AI for CAPA and Investigations: Where It Helps and Where It Can Get You in Trouble

Corrective and Preventive Action (CAPA) systems and investigation processes are among the most scrutinized elements of any quality system. These processes require structured problem-solving, objective evaluation of evidence, and defensible conclusions that can withstand regulatory review. With the introduction of AI tools such as ChatGPT, organizations are beginning to use automation to summarize data, draft investigation reports, and even suggest potential root causes. While this can improve efficiency, it introduces a fundamental risk: the substitution of generated logic for evidence-based analysis. Regulatory bodies, including the U.S. Food and Drug Administration, expect investigations to demonstrate clear linkage between observed events, supporting evidence, root cause determination, and corrective actions. AI-generated outputs, if not properly controlled, can weaken this linkage and create conclusions that appear logical but are not supported by data. This webinar addresses how to incorporate AI into CAPA and investigation workflows without compromising the integrity, depth, and defensibility of the process. WHY YOU SHOULD ATTEND Organizations are increasingly using AI to "assist" with investigations—but most are doing so without defined controls or clear understanding of the risks. AI can generate plausible but incorrect root causes, oversimplify complex multi-factor issues, miss critical contributing factors, and introduce bias based on prompt structure. These risks are not obvious on the surface. In many cases, the output looks structured and professional, which creates a false sense of confidence. From a regulatory standpoint, this is a major concern. Investigations must be evidence-based, thorough and complete, logically structured and defensible, and clearly attributable to qualified personnel. This webinar provides a structured approach to using AI as a support tool—without allowing it to replace critical thinking, analysis, or accountability. TOPIC BACKGROUND This webinar delivers a practical framework for integrating AI into CAPA and investigation processes while maintaining full control over analysis, decision-making, and regulatory compliance. The session begins by reviewing the core expectations for investigations within regulated environments, including the need for objective evidence, structured analysis, and defensible conclusions. Participants will examine how AI-generated outputs align—or conflict—with these expectations. The webinar then explores specific use cases where AI can provide value, such as organizing investigation data, structuring reports, summarizing large data sets, and improving documentation clarity. These applications are contrasted with higher-risk uses, particularly those involving root cause identification, causal reasoning, and corrective action determination. A key focus is understanding how AI can unintentionally introduce bias or unsupported conclusions. Participants will learn how prompt structure, incomplete data input, and over-reliance on generated outputs can lead to flawed investigations. The session emphasizes maintaining human ownership of the investigation process. This includes defining clear roles for reviewers, establishing standards for verifying AI-generated content, and ensuring that all conclusions are grounded in documented evidence. Participants will also learn how to integrate AI into existing CAPA workflows without disrupting established quality system requirements. The webinar concludes with a practical control model that organizations can implement immediately, ensuring that AI enhances efficiency while preserving the integrity and defensibility of investigations. Code: FDB1602