Why AI Risk Management Strategies Fail in Production

AI failures are costly and often preventable—yet most organizations undermine their own safeguards through predictable mistakes. This article exposes five critical pitfalls in AI risk management, from treating risk as a one-time launch gate to optimizing for the wrong performance metrics, that cause protective measures to fail in real-world conditions. Understanding these mistakes is essential for teams committed to responsible AI deployment at scale.