The executives succeeding in this role spend less time evaluating models and more time translating between the technical and the institutional.
As institutions rush to name a Chief AI Officer, most job descriptions read like a technical role: model evaluation, vendor selection, infrastructure oversight. The executives who actually succeed in the seat spend a minority of their time there. Their real work is translation — between data science teams who think in accuracy metrics and boards who think in risk and return, between vendors selling capability and business units who need a specific problem solved, between what a model can technically do and what an institution is actually prepared to trust it to do.
The most effective Chief AI Officers we work with are not the most technically credentialed people in the room. They are the ones who can sit with a skeptical business unit leader, understand the actual decision that leader is trying to make better, and come back with an AI-enabled approach that leader will actually adopt — not just one that performs well on a benchmark.
This has direct implications for how the role should be structured and staffed. It belongs closer to strategy and operations than to the technology organization alone, with a direct line to the executive committee and enough standing to say no to a use case that is technically impressive but institutionally premature.
Institutions that treat the Chief AI Officer as a purely technical hire tend to end up with an impressive slide on AI capability and very little that has actually changed how the institution makes decisions a year later.
Libra Scale Consult works directly with executive teams on the issues raised here.
