Dr. Alex Osterwalder, founder and CEO of Strategyzer, challenges conventional wisdom on innovation strategy, urging companies to embrace experimentation over rigid planning in today’s AI-driven business landscape.
I might disappoint you with my first statement: I’m not going to talk that much about AI because, for me, it’s just another technology. It’s more powerful than most things we’ve seen before, but at the end of the day, the real challenge for companies is learning how to reap benefits from AI. AI itself is not the real challenge.
Some of you may know me from co-authoring the Business Model Canvas with my colleague Eve. We’ve created various tools over the years. What I’ve learned is that leadership, processes, and how you use these tools determine how much value you create from new technologies. That might sound trivial, but it’s actually not.
Working with Leading Companies
One of the advantages I have is that I get to work with the world’s leading companies both from the top down and bottom up. In recent months, I’ve been working with Honeywell and L’Oreal leadership, giving me insights into what struggles these companies face. I also work with teams on the ground and draw inspiration from startups. This gives me a unique perspective spanning from very large companies to small companies who have started using our tools.
At Strategizer, I’m trying to innovate by giving away my secret sauce—everything I’ve learned and developed, packaged into playbooks that I share with others.
Two Different Worlds: Explore vs. Exploit
The fundamental insight I’ve discovered separates successful innovators from struggling companies: innovation and business execution are two entirely different worlds.
When I’m running a business, I focus on execution with clear KPIs and targets. However, most established companies make a critical mistake: they apply execution rules to innovation. This doesn’t work because these worlds operate under completely different principles.
I call this the “explore and exploit” paradigm. The execution side demands predictability and efficiency. The innovation side requires experimentation and tolerance for failure. I must consciously toggle between these two mindsets—something most leaders don’t do naturally.
When I’m running my company, I ask: “Are we on time and on budget?” Two minutes later, when I’m talking to my product team who are innovating, I need to switch and ask: “What did we learn? Should we kill the idea or continue investing?” It’s a very different mindset. Not many leaders consciously toggle between the two.
Learning from Bosch: The Innovation Funnel
Let me give you a concrete example from my work. Bosch, the conservative German company, hired me to help them innovate. When they were tasked with creating new business ventures, I showed them how to invest in 214 ideas over three years to eventually develop 19 potential winners. Only two became multi-billion-dollar businesses—a 1 percent success rate.
The critical lesson wasn’t the number of ideas explored but how I helped them manage the process. Each idea received only three months and $120,000 (mostly in people’s time). After three months, I asked teams one question: “What’s the evidence this is a good idea?”
Based on evidence, they killed 65 percent of first-round projects. In a second phase testing solutions, 74 percent were eliminated. The methodology worked because it killed bad ideas early, before wasting significant capital.
Most companies do the opposite: they build expensive prototypes first, then discover customers don’t want the solution.
Why Business Plans Fail at Innovation
Here’s a critical insight I’ve learned: business plans are excellent execution tools but “the death penalty of innovation.” When companies write detailed business plans for new ideas and investors fund them, the money gets spent building something. But what innovators need is experimentation to determine if the plan is even sound.
I worked with one of the top two food companies, and when I said we need to test desirability, feasibility, and viability, they told me: “Alex, we test viability early on.” I asked, “How?” They said, “We make spreadsheets.”
Spreadsheets aren’t evidence in innovation. They’re what I call “fantasies made explicit.” While I want the spreadsheet at the beginning because I need to know if this could be a big idea, everybody needs to know the spreadsheet is a fantasy. What I do is run experiments to support the spreadsheet with evidence.
This is why analysis alone fails. I’ve found that most established companies overthink and underexperiment. If you’re doing something genuinely new, there’s no historical data to judge if it’s good. You must experiment instead of planning.
The CEO’s Invisible Problem
I face a particular challenge as a CEO: nobody tells me my baby’s ugly. If I believe in an idea, my team members rarely provide honest reality checks. This makes evidence-based decision-making essential. I need my team to conduct rigorous experiments that prove or disprove my hypotheses rather than relying on my executive intuition.
Rather than believing in “best ideas,” I test three critical hypotheses for any new idea:
Desirability: Do people actually want it?
Feasibility: Can we build and scale it?
Viability: Can we make more money than we spend?
I use an innovation scorecard approach that tracks evidence at each phase. Ideas live or die based on demonstrated evidence, not spreadsheet projections.
Types of Experiments: From Lightweight to Strong
I’ve learned that not all experiments carry equal weight. My principle is simple: start cheap, increase confidence gradually.
Card Sort Experiments: I worked with ShapeScale, which needed to know which app features customers valued most. Rather than building everything, they created cards representing features and had customers rank them. This revealed which features outperformed before any development.
Brochure Testing: American Family Insurance created a brochure for a new farmer insurance product, distributed it at a convention, and included a call-to-action. Only 15 percent responded—and crucially, cattle farmers outresponded corn farmers. This revealed their true market before any product development.
Letters of Intent: For B2B ventures, I’ve found that a letter of intent demonstrates real commitment. FEX got four construction companies to sign letters promising to buy insulation foam products if made—strong evidence without building anything.
Wizard of Oz: In 1984, IBM tested speech recognition by having an admin type behind the scenes while users spoke into a microphone. The technology didn’t exist yet, but IBM learned whether people wanted it and how they’d interact with it.
The Tesla Precedent
I’ve studied Tesla’s approach extensively. The founders extensively tested before building. They showed existing electric vehicles to potential customers and discovered current EV buyers weren’t their target market. They wanted people who desired German cars that happened to be electric.
They then created a “mule”—a Lotus Elise with Tesla’s battery and drivetrain—for real-world testing. A landing page signup generated hundreds of interested buyers. Pre-sales of 100 Roadsters at $100,000 each proved demand before manufacturing began.
Later, Elon Musk’s Model 3 pre-sales generated $200,000-$300,000 in down payments within a week at $5,000 per reservation. He used this evidence to convince banks to provide additional funding. That’s the power of evidence.
What Changes and What Stays the Same with AI
AI will create completely new business models and value propositions, much of it predictive and ultra-personalized. But I’ve found that fundamental business principles remain unchanged: companies still need profitable models and must create genuine customer value. The technology is actually the easy part—cheap to access at $20 monthly for powerful tools.
The management mindset must shift, however. I believe “explorers will win over exploiters.” Companies that only optimize existing businesses will eventually become irrelevant. Entrepreneurial leaders who balance exploration and exploitation will thrive.
The Revenue Model Revolution
I use Laurastar—a Swiss steam iron manufacturer—as a small-company example of what I’m talking about. They initially tried a handheld steamer for travelers (a poor idea) until exploring multiple possibilities through a funnel approach.
The crucial insight I helped them discover came through understanding revenue models. Transactional revenues (selling once) get valued at 3-5x EBITDA. Recurring revenues (subscriptions or long-term contracts) get valued far higher.
Laurastar created the Aura device paired with replaceable wooden scent taps—delivering fragrance without chemicals. Customers repeatedly repurchase consumables, creating recurring revenue, lock-in effects, and patent protection. This business model transformation proved more valuable than product innovation alone.
Culture Comes from Leadership
Innovation culture doesn’t emerge from motivational slogans about “infecting everyone with innovation virus.” It requires explicit processes and clear rules. If I make evidence-based investments with transparent decision criteria, culture changes automatically.
The CEO is essential. Not all CEOs are naturally entrepreneurial, but those who consciously toggle between execution and exploration mindsets create environments where innovation flourishes. I’ve seen how Bracken Darrell, former CEO of Logitech, naturally manages this balance. Others struggle.
Key Takeaways
- Master both explore and exploit: These aren’t sequential phases but parallel capabilities I must balance as a leader
- Evidence over intuition: I test hypotheses systematically rather than betting on executive instinct
- Kill ideas early: I make small bets and ensure fast failure to prevent massive capital waste
- Business fundamentals matter: Technology enables but doesn’t create value; customer focus does
- Leadership shapes culture: An entrepreneurial CEO determines whether exploration becomes normal
- Start small and iterate: I begin with cheap experiments and increase investment as evidence accumulates
The message is clear: in an AI age where technology access is equal, competitive advantage goes to organizations that organize themselves for continuous exploration, make evidence-based decisions, and maintain entrepreneurial leadership alongside operational excellence.
