For decades, organizations validated software once and trusted it for years. That approach worked because traditional software followed fixed logic. If the code did not change, neither did the behavior.
AI changes that assumption.
An AI system can produce different outcomes over time even when no developer touches the code. As data patterns shift, operating conditions evolve, users interact with systems differently, and models encounter situations they were never trained to handle, performance can quietly drift away from what was originally validated.
For manufacturers adopting AI for quality management, complaint handling, predictive maintenance, supply chain decision-making, and operational intelligence, this presents a new challenge:
How do you know an AI system remains trustworthy after deployment?
This session explores a growing governance gap facing organizations as AI moves from experimentation into production environments. Using manufacturing-focused case studies, including AI-enabled complaint routing and predictive quality decision support, participants will examine how AI systems can change behavior without any traditional software change occurring.
The session introduces the concept of moving from a validated event to a validated state. Attendees will learn why validation approaches designed for deterministic software are often insufficient for AI-enabled systems and will be introduced to a practical continuous assurance framework built around three operational controls:
• Predefined change boundaries
• Continuous monitoring for drift and performance degradation
• Targeted reassessment when meaningful changes occur
At the center of the framework is qualified human oversight, ensuring governance evolves alongside system behavior rather than lagging behind it.
While the examples focus on manufacturing environments, the framework is equally applicable to AI systems operating in government, maritime, healthcare, and other regulated industries where trust, reliability, and accountability are critical.
Attendees will leave with a practical model they can immediately apply to their own AI initiatives to help maintain confidence, performance, and governance long after deployment.