From Digitalization to Assurance: Practical Use of Artificial Intelligence in GxP Validation

Abstract

The life sciences industry has spent the better part of the last decade digitizing its operations — replacing paper-based systems, automating workflows, and centralizing data across increasingly interconnected platforms. That transition, for the most part, followed a familiar playbook: validate the system, document the controls, and demonstrate to regulators that the technology does what it claims to do. Artificial intelligence does not follow that playbook, and the industry is beginning to feel the friction. AI-enabled systems are now appearing across pharmaceutical and biotechnology operations — from manufacturing analytics and deviation triage to clinical signal detection and quality monitoring. These are not experimental deployments. Organizations are making consequential GxP decisions based on AI outputs, and in many cases, the validation strategies supporting those decisions were built for a different kind of technology entirely. Traditional computer system validation assumed that a system's behavior was fixed at the point of deployment. AI does not make that assumption, and neither does the data it learns from. This paper examines what actually needs to change — and what does not. Regulatory agencies have been consistent: existing quality and risk management principles still apply to AI. What they have been less prescriptive about is how. Drawing on the Computer Software Assurance framework and direct experience implementing AI governance within a GxP-regulated advanced therapy organization, this paper proposes a practical, risk-based approach to AI assurance that focuses on lifecycle control, performance monitoring, and inspection readiness rather than exhaustive algorithmic testing. The intent is not to rewrite validation science, but to extend it honestly into territory it was not originally designed to reach.