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I have entered boardrooms where energy is high, budgets are approved and ambitions are clear. Everyone is talking about AI. Few can answer the one question that really matters.
Not “What can we create with AI?”
Not “How are we keeping up with the competition?”
But this: What problem are we actually trying to solve and for whom?
The question is simple. it isn’t.
It forces precision in environments that reward momentum. It shifts the conversation from excitement to responsibility. And it quickly reveals whether you’re building something meaningful—or simply responding to noise.
Why clarity is collapsing inside organizations
In the absence of a clear signal or confirmation, the mind fills in the blanks. Teams convince themselves they are right before anything is proven. Leaders have the green light before the problem is fully defined.
That’s where expensive mistakes begin.
A clear understanding of the problem—and confirmation that the proposed solution actually solves it in a measurable way—is what separates progress from activity. Without it, even well-funded initiatives sink 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’ve built powerful capabilities, but not everything we’ve built has created value. In some cases, we delivered features that customers never asked for and that we rarely used. The result was not an execution failure – it was an incorrect definition.
When scope creep hides the real problem
I see this pattern repeatedly. A company identified with a real, tangible problem. Then the execution begins – and the focus begins to blur.
For example, I worked with organizations that were trying to improve financial reporting. The problem was straightforward: it took two months to create a statement and income statement that should have taken one week. Clear problem. A clear opportunity. But instead of tackling it head-on, the teams broadened the scope. Dashboards have been added. The visualizations multiplied. There were new features that no one asked for. Meanwhile, the accounting team still needed only one thing: accurate data, delivered faster.
The result was predictable – more complexity, more effort and less impact. This happens when the original question no longer anchors the work.
When one question redirected a $1.5 billion strategy
I worked with a large private company where the chairman, CEO and CTO had a bold vision: AI-driven product recommendations. The ambition was to create a more personalized Amazon-like experience – and potentially turn it into a stand-alone product offering.
It was convincing on paper. But when we slowed down and asked the fundamental question — what problem are you actually solving, for whom and why — the cracks quickly appeared. Each manager had a different interpretation of the problem. None of the assumptions have been verified by the teams that would use the system or the customers that would benefit from it.
So they stopped. They conducted structured workshops, interviewed internal teams, and tested assumptions directly with users. Over the weeks, the settlement improved. Within a month, the strategy changed completely.
They moved away from a multi-million dollar direction that would grow into tens of millions of investments – 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 out of defining less.
When AI becomes a replacement for thinking
Another red flag emerges when leaders start reacting to headlines instead of their own business reality.
“We have to do AI because everyone else is doing it. This sentence alone often means that a strategy ceases to be a strategy.
I’ve seen organizations reallocate resources, launch initiatives, and end priorities not based on customer needs, but based on external narrative pressure. Thus begins the drift. Not out of bad intent, but out of borrowed urgency.
The problem is simple: competitors don’t share your context. What works for them may not work for 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 refocus this week
You don’t need a full transformation to fix it. You need better framing.
Start with one initiative your team is actively working on and get clear on the problem. Write it in one sentence. If it cannot be specific and measurable, this ambiguity will reflect further work.
Next, define who specifically benefits from his solution. Customers, employees or internal teams – if the “who” is vague, so will the value.
Then define what success looks like in measurable terms. What will change if the problem is solved? What will be faster, cheaper or easier? If you can’t answer that, you’re not ready to build yet.
Verify the assumption directly with the affected people before starting the implementation. Understand how they solve the problem today, where the friction really is, and what improvements would really matter. A handful of real conversations here trumps weeks of internal debate.
And when you move into execution, resist the natural tendency to expand your scope. Most projects fail not because they are too small, but because they try to be too complete before solving anything real.
The hidden trap of washing AI
We are in a time where almost every product, plan and offer involves artificial intelligence.
But the presence of AI does not guarantee the presence of value.
Many organizations fall into what might be called AI-washing – rebranding initiatives in the language of AI without ensuring that the underlying problem is real or meaningful to users.
A simple test will examine it:
If you removed the word “AI” from this initiative, would it still matter? Would it still solve a real human problem? Would it still be funded?
If the answer is no, the strategy is not ready.
Why this question is more important than ever
“Move fast and break things” worked when the cost of failure was low. That era is over.
Today, the winners are not the fastest builders. They are the clearest thinkers.
Because when the problem is well defined, the audience is specific, and the result is measurable, execution becomes significantly easier – and much more valuable.
It all starts with one question:
What problem are we actually trying to solve and for whom?
I have entered boardrooms where energy is high, budgets are approved and ambitions are clear. Everyone is talking about AI. Few can answer the one question that really matters.
Not “What can we create with AI?”
Not “How are we keeping up with the competition?”
But this: What problem are we actually trying to solve and for whom?
The question is simple. it isn’t.