The Operations Bridge

This starts a new series I’m calling AI Strategy Points — twelve things I’ve come to believe about adopting AI in an operational environment, one post at a time. Point one is about timing, and about being honest with yourself regarding where you actually stand.

I got a head start I didn’t fully appreciate at the time

I was fortunate enough to adopt AI — ChatGPT specifically — a few years before it became mainstream. At the time, it felt like a reasonable bet: something that looked like it might become a tidal wave, and I wanted to be ahead of it rather than caught flat-footed by it. What I didn’t expect was how fast that wave would actually arrive.

That early runway mattered. It gave me time to make mistakes on my own schedule instead of under organizational pressure. By the time AI adoption became an expectation rather than a curiosity, I had already paired ChatGPT with an early version of Microsoft Copilot to develop and deploy seven Copilot agents. More recently, I brought in Claude and built six data analysis skills with automation behind them.

None of that happened because I was especially technical. It happened because I started early, kept experimenting, and let the tools mature alongside my understanding of them.

Why I’m telling you this isn’t a brag — it’s a warning

If you’re just starting now, you may already be behind the curve. I don’t say that to be discouraging. I say it because I think a lot of leaders are underestimating how much runway other people already have, and that miscalculation affects how urgently they treat their own learning.

The gap isn’t really about who had access to the tools first — access is nearly universal now. The gap is about who has spent real hours failing with these tools, learning their limits, and building judgment about when to trust an output and when to challenge it. That kind of judgment doesn’t come from a single training session. It comes from repetition, over time, on real problems.

Behind the curve is not the same as out of the race

I don’t think being behind is disqualifying. I think it’s just information. If you’re starting now, the honest move is to acknowledge the gap rather than pretend it doesn’t exist, and then close it deliberately — through real use on real problems, not through a certificate or a single afternoon workshop.

The leaders I see struggle aren’t the ones who started late. They’re the ones who started late and then treated a single course or a single demo as sufficient. The tools reward accumulated hours of hands-on iteration far more than they reward credentials.

A question for leaders

Honestly assess where you are. Have you spent real hours building something with AI on a problem that mattered, or have you mostly watched demonstrations and read headlines?

The next post picks up a related thread: AI initiatives have to create value to stick. But that depends entirely on who gets to define what “value” means — and that’s rarely as obvious as it sounds.