AI and Decision Making in GMP: Resolving Conflicts Between Human Judgment and AI Recommendations

Artificial Intelligence (AI) is becoming an integral part of GMP-regulated operations, supporting activities such as deviation investigations, batch record review, predictive maintenance, quality risk assessments, CAPA effectiveness, and manufacturing decision support. As organizations increasingly rely on AI-generated recommendations, a critical question emerges: What happens when human judgment disagrees with AI? While AI can rapidly analyze vast amounts of data and identify patterns humans may overlook, it can also generate inaccurate, biased, or non-explainable recommendations. Conversely, human decision-makers may introduce cognitive biases, incomplete analysis, or inconsistent decision-making. Understanding how to manage disagreements between AI recommendations and human expertise has become essential for maintaining regulatory compliance, product quality, and patient safety. This session explores practical governance models for AI-assisted decision making within FDA-regulated environments. Participants will learn how to establish appropriate human oversight, determine decision authority, develop escalation pathways, document AI-assisted decisions, and create defensible processes that satisfy regulatory expectations while maximizing the benefits of AI. Rather than viewing AI and humans as competitors, this course demonstrates how organizations can build collaborative decision-making frameworks that leverage the strengths of both. WHY YOU SHOULD ATTEND: As AI becomes embedded in GMP processes, organizations must determine how AI recommendations should influence critical decisions without compromising compliance or accountability. Code: FDB1308