Understand where AI genuinely improves efficiency in CAPA workflows — and where it creates hidden regulatory risk in root cause analysis and investigation defensibility.
Includes live access & recording
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. This webinar addresses how to incorporate AI into CAPA and investigation workflows without compromising the integrity, depth, and defensibility of the process.
Organizations are increasingly using AI to "assist" with investigations — but most are doing so without defined controls or clear understanding of the risks. AI-generated outputs can look structured and professional, creating a false sense of confidence while introducing conclusions that are not supported by data.
From a regulatory standpoint, this is a major concern. Investigations must be evidence-based, thorough, logically structured, and clearly attributable to qualified personnel. If AI is used improperly, root cause determinations may be challenged, CAPAs may be deemed ineffective, and repeat deviations may occur due to incorrect conclusions.
This webinar provides a structured approach to using AI as a support tool — without allowing it to replace critical thinking, analysis, or accountability.
By the end of this session, participants will know exactly where AI can safely support CAPA workflows and where its use creates unacceptable regulatory risk.
Understand where AI can and cannot be reliably used in CAPA and investigation processes.
Identify FDA's regulatory expectations for investigations and what defensible conclusions require.
Recognize the specific risks of using AI for root cause analysis and causal reasoning.
Distinguish between acceptable low-risk and high-risk AI use cases in quality investigations.
Understand how prompt structure, incomplete data input, and AI bias can produce flawed investigations.
Maintain human ownership and accountability throughout the investigation process.
Establish standards for verifying AI-generated content and grounding conclusions in documented evidence.
Integrate AI into existing CAPA workflows without disrupting established quality system requirements.
Four critical focus areas that will sharpen your organization's approach to AI use in CAPA and investigations.
Understand how AI can generate plausible but incorrect root causes, oversimplify multi-factor issues, miss critical contributors, and introduce bias based on prompt structure.
Learn what FDA and other regulatory bodies require for investigations — evidence-based conclusions, clear root cause linkage, and documentation attributable to qualified personnel.
Identify where AI genuinely adds value — organizing data, structuring reports, summarizing large data sets, and improving documentation clarity — without replacing human analysis.
Leave with an implementable control model that ensures AI enhances efficiency while preserving the integrity, depth, and regulatory defensibility of your CAPA process.
A comprehensive 10-topic curriculum covering AI's role, regulatory expectations, risks, and controls for CAPA and investigation processes.
Quality, compliance, and regulatory professionals who use or are considering AI tools in investigation and CAPA processes.
CAPA Owners
QA Investigators
Quality Assurance Managers
Compliance Specialists
Regulatory Affairs Professionals
Manufacturing Quality Leads
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 — including 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. This includes aligning AI use with investigation procedures, documentation expectations, and approval processes. 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.
Charles H. Paul is the President of C. H. Paul Consulting, Inc. — a regulatory, manufacturing, training, and technical documentation consulting firm — celebrating its twentieth year in business in 2017. He has been a regulatory and management consultant and an Instructional Technologist for 30 years and has published numerous white papers on various regulatory and training subjects.
The firm works with both domestic and international clients designing solutions for complex training and documentation issues. He has held senior positions in consulting and in corporate training development prior to forming C. H. Paul Consulting, Inc. He also worked for several years in government contracting managing the development of significant Army-wide training development contracts impacting virtually all of the active Army and changing the training paradigm throughout the military.
He has dedicated his entire professional career explaining the benefits of performance-based training.
Don't miss this essential training on using AI responsibly in CAPA and investigation processes. Learn where AI helps — and where it can put your quality system at risk.