In the last issue of Execution Drift, I wrote about the productivity created by AI and the question nobody owns: who captures its value once the technology changes how work is done?
There is a question that comes before that one.
Who owns the decision when AI starts to influence how the organisation operates?
The First Ten Working Days
My approach to an enterprise AI adoption mandate starts with one question: what needs to be in place before a pilot becomes part of daily operations?
I would not begin with a policy document, a vendor selection or another steering committee.
I would take one live use case. One that is about to affect a customer interaction, an operational process, a risk decision or an investment case. Then I would establish the decision being influenced, the consequence of getting it wrong, and the executive who is prepared to own that consequence.
Data Access Is Not Decision Authority
AI programmes often treat data access, process ownership and decision authority as if they were the same thing. They are not.
A team may have permission to use the data. A business function may own the process. A technology team may operate the platform. Yet nobody may be clearly accountable for the outcome when the AI recommendation is wrong, incomplete or operationally inappropriate.
That is not an AI problem. It is an operating-model problem.
The Test Before Scale
Before scaling a use case, I would want a clear answer to four practical questions.
What remains under human judgement? Who monitors the quality and risk of the output? How is an exception handled? Who can intervene when the result is technically plausible but wrong for the business?
If those answers are vague, the organisation has not governed the use case. It has only deployed it.
Advance, Reframe or Pause
At the end of the initial assessment, the outcome should not be another presentation.
It should be a decision: advance because the business owner, data boundary and human accountability are clear; reframe because the technology is ahead of the business problem; or pause because the organisation is not ready to carry the consequence of deployment.
That is how I approach AI governance in a large enterprise. Not as a compliance layer applied after a tool has been chosen, but as the work required before AI changes a real decision.
The most expensive AI failure will not be a poor model. It will be an organisation that deployed one without knowing who was entitled to decide what it should do.
What is the first AI use case in your organisation that needs an executive decision, not another pilot?
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