Most organisations do not have a shortage of AI ideas. They have a shortage of ideas worth funding. Workshops generate long lists, vendors add their own, and every function can point to something that looks like an opportunity. The hard part is not generating candidates. It is telling the difference between a use case that will pay back and one that will only ever impress in a demo.
The two can look identical on a slide. Both promise efficiency, both use the same language, and both attract early enthusiasm. The gap only appears later, when one quietly delivers a measurable result and the other stalls somewhere between pilot and production. By then the money is spent and the appetite is gone. A better filter, applied earlier, avoids most of that waste.
The questions that separate value from noise
Start with the decision or task the AI is meant to improve, not the technology. Ask how often it happens, what it costs when it goes wrong, and who is accountable for the outcome today. A use case that touches a high-frequency, high-cost process with a clear owner is worth serious attention. One that improves something rare, cheap or ownerless rarely earns its keep, however clever the model.
Then test feasibility honestly. Is the data available, accessible and good enough to rely on? Can the output be trusted without a human checking every case, and if not, is that checking cheaper than the problem it solves? Will the people who are meant to use it actually change how they work? A use case that fails any of these is not ready, and no amount of model performance will rescue it.
A demo proves the model can work; a use case proves the business will be better off when it does.
The discipline is to score candidates on value and feasibility together, and to be willing to say no to the ones that only score well on novelty. That means retiring the impressive-but-marginal ideas early, while they are cheap to abandon, and concentrating money and attention on the few that will move a real number.
Done consistently, this turns AI from a series of experiments into a portfolio with a return. The organisations that get value from AI are rarely the ones with the most use cases. They are the ones that chose well and finished what they started.