Step 1: Start from a business problem, not the technology
List the workflows where your teams spend the most time or make the most costly errors. A strong AI use case begins with a measurable pain: hours lost to manual review, slow response times, inconsistent decisions, backlogs that never clear. Write each candidate as a problem statement with a number attached. If you cannot name the metric a use case would move, it is a science project, not a use case. This step keeps you from picking a use case because the technology is interesting rather than because the outcome matters.
Step 2: Score each candidate on value and feasibility
For every candidate, rate two axes. Value is the size of the outcome if it works: revenue, cost, risk reduction, or time saved, expressed as a range. Feasibility is how realistic delivery is given today's data, models, and integration effort. Plot the candidates on a simple two-by-two. The top-right quadrant, high value and high feasibility, is where you start. Resist the temptation to chase a high-value idea that depends on data you do not have or an integration no one will fund.
Step 3: Check data readiness honestly
Pick your shortlist and trace the data each one needs. Ask three questions for every data source: do we have access to it, is it accurate and current, and is it governed so we know its classification and permissions. Most AI use cases that fail in production fail here. A model fed ungoverned or low-quality data produces outputs no one can trust, and the project stalls. If a use case needs data you cannot vouch for, either fix the data first or move down the list.
Step 4: Assess governability before you commit
A use case is only worth building if you can run it safely once it is live. For each finalist, ask whether you could redact sensitive data before it reaches the model, enforce who is allowed to use it, and produce an audit trail of what it did. Use cases that touch regulated or personal data need a runtime control layer to reach production at all. Scoring governability now, alongside value and feasibility, stops you from building something that works in a pilot but cannot pass security and compliance review.
Step 5: Pick one, define success, and instrument it
Choose the single use case that scores highest across value, feasibility, data readiness, and governability. Write the success criterion as a target on the metric you identified in step one, with a date. Decide how you will measure it before you build, so the result is a number rather than an opinion. Put the governance controls in place around it from the start. Then ship that one use case under control, prove the outcome, and use the evidence to fund the next. A disciplined sequence of governed, measured use cases beats a long roadmap of ungoverned pilots.
Frequently asked questions
What makes a good AI use case?
One tied to a measurable business outcome, feasible with the data and models you have, backed by data you can govern, and safe to run under enforcement and audit once it is in production.
Why do most AI use cases fail?
They are chosen for novelty rather than value, depend on data the team cannot vouch for, or cannot be governed at runtime, so they stall in pilot and never pass security and compliance review.
How many AI use cases should you start with?
One. Pick the highest-scoring candidate across value, feasibility, data readiness, and governability, ship it under control, prove the outcome, and use that evidence to fund the next.