Small Bets, Big Wins

Greg

Hey everyone, and welcome to , a special series of the podcast, where we explore how AI—and innovative thinking—can help you build a truly . I’m , co-founder of .

Daniel

And I’m , Solve Next’s Chief Operating Officer. Today, we’re talking about a crucial shift in how organizations innovate—one that could make the difference between or .

Greg

And that shift begins with a simple truth: .

Daniel

Right. Too many teams go big on untested ideas—wasting time, capital, and credibility when those ideas bomb. Others move cautiously they never learn anything meaningful.

Greg

And the biggest issue? Most innovators are . They don’t have a system that tells them:

Greg

Which assumptions matter most,

Greg

What’s the smallest, smartest test we can run,

Greg

or how to turn learning into action.

Daniel

That’s why we developed the —to transform innovation from a high-stakes guessing game into a process.

Greg

And at the heart of that system are . Done right, they let you test critical assumptions, minimize risk, and maximize learning.

Daniel Buritica

But you design, execute, and measure those small bets——is where the comes in.

Greg Galle

Before we dive into the AI Assistant, let’s zoom out for a second. What does innovation management look like?

Daniel

It’s not just about ideas. You need a system—what we call the .

Greg

That system is built on : , , and .

Daniel

Greg, from your perspective, what’s the number one thing great innovation teams nail?

Greg

They never treat innovation as a side project. They bring the right mix of —people who embrace uncertainty and harness AI as an amplifier rather than a replacement.

Daniel

Exactly. Technology alone won’t solve the innovation dilemma—you need the right people who see it matters.

Greg

And who know to get it done. So, let’s talk process. How teams innovation is as vital as who’s on them.

Daniel

Yeah. The extremes of “go big or go home” or “analyze forever and never act” don’t work. The strikes that balance: structured yet nimble, letting teams test quickly, learn continuously, scale what works—and redirect or stop what doesn’t

Greg

That’s where our really shines.

Daniel Buritica

Right. It’s not just a tool, but an that helps teams small bets with .

Greg

So, Danny, give us the elevator pitch. What does the actually do?

Daniel

Think of it as your —it helps you: Identify and prioritize (SVAs), Design and run Validate hypotheses with real-world data, And track progress and around learning.

Greg

It also integrates —structured drills that break inertia, reframe challenges, and spark creative solutions.

Daniel

That’s right. Rather than a vague “let’s test something,” you get a method for designing small bets.

Greg

And the AI Assistant not only your experiments—it provides you with a way to analyze results in real time, giving you instant insights on what to change, amplify, or stop.

Daniel

Alright, let’s ground this in real-world examples. This isn’t just theory—it’s the difference between winning and crashing, literally.

Greg

Yeah. Let’s start with tragedy. Boeing, once the , made a —and it cost them

Greg Galle

Instead of building a new aircraft to compete with Airbus, Boeing to an older design. They introduced but that could have caught fatal flaws. And here’s the key mistake—they MCAS would work seamlessly. Instead of testing with real pilots, they

Greg Galle

The

Greg

Had they taken a , they would have: to assess MCAS behavior under real-world conditions. before full-scale deployment. And instead of betting everything on a rushed launch.

Greg Galle

Boeing’s failure wasn’t about

Daniel Buritica

Now, let’s contrast that with , which built its success

Daniel Buritica

Instead of launching globally overnight, Spotify before expanding.

Daniel Buritica

They to see what worked—freemium, invitation-only, and limited free streaming—before scaling the most effective one.

Daniel Buritica

And when they , they didn’t just —they , analyzed user engagement, and only then made bigger investments.

Daniel Buritica

That’s why they’re the —because they

Greg Galle

So, how do small bets fit into the broader framework of an ?

Daniel Buritica

Remember the five components of that system: . Every one of them benefits from structured, purposeful experimentation.

Greg Galle

Right, because small bets of your innovation engine—lowering risk, speeding up learning, and letting you adapt before you overcommit resources.

Daniel Buritica

And that’s what we call the —Learning From Investment. It’s not about “fail fast” or “move slow”—it’s about , data-driven steps that yield clarity and confidence.

Daniel

Alright, let’s wrap. might be “small,” but they deliver wins. They help you de-risk, learn more quickly, and only the best stuff.

Greg Galle

And that’s why the is such a powerhouse: it gives you a real-time, step-by-step approach to experiment smarter, not harder.

Daniel Buritica

So here’s your next move:

Daniel Buritica

First, —we’ve dropped the link in the show notes. Then, of —gain access to the AI assistants and see how they can shape your entire Intelligent Innovation System.

Greg Galle

If you’re ready to take the in building an , let’s talk. You can find us at or connect with us on .

Daniel Buritica

Until then, . Remember: —human, intellectual, political, social, reputational—AND financial.

Greg Galle

Thanks for tuning in to . We’ll catch you on the next episode—where we’ll keep exploring how to make innovation truly .

Daniel Buritica

Don’t forget to so you don’t miss our upcoming AI tools.