No accountable business owner
Do not fund a use case that only has a technical sponsor. A business owner must own the outcome and the operating change.
Free scorecard · eight criteria · no lead gate
AI Use Case Prioritization Framework.
Compare candidate AI use cases with one weighted method. Record the evidence, owner, decision, and next test for each use case.
An AI use case prioritization framework compares one defined workflow at a time. This framework weights business value at 25%, scale at 15%, data readiness at 15%, workflow fit at 15%, time to evidence at 10%, risk manageability at 10%, dependency burden at 5%, and adoption difficulty at 5%. A high score does not override a missing owner, baseline, data right, safety boundary, or measurement method.
Boundary: the weights are a starting point. Change them before scoring if regulation, safety, customer impact, or strategic dependence has greater importance in your company.
Eight criteria
Score value, feasibility, control, and adoption.
Use a score from 1 to 5 for each criterion. Write the evidence beside the score. Do not use confidence or enthusiasm as evidence.
| Criterion | Weight | Question | Evidence example |
|---|---|---|---|
| Business value | 25% | Will the use case change cost, revenue, quality, risk, or capacity? | Finance-approved baseline and value mechanism |
| Frequency or scale | 15% | Does the workflow occur often enough to matter? | Transactions, cases, hours, or users per period |
| Data readiness | 15% | Can the team lawfully access representative, usable data? | Rights, quality review, coverage, and lineage |
| Workflow fit | 15% | Can the task and its exceptions be defined and controlled? | Current process map, exception types, and service levels |
| Time to evidence | 10% | Can the company test the result within a useful period? | Test window, sample size, and acceptance threshold |
| Risk manageability | 10% | Can the team limit, detect, and respond to material harm? | Risk tier, human control, monitoring, and stop rule |
| Dependency burden | 5% | How much new integration, data, vendor, or platform work is required? | Named systems, owners, contracts, and critical path |
| Adoption difficulty | 5% | Will users change the workflow and use the output correctly? | User research, role changes, training, and adoption owner |
Five-step method
Move from a use case list to a funding decision.
- Define the candidate. Name one workflow, one user group, one business outcome, and one accountable owner.
- Fix the baseline. Record current volume, cost, cycle time, error rate, quality, risk, or revenue before the intervention.
- Score the eight criteria. Use the same evidence date and scoring rules for every candidate.
- Apply the hard-stop test. Defer any candidate that lacks data rights, safe operating limits, an owner, a baseline, or a valid measurement plan.
- Fund the next evidence step. Approve a bounded test with acceptance criteria, a review date, and an explicit stop condition.
Weighted score formula: multiply each 1-to-5 score by its weight, then add the results. For dependency burden and adoption difficulty, a score of 5 means the burden is low and manageable.
Worked example
Document exception triage.
This example shows the method. It is not a benchmark and does not predict a result for another company.
| Evidence | Illustrative finding | Decision effect |
|---|---|---|
| Value and scale | High manual review volume with measured delay and rework | Supports a bounded test |
| Data and workflow | Representative documents exist; exception classes need cleanup | Add a data-preparation gate |
| Risk and oversight | A reviewer can approve every high-impact exception | Do not allow autonomous final decisions |
| Measurement | Compare cycle time, precision, recall, rework, and reviewer effort | Set minimum quality and maximum error thresholds |
Hard-stop rules
Do not fund a score without operating evidence.
No stable baseline
Do not promise ROI if the current cost, quality, volume, or cycle time cannot be measured with a consistent method.
No safe operating boundary
Do not deploy if the team cannot define allowed actions, human review, monitoring, incident response, and a stop condition.
FAQ
AI use case prioritization questions.
How do you prioritize AI use cases?
Define each use case as one workflow and one measurable outcome. Score business value, frequency or scale, data readiness, workflow fit, time to evidence, risk manageability, dependency burden, and adoption difficulty. Apply a hard stop if the owner, baseline, data rights, safety boundary, or measurement method is missing.
What criteria should an AI use case scorecard include?
A practical scorecard should include business value, scale, data readiness, workflow fit, time to evidence, risk manageability, dependencies, and adoption difficulty. It should also name the metric owner, evidence source, decision, and next test.
Should the highest AI use case score always receive funding?
No. A weighted score helps compare candidates, but it does not override a material control gap. Stop or defer a use case if the company cannot establish lawful data use, safe operation, accountable ownership, a valid baseline, or a credible way to measure the result.
How many AI use cases should a company test first?
Start with the smallest portfolio that the company can govern and measure well. For many teams, this means one to three use cases with different risk and value profiles. The right number depends on delivery capacity, data access, control maturity, and executive ownership.
About Paul Okhrem
Paul Okhrem is an AI Transformation Consultant and Fractional Chief AI Officer. He helps leadership teams select AI use cases, define decision gates, assign owners, and measure results.
The original wording, criteria, and scoring structure are licensed under CC BY 4.0 with attribution to Paul Okhrem.