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I even have walked into boardrooms where the energy is high, the budgets are approved and the ambitions are clear. Everyone is talking about AI. Very few can answer the one query that basically matters.
Not “What can we build with AI?”
Not “How can we keep up with the competition?”
But this: What problem are we actually trying to unravel and for whom?
The query sounds easy. That’s not it.
It enforces precision in environments that reward dynamism. It shifts the conversation from excitement to responsibility. And it quickly shows whether you are constructing something useful – or simply reacting to noise.
Why clarity collapses inside organizations
In the absence of a transparent signal or confirmation, the mind fills within the gaps. Teams persuade themselves they’re right before anything is proven. Leaders give the green light for direction before the issue is fully defined.
This is where expensive mistakes start.
Clearly understanding the issue – and confirming that the proposed solution actually solves it in a measurable way – is the difference between progress and activity. Without it, even well-funded initiatives turn into mired in complexity that appears like progress but offers little value.
I learned this early in my leadership profession when working with highly expert engineering teams. We built powerful capabilities, but not all the pieces we created added value. In some cases, we delivered features that customers never asked for and barely used. The result was not an error in execution, but a faulty definition.
When scope creep hides the true problem
I see this pattern repeatedly. An organization identifies an actual, tangible problem. Then the execution begins – and the main focus begins to blur.
For example, I even have worked with organizations attempting to improve financial reporting. The problem was clear: it took two months to supply a profit and loss statement that ought to have taken per week. Clear problem. Clear opportunity. But as a substitute of solving it directly, the teams expanded the scope. Dashboards have been added. Visualizations multiplied. New features appeared that nobody had requested. Meanwhile, the accounting team needed only one thing: accurate data that may very well be delivered faster.
The result was predictable – more complexity, more effort and fewer impact. This happens when the unique query not anchors the work.
When one query redirected a $1.5 billion strategy
I worked for a big private company whose chairman, CEO, and head of technology had a daring vision: AI-driven product recommendations. The goal was to create a more personalized, Amazon-like experience – and potentially turn it right into a standalone product offering.
On paper it was convincing. But once we slowed down and asked a fundamental query—what problem are you solving, for whom, and why—cracks quickly began appearing. Each leader had a distinct interpretation of the issue. None of the assumptions were validated with the teams that will use the system or with the shoppers that will profit from it.
So they took a break. They ran structured workshops, interviewed internal teams, and tested assumptions directly with users. Within a number of weeks the alignment improved. Within a month the strategy completely modified.
They turned away from a multi-million dollar direction that will have resulted in tens of hundreds of thousands of dollars in investments – and as a substitute focused on a narrower set of use cases that truly improved customer experience and operational efficiency. The effect didn’t come from constructing more. It got here from defining less.
When AI becomes a substitute for pondering
Another warning sign occurs when executives begin to answer headlines as a substitute of their very own business realities.
“We have to do AI because everyone else is doing it.” This sentence alone is usually the explanation why strategy ceases to be strategy.
I’ve seen corporations reallocate resources, launch initiatives and set priorities based not on customer needs but on external pressures. This is how the drift begins. Not out of malicious intent, but out of borrowed urgency.
The problem is easy: competitors don’t share your context. What works for them may not apply to your customers, your data, or your limitations. Sometimes essentially the most strategic move is to decelerate long enough to regain clarity.
A practical method to refocus this week
A full transformation just isn’t required to resolve this issue. You need a greater framework.
Start with an initiative that your team is actively working on and supply clarity around the issue. Write it in a single sentence. If it can’t be made specific and measurable, the downstream work will reflect this ambiguity.
Next, define who specifically will profit from solving the issue. Customers, employees or internal teams – if the “who” is vague, so is the worth.
Then define what measurable success looks like. What will change when the issue is solved? What can be faster, cheaper or easier? If you may’t answer that, you are not able to construct.
Before execution begins, validate the belief directly with affected individuals. Understand how they’re solving the issue today, where the friction actually lies, and what improvement would really be vital. A handful of real conversations here will trump weeks of internal debate.
And whenever you start implementing it, resist the natural tendency to expand the scope. Most projects fail not because they’re too small, but because they fight to turn into too complete before solving anything real.
The hidden trap of AI washing
We are in a moment where almost every product, roadmap and pitch accommodates AI.
But the existence of AI doesn’t guarantee the existence of value.
Many organizations fall into what may be called AI-washing – rebranding initiatives in AI-speak without ensuring that the underlying problem is real or meaningful to users.
An easy test provides clarity:
If you removed the word “AI” from this initiative, would it not still be vital? Would it still solve an actual problem for an actual person? Would it still be funded?
If the reply is “no,” the strategy isn’t ready yet.
Why this query is more vital than ever
“Act fast and break things” worked when the fee of failure was low. This era is over.
Today the winners usually are not the fastest builders. They are the clearest thinkers.
Because if the issue is clearly defined, the goal group is restricted and the result’s measurable, implementation becomes much easier – and way more beneficial.
It all starts with an issue:
What problem are we actually trying to unravel and for whom?
I even have walked into boardrooms where the energy is high, the budgets are approved and the ambitions are clear. Everyone is talking about AI. Very few can answer the one query that basically matters.
Not “What can we build with AI?”
Not “How can we keep up with the competition?”
But this: What problem are we actually trying to unravel and for whom?
The query sounds easy. That’s not it.
