For organizations adopting AI Control Tower across ServiceNow and third-party AI, what architecture and governance model demonstrates that AI governance is actually changing enterprise risk rather than merely producing a better inventory of AI assets?
How should AI use cases, AI systems, models, agents, prompts, datasets, owners, policies, risks, controls exceptions, performance measures, and realized value be connected so that an executive or auditor can trace an AI capability from business justification through approval, deployment, monitoring, risk treatment and eventual retirement?
I am especially interested in the boundary between controls the AI Control Tower can technically enforce and controls that remain procedural, attestation, or dependent on external platforms.
What leading and lagging indicators would you use to prove that the governance model is reducing unmanaged AI exposure without creating an approval structure so restrictive that business units simply work around it?
