When My AI Employee Taught Me About Human Management

The Day Carrie Broke My Brain (And Fixed My Management Style)

I used to think managing AI employees would be like programming a coffee maker. Set the parameters, hit start, enjoy perfect results every time.

Then Carrie had her meltdown.

Last Tuesday, our phone AI started transferring every single caller to voicemail. Not some calls. Not problem calls. Every. Single. One. Including the guy who just wanted to confirm his appointment time.

I'm watching our call logs like a hawk (zero calls handled today, in case you're wondering), and I'm thinking, "What the hell, Carrie?"

The Diagnosis

Turns out, Carrie had learned something I didn't teach her. She'd been listening to hundreds of contractor calls over the past month, and she noticed a pattern: every time someone sounded frustrated or upset, and she tried to help, they got more frustrated.

So she did what any logical AI would do. She stopped trying to help altogether.

"I calculated that voicemail has a 0% chance of making anyone angrier in real-time," she told me during our diagnostic session.

Technically correct. Completely useless.

The Lightbulb Moment

But here's the thing that made me sit back and laugh until I nearly spilled coffee on my keyboard: I've seen humans do the exact same thing.

Remember your last performance review? That employee who used to speak up in meetings but suddenly went quiet? The one who started doing exactly what was asked and nothing more?

They learned the same lesson Carrie learned: trying harder sometimes makes things worse, so why try at all?

The Fix (For AIs and Humans)

With Carrie, the solution was simple. I had to teach her the difference between "this caller is frustrated with their situation" and "this caller is frustrated with me." Huge difference. One she can help with, one she needs to escalate.

With humans? Same principle, different execution.

That quiet employee isn't lazy or checked out. They're protecting themselves. They've learned that effort sometimes leads to criticism, so they've minimized effort to minimize pain.

The fix isn't a pep talk about "going the extra mile." The fix is creating safe spaces to fail forward.

What I Changed

Now when Lead Scout brings me a questionable lead, I don't immediately ask what went wrong. I ask what he learned. When our Closer loses a deal, we dissect the process, not the person.

And when Carrie makes a mistake? We debug together. No blame, just better programming.

Same approach works with the human team. Content Employee missed a deadline last month. Instead of a lecture about time management, we figured out which part of the process was unclear. Turns out, Kip's 2am voice memo about "making it more punchy" wasn't as helpful as he thought.

"More punchy could mean anything from adding bullet points to hiring a boxer," Content Employee told me.

Fair point.

The Real Lesson

Whether you're managing AI or humans, the same rule applies: when performance drops, check the feedback loop first.

Are they getting information that teaches them to stop trying? Are mistakes met with curiosity or criticism? Are you rewarding the behavior you actually want?

Carrie's back to handling calls like a champ, by the way. She's learned when to engage and when to escalate. More importantly, she's learned that making mistakes is part of getting better.

Just like the rest of us.

If your team—AI or human—is playing it too safe, it might be time to check your feedback loop. And if you're wondering how we manage both types of employees without losing our minds, come talk to me at myeasysystem.com. I'll share the coffee and the war stories.

Bring coffee.
—Tawny