For the last several years, enterprise AI maturity has been closely associated with governance frameworks. Organisations have created policy standards, ethical review boards, model validation processes, risk committees, compliance checkpoints and audit protocols. These measures remain important. They give enterprises structure, control and a clear way to align AI adoption with business risk appetite.
However, as AI systems become more embedded in real-time decision-making, traditional governance models are beginning to show their limits. A documented framework can prove that a process exists. It may not always prove what happened during an actual AI-assisted decision.