What a delight to hear from so many of you after we launched AI Leaps!

Whether you reached out to explore bringing the program to your organization, shared a resource to help us build, connected us with a collaborator, or simply said, “This is awesome!”– thank you. Hearing from you is always a bright spot, especially when we’re putting something new into the world.

One thing we heard again and again? How much our unofficial tagline, from hesitation to habit, resonated. It got me thinking: how does that shift actually happen? What does it look like for individuals? And how do organizations help it stick?

In our experience, it often starts with a bit of experimenting.

In every Ei Leaps program, individuals are encouraged to experiment: to try, reflect, and try again. When those experiments are seen, supported, and shared, something bigger begins to take shape. A culture of experimentation starts to grow. And when that culture takes hold, it gives others the safety and permission to keep experimenting. It becomes a virtuous cycle. That is exactly what AI Leaps is designed to spark.

Making a Culture of Experimentation Real

So when I came across a recent LinkedIn post from Amy Webb, CEO of the Future Today Strategy Group, it really hit home. She wrote, “genAI is basically three years old. There are no seasoned genAI leaders yet.” Rather than hiring for expertise that doesn’t exist, she encourages organizations to look for “an exceptional leader with a strong foundation of technical knowledge” and focus on operationalizing a genAI culture.

To be clear, Amy Webb hasn’t endorsed AI Leaps. In fact, she probably hasn’t even heard of it. But her five principles for building genAI culture confirmed that we’re on the right track, and they’re just too good not to share. Here’s what she suggests, and how we’re putting it into motion:

1. Democratize foundational knowledge
AI Leaps isn’t just for executives or early adopters. We work with intact teams and cross-functional groups to build shared understanding and shared language. Everyone’s in the learning together.

2. Build a culture of experimentation
Individuals can start experimenting, but culture is what sustains momentum and scales impact. It’s the fertile soil that allows experimentation to take root and grow. When people feel safe to test, learn, and apply AI to real work, you start to see the magic. Efficiency improves. Decisions get sharper. The speed of innovation picks up.

3. Identify high impact, low risk use cases
We focus on what people are already doing day to day and look for the places where AI can add immediate value. Low risk, high learning. Safe-to-fail Leaps.

4. Equip teams with tools and guardrails
We design the program around your organization’s approved tools and policies so your team can get building with clarity and confidence. And the CTO isn’t too worried they’ll get in trouble.

5. Build tiger teams and document learning
Each cohort functions as a tiger team. Every program ends with a Leap Talk, where teams share what worked, what didn’t, and what’s next.

So, thank you, Amy Webb, for articulating the roadmap so clearly. For more gems, we highly recommend following Amy Webb on LinkedIn.

And if this sounds like your kind of learning environment, we’d love to see you at our AI Leaps Sneak Peek on May 30th at 11am CT. Join us to get a feel for the program in action, and feel free to pass the invite to anyone looking to go from hesitation to habit with AI.