Core question
What questions should I be asking my team about our AI projects?
Short answer
Four questions keep any AI initiative honest. What problem is this actually solving? What would we lose if it failed? Who decided this was the priority, and on what basis? Where is the risk that is not showing up in this recommendation?
Those four do not require technical knowledge. They require a leader who stayed close enough to the work to know what to ask.
The first question is the one most often skipped, and it is the one that catches the most expensive mistakes. A team can describe what a tool does in great detail and still not be able to state the business problem it solves in one sentence. When that happens, you have found a solution looking for a place to land.
The second question forces an honest look at dependency and downside while it is still cheap to have that conversation. If this system failed next quarter, what stops working, who notices first, and how long would it take to go back to how you operate today? Teams rarely think this through voluntarily, because the answer is uncomfortable and the project is exciting.
The third question surfaces whether the priority came from a business case or from whoever presented most recently. Both happen in every organization. Only one of them is defensible when someone asks why this project and not the other three, and you can usually tell which you are dealing with by how quickly the answer arrives.
The fourth question is the one that protects reputations, including yours. Every recommendation presents its upside in detail. Very few volunteer the risk that did not make it onto the slide, not because anyone is hiding it, but because the people building something are the least likely to see what it exposes.
An executive who can ask a sharp question at the right moment is worth more to an AI initiative than an executive who built the system but cannot connect it to a business outcome.