How to identify a high-value AI agent use case

Start with operational friction

Do not begin with a list of model capabilities. Begin with work people already perform: recurring reports, queues, content operations, hand-offs, research and reconciliation. Ask where skilled people spend time gathering context rather than applying expertise.

The strongest opportunities are visible in backlogs, spreadsheets, inbox rules and repeated copy-and-paste between systems. They already have an owner and an informal process, which gives the build something concrete to improve.

Score value before novelty

Estimate frequency, handling time, delay, error impact and the value of a faster response. Use ranges where measurements are incomplete. A daily task affecting a revenue or service decision can be more valuable than a dramatic task performed quarterly.

Include the cost of review and exception handling. An agent that saves ten minutes but creates fifteen minutes of checking has not improved the operation. The baseline should describe total effort from trigger to accepted outcome.

• →Runs per week
• →Current minutes per run
• →Waiting time between steps
• →Rework rate
• →Consequence of a missed or wrong result

Test feasibility at the system boundary

List every source and destination. Confirm whether each has an API, an export, a stable identifier and an owner willing to grant access. Many apparently simple AI use cases are really integration projects with a model in the middle.

Inspect data quality early. Missing ownership, inconsistent labels and inaccessible history can dominate delivery. A narrow use case with clean inputs often reaches production sooner than a valuable use case dependent on six poorly understood systems.

Assess judgement and consequence

Separate interpretation from authority. An agent may be good at classifying a request while being the wrong place to approve a refund, publish regulated advice or change a customer entitlement. Consequential actions should have tighter validation or human approval.

Define unacceptable outcomes before designing the happy path. Consider privacy exposure, brand harm, financial loss, unfair treatment and operational disruption. If the team cannot name the boundary, the use case is not ready to scope.

Choose a first slice

Select a slice that can run end to end and produce a useful output within weeks. Reduce systems, audiences or action types rather than building half of every capability. A reporting agent might begin with one business unit and one daily brief.

Write a one-page charter: trigger, inputs, steps, output, owner, approvals, exclusions and success measures. If stakeholders agree on that page, technical discovery can proceed. If they do not, another workshop is cheaper than an ambiguous build.

• →Named operational owner
• →One measurable outcome
• →Accessible source data
• →Explicit exclusions
• →Real cases available for testing