By Naresh Nayar and Rick Hamilton
The Problem: AI Governance as It Exists Today Is Failing
In a previous piece, Point-of-View: AI Governance is Broken, we described a three-pillar approach to AI governance – the policies, principles, and accountability structures that define an organization’s intent. Yet across the enterprise, a familiar pattern persists: policies get written; principles are endorsed; and committees are formed. And when an AI system degrades quietly or creates unintended downstream consequences, leaders discover that governance stopped at the point of good intentions.
Imagine a demand-forecast model whose error rate drifts after a quiet upstream data change; revenue leakage accumulates for weeks before anyone can prove where the shift began. The postmortem is not about ‘AI ethics’ in the abstract, but rather, it is about telemetry, ownership, and escalation. The reality is that AI risk doesn’t live in policy documents. Instead, risk emerges through day-to-day decisions, unexpected system behavior, and operational tradeoffs, the very areas where AI risk management matters most.
In a mature AI program, governance sets direction and intent, while operational risk management determines how those intentions translate into real outcomes. Because risk manifests unevenly, not all AI systems require the same level of operational rigor. Controls must scale with business impact, ensuring speed for low-risk experimentation while demanding stronger discipline for systems that influence customers, critical decisions, or regulated outcomes.
Together, we explore this important topic more thoroughly in the full Substack article, including operational risk management, the five domains of operational AI risk management, and the feedback loop that makes the governance real. Read the full article here.