Source: Model Forward Governance — Board Governance Blueprint
There’s a statistic from this course that I’ve quoted in every conversation about AI strategy since I encountered it: 78% of organisations are using AI. Only 39% report measurable EBIT impact. That’s not a technology problem. That’s a governance and strategy problem.
The Model Forward Governance paper frames this as the “scale-and-value gap.” Adoption has become essentially frictionless. The tools are accessible, the cost is low, and the organisational pressure to be seen as “using AI” is enormous. But deploying tools is not the same as generating value.
The shift that’s happening — from AI as software feature to AI as agentic infrastructure — makes this governance gap more urgent. When AI was a recommendation engine, a failure mode was annoying but bounded. When AI is an agentic system with email access, scheduling authority, and the ability to initiate transactions, a governance failure has material consequences. Data centre power demand is projected to more than double to over 1,000 TWh by 2030. AI is no longer a line item in the IT budget.
The practical questions boards need to be asking: Who owns AI decisions in this organisation, and are they accountable for outcomes? What is AI actually doing on our behalf — not in aggregate, but in specific decisions? And critically: what’s the process for discovering when it’s wrong before it’s too late?