Consider a supplier payment flagged by an AI system as anomalous. The evidence behind the flag is solid. The transaction pattern is inconsistent with historical behavior. The risk indicators are clear. The system has access to the relevant data, the organizational context, and the analytical capability to generate a well grounded recommendation to place the payment on hold pending review. Should it actually place the payment on hold? That question sounds straightforward. In practice it opens one of the most consequential distinctions in enterprise AI governance, and one that most organizations have not yet seriously designed for. For years the primary question in enterprise AI was whether the system could identify the right action. Could it surface the anomaly? Could it generate the recommendation? Could it retrieve the relevant policy and match it to the current situation? Those are meaningful capabilities and the investment required to build them is significant and worthwhile. Th...