Framework
How to prioritize
AI use cases
Most enterprises have more AI ideas than they can fund. This Framework offers a repeatable way to decide which to build first based on business value and feasibility, then checked against how proven each one is.
The problem
Too many ideas, coming from several business departments, no shared way to choose
Every enterprise AI backlog looks the same: dozens of ideas from every function, each with an enthusiastic sponsor, and no consistent way to compare them. Choose by gut feel and the loudest voice wins. Choose by vendor demo and the flashiest tool wins. Neither tells you whether a use case is actually worth building. A prioritization framework replaces opinion with two questions you can answer the same way for every idea: how much value would it create, and how feasible is it to deliver.
The two axes
Business value vs feasibility
The whole framework rests on two scores, kept deliberately independent. A high-value, low-feasibility idea is a strategic bet, not a quick win, and collapsing them into one number is how prioritization goes wrong.
Business value
How strong is the generic business case: how painful and frequent the problem is, its economic leverage, and whether it matters across industries. Not how exciting the technology is.
Feasibility
How ready a typical enterprise is to deliver it: data availability and standardization, integration complexity, process maturity, and regulatory exposure.
Step by step
The framework in five steps
Score the business value
How strong is the generic business case? A high-value use case solves a painful, frequent, economically significant problem, ideally one felt across industries. This is not about how exciting the technology is.
Score the feasibility, independently
How ready is a typical enterprise to deliver it? Feasibility weighs data availability and standardization, integration complexity, process maturity, and regulatory exposure. A use case can be hugely valuable and still score low here, so keep the two scores separate.
Place it on the value by feasibility grid
Plotting both scores puts every use case into one of four groups: Do Now, Quick Wins, Big Bets, or Avoid. This is the base recommendation, before the ceilings in the next step.
Apply the maturity and risk ceilings
Two checks can only lower the result, never raise it. An emerging, unproven use case is capped below Adopt until the market shows it works. A use case with high regulatory exposure is capped until the governance path is clear (under the EU AI Act, risk is set by how the system is used, not by the industry).
Decide and sequence
Build the Do Now use cases, use Quick Wins to build momentum and capability, fund Big Bets as staged efforts rather than casual pilots, and hold the rest. Revisit when the market signal or your own readiness changes.
The four quadrants
The base signal from the grid
Plotting value against feasibility produces four groups. In this radar they map to the recommendation rings, Adopt, Trial, and Hold. Remember this is the base signal only, before the maturity and risk ceilings can lower it.
- Do Now (high value, high feasibility): Adopt. Build now.
- Quick Wins (lower value, high feasibility): Trial. Easy to deliver, useful for building momentum and capability.
- Big Bets (high value, lower feasibility): worth a funded, staged effort, not a casual pilot.
- Avoid (low value): Hold, regardless of how easy it is.
The ceilings
Maturity and risk can only lower the call
The grid gives a starting recommendation. Two checks adjust it downward, never upward.
Maturity: has anyone actually deployed this at scale? An emerging, unproven use case is capped below Adopt until the market shows it works. This is what keeps hype out of your do-now list.
Risk: use cases with high regulatory exposure are capped until the governance path is clear. Under the EU AI Act, risk is set by how the system is used, not by the industry it sits in.
For the exact formulas, weights, and thresholds behind each score, see the methodology.
See the framework applied
We have scored 350+ enterprise AI use cases on exactly these axes. Explore where they land.
Explore the use casesWant this run against your own backlog? We calibrate the scoring to your industry, data maturity, and strategic priorities.
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