Clinical ResearchAI/ML

Immediate Use Cases for GenAI in Pharma

Immediate Use Cases for GenAI in Pharma

AI’s promise for the life sciences industry is enormous. Equally enormous is the hesitation to leverage it. Many organizations never make it past the pilot (if that), convinced they must solve every data challenge or build complex, validated AI pipelines from the get-go.

In reality, for AI to create a positive impact and bring value within an organization, rather than starting with the most ambitious use cases, it starts with specific, low-barrier applications where the data is relatively clean and the risks are manageable, with low-hanging fruit for a solid return on investment.

In starting here, in the targeted use cases, it helps to build trust, establish operational readiness, and gain momentum for broader transformation across teams and workflows. Below are eight immediate, high-impact use cases for generative AI in pharma that can be safely implemented today to see tangible results without sacrificing compliance.

1. Multilingual Translation and Localization

Use Case: Translating regulatory, clinical, and pharmacovigilance documentation for global submissions and local affiliates.

Benefit/ROI: Translation, when enabled by AI and following a human-in-the-loop review model, significantly improves turnaround time and reduces costs for high-volume documentation while maintaining quality. Teams can translate hundreds of pages in hours instead of days, freeing up time for internal staff to focus on strategic review rather than repetitive text adaptation.

Data Input Requirements:

  • Source files in structured, readable formats (e.g., DOCX, XML, or standardized templates)
  • Existing translation memories (TMs) and term bases to fine-tune model accuracy
  • Validation workflows to ensure compliance with GxP documentation standards

2. Drafting Responses to Health Authority Queries

Use Case: Using GenAI to assist in the creation of responses to regulatory authority questions during the review process (e.g., FDA or EMA queries).

Benefit/ROI: Leveraging GenAI to respond to HA queries, which often follow predictable structures, can save valuable time for teams from having to synthesize information from multiple sources under tight timelines. GenAI can automatically retrieve and summarize relevant data, draft initial responses, and suggest supporting evidence, reducing turnaround time and improving quality and consistency. This is a clear, controlled way to reduce response cycle times while ensuring quality and traceability.

Data Input Requirements:

  • A centralized repository of approved submission documents, correspondence, and source data
  • Metadata tagging for product, indication, and submission region
  • Human oversight for scientific accuracy and compliance with local authority expectations

3. Literature and Safety Monitoring with AI Triage

Use Case: Automating the review of scientific literature and safety data to identify potential adverse events, new indications, or emerging regulatory trends—enhanced by AI-driven triage.

Benefit/ROI: AI can screen thousands of publications per week and apply intelligent triage, automatically categorizing articles as “relevant,” “review required,” or “irrelevant.” This allows pharmacovigilance and medical information teams to prioritize effectively by honing in on the highest-risk or most promising findings. Furthermore, AI can be used to detect safety signals and to triage safety cases between ‘critical’ and ‘non-critical’ cases. The result? When effectively combined with human review, AI triage enables faster safety signal detection and more efficient literature surveillance.

Data Input Requirements:

  • Access to relevant literature databases (PubMed, Embase, or internal libraries) and/or safety reporting systems
  • Defined search parameters and inclusion/exclusion criteria
  • Historical datasets to train relevance-scoring and triage models

4. Regulatory Intelligence and Monitoring

Use Case: Tracking and summarizing updates from global health authorities, such as new guidance documents, labeling changes, or procedural updates.

Benefit/ROI: Staying current with hundreds of agencies worldwide is a monumental task. With the use of AI tools, automatically extracting and summarizing updates from FDA, EMA, MHRA, PMDA, and other regulatory sources and health authorities can highlight what’s new and what’s relevant to each product portfolio. This means improving regulatory intelligence teams’ ability to be proactive rather than reactive, and in turn, turning a flood of information into actionable updates.

Data Input Requirements:

  • Structured feeds or crawlers connected to official regulatory websites
  • Defined taxonomies (e.g., labeling, CMC, clinical, safety)
  • Validation workflows to confirm accuracy before internal distribution

5. Assisted Authoring of Routine Regulatory Documents

Use Case: Drafting or pre-populating standard sections of regulatory submissions or clinical documentation, such as consent forms, lay summaries, clinical overviews, or protocol synopses.

Benefit/ROI: By pulling data from existing documents and templates, AI-assisted authoring tools can speed up drafting cycles by 30–50%. This ensures consistent structure, reduces manual repetition, and maintains alignment across global submissions.

Data Input Requirements:

  • Clean, templated GenAI authoring frameworks with specialised models and templates
  • Access to approved source content (previous submissions, study data, labeling text)
  • Governance for human review, annotation, and sign-off

6. Structured Content Assembly and Cross-Document Traceability

Use Case: Automating the assembly of submission components or linking data across related documents. For example: connecting information from the Clinical Study Report (CSR) to the Investigator’s Brochure or Summary of Clinical Efficacy.

Benefit/ROI: Generative AI can identify corresponding data points across multiple files, automatically populate linked sections, and flag inconsistencies. This enables faster submission compilation, improved traceability, and reduced human QC effort. Doing so is a natural progression toward more advanced document intelligence that’s achievable today with limited risk.

Data Input Requirements:

  • Structured or semi-structured documents with consistent section headers
  • Defined mappings between document types (e.g., CSR <—> CTD modules)
  • Controlled repository access for document versioning and validation

7. Training and SOP Management

Use Case: Generating and maintaining simplified summaries of standard operating procedures (SOPs) or training materials for new staff and auditors.

Benefit/ROI: AI can produce “human-readable” SOP summaries and FAQs that reduce onboarding time and ensure consistent understanding of key policies for employees and teams. It can also monitor for policy changes and flag affected content for re-training. Doing so offers high value for QA and compliance teams, where accuracy and consistency are paramount.

Data Input Requirements:

  • Approved SOPs in a structured, digital format
  • Training metadata (department, role, region)
  • Access controls to maintain version traceability

8. Regulatory Labeling Comparison and Harmonization

Use Case: Comparing product labels across countries to detect discrepancies or ensure global consistency.

Benefit/ROI: AI can automatically identify variations in wording, dosage instructions, or warnings across markets, dramatically reducing manual review time and regulatory risk.

Data Input Requirements:

  • Digital labeling text files in a consistent format
  • Reference label for comparison
  • Defined rules for “critical” vs. “non-critical” differences

Start Simple, Scale Smart

Pharma’s digital transformation doesn’t have to begin with complex, high-risk use cases like AI-driven submission assembly or automated risk assessment. In fact, it shouldn’t; doing so only creates more barriers for entry and stalls success before it even begins.

The key is to build confidence and capability incrementally, starting with use cases that:

  • Use structured or semi-structured data already available
  • Have limited patient or proprietary risk exposure
  • Offer clear, measurable ROI in speed, cost, or compliance

Each successful deployment strengthens your organization’s data readiness and governance maturity, making more advanced applications possible in the future.

The Role of Trusted Partners

Leveraging GenAI in regulated environments requires not only technological expertise but also intimate domain knowledge. That’s why it’s critical to find a strategic partner with to support finding the path of least resistance for AI within secure, validated frameworks. With the right partner, and by combining automation with expert human oversight, pharma organizations can lay the foundation for broader, organizational digital transformation.

Conclusion: The Future Starts with Small Steps

The most successful AI journeys don’t begin with grand ambitions. They begin with specific, solvable problems. Each use case, no matter how small, contributes to the ultimate goal: faster, more accurate, and more intelligent regulatory and clinical operations.

Start with data you can trust. Automate where it’s safe. Keep humans in the loop. The future of pharma isn’t just powered by AI; it’s built one intelligent step at a time. Ready to take your first intelligent step? Explore how TransPerfect Life Sciences helps pharma teams harness AI safely and effectively. Let’s chat!