The First FDA Warning Letter on AI: What It Means for Your Quality System

Artificial Intelligence (AI) is rapidly transforming regulated industries, from automated quality reviews and predictive maintenance to complaint handling and CAPA management. However, with innovation comes regulatory scrutiny. The FDA’s first Warning Letter involving the use of AI has become a defining moment for GMP-regulated organizations, signaling that regulators expect AI-enabled processes to be validated, controlled, monitored, and fully integrated into the organization’s Quality Management System (QMS).

This webinar explores the implications of FDA enforcement actions related to AI and explains what organizations must do to maintain compliance while adopting advanced technologies. Participants will gain insight into how FDA expectations for data integrity, validation, risk management, change control, and oversight apply to AI-driven systems and decision-making processes.

The session will also discuss practical approaches for integrating AI into existing quality systems without compromising regulatory compliance. From supplier qualification and algorithm validation to human oversight and deviation management, attendees will learn how to proactively prepare for FDA inspections and avoid compliance gaps.

By understanding the regulatory landscape surrounding AI, organizations can confidently leverage innovation while maintaining patient safety, product quality, and operational excellence.

Why You Should Attend:

AI adoption in regulated industries is accelerating, but many organizations remain unclear about FDA expectations regarding validation, oversight, documentation, and risk management. The FDA’s first AI-related Warning Letter demonstrates that regulators are closely evaluating how companies implement and control AI-enabled systems within their quality operations. This session provides practical guidance for understanding the compliance risks associated with AI and the steps organizations should take to ensure their quality systems remain inspection-ready.

Learning Objectives:

• Understand the significance of the FDA’s first AI-related Warning Letter.
• Identify FDA expectations for AI governance within GMP-regulated environments.
• Learn how AI impacts quality systems, validation, and data integrity requirements.
• Apply risk-based approaches for AI implementation and oversight.
• Understand documentation and change control requirements for AI-enabled systems.
• Evaluate AI-related risks including bias, cybersecurity, and automated decision-making.
• Develop strategies for integrating AI into CAPA, deviation management, and quality operations.
• Prepare for regulatory inspections involving AI-driven technologies.

Session Highlights:

• Regulatory Clarity: Understand the significance of the FDA’s first AI-related Warning Letter and its impact on GMP-regulated industries.
• Compliance Readiness: Learn how AI systems should be validated, monitored, and controlled within a compliant QMS.
• Risk-Based Strategies: Discover approaches for managing AI-related risks including bias, data integrity, cybersecurity, and change management.
• Practical Implementation: Gain actionable recommendations for integrating AI into quality operations while maintaining regulatory oversight.

By attending this training, participants will gain a clearer understanding of how to responsibly implement AI technologies while meeting FDA expectations and strengthening quality assurance practices.

Areas Covered:

• Overview of the FDA’s First AI-Related Warning Letter.
• Regulatory Expectations for AI in GMP-Regulated Industries.
• AI Governance and Quality System Integration.
• Computer System Validation (CSV) and AI Validation Considerations.
• Data Integrity and Audit Trail Requirements for AI Systems.
• Managing AI Risks: Bias, Hallucinations, and Model Drift.
• Change Control and Lifecycle Management for AI Applications.
• Human Oversight and Accountability in AI-Assisted Processes.
• AI in CAPA, Deviations, Complaints, and Risk Management.
• Supplier Qualification and Third-Party AI Vendor Oversight.
• Inspection Readiness and Documentation Best Practices.
• Future Trends in FDA Regulation of Artificial Intelligence.

Who Should Attend:

• Quality Assurance
• Regulatory Affairs
• Validation and Computer System Validation (CSV) Specialists
• Compliance Officers
• Manufacturing and Operations
• IT and Digital Transformation Managers
• CAPA and Deviation Management Teams
• Data Integrity Specialists
• Risk Management Professionals
• Internal Auditors and Inspection Readiness Teams
• Executive Leadership evaluating AI adoption strategies

Speaker Profile
Ginette Collazo, Ph. D. is an Industrial-Organizational Psychologist with 20 years of experience that specializes in Engineering Psychology and Human Reliability, disciplines that study the interaction between human behavior and productivity. She has held positions leading training and human reliability programs in the Pharmaceutical and Medical Device Manufacturing Industry.

Nine years ago, Dr. Collazo established Human Error Solutions (HES), a Florida based boutique consulting firm, where she has been able to position herself as one of the few Human Error Reduction Experts in the world. HES, led by Dr. Collazo, developed a unique methodology for human error investigations, cause determination, CA-PA development and effectiveness that has been implemented and proven amongst different industries globally. This scientific method has been applied in critical quality situations and workplace accidents.

She is the author of the book Human Error: Root Cause Determination Model, published in 2008. She is also a speaker at significant events like Interphex, FDAnews Annual Conference, Global Conference on Process Safety, International Conference on Applied Human Factors and Ergonomics, and of course, Pharmaceutical Industry Association.