10 Questions Every Executive Team Should Answer Before Scaling AI

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Most AI scaling decisions don't fail because the technology underperforms. They fail because the executive team never agreed, in the same room, on the answers to a short list of foundational questions — so every function scales on its own assumptions, and those assumptions collide later, at a much more expensive moment.

1. Who owns AI value delivery — a single accountable leader, or a diffuse set of function heads?
2. What does "success" mean across functions, in terms the CFO and the CTO would both sign off on?
3. Which initiatives require committee-level governance, and which can move through a fast, self-service path?
4. How is shadow AI usage measured today, and who's accountable for closing that gap?
5. What's the incremental funding model for initiatives that outgrow their original scope?
6. Who is accountable when an AI-assisted decision turns out to be wrong?
7. What's the workforce adoption plan, beyond a single training session?
8. Which vendor and data dependencies create concentration risk if they fail or change terms?
9. How will the board be briefed on AI risk, and how often?
10. What would make leadership pause or reverse a scaling decision already in motion?

None of these are technical questions. They're operating-model questions — and they're exactly the ones that surface, unresolved, in the middle of a scaling initiative if they aren't settled before it starts.

We walk executive teams through this list directly in an Executive Briefing, and in more depth in Leading the AI-Native Organization.