When a company decides to "do something with AI," the first meeting usually generates a list of twenty ideas. The second meeting, thirty. And that's where the problem begins: without prioritization criteria, either everything is attempted at once (and nothing reaches production) or choices are made by intuition (leading to building the wrong thing).
Each use case is evaluated along two axes: the impact on business metrics (hours saved, costs reduced, revenue generated) and the feasibility from a technical perspective, using the data and systems that exist today — not those that will exist "once we organize the data."
The quadrants structure the conversation: the top right contains the quick wins (high impact, high feasibility) that politically fund the rest of the roadmap. The top left contains the strategic bets that require preparation. At the bottom, what can be discarded without guilt.
"The question isn't what AI can do for your company. It's what it can do this quarter, with the data you already have."
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