The Strategy Professor's Guide to AI Adoption

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Last week, as I introduced Porter's Five Forces to my evening MBA students at the University of Washington Foster School of Business, I noticed something fascinating. The same principles that make strategy education effective can transform how product teams approach AI adoption. Here's how I see it: most product teams are approaching AI like a student cramming for an exam – chaotic, rushed, and ultimately superficial.

The Chaos of Unstructured Learning

Let's be honest: many product teams' approach to AI looks like any student's first attempt at case analysis – jumping straight to solutions without understanding the framework. They're playing with ChatGPT, experimenting with Midjourney, and tinkering with Copilot. But without a structured learning approach, it's just digital finger painting.

The Power of Intentional Learning

In my strategy class, each lecture introduces a new framework, but – and here's the key – we then immediately apply it to real-world cases. We analyzed which of Taylor Swift's many core competencies are responsible for her sustainable competitive advantage not just for fun (though it was), but because it helped us understand internal analysis in a tangible way.

Product teams need this same balanced approach to AI adoption. Yes, you need a structured framework that builds competency systematically – think of it as your team's AI curriculum. But you also need to create space for unexpected discoveries and organic learning moments. Some of your team's most valuable AI insights will come from unplanned experiments and spontaneous connections. The key is having enough structure to guide the learning journey while remaining flexible enough to explore promising detours.

"...the difference between good and great product teams won't be who used AI first - it'll be who learned to use it best."

Finding the Sweet Spot

Here's what it looks like in practice. In class, I provide the framework, but the real learning happens when students start discussing and debating its application to whatever case we happen to be discussing. Similarly, product teams need both structure and space for exploration in their AI journey.

A practical approach might look like this:

• Week 1: Focus on AI-powered customer research tools
• Week 2: Explore AI for roadmap prioritization
• Week 3: Experiment with AI in stakeholder communication

The key? Each area has clear learning objectives but leaves room for discovery and discussion.

The Leadership Imperative

As product leaders, we need to be both professor and student. Just as I carefully plan each strategy lecture while remaining open to unexpected insights from my students, you need to create structured learning environments while staying curious about your team's discoveries.

Here's your challenge: Take a look at your team's current AI adoption approach. Does it look more like a carefully crafted curriculum or a last-minute cram session? Because in the end, the difference between good and great product teams won't be who used AI first – it'll be who learned to use it best.

I'm currently working with product teams to develop more intentional and effective approaches to AI adoption. If you're interested in learning more about how to create a structured learning environment for your team or just want to compare notes on what's working, reach out. I'd love to hear about your experiences and share what I've learned helping other teams navigate this transition.

Break a Pencil,

Michael 

www.breakapencil.com 

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