What boards need to know about AI governance in 2024

Boards are increasingly being asked questions about AI systems they do not fully understand — how models are validated, what data they were trained on, and what happens when they fail.

Effective AI governance starts with an inventory: knowing every model in production, its purpose, its owner and its risk classification.

From there, governance should scale with risk. A recommendation engine and a credit decisioning model do not need the same oversight, and treating them identically wastes resources that should go toward the higher-risk systems.