Why AI in Pharmaceuticals Must Be Mapped to Sub-processes

Pharmaceutical operations depend on a growing volume of scientific, clinical, safety, manufacturing and regulatory records. Teams must process adverse event reports, investigate deviations and out-of-specification (OOS) results, review executed batch records and prepare regulatory submissions while maintaining data integrity, patient safety and compliance.

These activities require more than accurate information processing. Each assessment, exception and regulatory deliverable must move through defined procedures, controlled systems and accountable reviewers.

Consider an adverse event report entering a pharmacovigilance system. The report arrives with unstructured source documents that must be reviewed for case validity, potential duplication and completeness. Once the minimum criteria for a valid individual case safety report (ICSR) are available, applicable regulatory reporting timelines must be met. Case processing may involve extracting relevant information, applying Medical Dictionary for Regulatory Activities (MedDRA) coding, preparing a narrative and assembling evidence for medical review.

Existing safety databases, electronic data capture (EDC) systems and quality management systems (QMS) support these activities through structured records and established workflows. Rules-based automation handles predefined conditions, while traditional machine learning supports tasks such as classification, prediction and anomaly detection. However, workflows requiring the interpretation of multiple documents, evidence-linked drafting and coordinated review often involve additional manual effort.

AI can support these activities through document intelligence, retrieval-grounded analysis, content generation and workflow orchestration. Yet a broad initiative such as “AI in pharmacovigilance” does not specify which records AI can access, which tasks it can perform, where its outputs should be stored or who must review them.

This is why AI in pharmaceuticals must be mapped to sub-processes. The sub-process level connects AI capabilities to specific operational activities, source records, review requirements and measurable outcomes.

From Pharmaceutical Functions to AI-enabled Sub-processes

Pharmaceutical operations present AI opportunities across document-intensive activities, exception management and workflows involving multiple systems. Examples include clinical study report preparation, deviation investigations, adverse event processing, batch record review and electronic Common Technical Document (eCTD) submission assembly.

However, broad categories such as “AI in drug discovery,” “AI in manufacturing” or “AI in regulatory affairs” are too general to guide implementation. They do not establish the required data, expected outputs, applicable controls or accountable decision-makers.

A more practical approach maps opportunities across four levels:

• Function: The broader area of pharmaceutical operations, such as pharmacovigilance, clinical development, manufacturing quality or regulatory affairs.
• Process: A defined sequence of activities within that function, such as ICSR processing, deviation management or regulatory submission preparation.
• Sub-process: A specific activity within the process with identifiable inputs, outputs, operational responsibilities and review requirements.
• AI-enabled opportunity: The task AI can perform within the sub-process, supported by appropriate capabilities, system access and human oversight.

Consider pharmacovigilance, where adverse event narrative preparation is a sub-process of ICSR processing. AI extracts information from case records, organizes event chronology and drafts a source-grounded narrative for authorized review before acceptance into the case workflow.

This mapping identifies what the AI system needs to read, what it should produce and where human review occurs. It also establishes the boundaries for system integration, validation and performance measurement.

The same approach applies to drug discovery. Target identification is supported by sub-processes such as target evidence assessment, where AI can retrieve scientific literature, organize omics evidence and prepare a target assessment package. The responsible scientific lead evaluates the evidence and remains accountable for the target advancement decision.

The distinction matters because AI capabilities are not the same as operational use cases. Document intelligence can extract information, entity resolution can identify related records, and generative AI can prepare summaries. A deployable use case combines the relevant capabilities within a defined workflow, with clear responsibilities and controls.

How AI Agents Execute Governed Pharmaceutical Workflows

AI and agentic AI can support pharmaceutical operations at different levels.

AI capabilities such as information extraction, classification, retrieval-grounded analysis and content generation help teams interpret scientific and operational information, identify exceptions and prepare documentation. These capabilities can be incorporated into individual applications or broader workflows.

Agentic AI extends this approach by coordinating multiple activities across authorized systems, documents and review stages. An agent can respond to a defined event, determine the next permitted step, retrieve required information, invoke approved tools, prepare an output and route unresolved issues to the responsible reviewer.

Consider an agent supporting a pharmaceutical deviation investigation.

1. Trigger and scope definition: A deviation record is created in the QMS. The agent receives the assigned investigation task and identifies the records required under the applicable procedure.
2. Evidence retrieval: The agent accesses authorized batch records, standard operating procedures (SOPs), equipment logs, laboratory results and relevant historical deviations.
3. Evidence analysis: AI capabilities organize the available information, identify related events, highlight inconsistencies and surface missing documentation.
4. Draft preparation: The agent prepares an evidence-linked investigation summary, potential contributing factors and draft corrective and preventive action (CAPA) considerations for review.
5. Exception handling: Missing records, conflicting evidence or unresolved quality concerns are flagged and routed to the designated investigation owner or quality reviewer.
6. Review and controlled completion: Authorized personnel evaluate the evidence, determine investigation conclusions and approve required actions. Any updates to controlled records follow established procedures and permissions.

This workflow does not give the agent authority to establish root causes, approve CAPA measures or determine batch disposition independently. Those responsibilities remain with designated personnel.

The agent’s activities should operate within defined tool permissions, workflow rules, escalation conditions and logging requirements. When evidence is insufficient or a permitted tool cannot complete an action, the workflow should return the task for correction or authorized review rather than proceed on an unsupported assumption.

This makes agentic AI useful not simply for generating information, but for coordinating repeatable, evidence-based activities within pharmaceutical operating controls.

Examples of Sub-process Level AI Use Cases in Pharmaceuticals

Three pharmaceutical workflows demonstrate how AI capabilities, source records, review responsibilities and measurable outcomes can be defined at the sub-process level.

1. Adverse Event Case Preparation

Adverse event processing begins when safety information is received through sources such as healthcare professionals, patients, clinical programs or medical literature. The information must be assessed, structured and processed according to applicable pharmacovigilance procedures and reporting obligations.

How AI supports the sub-process:

• Document intelligence extracts patient, product, event and chronology information from unstructured case documents.
• Entity resolution identifies potential duplicate reports using available case attributes and previously recorded safety cases.
• Classification recommends MedDRA terms based on documented adverse event descriptions.
• Retrieval-grounded generation prepares draft case narratives with supporting source references.
• Workflow orchestration flags missing information, uncertain classifications and cases requiring medical review.

Human review and control: Authorized pharmacovigilance personnel verify case information, coding and narrative accuracy. Qualified medical reviewers assess medical questions, including seriousness and causality where required. Reporting decisions and submissions follow established pharmacovigilance responsibilities and approval procedures.

Measurable outcomes: Case preparation time, coding acceptance and override rates, reviewer correction rates, evidence completeness and reporting timeliness.

2. Deviation Investigation and CAPA Preparation

Deviation investigations require teams to determine what occurred, assess potential product-quality impact, establish supported causes and identify appropriate corrective and preventive actions.

The evidence may be distributed across batch manufacturing records, laboratory systems, equipment histories, approved SOPs and previous investigation files.

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How AI supports the sub-process:

• Document intelligence extracts relevant information from deviation descriptions, investigation records and supporting documents.
• Classification organizes deviations by approved categories to support triage and assignment.
• Retrieval-grounded analysis identifies relevant SOP requirements and historical investigations associated with the event.
• Pattern analysis highlights recurring deviations, related equipment events or previously documented contributing factors.
• Generative AI prepares investigation summaries, potential contributing-factor descriptions and CAPA drafts grounded in the available evidence.

Human review and control: The investigation owner evaluates the evidence and establishes supported conclusions. The quality unit reviews and approves investigation outcomes and CAPA decisions according to established procedures. Batch disposition remains under authorized quality responsibilities, and AI-generated drafts do not independently modify validated records.

Measurable outcomes: Investigation preparation time, draft acceptance rate, evidence completeness, reviewer rework and investigation cycle time.

3. Regulatory Submission Assembly

Regulatory submission preparation involves coordinating scientific, clinical, nonclinical and quality information into an accurate, complete and technically compliant submission package.

For eCTD submissions, this work includes document preparation, source verification, cross-referencing, technical validation and publishing readiness.

How AI supports the sub-process:

• Retrieval-grounded generation prepares draft Module 2 summaries using relevant approved source content and supporting study documentation.
• Document intelligence identifies missing sections, incomplete references and inconsistencies across submission documents.
• Rule-based validation tools check applicable eCTD structure, document formatting, metadata and hyperlink requirements.
• Change detection identifies differences between approved source documents and current submission drafts.
• Agentic orchestration gathers validation results, organizes publishing exceptions and prepares readiness summaries for regulatory review.

Human review and control: Regulatory authors and subject matter experts verify scientific accuracy and consistency with approved source material. The regulatory lead confirms content readiness, while authorized publishing personnel complete technical validation and submission activities.

Measurable outcomes: First-pass technical validation success, unresolved defects at publishing, document rework cycles and submission readiness against planned milestones.

These examples show how AI-enabled activities become measurable when each sub-process has defined inputs, outputs, responsible reviewers and acceptance criteria. They also demonstrate why a single AI capability may support several use cases without replacing the operational workflow around it.

Governance and Validation Requirements for AI-driven Pharmaceutical Operations

Pharmaceutical AI requires controls proportionate to its intended use, regulatory impact and potential consequences for patient safety, product quality and data integrity.

The governance model must account for both the AI system and the regulated processes it supports. A workflow that summarizes approved scientific literature has different operational risks from one that prepares information used in batch release or regulatory decision-making.

Several requirements should be addressed during design and implementation.

Approved data sources and access controls: AI systems should use authorized information sources, including validated study records, approved SOPs, controlled manufacturing records, regulatory documentation and permitted safety data. Role-based access and least-privilege permissions should determine which information and systems the application or agent can access.

Output verification and traceability: Generated outputs should retain appropriate links to supporting records so reviewers can verify their completeness and accuracy. Workflow evidence may include relevant inputs, source references, output versions, model or application versions, reviewer corrections, approvals and exceptions. Retention requirements should be determined by the applicable regulated-record obligations and documented risk assessment.

Computerized-system validation: Where AI supports GxP-regulated activities, organizations must assess applicable computerized-system validation and electronic-record requirements. These may include 21 CFR Part 11 and applicable FDA guidance, depending on the system’s intended use and the regulated activity. Testing should address intended functionality, output reliability, failure conditions, access permissions and controlled changes.

Context-specific model evaluation: AI performance should be evaluated against representative tasks and defined acceptance criteria. The FDA’s January 2025 draft guidance on AI supporting drug regulatory decision-making describes a risk-based credibility assessment framework built around a defined context of use. Although the guidance remains a draft, it provides a useful reference for assessing model credibility in relevant regulatory applications.

Human review and decision authority: Outputs supporting medical assessments, quality investigations, batch disposition or regulatory submissions must pass through the review and authorization controls established for those activities. AI can assemble evidence, identify inconsistencies and prepare recommendations, but accountable personnel retain responsibility for regulated conclusions and approvals.

Monitoring and change management: Organizations should monitor output quality, reviewer corrections, exception trends and system behavior after deployment. Changes to models, prompts, retrieval sources, workflow logic or system integrations should be assessed for their impact and evaluated before release under the applicable change-control process.

Governance therefore extends beyond restricting AI-generated content. It defines how the workflow uses regulated information, responds to uncertainty, preserves evidence and prevents unauthorized actions.

Prioritizing AI Use Cases Across Pharmaceutical Operations

AI adoption should begin with sub-processes where operational value, data availability, workflow boundaries and review responsibilities can be established clearly.

Prioritization should account for task volume and recurrence, the effort required to prepare evidence, the reliability of available source records, integration complexity and the potential consequences of an incorrect output.

The principles of ICH Q9(R1) Quality Risk Management provide a relevant foundation for applying risk-based thinking to pharmaceutical quality activities.

A practical adoption sequence can be structured around three stages.

Begin With Bounded Evidence Preparation and Drafting

Initial use cases can focus on activities that have accessible source records, clearly defined deliverables and established reviewers.

Examples include deviation evidence assembly, clinical study report (CSR) drafting support, medical, legal and regulatory (MLR) pre-review, and regulatory document completeness checks.

These opportunities allow teams to assess output quality, source traceability, reviewer corrections and time savings without initially granting AI authority to complete consequential regulated actions.

Expand to Controlled Multi-System Workflows

Once individual AI-enabled activities have demonstrated acceptable performance, organizations can evaluate workflows requiring coordination across multiple records and applications.

Examples include ICSR case preparation, EDC query management, deviation investigation support and regulatory submission readiness checks.

At this stage, implementation must account for data consistency, tool permissions, unavailable systems, exception handling, workflow status and reviewer escalation. Agentic capabilities can coordinate activities, while final decisions and controlled updates remain within authorized procedures.

Evaluate Complex, High-impact AI Use Cases

More complex applications may support scientific interpretation, manufacturing quality analysis, product-impact assessment and regulatory decision preparation.

These opportunities can offer substantial value but may require more extensive evidence, specialized validation, additional subject matter expertise and stronger monitoring.

The sequence is not a fixed ranking of pharmaceutical use cases. A narrowly scoped safety workflow may be easier to implement than a deviation investigation involving multiple manufacturing systems. Each candidate should be assessed on its specific risks, data readiness and operating requirements.

Organizations should also avoid common implementation failures: choosing use cases too broad to define, starting development before source data is usable, introducing agents without clear tool permissions and treating draft generation as equivalent to validated decision-making.

The objective is to establish a repeatable method for selecting, evaluating and scaling AI workflows rather than expanding deployment before operational controls are proven.

Conclusion

Successful AI adoption in pharmaceuticals begins with a clearly defined operational problem, not a broad technology initiative. Mapping opportunities to sub-processes establishes the records an AI system can access, the activities it can perform, the outputs it must produce and the decisions that require authorized review.

Organizations can begin with bounded activities such as case narrative preparation, deviation evidence assembly and regulatory submission checks, then expand to more complex workflows as data readiness, validation evidence and operational controls mature. AI consulting and development expertise can support this progression by connecting operational requirements to appropriate AI capabilities, existing enterprise systems and risk-based implementation practices.

Ultimately, the value of AI in pharmaceuticals depends less on the breadth of the technology deployment and more on how effectively AI is integrated into reliable, traceable and governed pharmaceutical processes.