Drugmakers have been quietly deploying AI on the factory floor for several years — for predictive maintenance, quality control and process optimization — largely without a dedicated regulatory framework telling them how the FDA expects that technology to be validated. That gap is starting to close. The FDA's Center for Drug Evaluation and Research listed "AI/ML Quality Considerations in Pharmaceutical Manufacturing" as a new item on its 2026 guidance agenda in February, alongside a companion item on digital health technologies in clinical trials.
The move follows a January 2025 framework, jointly proposed by CDER and the Center for Devices and Radiological Health, aimed at establishing the credibility of AI models used in drug and biologic submissions. It also builds on the FRAME initiative, which has focused on advanced manufacturing — including AI and machine learning integration — since 2021, and on requirements under PDUFA VII that direct the agency to build out a digital health technology framework and fund demonstration projects.
What the agency is expected to require
Based on the direction of CDER's existing guidance and public remarks from agency leadership, the draft is expected to require that pharmaceutical companies validate and verify AI models with documented performance metrics, maintain data integrity across cloud and IoT-connected systems, preserve audit trails and change-control records for any AI-driven adjustment to a manufacturing process, and demonstrate that AI-based digital controls are equivalent in reliability to the validated systems they replace or supplement — all while aligning with existing Current Good Manufacturing Practice requirements rather than replacing them.
In practice, that means a company using an AI model to flag a batch for quality deviation will need to show the FDA not just that the model works, but that its decisions are traceable, reproducible and auditable in the same way a human quality reviewer's sign-off would be.
Catching up to what's already happening
The guidance is arriving after adoption, not before it. Over 500 drug and biologic submissions since 2016 have already contained AI components, and companies including Perrigo have publicly described using AI for predictive maintenance and quality control on production lines. Roughly 20% of surveyed manufacturers reported running AI manufacturing pilots as of 2024, according to industry data cited by the agency's own advisory groups.
The FDA has said it intends to apply "human involvement" and a "risk-based approach" that balances innovation against patient safety, rather than mandating uniform validation standards regardless of how an AI system is used. For an industry that has spent the past two years dealing with high-profile OT ransomware incidents disrupting physical manufacturing, a formal quality framework for AI adds a second, software-integrity dimension to what "reliable pharmaceutical manufacturing" is now expected to mean — not just that a factory keeps running, but that the algorithms running it can prove their own decisions were sound.
