Not because it's a bad instinct - it's usually right. But because the answer tells us everything about where to start.
Sometimes it's strategic - a board asking questions, competitors making noise, a sense that the world is shifting and they're not sure how to shift with it. Sometimes it's painfully specific - a process that's bleeding time, a team that's drowning, a growth ambition that can't be met without hiring people they can't afford.
And sometimes - honestly - it's just that their team wants to throw their laptops out the window. That's as valid a starting point as any.
AI isn't one thing. It's a spectrum.
Where you should be on that spectrum depends on the problem you're solving, the risk you're comfortable with and what it's worth to get it right.
At one end, it might be as simple as helping your people use the AI tools you've already paid for. Copilot, ChatGPT, whatever's sitting there underutilised. No new tech, no big project - just showing people how to get more from what's already on their desktop.
At the other end, it's agentic systems - AI that doesn't just assist but actively works alongside your team, handling complex workflows with humans in the loop where it matters.
And in between? A whole range of possibilities. Automations that save hours. Workflows that scale without scaling headcount. Tools that turn junior people into experts and experts into superheroes.
The point is: you don't always need a massive data engineering overhaul to get value from AI. Sometimes you do. Often you don't. And the skill is knowing which is which.