Related question
Why can I not seem to ask the right questions about AI?
Short answer
Because good questions come from a working model of what the thing actually does, and most executives have never been given one. Without it, you are evaluating recommendations on delivery rather than substance. Strategy then rests on assumptions nobody tested, and governance never gets written because nobody knows what they are governing.
This is the honest version of a problem a lot of executives will not say out loud.
If you cannot see the big picture, you do not know what questions to ask. And without the right questions, everything downstream degrades. Strategy is built on assumptions nobody tested. Execution chases whatever was demonstrated most recently. Governance never gets written because nobody knows what they are governing.
The fix is not becoming technical. It is acquiring enough of a working model to evaluate what you are shown. Once you understand roughly how these systems arrive at an answer, the questions change on their own. You stop asking whether the demonstration was impressive and start asking what it was given, what it was not given, and what it would do with the messier version of the same input.
There is also a practical test you can run on any AI recommendation without knowing anything technical. What specifically improves if this succeeds: revenue, margin, capacity, response time, decision quality, or risk? Is the information it needs actually available? Were the people affected involved? What does full implementation cost, rather than the prototype? What alternatives were considered? Who owns the outcome, and what would cause us to stop? Working through those is usually worth more than another demonstration.
That foundation is what turns an executive from a spectator into a buyer who can tell the difference between capability and performance.