You don’t need a model. You need a reason.
If you’re trying to figure out where to begin with AI, you’re not alone. Most leaders are somewhere between curiosity and commitment—interested in the possibilities, but unsure what a smart starting point actually looks like.
The mistake many organizations make? They start with the technology. A model, a platform, a tool. But the most meaningful AI work doesn’t begin there. It begins with a question—rooted in the real work of your business.
Not a technical question. A leadership one.
Not “What can this model do?” but “What do we need to understand better?”
Not “Where’s our AI team?” but “Where are we stuck, slow, or guessing too often?”
That’s the shift. From capability-first to problem-first.
Because AI is just a tool for amplifying decision-making. But like any tool, it needs direction. It needs context. And it needs a reason to exist in the first place.
It usually emerges from friction—something that’s too manual, too inconsistent, too unpredictable. And when you frame that friction in terms of insight, foresight, or action, you create the conditions for a valuable AI opportunity.
Strong AI questions tend to share a few traits:
They’re tied to a real decision. If you had the answer, would you change something? Prioritize differently? Act faster?
They connect to business relevance. The outcome matters—to your customer, your team, or your operations.
They can be supported by data. Even if the data isn’t perfect, it exists. Signals are there to learn from.
Some examples:
“Can we predict which service calls will escalate before they do?”
“Which deals are most likely to stall based on past behavior?”
“Where are we consistently overdelivering without seeing a return?”
These aren’t tech questions. They’re business questions—ones that AI can help you answer faster, more consistently, or at greater scale.
You reduce risk. You avoid wasted effort. And you give your AI work a meaningful job to do.
You also make it easier to get buy-in. When the work is anchored in a problem people already care about, it’s easier to communicate the why—and to build momentum when the first answers start to land.
In your next leadership meeting, ask this one question:
“What’s a decision we make often—but inconsistently or with guesswork?”
Write down three examples. For each, ask:
If we could predict this better, would it change what we do?
Do we already capture data connected to it—even if it’s messy?
Who would benefit from getting it right more often?
If the answer to two of those questions is yes, you’ve found a strong candidate for your first AI question—and your real starting point.