The Difference Between an AI Use Case and an AI Wish

Every leadership offsite that included even a short session on AI tends to produce, without exception, a long and genuinely enthusiastic list of ideas by the end of it. "AI for predictive maintenance." "AI to optimize our pricing." "AI for demand sensing." "AI to reduce quality defects." The energy in the room during these sessions is usually real and worth taking seriously. People aren't making these suggestions cynically, and most of them reflect genuine, longstanding frustrations that a leader has been carrying around for years, hoping something would eventually come along to address them. The problem isn't the enthusiasm. The problem is that almost none of these list items, in the raw form they arrive in, are actually use cases. They're wishes, genuine, well-intentioned, and almost entirely unactionable in their current form, because nobody in the room has yet done the work of testing whether they could actually be built.

The gap between a wish and a use case is exactly where a huge amount of wasted AI investment happens, money and attention poured into "AI initiatives" that were never going to work because nobody checked, early, whether the basic ingredients a use case needs were actually present. There's a fast way to tell the difference.

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How AI wish lists usually get generatedlinkshare

AI wish lists get generated the way most brainstorms get generated: someone poses an open question, "where could AI help us?", and the room responds with associative, aspirational answers drawn from whatever frustrates them most in their day-to-day work, or whatever they've recently read about AI accomplishing somewhere else. This is a genuinely useful exercise for surfacing where the organization's pain actually is. It is a genuinely poor exercise for determining what's actually buildable, because those are two different questions, and conflating them is where most of the trouble starts.

The list that comes out of a session like this tends to read as a set of outcomes people want, less downtime, better pricing, fewer defects, rather than a set of specific, well-formed decisions that AI could plausibly improve. That's a completely natural output of a brainstorm. It's also, without further work, essentially useless as a basis for actually deciding what to build first, because "reduce defects" doesn't tell anyone what decision the AI would actually be informing, what data it would need, or who would act on what it produced.

The feasibility test: decision, data, authority, consequencelinkshare

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The test used to sort a wish list into genuine use cases is built around four specific questions, and an idea needs a real answer to all four before it earns the label "use case" rather than "wish."

First: what specific decision would this AI actually inform? Not a general outcome like "reduce defects," but a specific, recurring decision someone actually makes, "which batches to hold for additional quality inspection before shipment," for instance.

Second: what data would that decision need, and does it already exist in a usable, reasonably trustworthy form, or would it need to be built from scratch first?

Third: who has the authority to act on the recommendation once it's produced, is there a specific person, today, who could receive this output and actually do something with it, or would acting on it require an approval chain or an authority structure that doesn't currently exist?

And fourth: what's the actual, observable consequence of the decision going differently based on the AI's input, is there a real, measurable difference in outcome, or is this a decision where the AI's contribution would be marginal at best, however interesting the underlying analysis might be?

An idea with clean, specific answers to all four questions is a genuine use case, ready to be scoped and prioritized. An idea that stumbles on any one of them, usually data or authority, far more often than decision or consequence, isn't yet ready to be built, and trying to build it anyway is how organizations end up with expensive, sophisticated models that produce output nobody actually uses, because the underlying decision was never properly identified or the authority to act on it was never actually assigned.

Sorting wishes from use caseslinkshare

Running a wish list through this test is usually a fast, revealing exercise. It can be done credibly in a single working session with the right five or six people in the room, provided everyone commits to answering honestly rather than defending their favorite idea from the original brainstorm. What comes out the other side is almost always a much shorter list than what went in, and that's exactly the point: a shorter list of genuine use cases is worth infinitely more than a longer list of wishes, because the shorter list can actually be acted on this quarter, while the longer list mostly just sits there, generating a vague sense that the organization is "working on AI" without producing anything a leadership team could point to as an actual result.

It's worth resisting the instinct to feel disappointed when the list shrinks dramatically. A wish list of fourteen ideas that produces three genuine use cases isn't a failure of imagination in the original brainstorm. It's exactly what a healthy filtering process should produce, because most genuinely good ideas, in their raw form, haven't yet been tested against the specific, concrete requirements that separate an aspiration from something buildable.

What to do with the wishes that don't passlinkshare

The ideas that don't pass the feasibility test aren't necessarily bad ideas. Most of them fail on data or authority, which are both fixable conditions rather than fundamental flaws in the underlying concept. The useful move is to sort the failures by what specifically blocked them, rather than discarding them entirely. An idea that failed on data because the underlying information doesn't exist yet in usable form becomes a candidate for a future use case, once that specific data gap has been addressed through separate, deliberate data work. An idea that failed on authority because nobody currently owns the relevant decision becomes a prompt for an operating-model conversation, not about AI at all, but about who should actually own that decision, a conversation worth having regardless of whether AI ever gets involved.

Keeping this sorted list visible, rather than letting the failed wishes simply disappear from memory once the exciting three use cases move forward, does real work over time: it becomes the natural backlog for the second and third wave of AI initiatives, each one unlocked as its specific blocking condition gets addressed, rather than requiring an entirely new brainstorm from scratch every time the organization is ready to consider its next use case.

The two questions that do the most filteringlinkshare

Of the four questions in the feasibility test, two do a disproportionate share of the actual filtering work in most organizations, and it's worth knowing which ones in advance so you can move through the exercise efficiently rather than treating all four as equally likely to trip up a given idea.

Data readiness is the most common single point of failure, and it's usually not because the data doesn't exist anywhere. It's because it exists in a form too inconsistent, too recent, or too locally customized to actually train or validate a model against with any confidence.

The second most common point of failure is authority: an impressive number of genuinely good AI ideas turn out, on close inspection, to be aimed at a decision that nobody currently owns in any clear, accountable sense, which means there's no one positioned to actually receive and act on whatever the AI eventually produces.

Consequence, by contrast, rarely kills an idea outright. Most ideas that make it onto a leadership wish list do relate to a decision with real, observable stakes, because people don't tend to get excited about decisions that don't matter.

And the decision question itself, while sometimes genuinely unclear at first, is usually resolvable within a single focused conversation once someone insists on specificity rather than accepting a vague outcome statement. Knowing that data and authority are where most ideas actually stumble lets you test for those two conditions first, which speeds up the whole exercise considerably and keeps the room's energy focused on the items most likely to reveal something useful.

Running the test without deflating the roomlinkshare

There's a real risk, worth naming directly, that running fourteen enthusiastic ideas through a rigorous feasibility test and watching eleven of them fail can feel deflating to the people who proposed them, especially if the test is run in a way that feels like a rejection of their judgment rather than a genuine attempt to find what's actually buildable right now. Framing matters here more than people expect. Presenting the exercise explicitly as "let's find out which three of these we can actually start on this quarter," with an explicit, visible commitment to keep the other eleven on a tracked list rather than quietly discarding them, tends to preserve the room's enthusiasm far better than treating the exercise as a culling of bad ideas. Most of the eleven aren't bad ideas at all. They're good ideas that simply haven't yet had their specific blocking condition addressed, and treating them that way, visibly and consistently, keeps the people who proposed them invested in eventually seeing their idea revisited rather than feeling like it was dismissed.

The offsite that produced fourteen ideas and three real use caseslinkshare

A leadership offsite at one organization produced a genuinely thoughtful list of fourteen distinct "AI ideas," gathered over the course of an afternoon session with real enthusiasm and real domain knowledge behind each suggestion. Run through the four-question feasibility test over the following two weeks, decision, data, authority, consequence, tested honestly against each item, only three of the fourteen survived as genuine, immediately actionable use cases.

The other eleven fell into two rough categories. Several failed specifically on data: the underlying information existed somewhere in the organization, but not in a form clean or consistent enough to actually support a model yet, and building that foundation would need to happen first, as its own separate, smaller project, before those ideas could be revisited as genuine use cases. Several others failed on authority: the decision the idea was meant to inform didn't actually have a single clear owner today, which meant an AI recommendation, however accurate, would have had nowhere real to land, a gap that had nothing to do with AI at all and everything to do with an operating-model question the organization needed to resolve regardless.

The three that survived weren't necessarily the most exciting or ambitious ideas on the original list. One of them, a narrow maintenance-prioritization use case, had barely registered as memorable during the original brainstorm, overshadowed by flashier suggestions around pricing optimization and demand sensing. But it had a clean, specific decision behind it, trustworthy existing data, a clear owner ready to act on its output, and a genuine, measurable consequence if the decision improved. It became the organization's first successful pilot, delivered within the quarter, while several of the more exciting original ideas are still, patiently, waiting on the data and ownership work that would eventually let them graduate from wish to use case.

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An AI idea becomes a use case the moment you can name the decision, the data, and who acts on the answerlinkshare

An AI idea becomes a genuine use case the moment you can name, specifically and honestly, the decision it would inform, the data it would need, and the person who would actually act on what it produces, and it stays a wish, however good the underlying instinct behind it, until all three of those are in place. Before your next AI initiative gets scoped, run your own wish list through this test. You'll likely find, as most organizations do, that the list gets shorter, and that the ideas that survive are worth considerably more than the ones that don't.

Disclaimer

Industry situations in this chapter are composite illustrations unless explicitly attributed to a public source. They are not claims about any particular company, plant, vendor, or incident. External standards, research, and public case studies should be verified before publication. Implementations must be validated against local safety, quality, cybersecurity, regulatory, contractual, labour, privacy, and data-governance requirements. AI recommendations and autonomous actions should remain within clearly defined human authority, operational controls, and tested recovery procedures.

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