When an AI-assisted document ends up in a regulatory submission or a trial master file, someone must be accountable for its accuracy. This session addresses the governance infrastructure required to use AI responsibly in a regulated environment. We'll discuss the essential elements: an inventory of approved tools and use cases, defined human review requirements proportional to risk, documentation of who reviewed what, validation expectations, and clear escalation paths when output is wrong. We'll examine how existing quality management systems, SOPs, and vendor oversight frameworks can be extended to cover AI rather than rebuilt from scratch, and consider the emerging regulatory signals from FDA, EMA, and data protection authorities. Practical rather than theoretical — attendees will leave with a governance checklist.

Takeaways

• Build an AI use inventory and risk-tier framework for your organization
• Define proportionate human oversight and documentation requirements
• Extend existing QMS and vendor oversight processes to cover AI tools