Share with your CISO
Most boards now have AI governance structures in place, but governance frameworks are quietly becoming a false floor, giving executives confidence in the existence of controls rather than their effectiveness. Writing for BusinessDay Nigeria, digital trust adviser Amaka Ibeji argues that the field has conflated governance activity with governance outcomes, a category error that leaves boards approving policies and reviewing dashboards while strategic accountability stays fragmented. The corrective, as Ibeji frames it through the OECD Principles, the NIST AI Risk Management Framework, and ISO/IEC 38507, is to judge AI governance by what it produces, not by what it documents.
What this means for your business
The organizations most exposed to this argument are the ones that have invested heavily in governance infrastructure and assume the work is done. A mature policy library, a standing AI risk committee, and quarterly reporting to the board can coexist with poor decision quality, diffuse accountability, and strategic blind spots. If your board measures AI governance health by counting artifacts rather than tracing how those artifacts changed a specific decision, you are in the activity trap, and the gap between your documented controls and your actual risk posture is probably wider than anyone has formally acknowledged.
Ibeji’s framing is correct and underappreciated, though she writes from a position, digital trust consultancy, that benefits from organizations deciding their existing governance structures are insufficient and need external advisory input. That incentive tilts the argument toward overstating how universally broken current practice is, when the more precise claim is that governance frameworks designed for static compliance contexts are poorly suited to AI systems that update, drift, and expand scope continuously. The distinction matters because the remediation is different: this is mostly a measurement and accountability design problem, not a structural one requiring new committees or hired assurance providers.
The leading indicator to watch is whether your board asks “what decision changed because of this report” after any AI risk update. That single question separates boards doing oversight theater from those doing actual oversight. If the honest answer is “none,” then your governance process is producing documentation, not governance. A renewal of your AI risk management platform or an upcoming board presentation on AI strategy is precisely the moment to reframe what your team is asked to demonstrate, shifting from coverage metrics to decision influence records, before the next audit cycle locks in the wrong yardstick.
Based on reporting from Beyond AI governance: What every board should demand, originally published 2026-08-07 04:10:00.
