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AI in CQV: Navigating Innovation and Regulation

Donncadh Nagle
Joseph McCloskey
Sarah Rogers
Michael Brown
blue-hero-Jacobs

Artificial intelligence (AI) presents new opportunities to strengthen Commissioning, Qualification, and Validation (CQV) activities across Life Sciences. This paper outlines how deterministic, static AI models— aligned with emerging regulatory guidance—can responsibly support document‑heavy, repetitive CQV tasks while preserving traceability, compliance, and human oversight. It synthesizes regulatory expectations, industry readiness, cross‑sector lessons, and practical near‑term applications that reduce administrative burden without altering the core principles that define CQV.

Introduction 

The effective integration of AI within regulated pharmaceutical environments requires rigorous attention to regulatory compliance, robust data integrity practices, comprehensive risk management, and the complex challenges inherent to emerging technologies. Traditional CQV workflows rely heavily on manual document review and rigid, predefined processes. CQV engineers spend significant time analyzing drawings, technical packages, specifications, and traceability records – time that could otherwise be directed toward higher value work such as assessing risks, troubleshooting, and integration oversight. 

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