AI in Regulated Industry - Validating AI-Enabled Products with PLM and Digital Twins

AI is increasingly embedded in complex, highly automated products across regulated industries such as medical devices, industrial automation, aerospace, energy, and safety-critical manufacturing. While AI enables powerful new capabilities, validating these systems under regulatory and quality standards requires far more than accurate models. It demands a lifecycle approach that tightly integrates engineering, PLM, digital twin architectures, and human oversight to ensure traceability, verification, and audit readiness. This talk shares real-world lessons from usage of AI in regulated, high-stakes products, focusing on how PLM and digital twins can support end-to-end validation and verification. We will examine how AI models, training data, and inference pipelines become regulated product artifacts that must be versioned, governed, and linked to requirements, risks, and test evidence. Digital twins play a critical role in simulating AI-driven behaviour, enabling scenario-based verification that goes beyond deterministic software testing and supports regulatory evidence generation. A central theme is human-in-the-loop design. Expert input during training, operator oversight during operation, and feedback from real-world usage must be captured, controlled, and fed back into a closed-loop PLM process. We will discuss how this feedback influences model validation, controlled updates, and post-market monitoring while maintaining clear accountability and compliance. Rather than theory or compliance checklists, this session focuses on practical architectural patterns, trade-offs, and lessons learned from moving AI from proof of concept to production in regulated environments. I wish to cover concrete approaches for building compliant, scalable, and trustworthy AI-enabled products across regulated industries.