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.

Key Highlights

• Understand the roles and limitations of both AI and human decision-makers.
• Learn when AI recommendations should be accepted, questioned, or overridden.
• Develop governance frameworks for AI-assisted decision making.
• Understand FDA expectations regarding human oversight and accountability.
• Learn documentation practices that support inspection readiness.

| KEY LEARNING OBJECTIVES

• Explain how AI supports decision making within GMP operations.
• Differentiate between AI recommendations and human accountability.
• Identify situations where AI recommendations require additional human review.
• Recognize common cognitive biases affecting both humans and AI systems.
• Apply structured Human-in-the-Loop (HITL) decision-making principles.
• Develop escalation criteria when AI and human judgments conflict.
• Document AI-assisted decisions in a manner that supports regulatory compliance.
• Build governance processes that balance automation with human expertise.
• Prepare for FDA inspections involving AI-assisted decision-making.

| AREAS COVERED

• AI as a Decision Support Tool in GMP
• Human Judgment versus AI Recommendations
• Human-in-the-Loop (HITL) Decision Frameworks
• Decision Authority and Accountability
• Cognitive Bias in Human and AI Decision Making
• Risk-Based Decision Making Using AI
• Escalation Processes for Conflicting Recommendations
• Documentation Requirements for AI-Assisted Decisions
• AI Governance and Organizational Policies
• FDA Expectations for AI Oversight
• FDA Warning Letter on AI

| WHO SHOULD ATTEND

• Quality Assurance (QA) Departments
• Quality Control (QC) Departments
• Manufacturing Departments
• Operations Departments
• Regulatory Affairs Departments
• Validation and CSV Specialists
• Data Governance Professionals
• Risk Management Professionals
• Digital Transformation Leaders
• Compliance Officers
• Process Owners
• Executive Leadership responsible for AI implementation

| TOPIC BACKGROUND

Artificial Intelligence is increasingly influencing operational and quality decisions across regulated industries. However, regulatory responsibility cannot be delegated to AI. Organizations remain accountable for every GMP decision, regardless of whether AI contributed to the recommendation. As AI systems become more sophisticated, companies must establish clear governance defining when AI serves as an advisor, when human review is mandatory, how disagreements are resolved, and how decisions are documented. Regulatory agencies continue to emphasize transparency, explainability, risk management, and human oversight throughout the AI lifecycle. This session provides organizations with practical decision-making frameworks that integrate AI responsibly while preserving accountability, regulatory compliance, and patient safety. Participants will learn how to create collaborative human-AI decision processes that improve consistency, reduce risk, and support inspection readiness.

Course Director: Dr. Ginette Collazo

Dr. Ginette Collazo is an Industrial-Organizational Psychologist with over 20 years of experience specializing in human performance, decision making, and error reduction within GMP-regulated manufacturing environments.

She is the author of the Human Error Root Cause Determination Model and Founder of Human Error Solutions, a globally recognized consulting firm helping organizations improve quality systems, human performance, and operational excellence. In 2023, Human Error Solutions was recognized as a Top Ten Industrial Service Provider by Manufacturing Outlook and has been featured on ABC, NBC, Fox, and CBS.

Dr. Collazo is also the host of The Power of Why Podcast, where she explores critical thinking, leadership, human behavior, and decision making in complex organizational environments.

Her unique combination of psychology, quality systems, and AI governance expertise enables organizations to successfully integrate artificial intelligence into regulated operations while strengthening human decision making and maintaining compliance.