Related question
What kind of organization gets the most out of an AI diagnostic?
Short answer
One where leadership suspects the money is going somewhere and cannot say where, where several people have opinions about AI and nobody has evidence, or where a board question is coming and the honest answer right now would be improvisation. Certainty is what makes a diagnostic unnecessary, and it is rare.
There are three conditions where this work pays for itself quickly, and they are worth checking against your own situation before you spend anything.
The operation has drag nobody has located. Leadership knows the margin should be better, or that a function feels heavier than it should, but no one can point at the cause. That is a measurement problem before it is a technology problem, and it is the condition where an operational audit most reliably returns more than it costs.
Opinions have outrun evidence. Several capable people have strong and conflicting views about where AI belongs. Meetings on the subject produce advocacy rather than decisions. A diagnostic breaks that deadlock, not by being smarter than the people in the room, but by replacing the argument with a shared set of facts everyone had to agree to gather.
A defensible answer is about to be required. A board, an owner, or a major customer is going to ask what you are doing about AI. If the honest current answer is a tool announcement, the follow-up questions will not go well.
There is a fourth condition worth naming for the opposite reason. If you have already identified a specific, measurable problem, you know what a successful fix looks like, and someone owns the outcome, you may not need a diagnostic at all. Go solve it. Come back for the broader picture after you have a win behind you.