Listen In: Why Lean Innovation Matters 🔊
Mark Hallenbeck on Lean Innovation
About This Audio
In this clip, Mark Hallenbeck breaks down what Lean Innovation looks like in practice and why it matters before building solutions. He explains how understanding the problem, testing ideas, and identifying real opportunities can make the difference between building something useful and wasting time on the wrong solution. He also shares where AI fits into that process and why it’s most effective once the right problem has been clearly defined.
This approach is central to how he teaches innovation in the program, where students learn to validate problems before moving into solutions. You can read more about his approach in Turning Ideas Into Solutions.
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Audio Transcript
Lean innovation basically means learn as much as you can, as fast as you can. As cheap as you can. Learn before you burn through all of your resources.
It’s a very systematic way of learning about some issues, some problems, the human beings involved in that situation or problem, identifying pain points, identifying opportunities, and then iteratively developing prototypes of higher and higher fidelity, creating the tests for those, and very intentional tests where you hope to learn more about the thing that you’re trying to develop.
One thing that students often don’t understand is, if you’re gonna create a large system, you better be sure that you know it’s the right system. And what Lean innovation helps you do, is it helps you kind of answer all of those questions before you’ve sunk too much time and money into what you’re trying to do. This is really where innovation and lean innovation and AI meet.
AI is really, really good, once you know what your problems are. Once you’ve done the research, once you’ve identified the pain points, once you see those business opportunities, and you’ve validated that they’re real.
AI’s really good at helping create digital solutions to these things. What AI is not so great at is actually identifying those problems to begin with. And I think that’s where my course, and how I teach, I think I connect those two pieces. I help you understand what a real problem is, who the stakeholders are. I help you validate if your solution is viable, if it’s feasible. And then I help you figure out, okay, how can I use AI to digitally enable this solution, to scale it, to make it more effective?