By Naresh Nayar and Rick Hamilton
Why Every Owner Has a Blind Spot, and What to Do About It
A demand-forecasting model capable of making high-value inventory decisions is ready to deploy but never ships. Its technical performance is solid, and the business case is obvious. It stalls because no one will own the decision to approve it. Legal sees a risk decision. Risk sees a business decision. The business assumes the CTO owns the technology. The CTO points back to the process owner. Weeks pass, until a frustrated product manager finds a workaround, and an unvetted version goes live through a side door outside the governance process.
This pattern is increasingly common in enterprise AI. When AI governance promises strategy, budget, and corporate influence, multiple business lines may maneuver to claim ownership. But when governance requires approving a specific deployment’s risks or answering for an operational failure, ownership grows unclear. The same business lines can fight over AI before launch and disown it after something goes wrong. In both cases, accountability has emerged by default or force of personality rather than by design.
The resulting bottleneck, including stalled pipelines, proliferation of shadow AI, and paper-only compliance frameworks, points to a missing piece—an operating architecture for the AI governance. Before an executive team can assign an owner to AI governance, it must align on what that architecture is engineered to achieve. To define this, we return to the foundation established in our previous piece, Point-of-View: AI Governance Is Broken, in which we argue that effective AI requires a non-negotiable architecture of three pillars. Each supplies a critical safeguard that the others cannot.
Together, we explore this topic more thoroughly in the full Substack article, including Point of View, and Approach Read the full article here.