How to Prioritize AI Use Cases: A Four-Factor Scoring Method

To prioritize AI use cases, score each candidate on four factors: how often the work happens, how costly a mistake would be, how ready the underlying data is, and how easily you can measure the result. Add the scores, remove anything that fails a factor outright, and start with the highest total that a named person owns. The goal is not the most impressive use case. It is the first one that will run, be checked and be trusted.

Key takeaways

• Score every candidate from 1 to 5 on frequency, risk, data readiness and measurability, then add the four scores.
• Score risk in reverse: a high score means a mistake is cheap, visible and easy to undo.
• Treat a score of 1 on any factor as a stop sign for the first round, whatever the total says.
• Prefer a frequent, low-risk, well-documented task with a visible result over an ambitious one with a vague payoff.
• Name an owner and a success measure before building, and rescore the list after the first use case has run.

Why does the first use case matter more than the whole list?

Most companies can name a dozen things an AI agent might do. Brainstorming is easy. Choosing is where projects quietly go wrong, because without a method the loudest sponsor or the most exciting demo wins.

The first use case matters because it sets the pattern for everything after it. It decides how your team learns to review agent output, how approvals work, which systems get connected, and whether people believe the next proposal. A first use case that is too ambitious tends to stall in review. One that is too trivial teaches nobody anything and is forgotten. What you want is a task real enough to matter and contained enough to supervise.

It also helps to think in roles rather than technologies. A good first candidate reads like a small job: a clear responsibility, a few tools, a defined place where a human approves the work. If you are still fuzzy on what an agent is compared with a chatbot or an automation, the plain-language guide on what an AI agent for business is is a useful companion before you start scoring.

What do the four factors actually measure?

Each factor answers one practical question. Score each from 1 (poor) to 5 (strong).

• Frequency: how often does the task happen, and how many people touch it? A 5 is daily work shared across several people. A 1 is something that happens a few times a year. Frequent tasks give you fast feedback and enough volume to tell whether the agent is actually working.
• Risk, scored in reverse: what happens if the agent gets it wrong? A 5 is internal, reversible work where a person reviews the output before it goes anywhere. A 1 is anything that moves money, makes a legal commitment, touches sensitive personal data or reaches customers with no review step.
• Data readiness: does the information the task needs exist in one or two maintained places, with someone responsible for it? A 5 means current, findable and owned. A 1 means the knowledge lives in people's heads, scattered chats and outdated files.
• Measurability: can you state in one sentence what done looks like and check it from records? A 5 means a clear before and after you can observe. A 1 means success is a feeling.

Why these four and not something like strategic value? Because strategic value is argued, while these four can be observed. Frequency stands in for much of the practical value, since a task that rarely happens rarely pays back the effort. Risk, data and measurability decide whether you can run the agent safely and prove it worked.

Two of the factors deserve extra attention. Data readiness is usually where optimism is highest and evidence is thinnest, so it is worth reading how to give an agent a reliable source of truth before you score it. Measurability is easier once you have thought about success criteria for an AI agent pilot, because the same sentence that defines success will also earn you a high score here.

How do you score and rank candidates, step by step?

This can be done in a single working session with the people who do the work. Use this sequence.

• Write each candidate as a task, not a technology. Use a verb and an object, such as prepare the weekly client status summary. Avoid entries like use AI for operations.
• Assign an owner to each candidate. This is the person who would notice if the output were wrong and who cares if it is right. If nobody can be named, remove the candidate.
• Gather evidence. Ask to see a handful of recent real examples of the task, the documents it depends on, and the systems involved.
• Score the four factors from 1 to 5 in a short meeting that includes the people doing the work, not only the sponsor. Write one sentence of justification next to each score.
• Apply the stop rule. Any candidate with a 1 on any factor leaves the first round. It can return once the problem is fixed.
• Add the four scores and rank the rest. If two candidates tie, prefer the higher data readiness, then the lower risk.
• Run a sanity check. If the steps are fixed and the inputs are predictable, a plain workflow may do the job better. The guide on choosing between an AI agent and a deterministic workflow helps with that decision.
• Write the winner as a one-paragraph role with its tools, its approval point and its success measure, and set a date to review it.

Score from evidence, not hope. If someone says the data is all in the shared drive, open the shared drive. If someone says the task happens constantly, look at the last month of actual instances. The method is simple on purpose, so the honesty of the inputs matters more than the arithmetic.

What does scoring look like in practice?

Here is an illustrative scenario, not a real case. A services firm of moderate size lists four candidates and scores them with the people who do the work. The scores are listed as frequency, risk, data readiness and measurability.

• Weekly client status summaries: 4, 4, 4, 4, total 16. Account managers write them every week from project notes and a task tracker. Output is reviewed internally before it is sent.
• Customer email replies: 5, 2, 3, 3, total 13. Very frequent, but replies go straight to customers and the answers depend on policies that exist in several versions.
• Contract amendment drafts: 2, 1, 3, 2, total 8. The risk score of 1 removes it from the first round, whatever else is true.
• Supplier invoice reconciliation: 4, 3, 2, 4, total 13. Frequent and measurable, but the supporting data is spread across mailboxes and spreadsheets.

The status summary wins. It is not the most glamorous option, but it is frequent, reviewable and measurable, for example by checking how much editing the summaries need before they go out and whether they are ready on time.

The other results are useful too. Customer email replies become a sensible second use case once the firm has built review habits and a maintained set of policies. Invoice reconciliation is really a data cleanup task first, and the agent comes after. Contract amendments stay out until the firm has strict review rules and a clear view of what the agent may and may not touch.

Where do scores mislead you?

A scoring method gives structure, not certainty. These are the usual ways it goes wrong.

• Frequent but inconsistent work. A task that happens daily but is different every time earns a high frequency score and then fails because there is no stable pattern to learn or check.
• Risk judged by the task, not by the access. The question is not only what you are asking the agent to do, but what it could reach with the permissions it holds. Narrow permissions and least privilege change the risk score, which is why authorization guidance such as the OWASP Authorization Cheat Sheet is worth a read before you settle on a score.
• Data readiness assumed because a tool exists. Having a wiki or a shared folder is not the same as having a maintained, current, owned source. Ask who last updated it and who would notice if it were wrong.
• Measurability confused with activity. Counting messages handled or documents produced says little. A useful measure is tied to a result, such as time to a reviewed draft or the share of outputs that needed no correction.
• Sponsor enthusiasm overriding the score. If a senior sponsor wants a specific use case that scored poorly, treat that as information. Fix the weakest factor first, then revisit.

A score is a conversation tool. If the group disagrees on a number, the disagreement usually points to a missing fact, and finding that fact is the point of the exercise. Frameworks such as the NIST AI Risk Management Framework take the same view: risk is something you map and review with the people involved, not a number you settle once.

What should be true before you build the winner?

Picking a use case is not the same as being ready to build it. Before work starts, check the following.

• The role is written as a short job description with one clear responsibility. The guide to writing a job description for your first AI agent role shows one way to do it.
• A named person owns the outcome and reviews the output, and a backup is identified.
• The sources the agent will rely on are identified, current and have an owner.
• Permissions are limited to what the task needs, and the agent works through its own accounts rather than a person's login.
• There is a defined approval point for anything that leaves the company or changes a record.
• Success is written as a sentence you can check from records, with a date for the first review.
• You know what you will stop or change if the results are poor.

If any item is missing, that is useful: it tells you what the first weeks of work should address, and it is much cheaper to find out now than after the build.

How does Be Human handle prioritization in a managed engagement?

In a first engagement, Be Human defines one useful role, the memory and tools it needs, the approval boundaries and an observable success criterion. Prioritization is part of that scoping. Candidates are scored with the people who do the work, data and permissions are checked before anything is built, and the chosen role runs under human control with monitoring and recurring evaluations afterward. The discovery phase is where this scoring is done and written down, so the decision can be reviewed and revisited later.

Frequently asked questions

How many use cases should we score at once? Score as many as your team can describe with evidence, which is usually between five and ten. Fewer than that and you may be choosing by default. Many more and the session turns into a brainstorm instead of a decision.

Should we weight the factors differently? For a first use case, equal weights work well, and the stop rule already handles the worst cases. If you want to adjust, weight risk and data readiness more heavily for a company with sensitive data or messy records. Keep any adjustment written down so the reasoning is clear later.

What if the best-scoring use case is boring? That is usually a good sign. A dull, frequent, measurable task teaches the team how to supervise an agent at low cost, and it builds the sources, permissions and review habits that later, more ambitious roles will depend on.

When should we rescore the list? Rescore after the first use case has run for a full review cycle, and whenever your data, tools or policies change in a meaningful way. Scores move as sources get cleaned up and trust grows, so a candidate that was excluded at first may become the obvious next step.

Be Human editorial guidance. Examples are illustrative unless explicitly attributed. Sources provide background; implementation choices depend on your workflow.