PharmTech Explained: Human Accountability with AI in GMP

The second episode of PharmTech Explained, featuring industry experts Brian Drapeau and Richard Jaenisch, examines how AI is already reshaping pharmaceutical manufacturing and where the risks of getting it wrong are emerging.

AI is no longer a future consideration for pharmaceutical manufacturers; it is an operational reality. Regulatory submissions containing AI and machine learning components are rising sharply, and the pressure to deploy these tools across GMP environments continues to grow. But as adoption accelerates, a foundational question confronts every quality organization: where exactly does algorithmic decision support end and human accountability begin?

Deviation management has become the leading early use case, offering a clear proof of concept: if a process can be reduced to a fillable form, generative AI can enhance and accelerate it. Beyond time savings, the more consequential benefit is cognitive reallocation, freeing qualified professionals from administrative burden so their expertise can be directed toward investigation, root cause analysis, and risk-based decision-making that no algorithm can perform.

But accountability structures in GMP settings remain unchanged. As Drapeau illustrates with a recent FDA 483 observation, among the first issued specifically over AI use, manufacturers cannot delegate quality unit responsibility to a tool. The AI doesn't get cited. The human does.

The more systemic threat, Drapeau argues, is the slow, unnoticed drift toward autonomy. When AI outputs are consistently reliable, human verification tends to erode over time, an incremental shift that can embed unvalidated process changes into controlled manufacturing operations before anyone recognizes what has occurred.

To govern AI effectively, Drapeau identifies four emerging roles quality organizations should consider essential: AI-literate quality reviewers, a dedicated AI validation lead, a computational pharmaceutical scientist, and an IT and cybersecurity liaison embedded within quality.

The organizations best positioned for the next decade of AI integration, the episode concludes, will be those that invest in human capability with the same rigor they apply to the technology itself.