The problem isn't a shortage of ideas. It's that the prioritization framework most teams use scores ideas on the wrong axes. Here's a framework that works — three dimensions, each scored honestly, with a portfolio shape that compounds.
The three dimensions that actually matter
1. Business impact
Not "could this be valuable" — but: which specific business metric does this move, and by how much? If you can't name the metric and put a number on it, the idea isn't ready to be a use case. Either it's not actually valuable, or you haven't done the homework to know if it is.
The metric has to be on a dashboard someone already cares about. "Productivity" doesn't count. "Cycle time on claims adjudication," "first-contact resolution rate in tier-1 support," "percent of invoices processed straight-through" all count.
2. Feasibility
How confident are you that the AI can actually do this task reliably enough for production? Feasibility isn't binary — it's a spectrum from "this is a solved problem with off-the-shelf components" to "this is on the frontier of what current models can do."
A useful proxy: if you can't find at least three credible examples of the same capability working in production at other companies, you're not picking a use case — you're picking a research project. Research projects are fine, but they should be funded and managed differently.
3. Data readiness
Does the data the model needs exist, is it accessible, is it clean enough, and does someone own its quality? Each of these is a separate question, and "no" to any of them means the use case is dependent on a data project that hasn't been scoped, funded, or staffed yet.
The honest version of this conversation surfaces dependencies. The dishonest version pretends the data is ready because surfacing the dependency would slow down the AI program. Surface the dependency.
Scoring honestly
Score each idea on each dimension, 1 to 5. The total score isn't important — the shape is. Use cases that score 4–5 across all three dimensions are the ones to start with. Use cases that score 5/5/1 (high impact, high feasibility, low data readiness) tell you that the bottleneck is the data project, not the AI — and that's a different conversation to have.
The mistake is picking use cases that score 5/2/3 — high impact, low feasibility — because they look strategically important. They are strategically important. They are also the wrong place to start, because they will fail visibly and damage the credibility of every AI initiative that comes after them.
Why "easiest" is usually a trap
The instinct on a new AI program is to pick the easiest use case to "build momentum." This is usually wrong. The easiest use cases tend to be ones that are easy precisely because they're loosely connected to business outcomes — internal tools, draft generators, summarizers.
You ship them in a quarter. They work. Nobody can articulate what changed. Six months later the program is questioned because there's no measurable ROI. The momentum was real but the wrong kind.
A better instinct: pick the easiest use case that scores high on business impact. That's a different filter. It usually produces a slightly harder use case — but one where success is unambiguous.
The portfolio shape that compounds
Your first five use cases shouldn't be five independent bets. They should compound. A useful portfolio shape:
- One foundation use case — a capability that, once built, makes the next two easier. Often a RAG layer over a particular corpus, an evaluation framework, or a tool integration layer.
- Two adjacent use cases — different applications of the foundation capability. They prove the foundation generalizes and produce diverse outcomes.
- One stretch use case — higher feasibility risk, higher potential impact. The one that, if it works, becomes the case study you tell next year.
- One enablement use case — a capability that makes the AI program itself easier to operate. Often an evaluation harness, a governance workflow, or a cost-monitoring layer. Looks like overhead; pays back across the rest of the portfolio.
That mix gives you outcomes diverse enough to validate the program, reusability across use cases, and at least one ambitious bet that justifies the program's existence.
What to ignore
A few common framing patterns to be wary of:
- "This is what our competitors are doing." Maybe. Or maybe their press release is ahead of their production deployment. Don't pick use cases by trend report.
- "This is what the LLM vendor recommends." Their use cases are the ones that showcase their product. Yours should be the ones that move your numbers.
- "This is what an internal team is asking for." Sometimes legitimate, often a request shaped by what the asker thinks AI can do — not by what would actually be valuable. Ask "what business metric would this move?" and listen carefully to the answer.
The takeaway
The companies that get value from their first AI program are not the ones with the most use cases — they're the ones whose first five use cases were chosen well. The framework is simple: score impact, feasibility, and data readiness honestly; pick a portfolio shape that compounds; and ignore the easy use cases that don't move metrics.
The hard part isn't the framework. It's the discipline to say no to the 25 use cases that don't fit it.