When someone says, “we should use AI,” how do you unpack what’s really being asked?

One of the biggest signals for distinguishing different types of AI work is the expected outcome—whether the goal is automation, prediction, or content generation.
For example, if the focus is on insights and forecasting, it’s likely predictive AI; if it’s about creating text, images, or code, it points to generative AI.
What often goes wrong is when everything gets labeled simply as “AI” without clarifying the use case. This can lead to unrealistic expectations, poor tool selection, and misalignment with business objectives.
I’ve definitely been in conversations where “AI” meant different things to different stakeholders. Usually, I notice it when requirements are vague—like “we should use AI to improve efficiency” without defining how. That’s when I step in to ask clarifying questions about the problem we’re trying to solve, the data available, and the desired outcomes.
In my experience, the key is to shift the conversation from “using AI” to “solving a specific business problem with the right AI approach.”