Description We'll explore how AI can transform complex global regulations into structured, machine-readable requirements that integrate directly into engineering workflows. By connecting fragmented data from QMS, PLM, and RIM systems within a regulatory knowledge graph, AI agents can automate traceability, impact analysis, and compliance workflows. Learn how this approach reduces manual effort, rework, and review cycles while keeping documentation continuously audit-ready. A real-world case study with Philips demonstrates how REMATIQ enables automated traceability from design inputs through verification and validation.
Learning Objectives:
Identify the limitations and risks of manual, document-centric regulatory compliance processes in complex medical device development environments.
Assess their organization’s readiness to apply AI and structured regulatory data approaches to improve traceability and regulatory intelligence.
Determine practical next steps for introducing more automated, data-driven regulatory workflows across engineering, quality, and compliance teams.
