AAISM positions early and continuous risk assessment as a critical success factor. The guidance states that AI risk should be “identified, analyzed, and measured starting from the design and concept phases, before significant investment and deployment.” This ensures that high-impact risks (e.g., bias, privacy violations, safety issues) can be mitigated or designed-out before they become embedded in production systems. Frameworks (A) are valuable, but their effectiveness depends on when and how they are applied. Stakeholder participation (B) is important but is one component of a broader process. Creating completely separate risk processes for AI (D) may fragment governance and is not required; integration with enterprise risk management is preferred. Thus, the timing of risk measurement—early in the life cycle—is identified as the most important factor for effective AI risk management.