Organizations have spent the last years building AI principles, policies, approval processes, and risk assessments. While these foundations remain important, they were designed for a world where AI was developed by a limited number of specialists and governed through periodic reviews. Today, AI is embedded across enterprise applications, business processes, and increasingly autonomous agents, making traditional governance models difficult to scale.
What we explored during the session:
- Why existing governance approaches often struggle as AI moves into production;
- How organizations are creating better visibility across AI use cases, vendors, copilots, and agents;
- What continuous governance looks like beyond one-off assessments; and
- How privacy, data, risk, and technology leaders are working together to make governance more operational.
The aim was simple: to share practical ideas and approaches that participants could take back to their organizations and put into practice.