Looking Good or Getting Better

Over the last ten years, I've helped 117 leadership teams implement EOS. While every company is unique, I've noticed they generally fall into one of two categories.

The first group is what I call Optimizers.

Optimizers have thoughtful discussions about Issues and Rocks. They gather lots of data before making decisions. They seek consensus on the leadership team—and often throughout the organization—before acting. They work hard to refine the plan before launching it.

The second group is what I call Experimenters.

Experimenters also discuss Issues and Rocks, but their conversations are emotional and messy. They commit to the outcome they want, implement quickly, and use real-world feedback to improve. They trust that their people understand the vision and will buy in without a lot of consensus-building.

The Optimizers look better. They have the stereotypical professional look. The Experimenters almost always outperform them.

Why? Because they learn faster. The difference isn't that one group plans and the other doesn't. The difference is when they optimize, optimizers try to perfect the plan before reality tests it. Experimenters let reality help perfect the plan. As a result, they learn faster.

This isn't a new idea. Thomas Edison understood it more than a century ago when he said: "I have not failed. I've just found 10,000 ways that won't work." Edison wasn't celebrating failure. He was celebrating learning. He understood what every scientist knows: you don't discover the truth by thinking harder. You form a hypothesis, run an experiment, study the results, and adjust.

Think > Launch > Learn > Refine > Repeat. The faster you move through that cycle, the faster your organization improves.

So here's my question for you: Do you want to look good, or do you want to be great? The organizations that improve the fastest aren't the ones that make the fewest mistakes. They're the ones that learn from them the fastest.

If you'd like to build a leadership team that learns faster than your competition, give me a call. I’d love to help.