Free scoring matrix · rank your candidates in one sitting

AI Use Case Prioritization Tool

You have a list of AI use cases and need to choose where to start. Score every candidate on identical criteria, adjust the weights to your business, and see the order and the reasons.

Built by XONIK. Your scores stay in your browser.

Line drawing for an AI use case prioritization tool: use cases scattered at first, then ranked into bars of falling length, with one cut off.

In brief

This free AI use case prioritization tool ranks your candidates so you can see which to do first. Score each on the same criteria, from the value of the decision it improves to data readiness, ownership and the risk if it's wrong, and change the weights to suit your business. The tool explains the leader and runs in your browser.

Score each from 0 to 5. Leave a name empty to leave that column out.

Criterion	Weight	Use case 1: name	Use case 2: name	Use case 3: name	Use case 4: name	Use case 5: name	Use case 6: name	Use case 7: name	Use case 8: name
Value of the decision it improves How to score this Judge the decision the use case improves, not the technology. Lowest score: the decision is rare or changes little when it's made better. Middle score: it's a recurring decision, and the gain is easy to describe but hasn't been measured. Highest score: the decision is made often, the person who owns it can name what a poor decision costs today, and an improvement would show in business results.	weight Value of the decision it improves	Value of the decision it improves, Use case 1: score	Value of the decision it improves, Use case 2: score	Value of the decision it improves, Use case 3: score	Value of the decision it improves, Use case 4: score	Value of the decision it improves, Use case 5: score	Value of the decision it improves, Use case 6: score	Value of the decision it improves, Use case 7: score	Value of the decision it improves, Use case 8: score
Data readiness How to score this Whether the data it needs exists, can be reached and can be trusted today. Lowest score: the data doesn't exist, or sits where no one will give access. Middle score: the data exists and access is possible, but it needs cleaning or has known gaps. Highest score: the data is held in one place you can reach, someone vouches for its quality, and you've already used it for something similar.	weight Data readiness	Data readiness, Use case 1: score	Data readiness, Use case 2: score	Data readiness, Use case 3: score	Data readiness, Use case 4: score	Data readiness, Use case 5: score	Data readiness, Use case 6: score	Data readiness, Use case 7: score	Data readiness, Use case 8: score
Ownership available How to score this Lowest score: nobody has agreed to own it. Middle score: a sponsor supports it, but the person who would adjust it, learn when it's wrong and switch it off isn't named, or has no time. Highest score: one named person has agreed, has time set aside, and holds the authority to adjust it, trace a single answer back and halt it.	weight Ownership available	Ownership available, Use case 1: score	Ownership available, Use case 2: score	Ownership available, Use case 3: score	Ownership available, Use case 4: score	Ownership available, Use case 5: score	Ownership available, Use case 6: score	Ownership available, Use case 7: score	Ownership available, Use case 8: score
Effort to build into the workflow How to score this Score high when little has to change. Lowest score: it needs changes across the systems and the ways of working of different teams before anyone can use it. Middle score: it fits one team's workflow with modest changes to the systems it touches. Highest score: it fits into a tool the users already work in, needs little change to any system, and the people who would use it have helped define where it sits.	weight Effort to build into the workflow	Effort to build into the workflow, Use case 1: score	Effort to build into the workflow, Use case 2: score	Effort to build into the workflow, Use case 3: score	Effort to build into the workflow, Use case 4: score	Effort to build into the workflow, Use case 5: score	Effort to build into the workflow, Use case 6: score	Effort to build into the workflow, Use case 7: score	Effort to build into the workflow, Use case 8: score
Risk if wrong How to score this Score high when a wrong result is low risk. Lowest score: a wrong result could harm a person, break a rule or cost money that can't be recovered. Middle score: a wrong result is costly and takes effort to reverse, but would be found before it spreads. Highest score: a wrong result is seen by a person before it counts and is corrected in minutes.	weight Risk if wrong	Risk if wrong, Use case 1: score	Risk if wrong, Use case 2: score	Risk if wrong, Use case 3: score	Risk if wrong, Use case 4: score	Risk if wrong, Use case 5: score	Risk if wrong, Use case 6: score	Risk if wrong, Use case 7: score	Risk if wrong, Use case 8: score
Time to first use How to score this Score high for sooner. Lowest score: no one can say when real users would first have it, or approvals and purchases stand in the way. Middle score: a first version for real users is possible within a planning cycle, subject to one approval. Highest score: a first version for real users could run on a live process within weeks, with the approvals in hand.	weight Time to first use	Time to first use, Use case 1: score	Time to first use, Use case 2: score	Time to first use, Use case 3: score	Time to first use, Use case 4: score	Time to first use, Use case 5: score	Time to first use, Use case 6: score	Time to first use, Use case 7: score	Time to first use, Use case 8: score
Strategic fit How to score this Lowest score: it's unrelated to anything leadership has said matters, and would be a one-off. Middle score: it supports a stated priority, but only indirectly. Highest score: it serves a priority leadership has stated in writing, and the other use cases on this list could reuse it, for example through the data or the way of working.	weight Strategic fit	Strategic fit, Use case 1: score	Strategic fit, Use case 2: score	Strategic fit, Use case 3: score	Strategic fit, Use case 4: score	Strategic fit, Use case 5: score	Strategic fit, Use case 6: score	Strategic fit, Use case 7: score	Strategic fit, Use case 8: score

How the use case ranking is worked out

Name the use cases you're weighing, then give each a score on every criterion, anywhere from the lowest score up to the highest score on the scale you see. Higher always means better here, so give a high score for low effort and for a low risk of a wrong result. Each criterion has anchors that show what a low, a middle and a high score would mean in practice. Each criterion starts with a weight, with the value of the decision and the availability of an owner carrying the heaviest, and you can change any weight, down to zero if a criterion shouldn't count. A use case's weighted score multiplies every score by its weight, adds the results and divides by the total of the weights, which keeps the answer on the scale you used.

Use cases are listed highest first, with level scores sharing a rank. Against the runner-up, the leader is described by its margin, the criteria where it is ahead and those where the runner-up is ahead. Each use case is also shown with the criteria that lifted its score furthest and the one that held it back. The starting criteria and weights come from XONIK's reading of what carries a use case into daily work, taken from the reports on why pilots stall and who owns an AI system after launch. They don't come from a study of other organizations.

What this use case ranking can't tell you

The ranking reflects your own scores, and scores given in a meeting tend to follow the loudest voice. Ask a group of people to score separately, then compare their sheets. A use case that ranks first on a weighted average can still fail on one criterion, such as having no owner or no usable data, so read the individual scores as well as the total.

The tool doesn't size the value of any use case, check that its data exists, or know your constraints. It tells you which candidate your own scores favor and why, and it leaves the decision with you. Its order isn't an audit, a certification or a benchmark for your organization. What you enter stays in this browser tab.

Where do you start with AI use case prioritization, and what can't the ranking settle?

Look at AI for a while and you end up with a list of ideas, usually ordered by enthusiasm. The tool reorders that list by what carries a use case into daily work: the value of the decision it improves, an owner, usable data, the effort to fit it in, the harm if it's wrong, how soon real users could have it, and its fit with stated priorities.

It ranks the candidates you give it and explains the order. It doesn't size the value of any of them, check that the data exists, or know your constraints. Whether to proceed stays with you.

Who scores, and how long does a sitting take?

Describe each candidate in a sentence before anyone scores it, so everyone is scoring the same thing. A use case called customer service AI will get scored in very different ways. One described as drafting replies to a named kind of query, for review by a named team, won't.

Ask the heads of the functions affected, the technology lead and the sponsor to score separately. Once the candidates are described, a sitting takes about as long as a working session, and the argument afterward takes longer. Compare the separate sheets and look first at the criteria where people differ widely.

Criterion	Ask	What earns a high score
Value of the decision	The person who makes that decision today	They can say what a poor decision costs today
Data readiness	The data owner	One place you can reach, vouched for, already in use
Ownership available	The sponsor, then the named owner	One person has agreed and has time set aside
Effort to build into the workflow	The people who would use it	It sits inside a tool they already work in
Risk if wrong	Risk or the head of the function	A person sees a wrong result before it counts
Time to first use	The delivery lead	Real users, a live process, approvals in hand
Strategic fit	Leadership	A priority stated in writing that other use cases reuse

How should you read the order and the weakest score?

The result lists the use cases from highest to lowest and sets the leader against the runner-up. For each one it names the criteria that lifted its score furthest and the one that held it back. Where scores are level they share a rank.

A narrow margin means the scores haven't chosen for you yet, so look at the criteria where the runner-up leads. A wide margin deserves a different test: read the leader's lowest score. A use case can come first on average and still have no owner or no usable data.

A use case that ranks first but has no owner isn't first.

Turning the order into a decision

01
Take the lowest score to its owner
Ask the person who gave it what would raise it, and what that would cost.
02
Give the runner-up a fair test
If the runner-up leads on ownership or data, ask whether starting there and reaching the leader later is the safer path.
03
Decide how big the first wave is
Starting every promising use case at once splits attention. Choose only what the owners can carry.
04
Record why the others wait
One line each, with what would bring it back to the list.

When does this prioritization tool fit, and when doesn't it?

It fits when

You have a list of candidates, each described in a sentence
The people who would own and use them can take part

Look elsewhere when

You have only one candidate; the brief builder is your next step
The question is how the work should be done, which the build, buy or agent tool answers
You need each candidate's value in money; use the business case calculator on each

A scored ranking trades depth for a clear order. It's meant to end the argument about where to start, and the evidence for each use case still has to be gathered.

What do heads of AI and strategy leads ask before they rank?

Why doesn't it put the boldest use case first?

Because the boldest one often isn't the one that reaches daily use. That one tends to have an owner with time for it, data that can be reached and a place in a workflow people already use. The weights start with the decision and the owner for that reason.

Does it work out what each use case is worth?

No. It doesn't size the value of any use case, check that its data exists or know your constraints. It ranks the candidates you give it and explains the order. If you need the value of each in money, run the business case calculator on each one.

What if the top-ranked use case has no owner?

Then it isn't first. A use case can come first on average and still have no owner or no usable data, so read the leader's lowest score. Take it to the person who gave it, ask what would raise it, and give the runner-up a fair test.

How should we describe each use case before we score it?

In one sentence: what it does, for whom, and who reviews it. Customer service AI gets scored in different ways, while drafting replies to a named kind of query, for review by a named team, doesn't. Then have the affected heads, the technology lead and the sponsor score separately.

AI pilots stall before production: why, and what changes itWhy a pilot that worked still stops, and who has to catch it at the hand-off.Report AI strategy: decide where AI belongs in the businessWhere AI belongs, what to fund first and what to leave alone.Service AI Use-Case Prioritization MatrixA weighted matrix that ranks AI use cases and flags each one's weakest criterion.Download AI Investment Decision BriefA short brief that frames an AI investment for the people who must approve it.Download

Not sure which use case deserves to go first?

Send us your shortlist and who would own each item. A senior person reads it and writes back with the first question we would put to your sponsor.

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AI Use Case Prioritization Tool

Where do you start with AI use case prioritization, and what can't the ranking settle?

Who scores, and how long does a sitting take?

How should you read the order and the weakest score?

Turning the order into a decision

Take the lowest score to its owner

Give the runner-up a fair test

Decide how big the first wave is

Record why the others wait

When does this prioritization tool fit, and when doesn't it?

It fits when

Look elsewhere when

What do heads of AI and strategy leads ask before they rank?

Not sure which use case deserves to go first?