When to Talk and When to Practice

The Green Path - Shifting Knowledge Series

It was supposed to be a mini-workshop about how to react to AI disruption in software.

The facilitator had chosen an excellent exercise to structure the conversation. He gathered the current knowledge, helped them categorize and reason, then did some gap analysis and explored possible futures. From there, he led the team to explore responses to different potential futures.

And the conversation went nowhere.

It turns out only three people in the room had ever used AI for software development. No one else had the grounded experience to understand the difference between that and non-AI software development.

But that didn’t stop people from talking! There was a ton of discussion. They explored many possible futures and responses. The problem is that they just weren’t tied to reality. And so the conversation hit a dead end after the workshop. It was a thought exercise, nothing more.

Have you experienced this before?

We love to talk. And there’s a time for that, but too often, the learning journey is focused on theory. We explain concepts with more concepts, piling abstraction on top of abstraction, and then are disappointed when the information isn’t creating change in behavior.

But that doesn’t make information bad. It’s actually critically important, but not useful until they’ve had the grounded experience. People following recipes in Simple Robot experience the wins and successes, but in a very limited context.

Before they expand their practice to wider context, they need to add knowledge. I call this part of the Green Path the Smart Robot because it represents not just knowing things, but also knowing things about what one has done.

There’s no lecture needed on "how." Instead, it’s about recognizing patterns based on practical experience. Powerful learning happens when the goal isn’t to explain theory, but to help learners see the conditions under which certain actions succeed.

That’s the heart of skillful practice found in Smart Robot, and why grounded practice in Simple Robot has to happen before we talk about it.

In a workshop or learning environment, we can accelerate this by designing exercises that surface tacit knowledge they learned in Simple Robot and make it explicit through knowledge exploration in Smart Robot. We don’t just hand out one “recipe for success.” This is the stage where we help learners build a cookbook—multiple approaches tailored to different situations to take with them into Solo.

This means not just practicing the skill, but noticing what made it work: What changed? What stayed the same? What were the signals?

As a facilitator for practitioners in Smart Robot, your job represents the following:

• You’re not the source of answers.

• You’re the architect of practice.

• You choose exercises that give people just enough friction to notice the why behind their success (or failure).

Once learners can spot those patterns for themselves, they no longer need a script—they have flexible fluency. That’s how we move from “knowing about” to “knowing why what we do works”.

And that’s what makes a skill stick.