Source: Model Forward Governance — Board Governance Blueprint
This is one of those risks that sounds abstract until you imagine explaining it after the fact. Then it suddenly becomes very concrete, very expensive, and very difficult to hide behind a slide deck.
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 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?
In plain English, that is why the result matters beyond the chart. It changes where people should look, what they should question, and which comfortable assumption probably needs to be retired.
My takeaway: the danger is rarely the dramatic thing in the headline. It is the quiet gap between knowing a risk exists and assigning someone to do something about it. Very unglamorous. Very important.