AI Governance
The State of AI Governance: Turning Principle into Practice
As AI regulation matures across jurisdictions, boards are moving beyond policy statements toward accountable, measurable governance. Where does your organisation stand?
A threshold moment for AI oversight
Artificial intelligence has moved from an innovation agenda item to a board-level governance question. The EU AI Act is entering full force, sectoral regulators are issuing binding guidance, and insurers are beginning to price AI risk. The organisations standing out are not those with the most sophisticated models — they are the ones with credible, auditable governance around how AI is selected, deployed, and monitored.
From principles to a control framework
The gap between a published AI principle and a working control is where most programs stall. An effective framework translates high-level commitments into concrete, testable controls: who approves a high-risk use case, what evidence demonstrates fairness, how drift and incidents are logged, and who signs off on deployment. Without this control layer, an AI policy is an aspiration rather than an assurance control.
What boards should be demanding
Directors do not need to be data scientists, but they need the right questions asked and answered. That means an inventory of AI systems, an explicit risk classification for each, documented accountability for decisions, and a clear line of sight from incidents back to root cause. When the board can trace an AI decision to an owner, a rationale, and a review cycle, governance is functioning.