What I'm Learning About Leading Engineering Teams in an AI-First World

Over the last year, I’ve been running a sustained experiment that most engineering leaders can’t afford to run in the middle of delivery pressure: stress-testing AI-assisted workflows against the realities that actually matter—quality, trust, and how teams behave when stakes are real.

No quarterly goals to hit. No existing processes to defend. Just sustained exposure to what works, what breaks, and what quietly fails when AI meets real teams.

The headline isn’t “AI makes engineers faster.” The headline is: AI amplifies whatever your org already is. Healthy teams get sharper. Unhealthy teams get noisier, faster.

The experimentation advantage

Most engineering leaders are constrained by the need to keep things running. When delivery pressure is constant, experimentation stops feeling like learning and starts feeling like risk. You can’t tear down workflows to test something new, so you default to being conservative, incremental, careful. Even when the tools themselves are changing rapidly.

I’ve had the freedom to be more experimental. That’s meant spending time not just with the tools themselves, but with how they actually land inside real teams: what sticks, what gets ignored, and what looks promising at first but never quite survives day-to-day delivery.

Here’s what keeps showing up across those conversations and experiments.

The tools are ready. The leaders aren’t.

Most engineering leaders I talk to fall into two camps:

1. The skeptics who think AI is overhyped and are waiting for the dust to settle before committing resources
2. The enthusiasts who bought Enterprise licenses but struggle to integrate AI effectively into their workflows

But there’s a third element that matters more than either approach.

What actually matters

Through all my experimentation, three patterns keep emerging. Each one shows up less as a tooling problem and more as a leadership one.

1) AI doesn’t replace leadership: it exposes it

The teams that successfully integrate AI aren’t the ones with the best tools. They’re the ones with leaders who already built trust, clarity, and psychological safety. AI just amplifies what’s already there.

Leaders who haven’t built that foundation sometimes hope AI will paper over communication gaps. It won’t. It will make the gaps faster and more visible.

2) Bottom-up beats top-down every time

The failed implementations often start the same way: executive mandate, company-wide rollout, generic training.

The successful ones usually start with one engineer saying, “hey, this thing is useful,” and leadership getting out of the way. Then they add just enough structure to help the learning spread.

3) The real ROI isn’t speed: it’s space

Everyone talks about AI making engineers more productive. Fine. But the real value is giving engineering leaders space to actually lead.

Less time in status meetings. Less time tracking Jira. More time coaching, building trust, developing people, and doing the work that keeps teams healthy.

That’s the difference between retaining talent and watching it quietly walk out the door.

What I mean by “AI exposes leadership”

Here’s a pattern I’ve seen more than once.

A team rolls out AI access and velocity jumps immediately, especially for a handful of engineers who take to it quickly. But within a couple weeks, trust starts to wobble. Some engineers feel like using AI is “cheating.” Some are using it quietly because they don’t want to be judged. Managers start second-guessing output they didn’t personally observe being produced. Reviews get weird. People get defensive. Collaboration gets brittle.

The fix isn’t more training. It’s leadership making judgment explicit.

What “good” looks like now. What’s acceptable to offload to AI. What still requires human reasoning. What evidence we expect in a PR description. How we talk about risk and edge cases.

One leader I watched get traction quickly did something simple: they introduced lightweight reasoning reviews (not code reviews). The question wasn’t “did you write this yourself?” The question was: “Walk me through the trade-offs. What did you verify? What are the failure modes? What did the tool miss?”

Within two weeks, the tension dropped and adoption went up. Because the fear wasn’t the tool. The fear was being misjudged.

What I’m testing right now

I’m currently experimenting with:

• AI-assisted code review workflows (some are genuinely impressive, others are theater)
• Documentation automation that actually gets maintained
• Meeting summarization that doesn’t lose critical context
• Ways to integrate AI without creating security nightmares

The goal is to bring tested, practical playbooks into my next role. Not theory. Not hype. Just approaches I’ve seen succeed (and fail) in real organizations.

The opportunity

Engineering leaders are already stretched thin, focused on shipping and supporting their teams. Very few have the bandwidth to experiment seriously with every new AI tool that shows up.

But the leaders who work through AI integration thoughtfully over the next year are going to create a real advantage. Not just in velocity, but in hiring, retention, and the health of their teams.

What I’m looking for

The leaders who navigate this well won’t be the ones who waited for perfect answers. They’ll be the ones who were willing to experiment carefully, learn in public, and adjust as they go.

I’m not interested in hot takes. I’m interested in outcomes: healthier teams, higher trust, and sustained delivery, while adoption compounds instead of fragmenting.

If you’re leading an org through this transition, the work isn’t “pick the right tools.” It’s: protect judgment, build safety, and make learning spread.