Question Everything: Why the Question Matters More Than the Answer

Questions unlock better solutions. “How might we” is a powerful divergent thinking tool, especially when AI defaults to answers.

When my daughter spills her drink, she comes to me to tell me it happened.

In that moment, I have two options:

• Act: “Go grab a towel and clean it up.”
• Ask: “How do you think we can fix that?”

The first gets the mess cleaned the fastest. The second takes longer but lets her think.

She grabs paper towels, maybe uses her shirt. She asks for help, or she figures it out herself. Either way, she’s not just following instructions. She’s solving.

It’s a small moment, but it captures something important about how we approach problems.

When we turn a problem into a question, something shifts. Our brains respond differently to questions than to instructions. We get more creative and more motivated. We find solutions we wouldn’t have reached if we’d just executed.

The Push for Execution

In fast-moving product teams, that shift doesn’t always happen naturally. We’re under pressure with tight deadlines, sprints and bugs to fix. There’s a push for execution. The problem gets defined, and a solution shipped. This is efficient but not necessarily effective.

I see it with clients too. There’s a tendency to jump straight to conclusions. Create something, move to the next thing.

We’ve manufactured the wrong sense of progress. We’ve flattened our thinking to activity-based monitoring. We measure work done, not impact made.

And the systems we operate in make us fear being punished for slowing down. But slowing down doesn’t have to be negative. It’s adding depth and establishing foundations to build on.

I’ve seen it happen more than once. A team delivers exactly what was asked. On time and according to spec. But the problem isn’t solved. Not because of poor execution, but because questions were not asked.

The solution was locked in before the problem was understood.

Diverge Before You Converge

This is where design thinking offers something valuable. Divergence and convergence.

Divergence is expanding. You open up the problem space, explore possibilities, challenge assumptions and resist the urge to settle too quickly.

Convergence is getting to the point. You bring constraints back in. Budget, technical feasibility, timeline. And you shape your exploration into something actionable.

Most teams are good at converging. They ship.

But divergence? That takes discipline. It means staying in the question longer than feels comfortable.

How Might We ... ?

A practical tool for divergence is the how might we method. It’s simple. Take any problem and reframe it as a question starting with “how might we.”

The phrasing matters. How might we is optimistic without being naive. It assumes a solution exists but leaves the how open.

Here’s what it looks like in practice.

• “Users keep dropping off at checkout.”
  How might we reduce the friction between cart and payment?

• “Our onboarding takes too long.”
  How might we get new users to their first success faster?

• “Admins can’t manage permissions at scale.”
  How might we make it easier to give multiple people the right access?

Each reframe does the same thing. It takes a complaint or observation and turns it into a space you can explore.

The problem stays the same. But the number of possible solutions grows. That expansion is where the real value lives.

The Question Before the Prompt

This matters even more now that AI has become part of how teams work.

AI is good at execution. Like generating code or drafting copy. It’s a powerful convergence engine. But that’s also the problem.

AI defaults to answers. Give it a problem, it will solve it immediately. It won’t ask if it’s the right problem. It won’t explore alternatives.

That doesn’t keep the frame open. Convergence without divergence is just fast guessing. You end up with the wrong sense of progress again. Shipping solutions to problems nobody questioned.

So the skill that matters most right now is one AI doesn’t default to. Asking more questions. Keeping the frame open longer. Resisting the pull to converge before you’ve explored.

Give AI a vague problem, you get a vague answer. Give it a sharp question, you get a sharper solution.

Takeaways

The question mark might be the most underrated tool in your workflow. Not because questions slow things down. But because they open things up.

Before you assign, build or prompt, ask the question that reframes the problem: How might we?

Because the quality of your answer will never exceed the quality of your question.

Question everything. On purpose.