Can I use AI in a validated workflow?
Two different questions usually hide in that one. Drafting a document a human owns is not the same as putting AI inside a validated system of record, and mostly the second is where validation bites.
Regulatory Two different questions usually hide inside that one, and much of the time they have different answers. What follows is the shape of the constraint, with the controlling sources linked, not legal advice. Confirm the specifics with your own quality and regulatory function.
If AI is drafting a document a qualified human owns and reviews, a clinical study report, a safety narrative, a submission summary, you are mostly in People and Programs territory. The human is the author of record, your normal quality review still applies, and the main environment question is the data class from your AI Use Policy: patient data and unfiled results stay in your approved, enterprise-secure environment. That is closer to your existing accountability applied to a faster first draft than to a new regulatory frontier. It is not automatically free of validation questions, though: if that draft is generated or maintained inside a system that keeps a regulated record, the intended use, the inputs, and how the output is used can still pull it across the line.
If AI is becoming part of a system that creates or maintains a GxP record, that is a different matter. Now you are in computer-system-validation and 21 CFR Part 11 territory, records the agency has to be able to trust, with audit trails, access controls, and validation. It is your quality and regulatory function’s call, not something to route around. Do not relitigate that gate out of principle. Do the Permission work once, choose controls you can stand behind, and bring quality in early. If the AI is being used to help establish safety, effectiveness, or quality, the FDA’s 2025 draft guidance on AI to support regulatory decision-making sets out a risk-based credibility framework tied to the model’s context of use.
Most of the early value sits in the first case, not the second. So start where a human owns the output and the data is already yours to use, prove it, and let your quality function help you move the harder line deliberately. The full picture is Where AI Touches a Clinical-Stage Biotech.
Sources
- FDA and EMA, Guiding Principles of Good AI Practice in Drug Development (January 2026), ten joint principles spanning the medicine lifecycle, from research through manufacturing and safety monitoring (EMA announcement)
- FDA, 21 CFR Part 11, Electronic Records; Electronic Signatures (eCFR, current)
- FDA, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products (draft guidance, Federal Register, January 2025)
- ICH, E6(R3) Good Clinical Practice (Step 4 reached January 2025; linked here as EMA’s Step 5 implementation), on computerised systems and data integrity
- EMA, Reflection paper on the use of AI in the medicinal product lifecycle (2024)
Cheers,
-Titus
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