I've been struggling with something lately: how do you create a three-year strategic plan when you have no idea what the world will look like in six months?

Working in education, I'm constantly being asked to think about how AI will transform the way we learn and work. The challenge? Things that seemed impossible three years ago are happening today. And technologies that were experimental six months ago are becoming increasingly capable. I'm participating in forums, speaking with experts, and listening to predictions about the future of AI. But I've come to a realization: Nobody really knows what's going to happen. We have educated guesses, but they're still guesses.

So how do we plan for a future we cannot predict? Perhaps the answer isn't to become better at predicting, but to build systems that prepare us for uncertainty.

Tip of the Week: Don't build a plan that depends on predicting the future. Build a system that helps you adapt to it. The goal isn't to eliminate uncertainty, but to avoid being caught off guard.

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The Theory Behind

Known Unknowns vs. Unknown Unknowns: We don't know what we don't know. Former US Secretary of Defense Donald Rumsfeld famously popularized this distinction in 2002, although its roots go back further. There are known knowns (things we understand), known unknowns (questions we recognize but cannot answer), and unknown unknowns (things we don't even realize we should be questioning). In AI, we have plenty of known unknowns: How will jobs evolve? What skills will become essential? But the real challenge is the unknown unknowns: breakthroughs, applications, or disruptions we haven't even imagined. This is why traditional planning becomes increasingly difficult as uncertainty grows.

Scenario Planning: Prepare for multiple futures instead of predicting one. Popularized by Royal Dutch Shell and strategist Pierre Wack, scenario planning challenges the traditional approach of building a strategy around a single forecast. Instead, we explore several plausible futures and identify actions that would make sense across them. For example, rather than predicting exactly how AI will transform education, we can imagine different scenarios: AI supporting teachers, fundamentally changing their roles, or enabling entirely new learning models. We don't need to know which scenario will happen to start preparing for all three. The goal is not to predict correctly, but to make better decisions today.

Optionality: Build flexibility into your long-term plans. In Antifragile, Nassim Nicholas Taleb discusses the value of optionality: creating opportunities that allow us to benefit from positive surprises while limiting exposure to negative ones. In an uncertain environment, committing everything to one prediction can be dangerous. Instead, we can invest in transferable skills, experiment with emerging technologies, and maintain multiple pathways forward. This doesn't mean avoiding commitments. It means making deliberate commitments while preserving the flexibility to change direction as we learn more. The best strategy may not be the one with the most detailed roadmap, but the one with the most room to adapt.

What I’ve Learned

You don't need to know the future, but you need to stay close to those shaping it. One thing I've realized is that nobody has all the answers about AI, including the experts. But collectively, we can get closer to understanding what's emerging. That's why I've been participating in forums, exchanging ideas with leaders across industries, and attending conferences. Next week, I'm flying to São Paulo for an AI and Future of Education conference. I'm not expecting to come back with a crystal ball. I'm hoping to return with better questions, new perspectives, and stronger connections. [Side note: If you've read this far and you're in São Paulo, let's connect!]

Make It Happen

Here's a practical system to plan for the future without pretending you can predict it.

1. Define your three-year direction, not your three-year roadmap. Identify what you want to achieve and why. Keep the destination relatively stable, but allow the path to evolve.

2. Map your knowns and unknowns. Create three columns: What You Know, What You Know You Don't Know, and Assumptions to Challenge. You can't list true unknown unknowns, but you can identify blind spots worth exploring.

3. Build three future scenarios. Imagine a conservative future, an accelerated-change future, and a disruptive future. Ask yourself what decisions would work across all three.

4. Create options instead of betting everything on one outcome. Invest in transferable skills, pilot new technologies, and test small initiatives before making large commitments.

5. Build your uncertainty network. Identify five to ten leaders facing similar challenges. Connect regularly, exchange insights, and compare what you're learning. Think of it as your personal early-warning system.

6. Set a quarterly strategy review. Every 90 days, revisit your assumptions, update your scenarios, and adjust your priorities. Ask: What changed? What did we learn? What should we do differently?

7. Maintain a rolling execution plan. Keep a detailed plan for the next 90 days, a directional plan for the next year, and strategic ambitions for the next three years. The further away the horizon, the more flexibility you preserve.

Uncertainly yours (at least for now),

Jorge Luis Pando

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