How to Choose the First Measurable AI Use Case

1. Collect problems rather than tool ideas

Ask departments for time-consuming tasks, slow decisions, recurring errors, repeated questions, and information that is hard to reach. Keep the first round from becoming a list of bots and agents. The purpose is to understand pain and outcomes.

Turn each opportunity into a sentence: “When this happens, this role spends time doing this, which causes that result, and we want to change this measure.” For example, customer requests arrive as free text and service staff classify them manually, delaying routing; the desired contribution is faster suggested classification while unclear cases stay with a reviewer.

2. Score business value

Assess frequency, volume, time, and impact on the customer, revenue, cost, or risk. A task that takes minutes but occurs thousands of times may matter more than a complex monthly executive report. Ask whether improvement changes a result or merely makes an unimportant step more elegant.

Name a benefiting owner who is prepared to change the process, not only a sponsor interested in technology. Without an owner who supplies experts and data and settles scope decisions, the use case remains a demonstration.

• Recurring, material pain.
• An outcome connected to a business goal.
• An owner able to change the work.
• A baseline that can be captured.

3. Score feasibility and readiness

Ask whether real examples, knowledge sources, an evaluation method, and a place in the workflow exist. Summarizing repeated documents with historical examples is easier to evaluate than a rare strategic decision with no reference answer. Do not reject a case because it needs cleanup, but distinguish a fixable gap from ownerless disorder.

Assess user readiness as well. An assistant will fail if the team has no time to review it or distrusts the source. A better interface, source cleanup, or training may be a prerequisite to the pilot.

4. Subtract risk from attractiveness

A high-value opportunity may still be a poor first use case if errors are consequential and irreversible, data rights are unclear, or outputs are hard to explain. Start with a reviewable contribution rather than a final decision affecting people, money, or obligations.

Drafting a proposal from an approved catalog is lower risk than approving a discount. Summarizing a complaint is lower risk than rejecting compensation. A large use case can often be divided into a lower-risk step that proves value and builds controls.

5. Write a one-page use-case card

Document the problem, user, task, inputs, output, exclusions, human review, success metrics, and guardrails. Add the largest assumption the experiment is designed to test. If the card requires many broad phrases, the scope is not yet ready.

Capture baseline cycle time, errors, rework, volume, and quality before launch. Define the pilot target as a realistic direction or decision threshold without inventing a number unsupported by history. The team must know what evidence will lead it to scale, revise, or stop.

• One primary outcome measure.
• A quality or risk measure.
• A real-adoption measure.
• Full operating cost.

6. Test in a limited real setting and decide explicitly

Choose a representative sample, small team, and period long enough to include exceptions. Keep a comparison with current work and record edits, rejections, and escalations. Do not showcase only the best examples; difficult cases determine operational viability.

End with one of four decisions: scale, make a defined improvement and retest, narrow the scope, or stop. Tie the decision to evidence rather than enthusiasm or sunk cost. A good pilot reduces uncertainty even when it proves the opportunity is not ready.