How to Evaluate an AI Automation Use Case Before Building It

1. Describe the workflow, not the AI feature

Write down the trigger, inputs, decisions, actions, participants, systems, exceptions, and final outcome of the existing workflow. Then identify exactly where time, cost, delay, inconsistency, or missed opportunity occurs. A request such as ‘add an AI assistant’ is not yet a use case.

Good candidates usually contain language, documents, images, judgment under known policies, or repetitive coordination across systems. Deterministic rules remain preferable when the inputs and outcomes can be expressed reliably in code. AI should handle the uncertain portion, not replace stable logic that already works.

• Name the workflow owner and the people affected
• Measure current volume, cycle time, error rate, rework, and cost
• Separate deterministic steps from judgment-heavy steps
• Identify the decision that remains accountable to a person or system
• Define what a safe refusal or fallback looks like

2. Test strategic value and adoption conditions

A technically feasible automation can still fail because the outcome is not important enough, no team owns adoption, or the workflow is changing for unrelated reasons. Connect the proposal to a funded business priority and quantify the value of faster processing, reduced rework, improved service, or newly possible work.

Include change-management cost. Users need a clear reason to trust and adopt the result, especially when AI changes how expertise or approvals are exercised. Confirm who owns training, support, exceptions, and the operational metric after launch.

3. Assess data, security, and consequence

Inventory the data required to perform the task and determine whether it is accessible, current, representative, permitted for the intended use, and labeled with the access metadata the solution needs. A use case that depends on unavailable or poorly governed data is not ready for a model comparison.

Rate the consequence of a wrong answer or unintended action. Low-impact drafting can tolerate review and correction. Financial approval, safety decisions, regulated communication, and irreversible system changes require stronger controls, traceability, and human authorization—or may be unsuitable for automation.

• Classify personal, confidential, regulated, and licensed content
• Identify authorization boundaries and data residency requirements
• Model prompt injection, data leakage, and unsafe tool execution
• Define human review for high-impact outputs
• Record retention, audit, deletion, and incident-response requirements

4. Define evaluation before building the prototype

Create a representative evaluation set from real, approved examples. Include normal cases, ambiguous inputs, missing information, adversarial instructions, permission boundaries, and cases where the correct response is to stop. Score task correctness as well as safety, latency, cost, and user effort.

Agree on go, revise, and stop thresholds before seeing results. This prevents a visually impressive demonstration from replacing evidence. Compare the AI-assisted path with the current process and with a simpler rules or search-based alternative.

5. Fund a bounded experiment with an exit decision

The first implementation should test the riskiest assumptions with the smallest useful scope. Limit users, data, tools, integrations, and actions. Instrument every step so the team can explain success, failure, latency, token use, human corrections, and operational effort.

End the experiment with a decision: productionize, revise the use case, adopt a simpler solution, or stop. Productionization requires ownership, security review, evaluation in delivery pipelines, monitoring, support, and a model-change strategy—not only a more polished interface.