Who Checks the Work?

You ask AI to turn a few project notes into an update. Seconds later, you have something you could send to your boss. Clear sentences. A confident recommendation. Even a next step.

Then you read it against the notes.

A target date became a confirmed date. Someone who offered to help became the owner. A decision nobody made is sitting in the last paragraph, written like everyone agreed.

The writing is good. That’s what makes this easy to miss.

In the last issue, I wrote about how quickly organizations are moving to adopt AI. The question I keep coming back to now is what happens after the tool produces the work. Who knows enough to check it? What are they actually checking? And did anyone account for the time that takes?

My working theory is that reviewing AI output will become a much larger part of professional work than many teams are preparing for.

Think about a proposal. AI can help assemble the scope, clean up the language, and organize the pricing. Someone still needs to notice that the installation date depends on equipment arriving, or that the customer asked for something the quote doesn’t include. Those details can determine whether the proposal is usable.

That changes what it means to be good at using AI. You need to describe the job clearly, recognize what a successful result looks like, and know where to look when something seems a little too complete.

There’s a learning question here, too. If someone is new to the work, how do they develop the judgment to review an answer they couldn’t yet produce themselves? A polished first draft can help them learn, provided they spend time comparing it with the evidence and understanding the corrections.

I’d like to see more AI training include that exercise. Give people the source material and a believable answer. Ask them what they would change before putting their name on it. Discuss why.

We’ve spent the first five workshops getting better at asking. This one starts practicing what happens when the answer comes back.