Your First AI Move? Ask a Better Question.

You don’t need a tool. 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 laser in on the tech. An LLM, 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.

It’s not a technical question. It’s a leadership one.
Not “What can this model do?” but “What do we need to understand better?”
Not “Who owns AI?” but “Where are we stuck, slow, or guessing too often?”

That’s the shift. From capability-first to problem-first.

AI is just a tool for amplifying decision-making, and like any tool, it needs direction. It needs context. And it needs a reason to exist in the first place.

What does a good AI question look like?

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 reduce administrative burden and help teams close out support tickets faster?
• Which deals are most likely to stall based on past behavior?
• What patterns show up before a customer stops responding or disengages?
• Are there signals that tell us which claims or requests will need escalation?

These are business questions that AI can help you answer faster, more consistently, or at greater scale.

What happens when you start this way?

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.