AI use-case prioritization matrix
You have more ways to use AI than time to try them. Decide which deserve attention first, and leave with a shortlist you can defend.
This free workbook scores each use case for value, feasibility, risk and confidence on anchored scales, places it on a priority matrix, and ends with one to three candidates and a clear next step for each.
5 worksheets · Room for 40 use cases · Risk kept visible, never averaged away
The problem
Every AI idea sounds like a good one.
Without a consistent way to compare them, the choice comes down to enthusiasm. The goal isn’t the highest score. It’s a small list you can explain.
The most exciting idea wins
Whoever asked loudest goes first
A high score hides a serious risk
Guesses get scored like facts
Ten pilots start, none finish
Definition
What is an AI use case?
A specific piece of real work where AI contributes something defined, toward an outcome, while a person keeps a defined responsibility. If it doesn’t name all four, it’s still an aspiration.
“Use AI to summarise recurring customer feedback and draft monthly themes; a human validates the themes and decides what action to take.”
How it scores
Eleven criteria, each with an anchor.
Every criterion is scored 1–5 against a written definition, so a 4 means the same thing for every candidate. A 2 or a 4 means “between the anchors”.
Value
How much it would matter.
Business / work impact
Time / effort reduction
Frequency / volume
Strategic leverage
Feasibility
How ready the work is for AI.
Process clarity
Input / context readiness
AI task fit
Implementation ease
Risk
Higher scores mean more risk.
Consequence of error
Sensitivity / privacy
Human judgement required
Each use case also gets a confidence rating, from Low to High. A score built on guesses is flagged for validation, not treated as a fact.
The important part
Position first. Then risk.
Value and feasibility decide where a use case lands. Risk is never averaged in. It sits on top as its own overlay, so a strong opportunity can’t hide a serious consequence of error.
Risk overlay
Value ↑ · Feasibility →
Strategic opportunity
Higher value · lower feasibility
Worth having, not ready yet. Solve the constraints first.
Prioritize / quick win
Higher value · higher feasibility
Your strongest candidates to move forward now.
Defer or reject
Lower value · lower feasibility
Park it, or drop it, and say so.
Selective experiment
Lower value · higher feasibility
Easy, but only worth it as a small, cheap test.
Worked examples
Same value. Very different decisions.
The three examples in the workbook all score 3.75 for value. Feasibility, risk and confidence are what separate them.
How to run it
Five steps to a shortlist.
The matrix supports the decision. It doesn’t make it. Every use case carries your own Now, Next, Later or Reject alongside the calculation.
- 01
Define the assessment
The work area, the objective, the planning horizon and the decision you need to make.
- 02
List and score use cases
One specific use case per row, scored for value, feasibility, risk and confidence.
- 03
Use the anchors
Score against the 1–5 definitions so the numbers mean the same thing across candidates.
- 04
Read the matrix
Check each position, then the risk and confidence overlays, then record your own decision.
- 05
Shortlist and hand off
Choose one to three, and document why, what is uncertain, the safeguard and the next action.
After the matrix
A shortlist that knows what happens next.
For each of your one to three choices, the handoff records enough to move it into validation or design, without turning it into an implementation plan.
The next resource is often one of ours: an automation canvas, an agent specification or a verification checklist.
For each priority candidate
- 1Why this is a priority
- 2Most important assumption
- 3Primary success signal
- 4Main risk / safeguard
- 5Human checkpoint
- 6Next action
- 7Recommended next resource
The workbook
Score them once. Pursue the right few.
Everything above is on this page for free. The workbook does the part a webpage can’t: calculate every score, place every use case, rank them and carry your choices into a handoff.
3 optional AI prompts
ai-use-case-prioritization-matrix.xlsx
XLSX · 5 worksheets
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Keep it where you work
Make your first use case stick.
Once a use case is chosen, it runs on the same prompts and context every time. Workbench is our browser extension for exactly that — keep prompts and context organised, fill the variables in a form, and run them without leaving ChatGPT, Claude or Gemini.
FAQ
Questions, answered.
How do I prioritise AI use cases?+
Score each one on what it is worth and how ready the work is, keep the risks and your confidence visible alongside, and choose a small shortlist. The workbook scores value and feasibility from four criteria each, places every use case in a quadrant, and never lets a strong score hide a serious risk.
What makes a good AI use case?+
One that describes real work, not an aspiration. A useful pattern is: the current work or problem, what AI contributes, the intended outcome, and what the human stays responsible for. “Use AI for marketing” is not a use case. “AI drafts monthly feedback themes and a person validates them” is.
Why is risk scored separately from value and feasibility?+
Because averaging it in would let a valuable, easy use case hide a serious consequence of error. Risk stays its own overlay. One criterion scored 5 marks the use case High, safeguards required, whatever its opportunity score.
What if my scores are mostly guesses?+
Record that. Every use case carries a confidence rating, and low confidence reads as “validate assumptions before commitment”. A high score built on guesses should lead to a test, not a rollout.
How many AI use cases should I pursue at once?+
Usually one to three. The workbook is built to end with a small, defensible shortlist rather than a long list of approved ideas. That is what makes it likely any of them actually gets finished.
Can I change the weights and thresholds?+
Yes. Every criterion has an optional weight, and the quadrant boundary and risk thresholds are settings. The defaults treat criteria equally and put the boundary at 3.5. Change them only when you have a reason you could explain.
What happens after the shortlist?+
Each chosen use case gets a handoff: why it matters, the key assumption, the success signal, the main risk and safeguard, the human checkpoint and the next action. Then it moves into validation, or into a planning tool such as an automation canvas or an agent specification.