There’s a chart I keep coming back to. It’s simple: Waterfall → Agile → AI.

It’s a snapshot of history, a forecast of what’s next, and for those of us who’ve lived through the transitions—it’s also a bit of a mirror.

I remember when Agile was the revolution. I spent years helping teams shift away from Gantt charts and sign-off meetings and into something more human, iterative, and empowering. And for a while, it felt like we’d cracked it. We had standups. We had retros. We had real conversations about value.

But like every wave of change, Agile hardened into process. The ceremonies multiplied. The heart of it—working closely with customers, shipping quickly, learning as we go—got buried under velocity charts and frameworks that seemed to matter more than outcomes.

Now AI is shifting the ground under us again. Not just in how we write code, but in how we think about what work is. What’s worth doing manually? What can be delegated to a model? What does “done” mean when a prototype takes minutes, not weeks?

This isn’t just a tooling upgrade. It’s a change in the structure of work.

The teams I lead now are starting to look different. There’s a new tempo. Ideas turn into code faster. “Planning” looks more like real-time decision-making. And the skills that matter most? Curiosity. Judgment. A deep understanding of users and context.

To engineering leaders and executives: this is the moment to revisit how we work—not just what we’re building. We’ve been here before. We’ve watched systems ossify. We’ve untangled teams from “best practices” that no longer served them.

This time, we have a head start.

We know that great work isn’t about rigid roles or perfect processes. It’s about clarity, trust, and fast feedback. The organizations that thrive in this new wave won’t be the ones that bolt AI onto a Jira board. They’ll be the ones that rethink the board itself.

The wheel is turning again. Let’s steer it with care.