When AI is brought up in the boardroom, many leaders feel like they are stuck. Their teams are writing software with it, the tech is moving rapidly, and there is a whiff of risk in there somewhere and then they feel like asking about this trade-off involves revealing how little they follow any technical detail.
The reassuring part is this. You still don't need to know what contributes to AI writing code in order to lead a team that does it well. One great question, and the ability to dare read the answer
The question
Here it is:
Validate AI output — "How do we know that when the AI writes code, the code is correct?
That's it. You ask it boldly, and then you listen not so closely to the words as to the form of response that comes back.
How to read the answer
There are two types of answers, and the sound extremely different once you start listening for them.
Your weak answer rests on the reputation of the tool. "It is: "AI is pretty darn good now," "the latest version has high fidelity," and most common of all, "everyone uses it; it's fine." None of this is anything the team actually does. You're borrowing trust from the tool's brand. This is exactly how organizations wind up shipping software that went untested.
A strong response describes a process. You write down all the things each piece is supposed to do. We have automated tests that show that it does do that. And before anything ships, a change has to be explained by an engineer senior enough not just approved.
You may not need to follow the second-given answer entirely through its tech lingo. You listen for one thing: Is there something deliberate and methodical about catching mistakes or is it simple overconfidence?
Why this particular question works
The reason this works is because it gets right to the risk and that risk isn't what many think.
The problem with AI is not bad code. In general the good code looks totally fine. The danger is it quickly writes a whole lot of code that looks plausible, and we all know how easy to wave through plausible looking code is. It passes the glance test. There is no incentive to dig any deeper because nothing appears amiss.
A good team is aware of this, and proactively sets up a counter for it. A team running on confidence doesn't, and it won't discover which kind until something fails in front of a customer, at which point the cost is way more than a few minutes review would've been.
Before the failure instead of after, your one question emerges showcasing what sort of team you have.
This is not a technical question, but really a leadership one —
You do not have to out-code your engineers or understand the models. You must also ensure that the check exists, and that people respect it.
How do we know the AI is writing the right code?
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