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I’ve walked into boardrooms where the energy is high, the budgets are approved and the ambition is clear. Everyone is talking about AI. Very few can answer the one question that actually matters.
Not “What can we build with AI?”
Not “How do we keep up with competitors?”
But this: What problem are we actually trying to solve, and for whom?
The question sounds simple. It isn’t.
It forces precision in environments that reward momentum. It shifts the conversation from excitement to accountability. And it quickly exposes whether you are building something meaningful—or simply reacting to noise.
Why clarity breaks down inside organizations
In the absence of a clear signal or validation, the mind fills in the gaps. Teams convince themselves they are right before anything has been proven. Leaders greenlight direction before the problem has been fully defined.
That’s where expensive mistakes begin.
Understanding the problem clearly — and confirming that the proposed solution actually resolves it in a measurable way — is what separates progress from activity. Without it, even well-funded initiatives drift into complexity that looks like progress but delivers little value.
I learned this early in my leadership career, working with highly capable engineering teams. We built powerful capabilities, but not everything we built created value. In some cases, we shipped features customers never asked for and rarely used. The result wasn’t failure in execution — it was misalignment in definition.
When scope creep hides the real problem
I see this pattern repeatedly. A company identifies a real, tangible problem. Then execution begins — and focus starts to blur.
For example, I worked with organizations trying to improve financial reporting. The problem was straightforward: it took two months to produce a P&L that should have taken one week. Clear problem. Clear opportunity. But instead of solving it directly, teams expanded the scope. Dashboards were added. Visualizations multiplied. New features appeared that no one requested. Meanwhile, the accounting team still just needed one thing: accurate data, delivered faster.
The result was predictable — more complexity, more effort, and less impact. That’s what happens when the original question is no longer anchoring the work.
When one question redirected a $1.5 billion strategy
I worked with a large private company where the chairman, CEO and head of technology were aligned on a bold vision: AI-driven product recommendations. The ambition was to create a more personalized, Amazon-like experience — and potentially turn it into a standalone product offering.
On paper, it was compelling. But when we slowed down and asked a basic question — what problem are you actually solving, for whom and why — the cracks appeared quickly. Each leader had a different interpretation of the problem. None of the assumptions had been validated with the teams who would use the system or the customers who would benefit from it.
So they paused. They ran structured workshops, interviewed internal teams and tested assumptions directly with users. Within weeks, alignment improved. Within a month, the strategy changed entirely.
They walked away from a multi-million-dollar direction that would have scaled into tens of millions in investment — and instead focused on a narrower set of use cases that actually improved customer experience and operational efficiency. The impact didn’t come from building more. It came from defining less.
When AI becomes a substitute for thinking
Another warning sign appears when leaders start reacting to headlines instead of their own business realities.
“We need to do AI because everyone else is doing it.” That sentence alone is often where strategy stops being strategy.
I’ve seen organizations reallocate resources, launch initiatives and sunset priorities based not on customer need, but on external narrative pressure. That’s how drift begins. Not through bad intent, but through borrowed urgency.
The problem is simple: competitors don’t share your context. What works for them may not apply to your customers, your data, or your constraints. Sometimes the most strategic move is to slow down long enough to regain clarity.
A practical way to reset focus this week
You don’t need a full transformation to fix this. You need better framing.
Start with one initiative your team is actively working on and force clarity around the problem. Write it in a single sentence. If it can’t be made specific and measurable, the work downstream will reflect that ambiguity.
Next, define who specifically benefits from solving it. Customers, employees, or internal teams—if the “who” is vague, the value will be too.
Then define what success looks like in measurable terms. What changes if the problem is solved? What becomes faster, cheaper or easier? If you can’t answer that, you’re not ready to build yet.
Before execution begins, validate the assumption directly with the people affected. Understand how they solve the problem today, where the friction actually is and what improvement would genuinely matter. A handful of real conversations here will outperform weeks of internal debate.
And as you move into execution, resist the natural tendency to expand scope. Most projects fail not because they are too small, but because they try to become too complete before solving anything real.
The hidden trap of AI-washing
We are in a moment where nearly every product, roadmap, and pitch includes AI.
But the presence of AI does not guarantee the presence of value.
Many organizations fall into what could be called AI-washing—rebranding initiatives in AI language without ensuring the underlying problem is real or meaningful to users.
A simple test cuts through it:
If you removed the word “AI” from this initiative, would it still matter? Would it still solve a real problem for a real person? Would it still get funded?
If the answer is no, the strategy isn’t ready.
Why this question matters more than ever
“Move fast and break things” worked when the cost of failure was low. That era is over.
Today, the winners aren’t the fastest builders. They are the clearest thinkers.
Because when the problem is well-defined, the audience is specific, and the outcome is measurable, execution becomes significantly easier—and far more valuable.
Everything starts with one question:
What problem are we actually trying to solve, and for whom?
I’ve walked into boardrooms where the energy is high, the budgets are approved and the ambition is clear. Everyone is talking about AI. Very few can answer the one question that actually matters.
Not “What can we build with AI?”
Not “How do we keep up with competitors?”
But this: What problem are we actually trying to solve, and for whom?
The question sounds simple. It isn’t.
