AI in pharmaceuticals: Use cases, benefits, operating model and future trends

Pharmaceuticals is one of the strongest candidates for AI adoption because the industry operates at the intersection of science, data, documentation, regulation, patient safety, manufacturing quality, and globally distributed operations. Every function, from discovery and clinical development to regulatory affairs, pharmacovigilance, quality, manufacturing, supply chain, medical affairs, and commercial operations, depends on the accurate interpretation of scientific evidence, operational records, regulatory requirements, and approved content.

The need for this capability is growing as pharmaceutical work becomes more complex and higher in volume. According to IQVIA [1], global use of medicines grew by 14% over the past five years and is expected to reach 3.7 trillion defined daily doses by 2029, underscoring the need for pharmaceutical companies to enhance efficiency as growth moderates across developed and developing economies. As medicine use expands globally, pharmaceutical teams must manage more trial records, safety cases, quality events, regulatory documents, supply chain data, and customer-facing materials without compromising accuracy, compliance, or patient safety.

AI creates value when it is embedded in specific operational steps, not when it sits alongside teams as a generic chatbot. In clinical operations, AI can flag missing site documents, summarize trial updates, and draft follow-up notes for review. In pharmacovigilance, it can prioritize case queues, extract relevant safety details, and prepare case narratives for medical assessment. In quality operations, it can classify deviations, identify similar historical events, and suggest investigation pathways before a qualified reviewer makes the final decision.

This is why AI opportunities in pharmaceuticals should be mapped at the operating-model level. Each function should be broken down into processes, sub-processes, tasks, systems, artifacts, owners, and controls. At that level, organizations can distinguish predictive use cases from content generation use cases, identify where human review is mandatory, and prioritize AI initiatives based on value, risk, feasibility, and operational readiness.

The economic potential is substantial. McKinsey [2] estimates that generative AI could create $60 billion to $110 billion in annual economic value for the pharmaceutical and medical products industries, with impact across discovery, development, regulatory approval, marketing, and customer engagement. But realizing that value requires more than selecting tools. It requires designing AI around governed pharmaceutical workflows, where AI can draft, summarize, retrieve, classify, recommend, or coordinate work while designated human reviewers remain accountable for decisions that affect patients, product quality, regulatory submissions, or customer-facing communication.

This article applies the operating-model lens to AI in pharmaceuticals. It breaks pharmaceutical work into major functions, processes, and sub-processes, and shows where AI can create practical, workflow-specific value. The focus is on helping pharmaceutical organizations identify high-impact AI opportunities, integrate them into existing workflows, and maintain human accountability across every critical decision point.

Table of Contents

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

How AI is reshaping pharmaceutical operations

Pharmaceutical companies have long relied on computational chemistry, statistical modeling, laboratory information management systems (LIMS), electronic data capture (EDC), quality management systems (QMS), enterprise resource planning (ERP), and machine learning to improve research, development, manufacturing, quality, and commercial operations. These technologies remain essential, but AI introduces a broader capability: it can help teams interpret information, generate controlled outputs, identify exceptions, and coordinate work across regulated processes.

Traditional automation follows predefined rules. Machine learning predicts, scores, detects, forecasts, and classifies from historical patterns, which is why molecular property prediction, demand forecasting, anomaly detection, and safety-signal scoring became some of pharma’s earliest durable AI use cases. Generative AI expands the opportunity by reading, summarizing, drafting, comparing, explaining, and transforming scientific and operational information. Agentic AI goes further by coordinating multi-step workflows across systems, documents, teams, approvals, and operational handoffs.

This impact is especially visible in areas where pharmaceutical work depends on regulated content, scientific judgment, and cross-functional review.

• Document-heavy work: Pharmaceutical teams manage clinical study protocols and reports, electronic Common Technical Document (eCTD) modules, investigator brochures, executed batch records, SOPs, validation protocols, safety case narratives, contracts, and supplier qualification files. AI can extract relevant information, compare sections of documents, identify missing evidence, summarize source materials, and prepare drafts for review.
• Narrative-heavy work: Clinical study reports, Module 2 summaries, periodic safety update reports, deviation investigations, CAPA records, medical information responses, publication drafts, and regulatory response letters require consistent, evidence-backed narratives. AI can draft structured text, check chronology, highlight source discrepancies, and align outputs with approved templates and controlled terminology.
• Exception-heavy work: Protocol deviations, manufacturing deviations, out-of-specification results, batch failures, safety signals, health-authority queries, serialization exceptions, temperature excursions, and supply disruptions all require timely assessment. AI can classify cases, score urgency, detect unusual patterns, retrieve similar historical events, and recommend the next step for a qualified reviewer.
• Knowledge-heavy work: Pharmaceutical decisions often depend on ICH and GxP guidelines, labeling standards, country-specific filing requirements, PhRMA and EFPIA codes, MedDRA coding conventions, SOPs, regulatory commitments, and therapeutic-area evidence. AI can retrieve controlled guidance, cite source clauses, compare requirements, and surface relevant context at the point of work.
• Workflow-heavy work: Processes such as target-to-lead progression, study start-up to database lock, dossier authoring to submission, case intake to E2B submission, deviation to batch release, change control routing, and demand planning to distribution involve many systems, owners, artifacts, and approvals. AI can sequence tasks, forecast bottlenecks, prepare handoffs, trigger follow-ups, and route work to the right reviewer.

Typical pharmaceutical AI use cases do not remove the human from the process. Instead, they prepare the case, retrieve context, draft the output, identify exceptions, recommend the next step, and route the work to the right qualified reviewer. Scientists, clinicians, biostatisticians, regulatory writers, safety physicians, QA reviewers, manufacturing leads, and commercial owners retain accountability for scientific judgment, patient safety, product quality, and regulatory decisions.

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

AI can create meaningful value across pharmaceuticals only when it is tied to specific workflows. High-level labels such as “AI in pharma,” “AI in R&D,” “AI in quality,” or “AI in commercial” may be useful for strategy discussions, but they are too broad to guide implementation. They do not define the data required, the controls involved, the validation expectations, the approval path, the success metrics, the system of record, or the accountable decision owner.

A more practical approach maps AI opportunities to the pharmaceutical operating model:

• Function: the major scientific, operational, or control area, such as drug discovery, clinical development, regulatory affairs, pharmacovigilance, quality, manufacturing, supply chain, medical affairs, or commercial.
• Process: the workflow area within that function, such as lead optimization, study start-up, dossier assembly, case processing, deviation management, batch release, or demand planning.
• Sub-process: the specific activity inside the workflow, such as ADMET prediction, site feasibility scoring, Module 2 summary drafting, MedDRA coding, root-cause analysis, batch-record review, or signal validation.
• AI-enabled opportunity: the way AI supports that activity, such as predicting a molecular property, classifying an exception, drafting a narrative, extracting structured data, retrieving guideline context, or routing a case for approval.

This level of detail matters because pharmaceutical workflows are tied to specific regulations, validated systems, documents, scientific standards, and decision rights. Drafting a clinical study report is different from coding an adverse event. Responding to a health authority query is different from dispositioning a manufacturing deviation. Predicting compound toxicity requires a different scientific context, evidence base, and validation approach than reconciling a serialized shipment or matching a supplier invoice.

The sub-process layer is where AI becomes buildable, governable, and measurable. For example, “use AI in research” is too vague to implement or validate. By contrast, “rank literature and omics evidence for a target biology decision package” clearly defines the artifact, the input sources, the AI-supported activity, and the reviewer. The translational biology lead can then assess the evidence, approve or reject the recommendation, and remain accountable for the decision on target advancement.

The same principle applies across pharmaceutical operations. In regulatory affairs, AI may draft a Module 2 summary from approved source documents, while regulatory writers and subject-matter experts verify its accuracy before submission. In pharmacovigilance, AI may support MedDRA coding or safety case narrative drafting, while the safety physician or qualified reviewer confirms the final assessment.

Sub-process mapping turns AI from a broad technology concept into an executable, governable workflow. It clarifies what AI reads and what it predicts, drafts, extracts, classifies, or recommends, and then pinpoints where human review is mandatory, which controls apply, what evidence is required, and which validated system of record must be updated. The result is a way to group opportunities by function without losing accountability at the point where the work happens.

Pharmaceutical operating model and AI opportunity mapping across pharma processes

The following sections map AI opportunities across the operating model of a modern pharmaceutical organization, from discovery and development to manufacturing, commercialization, and enterprise functions. Each section includes a brief overview, a process and sub-process table, and high-value AI opportunities.

Function 1. Drug discovery and target biology

Drug discovery owns the path from target biology through hit discovery, lead optimization, and candidate nomination. These workflows are data-rich and scientifically complex, combining genomics, chemistry, structural biology, high-throughput screening, and large volumes of internal and published literature.

AI is highly relevant because discovery pairs pattern-rich biological and chemical data with repetitive predictions, prioritizations, and literature syntheses. AI can analyze omics and literature, predict molecular properties, rank candidates, and orchestrate multi-step workflows such as target evidence assembly, virtual screening triage, and lead-optimization cycles, while scientists retain accountability for which targets, hits, and molecules advance.

The highest-value AI opportunities are high-throughput screening data review, the medicinal-chemistry design cycle, de novo molecular design, ADMET prediction, and omics-and-literature synthesis, because they produce structured assay, chemical, and literature data that AI can classify, predict, and rank while scientists’ own advancement decisions.

An example agentic workflow is lead-optimization support. The agent retrieves the active series and assay history, predicts ADMET and potency for proposed analogs, ranks candidates against the target product profile, drafts SAR commentary, and and routes a prioritized synthesis list to the medicinal chemistry team for review.

Function 2. Translational medicine and biomarker strategy

Translational medicine connects discovery biology to clinical proof of mechanism through biomarker strategy, patient segmentation, companion-diagnostic feasibility, and translational evidence planning. These workflows integrate biomarker data, clinical endpoints, omics, assay metadata, and real-world evidence (RWE).

AI is highly relevant because translational work integrates biomarker, endpoint, omics, and assay data that must be linked and reconciled. AI can summarize mechanism-of-action evidence, standardize assay data, support subgroup discovery and endpoint linkage, and assemble translational packages, while scientific reviewers retain control over translational conclusions.

The highest-value opportunities are clinical-omics-RWE data linkage, biomarker standardization, responder subgroup definition, and companion-diagnostic feasibility, because they combine structured EDC, LIMS, omics, and RWE data with clear review points for bioinformatics, programming, and translational leads.

An example agentic workflow is translational evidence assembly. The agent retrieves assay results, clinical endpoints, omics records, and RWE, drafts a cited translational package with explicit evidence gaps, and routes it to the translational-medicine lead for confirmation.

Function 3. Preclinical development and toxicology

Preclinical development manages candidate entry, IND-enabling safety packages, Good Laboratory Practice (GLP) toxicology, safety pharmacology, toxicokinetics, and nonclinical submission content. These workflows combine in vitro and in vivo study data, modeling, and document-heavy reporting that feeds the investigational new drug (IND) application.

AI is highly relevant because preclinical work involves substantial data analysis, modeling, and structured scientific writing. AI can compare protocols, detect toxicology signals, summarize study findings, and assemble IND module content, while study directors, pathologists, and regulatory toxicology leads retain approval.

The highest-value opportunities are GLP protocol development, in-life and toxicology study interpretation, toxicokinetic assessment, and IND nonclinical module assembly, because they reduce manual analysis and documentation effort while qualified specialists retain interpretive accountability.

An example agentic workflow is the IND nonclinical module assembly. The agent retrieves GLP protocols, final study reports, toxicokinetic tables, deviations, and certificates of analysis. It then drafts the nonclinical overview and study-report summaries, flags data inconsistencies, and routes the package to the regulatory toxicology lead for review.

Function 4. Clinical development strategy

Clinical development strategy supports the clinical development plan, indication strategy, trial design, protocol governance, endpoints, feasibility assumptions, and the benefit-risk evidence path. These workflows reconcile eligibility criteria, endpoints, amendments, and prior evidence across many documents.

AI is highly relevant because design work reconciles eligibility criteria, endpoints, amendments, and prior evidence across many documents. AI can summarize prior evidence, draft protocol synopses, check cross-document consistency, and support scenario analysis, amendment impact assessment, and design-review packaging, while clinical science, biostatistics, safety, and governance reviewers make final decisions.

The highest-value opportunities are protocol amendment impact assessment, eligibility and endpoint definition, and feasibility and enrollment-scenario modeling, because they connect protocol, CRF, CTMS, and trial master file artifacts with clear review points for governance, clinical science, and feasibility leads.

An example agentic workflow is protocol amendment impact assessment. The agent retrieves the current protocol, enrollment milestones, CRF fields, and trial master file evidence, drafts an impact summary across sites, data, and safety, and routes it to the protocol-governance committee for confirmation.

Function 5. Clinical operations and trial management

Clinical operations owns trial execution from startup through activation, monitoring, enrollment, vendor coordination, trial master file quality, milestones, and inspection readiness. These workflows are protocol-driven, document-heavy, and operationally complex across many studies and sites.

AI is highly relevant because clinical operations combine structured trial data, large document sets, and repetitive monitoring and coordination across many studies and sites. AI can classify start-up documents, summarize monitoring findings, and draft site communications. It can also orchestrate site selection, central monitoring, recruitment forecasting, and electronic trial master file (eTMF) completeness checks, while Good Clinical Practice (GCP) accountable roles retain oversight.

The highest-value opportunities are site identification and selection, central monitoring signal review, patient identification and matching, and eTMF completeness review, because they combine high operational volume with artifact-rich CTMS, EDC, and eTMF evidence and clear review boundaries.

An example agentic workflow is site selection and startup. The agent scores candidate sites on historical enrollment and quality, models enrollment scenarios, drafts a feasibility and site-ranking summary, validates essential documents against trial master file requirements, and routes the recommendation to the clinical-operations lead for approval.

Function 6. Clinical data management and biostatistics

Biometrics owns clinical data capture, data standards, query management, medical coding, statistical programming, analysis datasets, database lock, and clinical reporting outputs. These workflows are precision-critical, deadline-driven, and central to regulatory submissions and publications.

AI is highly relevant because biometrics combines structured data review, repetitive query and coding tasks, and large-volume scientific writing. AI can suggest codes, draft query text, reconcile labs, support SDTM and ADaM mapping, and draft report sections, while data managers, coders, statisticians, and medical writers retain documented decisions before lock.

The highest-value opportunities are EDC query management, MedDRA and WHODrug coding, SDTM and ADaM mapping, TLF production, and CSR drafting, because they are repetitive and documentation-heavy, while statistical and medical-writing accountability is preserved.

An example agentic workflow is CSR drafting. The agent ingests the locked dataset, statistical outputs, and source documents, drafts CSR sections aligned to the template and SAP, checks internal consistency and cross-references, and routes the draft to the medical writer and biostatistician for review.

Function 7. Regulatory affairs and submissions

Regulatory affairs owns regulatory strategy, registration planning, submission orchestration, electronic common technical document (eCTD) publishing, labeling governance, health-authority interactions, and lifecycle submissions. These workflows are document-intensive, deadline-driven, and governed by ICH standards and country-specific requirements.

AI is highly relevant because regulatory work combines structured authoring, cross-document consistency checking, eCTD assembly and validation, and global change management. AI can draft module summaries, compare labeling, draft query responses, and run dossier gap checks, commitment tracking, and variation planning, while regulatory professionals approve final positions and submissions.

The highest-value opportunities are CTD module drafting, eCTD compilation and validation, labeling harmonization, health-authority query response drafting, and variation classification, because they are repeatable and rules-bound, reducing cycle time and technical-rejection risk while regulatory professionals retain accountability for content.

An example agentic workflow is submission assembly. The agent drafts Module 2 summaries from source modules, runs an eCTD gap and validation check, flags formatting and hyperlink errors, drafts a cover letter with correct references, and routes the package to the regulatory lead and publisher for review.

Function 8. Pharmacovigilance and patient safety

Pharmacovigilance covers adverse-event intake, case processing, medical review, safety coding, expedited and aggregate reporting, signal management, risk management plans, and benefit-risk governance. These workflows are case- and alert-heavy, governed by strict reporting timelines under Good Pharmacovigilance Practices (GVP).

AI is highly relevant because safety operations process very large volumes of cases and signals through repetitive intake, coding, and narrative steps. AI can extract case data, suggest codes, draft narratives, and support intake-to-submission case processing, signal detection, and aggregate report drafting, while safety physicians and qualified reviewers retain accountability for causality, seriousness, and reporting decisions.

The highest-value opportunities are ICSR intake and extraction, case triage and duplicate detection, MedDRA coding, narrative generation, disproportionality signal detection, and aggregate report drafting, because they are high-volume and repetitive while qualified safety physicians retain accountability.

An example agentic workflow is ICSR processing. The agent ingests a case from intake, extracts and structures case data, suggests MedDRA coding, drafts the narrative, summarizes causality and seriousness evidence, and routes the case to the safety physician for medical review and reporting decision within regulatory timelines.

Function 9. Quality assurance and GxP compliance

Quality assurance oversees the pharmaceutical quality system, GxP compliance, SOP governance, deviations, out-of-specification (OOS) and out-of-trend (OOT) investigations, CAPA, change control, validation quality, supplier quality, audits, inspections, and data integrity. These workflows are document-intensive, control-critical, and inspection-sensitive.

AI is highly relevant because quality work centers on deviation investigations, CAPA, batch-record review, change control, and audit readiness. AI can draft investigations, summarize evidence, check SOP alignment, and support deviation-to-disposition, recurrence trending, change-impact assessment, and inspection response, while the quality unit retains disposition and GxP accountability.

The highest-value AI opportunities are deviation classification and investigation, OOS/OOT investigation, CAPA drafting, batch-record review, change-impact assessment, and inspection readiness, because they are documentation-heavy and slow when manual, while the quality unit retains disposition authority.

An example agentic workflow is deviation-to-disposition support. The agent classifies a deviation, retrieves batch and prior-investigation context, drafts a root-cause investigation and CAPA, summarizes batch impact, and routes the disposition package to the quality unit for review, keeping draft and validated-record environments strictly separate.

Function 10. Manufacturing, CMC, and batch release

Manufacturing and CMC oversee process development, technology transfer, process validation, continued process verification, current Good Manufacturing Practice (CGMP) execution, the quality-control testing interface, batch-record review, CMC submission content, and final disposition. These workflows are process-intensive, data-rich, and tightly coupled to quality and regulatory commitments.

AI is highly relevant because manufacturing combines process and equipment data, batch documentation, and technical reporting. AI can draft validation and CMC documents, summarize process trends, and support review-by-exception, continued process verification trending, and release-packet assembly, while process owners and the quality unit retain control over validated operations and disposition.

The highest-value AI opportunities are continued process verification trending, PAT and process monitoring, batch-record review, CMC Module 3 authoring, and batch-release disposition, because they are high-volume and artifact-rich while the quality unit retains final authority.

An example agentic workflow is batch-release exception triage. The agent retrieves batch exceptions, CoA status, deviation and CAPA records and drafts a disposition summary scored for readiness, and routes risks to the QA-release manager for decision.

Function 11. Supply chain planning and product lifecycle management

Supply chain owns demand and supply planning, materials planning, supplier coordination, distribution readiness, serialization, traceability, stability coordination, and lifecycle execution across a regulated, often cold-chain network. These workflows are calculation-intensive and exception-prone, with patient access and compliance implications.

AI is highly relevant because the supply chain combines structured planning data, serialization and traceability data, and recurring exception handling across many partners. AI can draft planning commentary, summarize exceptions, draft partner communications, and support demand forecasting, shortage detection, serialization exception handling, and cold-chain disposition, while supply, quality, and regulatory owners approve decisions.

The highest-value AI opportunities are demand forecasting and sensing, inventory optimization, serialization exception handling, cold-chain excursion monitoring, and Q12 post-approval change planning, because they are high-volume and rules-driven, while planners and quality teams retain decision authority.

An example agentic workflow is serialization exception handling. The agent ingests traceability data, classifies exception types, identifies likely root causes, drafts corrective tasks and partner outreach, and routes unresolved cases to the supply chain and quality teams, thereby preserving regulatory governance throughout.

Function 12. Medical affairs and scientific communications

Medical affairs owns medical strategy, scientific exchange, medical information, publications, evidence communication, field-medical enablement, non-promotional review, and insight capture. These workflows are scientific, compliance-sensitive, and document-heavy, governed by codes that separate medical from promotional activity.

AI is highly relevant because medical affairs combines literature synthesis, engagement data, medical information drafting, and publication management. AI can summarize literature, structure field notes, draft medical-information responses, and support inquiry triage, standard-response drafting, and insight clustering, while medical reviewers approve all external-facing materials and confirm consistency with approved labeling.

The highest-value AI opportunities are medical information intake and response authoring, KOL mapping and MSL support, evidence-gap analysis, publication drafting, and field-insight triage, because they reduce preparation and documentation effort while medical and compliance accountability stays with medical teams.

An example agentic workflow is medical-information support. The agent classifies an inbound inquiry, retrieves the relevant standard response document and approved evidence, drafts a cited response, flags adverse-event or product-complaint content for pharmacovigilance routing, and routes the draft to the medical-information team for review.

Function 13. Market access, pricing, HEOR, and commercial operations

This function covers value evidence, health economics, payer strategy, pricing governance, access planning, brand marketing, omnichannel content, healthcare-professional (HCP) engagement, and promotional review operations. These workflows are evidence, content, and compliance-intensive, with high commercial impact, and all promotional materials must pass medical, legal, and regulatory (MLR) review.

AI is highly relevant because this function combines evidence synthesis, economic modeling, large-volume content creation, and compliance review. AI can synthesize evidence, draft dossier sections and brand content, run pre-MLR checks, and support value-dossier assembly, pricing-scenario evaluation, content-to-MLR routing, and next-best-action recommendation, while access, HEOR, legal, medical, regulatory, and commercial reviewers make final decisions on value, claims, and price.

The highest-value AI opportunities are value-dossier development, pricing-scenario and gross-to-net governance, formulary submission development, promotional content drafting, pre-MLR checks and MLR triage, and HCP targeting with next-best-action, because they combine high-volume or high-stakes decisions with artifact-rich inputs while reviewers retain approval on value, claims, and price.

An example agentic workflow is content-to-MLR support. The agent drafts brand-aligned content from approved references, runs pre-MLR compliance checks for claims and fair balance, assembles the reference pack, and routes the content in parallel to medical, legal, and regulatory reviewers—keeping final approval with the reviewers.

Function 14. Patient services and patient support programs

Patient services owns patient support programs (PSPs), hub services, benefit verification, adherence support, and access assistance. These workflows are interaction-heavy and document-driven, and they are sensitive because they involve patient data and may surface adverse events.

AI is highly relevant because patient services combine high-volume inquiries, enrollment documentation, and benefit verification. AI can extract enrollment data, retrieve approved education content, draft policy-grounded responses, and support enrollment, benefit verification, inquiry handling, and adverse-event routing, while staff retain accountability for patient-impacting decisions and mandatory safety reporting.

The highest-value AI opportunities are enrollment intake and verification, benefit verification support, inquiry handling, adherence support, and adverse-event detection and routing, because they reduce administrative effort and improve consistency while preserving human accountability and mandatory safety reporting.

An example agentic workflow is enrollment and inquiry support. The agent validates enrollment data, summarizes coverage and eligibility, drafts prior-authorization documentation, responds to routine inquiries from approved content, and routes any adverse-event mentions to pharmacovigilance and complex cases to case managers.

Function 15. Procurement and supplier management

Procurement sources materials, services, and contract partners (CMOs/CDMOs and CROs) and manages supplier qualification, contracts, and performance. These workflows are document-heavy and exception-prone, with quality and compliance dependencies that distinguish pharmaceutical procurement from generic spend management.

AI is highly relevant because procurement involves repetitive document handling, supplier-qualification review, invoice matching, and contract analysis across many vendors. AI can summarize spend, extract contract obligations, draft scorecards, and support supplier qualification, bid evaluation, invoice matching, and performance monitoring, while procurement and supplier-quality teams approve qualifications and awards.

The highest-value AI opportunities are spend analysis, supplier qualification review, contract obligation extraction, invoice and PO matching, and supplier performance monitoring, because they reduce manual reconciliation and strengthen supplier controls while procurement and supplier-quality teams retain approval.

An example agentic workflow is supplier qualification support. The agent extracts and validates supplier documents and certifications, summarizes ownership and risk indicators, flags gaps against GxP requirements, and routes the qualification package to the procurement and supplier-quality teams for approval.

Function 16. Business development, licensing, and alliance management

Business development pursues partnerships, in and out-licensing, mergers and acquisitions, and alliance management across a science- and deal-intensive landscape. These workflows are research, document, and coordination-heavy.

AI is highly relevant because business development combines scientific and commercial due diligence, document analysis, and ongoing alliance coordination. AI can summarize assets, cluster data-room documents, draft diligence responses, and support asset screening, diligence document analysis, and alliance obligation tracking, while dealmakers retain judgment on valuation and terms.

The highest-value opportunities are asset and pipeline screening, market and competitive analysis, diligence document analysis, and alliance obligation tracking, because they reduce research and documentation effort while preserving deal judgment.

An example agentic workflow is diligence support. The agent ingests data-room documents, clusters and summarizes them by workstream, drafts source-grounded responses to diligence questions, flags risks, and routes the package to the deal team for review.

Function 17. Legal, compliance, ethics, and risk

This function manages contracts, healthcare-compliance obligations, anti-bribery and transparency requirements, privacy, and enterprise risk. These workflows are document-intensive and policy-driven, with significant regulatory exposure that is specific to the life-sciences environment.

AI is highly relevant because these functions involve contract analysis, policy interpretation, monitoring, and case documentation. AI can extract obligations, summarize spend against transparency rules, draft policy answers, and support contract obligation management, transparency monitoring, and investigation documentation, while legal and compliance owners retain accountability.

The highest-value opportunities are contract review and obligation tracking, legal request triage, transparency and spend monitoring, privacy and consent review, and policy support, because they improve responsiveness and consistency while preserving legal and compliance accountability.

An example agentic workflow is contract-obligation management. The agent extracts obligations and renewal dates, flags non-standard clauses against playbooks, summarizes compliance commitments, and routes contracts to the legal and compliance owners for review.

Function 18. Data, technology, cybersecurity, and AI governance

This function manages the validated and enterprise systems that connect pharmaceutical operations—LIMS, EDC, QMS, RIM, safety databases, ERP, and CRM—along with integration, data quality, cybersecurity, and AI governance. It is foundational because AI cannot scale without secure data access, validated integration, and model governance.

AI is highly relevant because technology operations involve large volumes of incidents, integrations, data-quality exceptions, and governance documentation across regulated systems. AI can draft release notes, summarize incidents, document AI use cases, and support incident triage, integration monitoring, data-quality remediation, and model-release evidence assembly, while platform owners, validation leads, and model-risk officers make release and escalation decisions.

The highest-value AI opportunities are incident and integration triage, data quality and lineage management, cloud ingestion validation, GxP computerized system validation, and AI governance and model monitoring, because they are foundational to scaling AI safely across regulated pharmaceutical operations.

An example agentic workflow is AI model-release evidence assembly. The agent retrieves training lineage, test evidence, change approvals, and validation requirements, drafts a risk-based validation and credibility summary, routes exceptions to the validation lead, and records the model-risk officer’s confirmation.

High-value AI use cases in pharmaceuticals

The pharmaceutical AI use-case map is broad, but not every workflow should be prioritized first. The strongest early opportunities are usually high-volume, document-heavy, exception-heavy, scientifically repetitive, or narrative-heavy workflows where AI can prepare a draft, recommendation, summary, classification, or prediction for human review.

A use case becomes high-value when the business impact is clear, and the review boundary is well-defined. In pharmaceuticals, that usually means AI reduces backlogs, shortens cycles, improves documentation quality, strengthens oversight, or accelerates time-to-decision while a qualified reviewer remains accountable for the final outcome.

These use cases work well because they support human review rather than bypassing it. AI can prepare evidence, draft outputs, flag exceptions, and suggest next steps, while domain experts remain accountable for scientific, safety, quality, regulatory, and commercial decisions.

How agentic AI works in pharmaceutical workflows

Generative AI can draft, summarize, classify, predict, and retrieve. Agentic AI can coordinate a workflow. In pharmaceuticals, this distinction matters because many valuable use cases are not single tasks. They require multiple steps across validated systems, controlled documents, teams, regulations, and approvals.

When a new safety case, filing need, quality event, protocol change, or content request appears, an agentic workflow follows a governed sequence: planning the work, retrieving approved context, drafting or analyzing the required output, routing it to the right reviewer, and recording the decision. Its access should be limited to approved systems, and each step should be logged for review.

Individual case safety report processing

• Work plan: The agent defines the case-processing checklist and identifies missing information.
• Evidence retrieval: It gathers case intake data, source documents, prior reports, and relevant safety database records.
• Drafting and analysis: It extracts and structures case details, suggests MedDRA coding, checks for duplicates, and drafts the safety narrative.
• Final confirmation: The safety physician or qualified reviewer confirms the medical assessment and reporting decision.

Regulatory submission package assembly

• Submission plan: The agent defines the submission checklist and identifies required dossier sections.
• Source retrieval: It gathers approved source documents, study outputs, labeling references, and regulatory commitments.
• Drafting and validation: It drafts Module 2 summaries, checks cross-references, flags gaps, and prepares the package for publishing review.
• Final confirmation: The regulatory lead and publisher confirm the final package before submission.

Quality deviation and CAPA workflow

• Investigation plan: The agent defines the deviation investigation checklist and identifies required evidence.
• Evidence retrieval: It gathers batch records, deviation history, SOPs, test results, and similar prior events.
• Investigation support: It classifies the deviation, drafts root cause analysis and CAPA content, summarizes the batch impact, and routes the disposition package.
• Final confirmation: The quality unit confirms the investigation outcome, CAPA plan, and batch disposition decision.

Commercial content and MLR workflow

• Review plan: The agent defines the content review checklist against brand, labeling, claims, and compliance requirements.
• Reference retrieval: It gathers approved claims, references, prescribing information, brand guidance, and prior MLR comments.
• Content review support: It drafts or checks content, flags unsupported claims, assembles the reference pack, and routes the material for review.
• Final confirmation: Medical, legal, and regulatory reviewers confirm the final content before release.

The design rule is clear: agentic AI can prepare evidence, draft outputs, recommend next steps, route work, and update approved workflow stages, but it should not bypass accountability. Pharmaceutical organizations must define where human review is mandatory, which evidence must be retained, which systems of record can be updated, and how exceptions are escalated. This is especially important where AI output affects patient safety, product quality, regulatory submissions, promotional claims, or patient and personal health data.

How to prioritize AI use cases in pharmaceuticals

Pharmaceutical organizations should not select AI use cases only because they sound innovative. The strongest opportunities are those where the workflow is frequent, evidence is available, business value is clear, and review can remain with the accountable pharmaceutical role. In practice, prioritization should balance value, feasibility, risk, and control readiness.

A practical first wave should focus on high-volume, artifact-rich, cleanly reviewed sub-processes. Examples include ICSR intake support, deviation and CAPA drafting, CSR and CTD module drafting, MLR pre-review, medical information response drafting, EDC query management, demand forecasting, and serialization-exception handling. These workflows are strong candidates because AI can prepare evidence, draft outputs, classify exceptions, or recommend next steps while a qualified reviewer remains in control.

More sensitive use cases require stronger governance from the beginning. AI outputs that directly influence regulatory decisions, causality assessments, final batch disposition, product-quality conclusions, promotional claims, or patient-facing actions should be subject to clear validation expectations, retained evidence, exception handling, and mandatory human approval.

This prioritization discipline prevents common AI roadmap failures: selecting use cases too broad to implement, starting without usable data, bypassing governance, or promising savings before proving the workflow. Leading pharmaceutical AI programs begin with bounded workflows where value is clear, evidence is available, and accountability is assigned.

Governance, risk, and responsible AI in pharmaceuticals

AI in pharmaceuticals must operate within the organization’s existing quality, regulatory, safety, compliance, cybersecurity, and computer system validation environment. The most important principle is clear accountability. AI can assist, recommend, draft, classify, predict, retrieve, and route work, but the responsible qualified human owner must remain accountable for consequential decisions, regulated outputs, and anything that affects patient safety, drug quality, study reliability, or customer-facing communication.

This is especially important because pharmaceutical AI increasingly produces information that supports regulated decisions across safety, quality, submissions, and other compliance-critical workflows. Key governance requirements include:

• Human review and accountability: AI outputs that support regulatory decisions, batch disposition and release, causality and seriousness assessment, safety reporting, promotional claims, or patient-impacting actions should be reviewed by qualified owners before they affect controlled records, submissions, production decisions, or customer-facing messages.
• Source-grounded outputs: AI responses should be grounded in approved pharmaceutical sources, including validated study records, protocols, SOPs, labeling, regulatory commitments, controlled research data repositories, safety databases, and other approved evidence sources.
• Traceability and audit trails: The audit trail should capture prompts, inputs, cited sources, outputs, model versions, reviewer actions, approvals, overrides, escalations, and downstream system updates. These controls should be reviewable under applicable electronic-record and electronic-signature expectations, including 21 CFR Part 11 and Annex 11.
• Validation and credibility assessment: AI systems should be assessed using risk-based validation and credibility principles, with evidence proportionate to the model’s context of use. This may include computer system validation, software assurance, GAMP 5 alignment, ICH Q9 quality-risk principles, and model-specific testing for accuracy, reliability, robustness, and intended-use performance.
• Role-based access and scoped tool use: AI systems should follow least-privilege access. A user or agent should retrieve only the scientific, clinical, safety, quality, commercial, or patient data permitted by the approved workflow. For agentic AI, access to tools should be tightly scoped, especially where the workflow connects to validated systems or controlled records.
• Data protection and data integrity: Controls should protect patient data, study data, and regulated records in line with applicable privacy, cybersecurity, and data integrity expectations, including GDPR, HIPAA (where relevant), ALCOA+ principles, and internal security policies.
• Bias, drift, and performance monitoring: AI systems should be monitored for accuracy, completeness, hallucination risk, bias, drift, latency, adoption, escalation frequency, reviewer overrides, unresolved low-confidence outputs, and exception rates. Monitoring should be stronger where the AI output influences safety, quality, regulatory, or clinical decisions.
• Escalation and exception handling: Workflows should define what happens when AI confidence is low, evidence conflicts, required data is missing, guidance is ambiguous, or the scenario has unusual patient safety, quality, regulatory, or privacy sensitivity.
• Third-party and vendor risk management: AI platforms, models, infrastructure, integrations, and service providers should be reviewed for security, validation support, auditability, data handling, model governance, and contractual controls before connection to regulated workflows.
• Regulatory and standards alignment: Responsible AI programs should align with applicable GxP expectations, ICH guidelines, pharmacovigilance and GVP requirements, records-retention rules, cybersecurity frameworks, the PhRMA and EFPIA codes for commercial activity, and broader AI risk-management frameworks such as NIST AI RMF 1.0 and NIST AI RMF 1.0 and its Generative AI Profile (NIST AI 600-1).

Governance should not be treated as a blocker to AI adoption. It is what makes AI usable in pharmaceuticals. A well-governed AI workflow can provide stronger documentation, clearer accountability, better auditability, more consistent execution, and greater transparency than unmanaged manual processes.

How ZBrain operationalizes AI use cases in pharmaceuticals

Identifying use cases is only the first step. Pharmaceutical organizations also need a way to design, build, validate, deploy, govern, and scale AI workflows across discovery, development, regulatory, safety, quality, manufacturing, supply chain, medical, and commercial functions, and across sites and regions. 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 pharmaceutical organizations identify, evaluate, and design AI solutions by leveraging their operating model, scientific and operational workflows, validated systems, and historical 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, reducing fragmented experimentation.

Preparation (Foundation)

Establishes a clear understanding of the organization’s current operating environment, including discovery, clinical, regulatory, safety, quality, manufacturing, supply chain, medical, and commercial workflows, along with LIMS, EDC, QMS, RIM, safety, ERP, and CRM systems, plus KPIs and data-readiness assessment.

Ideation and prioritization (Discovery)

Uses enterprise and operational data to identify AI opportunities and prioritize them based on feasibility, cost, benefits, ROI, data readiness, patient-safety and quality impact, governance requirements, and the ability to embed into existing pharmaceutical workflows.

Solution design (Validation)

Translates prioritized opportunities into ROI-validated and KPI-mapped solution blueprints, defining where AI can assist, augment, or act under approval gates within workflows such as ICSR processing, deviation and CAPA, CSR and CTD drafting, MLR pre-review, or demand planning.

Technical design (Build-ready)

Transforms solution requirements into structured technical design artifacts, including architecture diagrams, schemas, integrations, agentic workflows, user stories, epics, and business requirement documents, aligned with validated systems and GxP controls.

Proof of Concept / PoC (Validation)

Tests selected AI workflows in controlled environments to validate feasibility, business value, data quality, system connectivity, credibility for the intended context of use, and human-review design before scaling.

Scaled product

Deploys validated AI solutions as governed, production-grade workflows across enterprise environments, supported by performance metrics, observability, audit trails, and continuous improvement loops to sustain impact across functions, sites, and regions.

Future of AI in pharmaceuticals

AI in pharmaceuticals will evolve from copilots to workflow agents. The first wave of adoption helps teams draft, summarize, search, classify, predict, and retrieve information. The next wave will coordinate larger workflows across validated systems, functions, and teams, with people entering at defined points for review, approval, and scientific judgment.

This shift will not be defined only by more powerful models. In pharmaceuticals, the future of AI will depend on how well organizations connect AI to governed workflows, controlled data, validated systems, audit trails, and accountable human review. Three trajectories are likely to shape the next stage of adoption.

Trajectory 1: Federated AI platforms

Pharmaceutical organizations will move from isolated AI pilots to federated AI platforms with shared orchestration, governance, observability, and integration. Instead of each function building disconnected tools, clinical development, regulatory affairs, pharmacovigilance, quality, CMC, supply chain, medical affairs, and commercial teams will reuse approved AI services while preserving function-level ownership and review controls.

For example, a regulatory affairs team preparing a submission update may need the same evidence trail used by safety, quality, or CMC teams. A federated platform approach allows these groups to work from controlled sources, shared workflows, and consistent governance without losing accountability for their own decisions.

Trajectory 2: Long-horizon agentic workflows

AI will increasingly support multi-step workflows that extend across systems and teams. A protocol amendment, for instance, may trigger downstream updates in site readiness, safety review, regulatory tracking, and the electronic trial master file. Agentic workflows can help track status, retrieve evidence, classify gaps, draft updates, recommend next actions, and route work to the right owner.

Human confirmation will remain mandatory at risk-bearing decision points. A clinical operations lead, pharmacovigilance physician, regulatory reviewer, quality reviewer, or commercial approver must confirm any action that affects patient safety, drug quality, regulatory commitments, controlled records, or customer-facing communication.

Trajectory 3: Workflow design over model selection

As model capabilities continue to improve, competitive advantage will come less from selecting the newest model and more from designing the right workflow around it. A quality team reviewing deviations, for example, will gain more value from well-defined handoffs, controlled data access, validation evidence, reviewer placement, and audit trails than from switching among similar AI models.

In pharmaceuticals, successful AI adoption will depend on embedding AI into SOPs, validated systems, governed review loops, and measurable operating processes. The strongest organizations will not be those with the longest list of AI pilots. They will be those who connect AI to how discovery, development, manufacturing, safety, quality, regulatory, medical, and commercial work actually happens at the function, process, and sub-process level.

Endnote

AI has the potential to reshape pharmaceutical work, but only when it is applied at the right level of detail. Broad statements such as “AI in pharma,” “AI in R&D,” or “AI in quality” are not enough to guide implementation. Real value comes from applying AI to specific pharmaceutical workflows, from target prioritization and lead optimization to protocol design, site selection, regulatory authoring, safety processing, quality review, manufacturing documentation, demand forecasting, and medical-commercial review.

The pharmaceutical operating model is complex, spanning discovery, development, regulatory, safety, quality, manufacturing, supply chain, medical, commercial, and enterprise functions. Across these functions, GenAI can predict properties, classify exceptions, summarize evidence, draft documents, retrieve guideline context, reconcile records, and coordinate multi-step workflows. Agentic AI extends this value by connecting steps across validated systems, controlled documents, and cross-functional teams, while maintaining human review.

For pharmaceutical organizations, the path forward is clear. Pharma companies should map AI opportunities at the sub-process level, prioritize workflows with clear value and review models, connect AI to approved data and validated systems, and scale through governed pilots, context-specific validation, and reusable agents across functions, sites, and regions.

Generic chatbots or isolated pilots will not define the future of pharmaceutical AI. It will be defined by governed, workflow-specific AI systems that help pharmaceutical organizations accelerate research, strengthen quality and safety, improve compliance, and give scientists, clinicians, quality teams and regulatory experts more time to apply judgment where it matters most.

Bring AI into everyday pharmaceutical workflows with ZBrain. From discovery, clinical operations, and regulatory affairs to quality, manufacturing, supply chain, medical affairs, and commercial operations, ZBrain helps pharmaceutical organizations identify, build, and scale governed AI solutions that deliver measurable value. Contact the ZBrain team today!