on buildability, risk, and business value.

This article uses the biopharma operating model to break work into functions. Each function is then divided into processes and sub-processes. For each area, it shows where AI enablement opportunities can fit in to get the maximum business value.

• How AI is transforming biopharma operations
• Why biopharma AI use cases must be mapped at the sub-process level
• Biopharma operating model and AI opportunity mapping across biopharma processes
• High-value AI use cases in biopharma
• How agentic AI works in biopharma workflows
• How to prioritize AI use cases in biopharma
• Governance, risk, and responsible AI in biopharma
• How ZBrain operationalizes AI use cases in biopharma
• Future of AI in biopharma

How AI is transforming biopharma operations

AI is transforming biopharma operations by moving work beyond task routing and prediction toward contextual decision support. Its value becomes clearer in everyday operational moments where teams must connect structured data, regulated documents, historical decisions, and expert judgment before work can move forward.

For example, a protocol amendment review often starts with a clinical operations manager comparing a revised protocol with site feasibility comments and prior regulatory correspondence across different systems. Rules can send the amendment to the next queue, and predictive AI can estimate which sites are most likely to miss a startup milestone, which helps when inputs are structured, and past patterns still apply. The gap appears when the team needs a reasoned view of what the amendment changes, because those tools do not assemble the rationale from scattered text or make the tradeoffs clear enough for review. AI changes the work at that boundary by turning the scattered record into a reviewable impact summary, so manual reconciliation falls, and the clinical operations manager can seek regulatory or quality input before the change moves forward.

That same boundary appears across biopharma operations wherever regulated evidence, expert judgment, and repeatable handoffs meet:

• Document-heavy work: regulatory submission modules, trial master file artifacts, batch records, and quality standard operating procedures can be checked for missing context and version conflicts before a reviewer spends time on them.
• Narrative-heavy work: clinical study report summaries, safety case narratives, medical information responses, and inspection readiness briefings become faster to assemble because AI can shape an initial draft from approved source material while showing where evidence is thin.
• Exception-heavy work: batch deviations, protocol deviations, adverse event triage, and supply allocation exceptions can be classified and prioritized so that quality, clinical, and safety teams focus first on the cases with higher operational or patient-risk implications.
• Knowledge-heavy work: regulatory requirement interpretation, chemistry, manufacturing, and controls (CMC) precedent searches, medical affairs evidence review, and quality risk assessment improve when AI retrieves relevant prior decisions and flags conflicting guidance for expert review.
• Workflow-heavy work: study startup, change control, pharmacovigilance case processing, and demand planning benefit when AI forecasts the likely bottleneck and assembles the next work packet, which reduces avoidable rework between functions.

The practical design rule is to keep AI narrow and controlled. AI prepares the case by retrieving relevant evidence, shaping the draft output and routing the package to the appropriate regulatory or quality assurance reviewer before any production change, customer-facing message or risk-bearing action proceeds. That keeps accountability visible while reducing manual effort, because people spend less time assembling records and more time testing the recommendation against science, regulation, and patient impact.

Why biopharma AI use cases must be mapped at the sub-process level

Broad AI ideas become useful only when they are tied to a specific workflow, input, output and review point. For example, a discovery program review might include two requests under the same “AI for biopharma” label: One group may want support for target-biology evidence mapping, while another may need high-throughput screen result triage before committing follow-up chemistry time The evidence work draws on research informatics and electronic lab notebook records, while the screen triage step depends on assay output and quality flags, so the approver and review questions are not the same. At that altitude, the high-level label is too loose to build, govern, or measure, since no one has named the artifact being prepared or the point where scientific judgment enters.

A better approach is to map AI use cases to the biopharma operating model:

Function: the major business, scientific, or operational area, such as discovery research, translational science, clinical development, regulatory affairs, pharmacovigilance, manufacturing, quality, medical affairs, or commercial access.

Process: the workflow area within that function, such as target nomination, assay development, lead optimization, clinical study planning, regulatory submission preparation, adverse event case processing, batch release, quality event management, or market access evidence generation.

Sub-process: the specific work activity, such as mapping target-biology evidence, registering critical reagents and samples, preparing a design of experiments plan, drafting a clinical protocol synopsis, assembling a submission module, summarizing safety narratives, reviewing batch records, or preparing payer evidence summaries.

AI-enabled opportunity: the specific way AI can support that sub-process, such as classifying evidence strength, detecting missing metadata, recommending experimental ranges, drafting a first-pass narrative, extracting information from regulated documents, summarizing deviations, or assembling reviewer-ready evidence.

This level of detail matters because biopharma workflows are tied to specific scientific artifacts, regulated records, quality systems, data sources, review checkpoints, and accountable decision-makers. An AI workflow for target-biology evidence mapping is different from one for adverse event narrative drafting. A sample registration workflow is different from a design of experiments planning workflow. A regulatory submission assistant is different from a laboratory operations copilot or a quality review assistant.

Sub-process mapping makes each AI opportunity concrete because it has to define what AI is allowed to do, which artifact it touches, and who accepts or rejects the proposed output. For example, in target-biology evidence mapping, AI may classify evidence strength and draft supporting rationale in a target nomination package, but the target biology lead confirms whether it is ready for nomination review. In critical reagent and sample registration, AI may detect missing metadata in a laboratory information management system record, but the laboratory operations manager approves corrections before the record is accepted. In the design of experiments for lead optimization, AI may recommend test ranges in an experimental plan, but the lead optimization scientist confirms the plan before work proceeds.

By mapping AI opportunities at the sub-process level, biopharma organizations can move from broad innovation ideas to executable workflows with clear business or scientific value, data requirements, system integration needs, governance controls, and review accountability.

Biopharma operating model and AI opportunity mapping across biopharma processes

The biopharma operating model below is organized into key industry-native functions that practitioners recognize. Each function is decomposed into its major processes and their sub-processes, and each sub-process carries the AI-enabled opportunity that applies to it. The focus is on software-only AI opportunities that support human review at key workflow and decision points.

Function 1. Discovery research, target biology, and translational biomarkers

This function owns disease biology, target nomination, assay strategy, candidate evidence, and early biomarker hypotheses from exploratory research through translational handoff. Discovery biologists, pharmacologists, bioinformatics scientists, translational scientists, and lab operations teams work across electronic lab notebooks, laboratory information management systems, and research analytics platforms.

AI is most useful when evidence is fragmented across literature, omics data, assay results, lab records, and biomarker plans. It helps teams reduce manual curation, sharpen prioritization, and create clearer review trails, while scientists remain accountable for target rationale, experimental interpretation, and candidate nomination.

Highest-value opportunities: Target biology evidence mapping, high-throughput screen result triage, and biomarker data transfer to clinical data management should receive priority because they combine high-volume evidence review with clear scientific review boundaries. In these workflows, AI can reduce manual curation, strengthen target and hit-selection decisions, and lower the volume of downstream queries by preparing evidence in a more structured and review-ready form. Final accountability remains with the discovery biology lead, screening scientist and clinical data management lead.

Example agentic workflow: One example of an agentic workflow is target nomination evidence assembly. The agent plans the evidence checklist, retrieves approved lab and omics records, drafts rationale and gap sections, routes uncertain claims to the discovery biology lead, and records confirmation before advancement.

Function 2. Preclinical development and toxicology

This function manages a nonclinical development strategy, toxicology, toxicokinetics, safety pharmacology, nonclinical reporting, and evidence packages that support first-in-human and later regulatory milestones. Toxicologists, pathologists, pharmacokinetic scientists, study directors, and regulatory writing partners work across research informatics, laboratory information management, and analytics platforms.

AI creates value where teams must reconcile observations, pathology findings, toxicokinetic data, and submission datasets across regulated evidence packages. It helps shorten quality control cycles and improve safety interpretation, while study directors, toxicologists, and governance forums retain scientific and regulatory accountability.

Highest-value opportunities: Toxicology observation and pathology finding reconciliation, SEND domain mapping, and nonclinical study report quality control offer the strongest near-term value because they are high-volume, artifact-rich, and bounded by clear review decisions. AI reduces manual cross-checking across study reports and laboratory records, shortens quality control cycles, improves decision quality, and preserves accountability with the study pathologist, nonclinical data standards lead, and study director.

Example agentic workflow: An example agentic workflow is nonclinical study report quality control: it plans the quality control run, retrieves approved protocol and laboratory evidence, drafts a discrepancy summary, routes interpretation issues to the study director, and records approval before final disposition.

Function 3. Clinical development and protocol design

This function manages clinical development strategy, study design, protocol authoring, endpoint selection, informed consent alignment, and cross-functional study design handoffs. Clinical scientists, medical directors, biostatisticians, regulatory partners, patient engagement leads, and protocol authors work across clinical trial management, electronic data capture, trial master file, and regulatory platforms.

AI addresses the manual cross-checking burden that slows protocol development and amendment governance. It helps identify inconsistent endpoints, downstream document impacts, and ambiguous data collection requirements. Clinical and statistical owners remain accountable for medical judgment, patient protection, and good clinical practice compliance.

Highest-value opportunities: Clinical study protocol authoring, protocol amendment impact assessment, and electronic data capture build requirements handoff offer the strongest near-term value because they are document-heavy worksteps with clean review boundaries. AI reduces manual cross-checking across the protocol, informed consent form, data capture specifications, statistical analysis plan, and trial master file. It can shorten the amendment and build cycles while preserving medical, statistical, and good clinical practice accountability.

Example agentic workflow: An example agentic workflow is protocol amendment impact triage: it plans the amendment review, retrieves current protocol and data capture evidence, drafts impact notes, routes patient-facing and submission impacts to the clinical study lead, and records the final disposition.

Function 4. Clinical operations and site management

This function manages study startup, site identification, feasibility, site activation, investigator site management, monitoring, vendor oversight, and trial master file readiness. Clinical operations leads, study managers, clinical research associates, site activation specialists, and trial master file owners use clinical trial management, electronic data capture, and electronic trial master file platforms.

AI is valuable because startup, monitoring, and inspection readiness depend on coordinated documents, milestone data, and site-level decisions. It helps reduce manual follow-up, focus monitoring on higher-risk sites, and improve readiness accountability, while clinical operations and quality roles retain final judgment.

Highest-value opportunities: Site identification and feasibility questionnaire management, risk-based monitoring plan execution, and trial master file completeness review offer the strongest near-term value because they are high-volume workflows with clear review boundaries. AI combines structured data from clinical trial management, electronic data capture, and electronic trial master file platforms, helping teams shorten startup timelines, focus monitoring efforts, and reduce remediation costs before inspection readiness reviews.

Example agentic workflow: An example agentic workflow is the site activation readiness workflow: it plans the readiness check, retrieves protocol, consent, training, and trial master file status, drafts a gap summary, routes missing items, and records confirmation after the site activation specialist approves.

Function 5. Clinical data management, biostatistics, and statistical programming

This function manages clinical data standards, electronic data capture design, data cleaning, medical coding, statistical analysis planning, statistical programming, submission datasets, and clinical study report outputs. Clinical data managers, medical coders, biostatisticians, statistical programmers, and standards leads work across electronic data capture, analytics, and safety case management integrations.

AI helps where high-volume data issues, coding decisions, and traceability checks slow down database lock and submission preparation. It supports query detection, coding assistance, standards mapping, and programming review, while human reviewers remain responsible for data integrity, statistical validity, and submission-ready outputs.

Highest-value opportunities: Data discrepancy and query management, MedDRA and World Health Organization (WHO) Drug coding, and Clinical Data Interchange Standards Consortium (CDISC) SDTM and ADaM mapping offer the strongest AI return. These areas combine high transaction volumes with structured artifacts and clear reviewer handoffs. Prioritizing these areas reduces manual cleaning and recoding effort, shortens database lock and submission dataset cycles, and improves decision quality without moving accountability away from qualified reviewers.

Example agentic workflow: An example agentic workflow is daily electronic data capture query triage: it plans discrepancy triage, retrieves case report data and protocol references, drafts prioritized query text, routes cases to the clinical data manager, and records each approved or rejected query.

Function 6. Regulatory affairs, labeling, and submissions

This function manages regulatory strategy, agency interaction planning, submission content planning, labeling coordination, lifecycle submissions, and eCTD publishing. Regulatory strategists, regulatory operations, labeling leads, medical writers, chemistry, manufacturing, and controls (CMC) regulatory leads, and submission publishers work across regulatory information management and submission platforms.

AI helps when regulatory teams must reuse content, reconcile cross-module claims, evaluate labeling impacts, and confirm publishing readiness under time pressure. It reduces manual comparison effort and strengthens review accountability, while regulatory professionals retain interpretation, sign-off, and compliant submission dispatch responsibilities.

Highest-value opportunities: eCTD publishing and submission readiness review, common technical document quality check, and labeling change impact assessment offer the strongest near-term AI value because they are artifact-rich worksteps with traceable sources and clean review boundaries. Applying AI here reduces manual reconciliation, shortens submission cycle time, strengthens compliance, and improves review accountability without shifting regulatory sign-off.

Example agentic workflow: An example agentic workflow is the submission readiness exception workflow: it plans the eCTD readiness checklist, retrieves submission metadata and source evidence, drafts an exception log, routes publishing tasks, and records confirmation when regulatory operations approve dispatch readiness.

Function 7. Pharmacovigilance and patient safety

This function manages adverse event intake, case processing, medical review, expedited reporting, signal detection, aggregate safety reporting, risk management, and benefit-risk governance. Safety operations, safety physicians, safety scientists, medical reviewers, case processors, and pharmacovigilance quality teams work in safety case management platforms connected to clinical, regulatory, medical information, and analytics systems.

AI helps most where case volume, coding variation, duplicate review, and aggregate evidence preparation create bottlenecks. It supports intake triage, narrative drafting, signal detection, and benefit-risk review, while safety physicians and pharmacovigilance governance retain good pharmacovigilance practice and reportability accountability.

Highest-value opportunities: Adverse event intake from medical information and field sources, MedDRA and WHO Drug coding, and PBRER and PSUR aggregate safety review offer the strongest near-term AI value. These are high-volume workflows with repeatable source documents and clean review boundaries. AI helps safety operations reduce intake and coding cycle time, gives safety physicians better-organized evidence, and keeps accountability with case processors, medical coders, and safety physicians before reporting decisions are made.

Example agentic workflow. An example agentic workflow is the individual case safety report intake triage workflow: it plans validity and duplicate checks, retrieves field notes and prior cases, drafts the triaged report and Council for International Organizations of Medical Sciences (CIOMS) I form, routes exceptions, and records case processor approval.

Function 8. CMC development and pharmaceutical sciences

This function manages formulation development, process development, analytical development, specifications, stability, comparability, control strategy, and CMC regulatory content. Process scientists, formulation scientists, analytical scientists, stability leads, CMC regulatory leads, and technical operations teams use electronic lab notebooks, laboratory information management systems, quality systems, and regulatory platforms.

AI is most useful when experimental evidence, stability trends, quality records, and submission content must be linked into a defensible control strategy. It reduces collation effort and sharpens exception prioritization, while CMC experts remain accountable for scientific rationale, control strategy, and regulated lifecycle decisions.

Highest-value opportunities: Design of experiments execution and result review, stability data trending and shelf-life assessment, and eCTD Module 3 CMC section authoring are the strongest opportunities. They combine experiment results, stability pulls, and submission updates with artifact-rich inputs. AI reduces manual collation and first-pass review time, sharpens exception prioritization, and preserves clean review boundaries with process scientists, stability leads, and CMC regulatory leads.

Example agentic workflow: One example of an agentic workflow is the CMC information request response. The agent plans the response scope, retrieves approved lab, quality, manufacturing and regulatory evidence, drafts cited response text, routes the package, and prompts the CMC regulatory lead to confirm readiness.

Function 9. Manufacturing operations and batch release

This function manages current good manufacturing practice production execution, master batch records, electronic batch records, in-process controls, deviations, continued process verification data capture, batch record review, and batch disposition handoffs. Manufacturing supervisors, operators, process engineers, manufacturing science and technology teams, quality assurance, quality control, and release roles use manufacturing execution, laboratory, quality, planning, and enterprise resource planning platforms.

AI helps where production teams face high-volume record review, process exception triage, deviation initiation, and release package reconciliation. It supports review by exception and anomaly detection on existing digital records, while the quality unit and Qualified Person roles remain accountable for batch disposition and patient safety.

Highest-value opportunities: Batch production record review by exception, critical process parameter monitoring, and deviation report initiation and classification are the strongest fits because they are high-volume, artifact-rich workflows with clear review boundaries. Applying AI to these steps reduces manual record review effort, shortens deviation triage cycle time, and improves decision quality while preserving accountability for batch disposition and patient safety.

Example agentic workflow: An example agentic workflow is the batch release readiness workflow: it plans the release-readiness sequence, retrieves batch, laboratory, quality, and inventory evidence, drafts an exception checklist, routes it to quality assurance, and records the quality unit disposition.

Function 10. Quality assurance and quality control

This function owns the pharmaceutical quality system, quality control testing, deviations, CAPA, change control, out-of-specification investigations, audit readiness, product quality review, validation oversight, and data integrity. Quality assurance, quality control laboratories, validation, compliance, data integrity, and quality systems teams use quality management, laboratory, manufacturing execution, batch record, and analytics platforms.

AI helps when quality teams must collect evidence across investigations, detect recurrence, review audit trails, and prepare inspection-ready packages. It can reduce manual evidence gathering and closure delays, while quality personnel retain accountability for good practice (GxP) decisions, investigation conclusions, and inspection commitments.

Highest-value opportunities: Deviation report triage and classification, CAPA effectiveness checks, and Attributable, Legible, Contemporaneous, Original, Accurate (ALCOA)+ data integrity review carry the strongest AI value. These are high-volume workflows with clear handoffs to quality assurance, quality systems, and data integrity reviewers. AI reduces manual evidence gathering, highlights recurrence and audit-trail risks earlier, and shortens closure or inspection-readiness cycles. Qualified quality roles still confirm the final decisions.

Example agentic workflow. An example agentic workflow is the deviation-to-CAPA closure workflow: it plans the closure worklist, retrieves deviation, batch, CAPA, and lab evidence, drafts a recurrence summary, routes ownership actions, and records the quality systems lead decision.

Function 11. Supply chain planning, trade, and serialization

This function owns demand planning, supply planning, materials availability, distribution readiness, trade operations, returns, recall logistics, serialization, and product traceability. Supply planners, demand planners, procurement, logistics, trade operations, serialization leads, quality assurance, and manufacturing planning teams work across enterprise resource planning, supply chain planning, manufacturing, quality, and analytics platforms.

AI helps when planning and traceability decisions require rapid reconciliation across forecasts, batch status, inventory, shipment records, and serialization events. It improves demand forecasting, constraint triage, inventory decisions, and exception investigation, while supply chain and quality roles remain accountable for regulated distribution, patient supply, and traceability.

Highest-value opportunities: Forecast demand plan creation, supply constraint and allocation planning, and serialization exception report investigation are the strongest opportunities because they are high-volume, artifact-rich, and have clear review boundaries. AI reduces manual reconciliation across forecast, enterprise resource planning, batch, and serialization data, shortens planning and investigation cycles, and improves decision quality without moving accountability away from regulated supply chain and quality roles.

Example agentic workflow: An example agentic workflow is cold-chain shipment exception triage: it plans the release-risk review, retrieves shipment, temperature, batch, and quality status, drafts deviation triage, routes the case, and records quality assurance release manager approval.

Function 12. Medical affairs and medical information

This function manages scientific exchange, medical information, field medical engagement, external expert planning, evidence gap assessment, publications support, medical insights, and medical input to promotional review. Medical information specialists, medical directors, medical science liaisons, publications teams, evidence generation leads, and medical reviewers use customer relationship management, medical information workflow, and analytics platforms.

AI helps where teams must retrieve approved content, triage inquiries, summarize field insights, and check promotional claims against evidence. It reduces retrieval and drafting effort, improves prioritization, and strengthens compliance, while medical affairs professionals remain accountable for scientific accuracy, nonpromotional standards, and patient safety escalation.

Highest-value opportunities: Medical information response letter preparation, medical insight categorization and escalation, and promotional claims substantiation medical checks create the strongest AI value. These workflows combine high inquiry or review volume with structured artifacts and clean review boundaries. AI reduces retrieval and drafting effort, sharpens prioritization, strengthens compliance, and preserves human accountability with medical information specialists, medical affairs leads, and medical reviewers.

Example agentic workflow: An example agentic workflow is the medical information response workflow: it plans the response path, retrieves approved content and safety context, drafts a citation-linked package, routes adverse event cues to patient safety, and presents the response for specialist confirmation.

Function 13. Commercial operations, market access, contracting, and Gross-to-Net revenue management

This function oversees brand execution, launch readiness, field operations, promotional content governance, market access, payer dossiers, contracting, gross-to-net, chargebacks, and revenue leakage management. Brand teams, market access, field operations, contracting, pricing, finance, revenue management, and review committee roles use commercial customer relationship management, revenue management, enterprise planning, and analytics platforms.

AI helps where commercial teams must segment accounts, forecast demand, govern compliant engagement, monitor access changes, analyze contracts, and detect revenue exceptions. It improves prioritization and control quality, while commercial, medical, finance, and review roles remain accountable for compliant promotion, payer commitments, and financial controls.

Highest-value opportunities: Health care professional segmentation and targeting, promotional claims substantiation, and gross-to-net accrual and chargeback reconciliation offer the strongest near-term value. These workflows combine high transaction volume, artifact-rich inputs, and clear review boundaries. AI reduces manual effort, accelerates approvals, improves prioritization, and strengthens financial control quality while brand analytics, medical affairs, and revenue accounting roles retain accountability.

Example agentic workflow: An example agentic workflow is gross-to-net exception triage: it plans the close-period review, retrieves chargeback, rebate, contract, and sales evidence, drafts a variance package, routes it to the analyst, and records confirmation in the case log.

Function 14. Enterprise data, AI platforms, and GxP digital validation governance

This function manages enterprise data architecture, data governance, integration, analytics enablement, AI platforms, model operations, Good practice (GxP) validation, cybersecurity alignment, privacy controls, and controlled digital change management. Data engineering, data science, AI engineering, platform operations, information security, privacy, quality assurance, validation, and business data owners govern platforms integrated with regulated business systems.

AI value depends on governed data, workflow integration, validation evidence, audit trails, human oversight, explainability, privacy, security, and controlled model change management. This function provides the operating model anchor for scaling AI safely across regulated workflows without weakening review accountability.

Highest-value opportunities: CSV V-model validation evidence package, user requirements specification traceability, and production monitoring for model drift and hallucination risk offer the strongest near-term value. These workflows are high-volume and artifact-rich, with clear review boundaries. AI reduces evidence assembly effort, shortens audit and release cycles, and improves model-change decision quality while final acceptance stays with the quality assurance validation lead and model risk owner.

Example agentic workflow. An example agentic workflow is the CSV evidence readiness workflow: it plans the validation evidence checklist, retrieves requirements, qualification, change, and test metadata, drafts a traceability summary, routes gaps, and records quality assurance validation lead confirmation.

Function 15. Clinical supply chain and investigational product (IMP) management

This function manages clinical trial supply strategy, IMP manufacturing, labeling and booklet/translation management, comparator and ancillary sourcing, randomization and trial supply management (RTSM/IRT), depot and site distribution, drug accountability, expiry and relabeling, and IMP returns and destruction. Clinical supply leads, packaging and labeling specialists, RTSM/IRT managers, clinical supply planners, depot and logistics partners, and Qualified Person (QP) release roles work across interactive response technology (IRT/RTSM), clinical supply planning, manufacturing, and trial master file platforms.

AI is valuable because trial supply decisions require constant reconciliation across enrollment projections, randomization design, batch and expiry status, depot inventory, and dispensing events, where overage wastes costly drug and shortage stops patient dosing. It improves forecasting, accountability, and exception triage, while clinical supply, regulatory labeling, and QP roles retain accountability for patient supply continuity and regulated release.

Highest-value opportunities. Trial demand forecasting and supply simulation, IRT/RTSM configuration and accountability reconciliation, and expiry and relabeling management are the strongest fits because they are simulation-friendly, high-volume, and bounded by clear review decisions. AI reduces overage and stockout risk, shortens reconciliation cycles, and preserves accountability with the clinical supply lead, RTSM manager, and QP.

Example agentic workflow. Clinical resupply exception triage: it plans the resupply review, retrieves enrollment, IRT dispensing, depot inventory, and expiry status, drafts a shortage-risk exception list, routes constrained sites, and records clinical supply lead confirmation before resupply release.

Function 16. Translational medicine, clinical pharmacology, and model-informed drug development

This function manages clinical pharmacology strategy, PK/PD and exposure-response analysis, population PK, dose selection and optimization, drug-drug interaction and special-population assessment, cardiac safety, bioanalytical strategy, immunogenicity, and model-informed drug development (MIDD) evidence. Clinical pharmacologists, pharmacometricians, PK/PD modelers, bioanalytical leads, and translational scientists work across modeling and simulation, bioanalytical, and clinical data platforms.

AI helps where dose and exposure decisions depend on assembling and modeling large, heterogeneous datasets and translating them into defensible regulatory positions. It accelerates dataset assembly, model diagnostics, and summary drafting, while clinical pharmacology and pharmacometrics owners retain accountability for dose rationale and model-based conclusions.

Highest-value opportunities. Population PK model development, exposure-response analysis, and concentration-QTc analysis offer the strongest near-term value because they are data-intensive, diagnostics-driven, and bounded by clear modeler sign-off. AI shortens dataset assembly and first-pass diagnostics while keeping dose and exposure conclusions with the clinical pharmacologist and pharmacometrician.

Example agentic workflow. Dose-optimization evidence assembly: it plans the exposure-response review, retrieves PK, PD, and endpoint data, drafts dose-range options with diagnostics, routes weak relationships to the clinical pharmacology lead, and records confirmation before dose recommendation.

Function 17. Real-world evidence, epidemiology, and health economics and outcomes research (HEOR)

This function manages real-world data (RWD) strategy and partnerships, epidemiology and natural-history studies, observational and RWE study design and execution, registries, HEOR economic modeling, indirect treatment comparisons, patient-reported and value evidence, and evidence for health technology assessment (HTA) and payers. Epidemiologists, RWE scientists, HEOR researchers, data scientists, biostatisticians, and outcomes liaisons work across RWD, analytics, and HTA platforms.

AI helps where evidence questions require assessing data fitness, designing bias-resistant studies, and synthesizing dispersed clinical and economic evidence. It accelerates feasibility, design drafting, and synthesis, while epidemiology, HEOR, and biostatistics owners retain accountability for study validity and value conclusions.

Highest-value opportunities. RWD fit-for-purpose evaluation, cost-effectiveness and budget-impact modeling, and indirect treatment comparison are the strongest fits because they combine large dispersed evidence with clear methodological review boundaries. AI reduces feasibility and synthesis effort while keeping validity decisions with epidemiology, HEOR, and biostatistics roles.

Example agentic workflow. RWE feasibility and design package: it plans the feasibility assessment, retrieves candidate data sources and prior evidence, drafts a fit-for-purpose summary and study design options, routes bias risks to the epidemiologist, and records RWE lead confirmation.

Function 18. Patient services, access, and adherence (hub and patient support programs)

Suggested placement: alongside Function 13 (Commercial), commercial/launch stage.

This function manages patient onboarding and hub services, benefits verification and prior authorization support, copay and financial assistance, free and bridge drug programs, specialty pharmacy and dispensing coordination, adherence and nursing support, and patient program compliance. Hub operations, reimbursement specialists, case managers, nurse educators, specialty pharmacy liaisons, and program compliance teams work across patient services CRM, hub, and specialty distribution platforms.

AI helps where high patient and case volume requires rapid coverage determination, documentation drafting, and adherence triage. It accelerates intake, coverage, and outreach prioritization, while case managers, reimbursement specialists, and compliance roles retain accountability for patient eligibility, privacy, and program integrity. Adverse-event cues detected in any patient interaction are routed to pharmacovigilance intake.

Highest-value opportunities. Benefits verification and coverage determination, prior authorization and appeals support, and adherence monitoring are the strongest fits because they are high-volume, document-heavy, and bounded by clear specialist review. AI shortens time-to-therapy and improves adherence outreach while keeping eligibility and privacy accountability with case managers, reimbursement specialists, and compliance.

Example agentic workflow. Patient onboarding and coverage triage: it plans the onboarding path, retrieves enrollment, consent, and payer data, drafts benefits and prior authorization documentation, routes adverse-event cues to pharmacovigilance, and presents the case for reimbursement specialist confirmation.

Function 19. Healthcare compliance, ethics, and transparency

This function manages the healthcare compliance program, HCP/HCO engagement compliance, fair market value (FMV), transparency and aggregate spend reporting (US Open Payments / Sunshine Act and ex-US equivalents), anti-bribery and anti-kickback monitoring, speaker and event compliance, monitoring and auditing, and investigations. Compliance officers, transparency reporting teams, monitoring and auditing analysts, and legal partners work across spend, CRM, expense, and compliance platforms.

AI helps where compliance teams must reconcile spend and engagement data across systems, detect anomalous activity, and validate reportable transfers of value before filing. It improves monitoring coverage and reporting accuracy, while compliance officers and legal retain accountability for compliance determinations and disclosures.

Highest-value opportunities. Aggregate spend data validation, Open Payments / Sunshine Act reporting, and field activity and expense monitoring are the strongest fits because they are high-volume, reconciliation-heavy, and bounded by clear compliance review. AI improves reporting accuracy and monitoring coverage while keeping compliance determinations and disclosures with compliance and legal roles.

Example agentic workflow. Aggregate spend reporting reconciliation: it plans the reporting cycle, retrieves spend, CRM, and expense data, drafts a categorized transfer-of-value file with exceptions, routes misclassifications, and records transparency lead confirmation before submission.

High-value AI use cases in biopharma

High-value AI opportunities in biopharma usually emerge where there is enough workflow volume, a clear artifact to prepare, and a defined review point for an accountable role. These opportunities use AI to classify work or draft evidence-backed outputs, while the final decision stays with the function that owns the risk.

A use case earns ‘high-value’ when its business impact is obvious, and its review boundary is well-defined. In practice, that means a visible backlog and a repeatable artifact set. It also means a named role can confirm the output before it affects a submission or a safety case.

How agentic AI works in biopharma workflows

In biopharma, delays often come from evidence that is valid but scattered across lab, clinical, and regulatory repositories, so an agentic workflow is useful only when it is tightly governed. The pattern is a governed sequence: plan the work, retrieve approved evidence, draft a reviewable output, route exceptions, and confirm the result, with tool access limited to approved systems.

Here are some examples:

Target nomination evidence assembly

• Agent role: builds the evidence checklist from the approved target nomination template.
• Retrieves electronic lab notebook (ELN) experiments and assay records from discovery systems.
• Drafts disease rationale and tractability sections, then flags evidence gaps.
• Routes low-confidence claims to the discovery biology lead, who confirms before advancing.

Nonclinical study report QC workflow

• Agent role: plans the quality control (QC) run from the approved protocol and checklist.
• Retrieves protocol versions and laboratory information management system (LIMS) exports.
• Uses ELN entries and draft report tables to draft a linked discrepancy summary.
• Routes integrity or interpretation issues to the study director, who confirms the final disposition.

Protocol amendment impact triage

• Agent role: plans the amendment review sequence against current clinical records.
• Retrieves the clinical study protocol and informed consent form from approved repositories.
• Checks case report forms, the analysis plan, and trial master file records for impact.
• Routes impacts to the clinical study lead, who confirms the amendment disposition.

Site activation readiness workflow

• Agent role: plans the readiness check from the study startup milestone.
• Retrieves the clinical study protocol and approved informed consent form.
• Drafts a gap summary and activation-risk note from the training and trial master file status.
• Routes missing-item requests to the study team; the site activation specialist confirms readiness.

The review boundary is the safety property: the agent prepares evidence and drafts, but the accountable process owner confirms before any production change.

How to prioritize AI use cases in biopharma

In biopharma, AI prioritization is a sequencing exercise, not a catalog of potential use cases. Each opportunity should be scored on value and feasibility, with initial efforts focused on workflows where AI can reduce review bottlenecks, shorten cycle times and improve decision quality while keeping scientific, medical, regulatory and quality accountability clear.

Biopharma AI roadmaps often stall in four classic patterns: misaligned scope, missing data, bypassed governance, and premature quantified savings. Avoid this by sizing work at the sub-process level, proving that the source artifacts are usable, and keeping the accountable reviewer in the workflow. In practice, the strongest first projects are the high-volume, artifact-rich, cleanly reviewed sub-processes flagged in the operating model above.

Governance, risk, and responsible AI in biopharma

In biopharma, AI governance must account for scientific judgment, regulated records, patient safety, data integrity, and accountable human review. Responsible AI practices help ensure that AI-enabled workflows improve speed and consistency without weakening compliance, quality, or decision ownership.

Human-in-the-loop (HITL) oversight: In biopharma, AI may draft a target nomination package section, summarize nonclinical evidence for an investigator’s brochure, or classify high-throughput screen results, but it should not finalize regulated or risk-bearing work on its own. A translational medicine reviewer, toxicology study director, clinical protocol owner, or regulatory submission lead confirms the output before a candidate selection decision, protocol amendment, submission update, or external medical communication moves forward.

Regulatory and standards alignment: AI governance in biopharma should not sit apart from the regulatory and quality systems that already guide the industry. A practical starting point is to use the NIST AI Risk Management Framework and NIST AI 600-1 to structure AI risk controls, then map those controls to FDA requirements such as 21 CFR Part 11 for electronic records and 21 CFR Part 312 for investigational drug applications.

For development, clinical, safety and quality workflows, ICH guidance such as ICH Q9(R1), ICH E6(R3) and ICH E2E can help anchor expectations for risk management, evidence review and accountable oversight. The EU AI Act remains relevant for global operating models, especially when AI systems may be deployed across regions with different compliance obligations.

Bias mitigation and evidence retention: Bias can enter when historical target biology evidence overweights well-studied pathways, when biomarker hypotheses are anchored to prior trial assumptions, or when screening models rank compounds based on uneven training data. Reviewers should retain named source artifacts such as electronic lab notebook entries, assay feasibility reviews, toxicology study reports, and clinical study protocol versions so that the basis for each AI-assisted recommendation remains inspectable.

Key governance requirements: Biopharma teams need a use-case inventory that distinguishes low-risk summarization from higher-risk scoring, ranking, or recommendation in target nomination, lead optimization, protocol authoring, and biomarker context of use definition. Risk tiering should define approval gates, monitoring frequency, and escalation paths, because an AI error in a candidate selection criteria review can affect development spend and patient-facing study design more directly than an internal literature digest.

Design principles: AI responses should be grounded in approved biopharma sources, such as validated research informatics records, laboratory information management system data, controlled protocol templates, and authorized regulatory submission content. Least privilege and role-based access control reduce the chance that a discovery user sees clinical data they do not need, while scoped tool access prevents an agent from changing a study schedule or submission dataset without confirmation from the clinical operations owner or regulatory data standards lead.

Traceability and data security: Each AI-assisted workflow should keep an audit trail of prompts, retrieved sources, model version, reviewer disposition, approvals, rejected suggestions, and downstream system updates, so records remain reviewable under controls such as 21 CFR Part 11, NIST Cybersecurity Framework (CSF 2.0), International Organization for Standardization / International Electrotechnical Commission (ISO/IEC) 27001:2022, and SOC 2 Trust Services Criteria. Data protection also has to cover confidential compound structures, nonclinical findings, biomarker data transfers, and protocol drafts, because stronger security and clearer review accountability are what allow AI to shorten cycle time without weakening compliance.

How ZBrain operationalizes AI use cases in biopharma

Identifying use cases is only the first step. Biopharma organizations also need a way to design, build, validate, deploy, govern, and scale AI workflows across functions. This is where ZBrain helps.

ZBrain is an end-to-end AI enablement platform that provides enterprises with a structured pathway from identifying where artificial intelligence can deliver value to deploying it as a governed, scalable capability. The platform operates across two core dimensions: strategy and execution. In the strategy phase, ZBrain helps organizations identify, evaluate, and design AI solutions by leveraging their own business processes, technology landscape, and operational data. The execution phase ensures these AI opportunities are systematically developed into scalable solutions. By covering the full AI lifecycle in six connected stages, ZBrain enables each initiative to progress from strategic insight to enterprise deployment, eliminating fragmented efforts.

Preparation (foundation)

Establishes a comprehensive understanding of the organization’s current enterprise environment, including processes, technology systems, workforce metrics, and KPIs, providing the insight needed to identify where AI can deliver meaningful value.

Ideation & prioritization (discovery)

Leverages enterprise data to identify AI opportunities and then prioritizes them based on feasibility, cost, benefits, and potential ROI, with priority given to those that can be embedded within existing processes.

Solution design (validation)

Translates prioritized opportunities into ROI-validated and KPI-mapped solution design blueprints, defining where AI can assist, augment, or act autonomously within workflows.

Technical design (Build-Ready)

Transforms solution requirements into structured, build-ready technical design artifacts, including architecture diagrams, schemas, agentic workflows, user stories, epics, and business requirement documents. This provides the build team with a complete technical design to serve as a foundation for development.

Proof of concept / PoC (validation)

Tests selected AI solutions in controlled environments to validate feasibility, business value, and implementation readiness before scaling.

Scaled product

Scale validated proof-of-concept, supported by performance metrics and observability data, are deployed as governed, production-grade AI solutions across enterprise environments, with continuous improvement loops to sustain impact.

Future of AI in biopharma

In the coming years, AI in biopharma is likely to move away from isolated pilots toward federated platforms that let research and development (R&D), clinical operations, regulatory affairs, and safety functions use shared orchestration with common governance, observability, and integration layers. That matters because many AI use cases now fail at the handoff: a model may classify a protocol deviation correctly, but the evidence trail, system update, and review record still sit in separate workflows. A federated platform gives each function room to tailor AI to its process while keeping approved data access, monitoring, audit logs, and system connections consistent, so a regulatory affairs reviewer or clinical quality reviewer can confirm the proposed action before it changes a submission record or trial file. Global medicine use is projected to approach four trillion defined daily doses by 2030 (IQVIA Institute – Global Medicine Use Trends 2026 – YouTube [3].

Once that shared operating layer is in place, the next trajectory is the rise of long-horizon agentic workflows that can stay oriented around a multi-step biopharma goal rather than answering one prompt at a time. In clinical development, for example, an AI workflow could maintain the thread across site feasibility scoring and enrollment forecasting, then surface the risks that need attention before a country start-up plan is revised. In pharmacovigilance, it could carry context from case intake through signal prioritization, while a safety physician confirms each risk-bearing judgment before any safety position or external response advances. The value is not that AI acts alone, but that it reduces the coordination burden between handoffs and gives accountable reviewers a clearer queue of decisions.

As those agentic patterns mature, the main source of advantage will shift from picking one frontier model to designing the workflow around the decision that biopharma actually needs to make. When high-performing models converge, differences in outcomes will depend more on whether the process has clean source data, evidence-linked outputs, escalation rules, and review checkpoints that match regulated work. A well-designed workflow for chemistry, manufacturing, and controls (CMC) response preparation, for instance, can combine retrieval, anomaly detection, and drafting in a controlled sequence so that the CMC lead reviews the evidence before a response is finalized.

The future of AI in biopharma will depend not only on better models, but on better workflow design. The impact will come from embedding AI into processes in ways that accelerate work, improve decision quality and maintain clear scientific, medical, regulatory and quality accountability.

Endnote

This article treated AI as part of the biopharma operating model, not as a general productivity layer added after the fact. It followed work from function to process to sub-process, then placed AI where it could relieve a real bottleneck, such as slow evidence review or inconsistent handoffs. Text models were only one part of that map, while predictive models, optimization, computer vision, anomaly detection, and analytics mattered when they supported a specific reviewable decision.

Value showed up where teams already work through real artifacts and governed systems. AI can draft a first-pass evidence rationale, which the scientific lead confirms before it supports target nomination package preparation.

The first projects should therefore come from the high-volume, artifact-rich, cleanly reviewed sub-processes identified across the model. They are the places where inputs are available, review roles are already defined, and value can be scored against feasibility without redesigning the whole workflow. Target biology evidence mapping is a practical example, because AI can compare external literature with internal summaries against a defined review rubric, while the disease biology lead confirms the mapped evidence before it shapes the next scientific decision.

The governance posture is just as important as the use case. AI needs to sit inside the US regulatory and assurance framework, including the National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF), US Food and Drug Administration expectations, and the industry’s own quality standards. Traceability across inputs and outputs, together with documented reviewer approval, makes it clear why a suggestion was accepted, changed, or rejected, which supports compliance and human accountability.

The forward view moves from single drafts to agentic workflows that prepare governed sequences of work. An agentic workflow might gather evidence, check consistency, and prepare an exception note, but each step still remains bounded by workflow rules and human confirmation. The durable advantage goes to teams that map AI to specific sub-processes, keep humans accountable by role, and scale only what proves value under control.

Turn biopharma AI opportunities into scalable solutions with ZBrain. Identify high-value workflows, map sub-processes, validate fit, and scale AI across discovery, clinical development, regulatory, safety, quality, manufacturing, medical, and commercial operations. Contact the ZBrain team today!