Walking the AI Dog: How to Build an Operating Model That Actually Reduces Your Workload

My team and I have been working with AI every day for well over a year now. Like everyone in our industry, we've run the full range on it: intrigued, impressed, frequently frustrated, occasionally furious.

A few weeks ago I hit furious. Chat had gotten something wrong for the third or fourth time, confidently answering questions about a document it plainly hadn't read. So I did what you'd do with any employee who kept turning in work like that: I PIPd it. Figure it out, I told it, or I'm going in another direction (e.g., Claude!).

Then it did something I didn't expect. For more than a year, I'd been managing these tools one correction at a time. A fix here, a rule there, always reactive, always in the moment. I had never once stepped back and set the terms for how we should actually work together. Instead of apologizing again, that's exactly what it proposed: stop patching and instead build a real operating model. A wholesale set of standards, not another one-off instruction. The tool I was about to fire handed me the idea I'd been missing for more than a year.

That exercise clarified something I'd been circling for months but couldn't quite put my finger on. The real question wasn’t whether AI was getting better. It was whether I was walking the AI dog, or it was walking me.

That's the real question with AI. Not which model you use, but who's leading whom. The tool won't make you sharper or lazier, more valuable or less. How you use it decides that.

When it works, it clears the low-value work off the table and hands back the one resource we can't manufacture more of: our attention. My team gets to spend theirs on the thinking clients actually hire us for. It's how we make sure the value we say we bring is the value we truly bring.

When it doesn't, it does the opposite. It hallucinates. It answers confidently about a document it never opened. It hands me five mediocre options when I asked for one good recommendation. Now I'm the one fact-checking and cleaning up, and the tool has made more work for me, not less.

I love where Chat and I netted out: as it told me, the AI assistant's job is to reduce our cognitive load, not increase it. That's the test. If a response leaves me with less work than I started with, it's doing its job. If it leaves me verifying, filtering, or cross-checking, it hasn't met the bar, no matter how polished it reads.

But the tool won't hold that line on its own. The dog won't walk itself. That part is on us: clear rules, tight instructions, and a flat refusal to accept work that just makes more work.

So I'll put the question to you. If you've built your own operating model with these tools, what's the one rule that's made the biggest difference? Put it in the comments. I'm always looking to steal a good one.