A workflow complaint becomes a testable AI use case only when the team can name the event that starts the work, the evidence available at that moment, the result to be produced, the person who reviews it, and the condition that would disprove the idea. Start with observed work, not with a model or feature. The output should be a short use-case contract that a process owner can approve and an independent reviewer can test.
Diagnose the pain behind the request
Suppose a founder hears that weekly reporting is slow and asks for an AI reporting assistant. That request hides several possible causes. Inputs may arrive late. Teams may use inconsistent labels. A manager may spend time reconciling exceptions. The final narrative may be the smallest part of the delay. Automating the narrative first would produce faster prose while leaving the actual queue untouched.
Observe a complete reporting cycle and record the trigger, every handoff, the source fields used, waiting time, rework, exception reasons, and the person who accepts the final report. This is direct workflow evidence. It does not prove that AI will help, but it identifies where a change could be tested. If the delay comes from missing inputs, the useful use case may be detecting incomplete submissions and routing them back, not drafting a summary.
Separate constraints that belong to the workflow from constraints that belong to the technology. The AI infrastructure bottleneck analysis contributes a check on power, cooling, capacity, maintenance, and utilization when a proposed use case requires owned compute. For a small reporting test, those dependencies may be irrelevant. Writing that down prevents a simple experiment from inheriting an enterprise architecture by accident.
Write a use-case contract
A useful contract fits on one page. It should answer who owns the process, what event activates the system, which records are allowed, what transformation is permitted, what output is expected, who reviews uncertain cases, and what is explicitly outside scope. Use verbs that can be observed. Classify, extract, compare, flag, and draft are testable. Improve reporting is not.
For the reporting example, the contract could say: when department submissions close, the system checks required fields and label consistency, flags incomplete records, and drafts a reconciliation note for the reporting manager. It may not change source records, invent missing values, or send the final report. The reporting manager accepts, corrects, or rejects each output and records a reason. Any low-confidence or novel case returns to that manager.
This boundary matters because a local experiment can answer a narrow technical question without proving a business case. The local AI experiment shows how a founder can reduce setup friction and test capability on existing hardware. Its contribution here is reversibility: learn whether the task is feasible before committing to a larger operating model, while still treating data access and review as separate decisions.
Define exclusions before choosing a tool. Sensitive fields may be removed. Certain departments may be excluded until labels are stable. External sending may remain prohibited. These exclusions are not weaknesses. They make the causal claim legible: if the bounded intervention changes the chosen part of the workflow, the team can see why.
Test the causal claim
Run the contract against representative completed cases and then against live work under supervision. Compare the proposed output with the current procedure. Record acceptance, correction, rejection, correction reason, and any new work created for the reviewer. A high volume of polished drafts is not success if every draft requires a full rebuild.
The decision metric is accepted outputs with documented correction reasons. The process owner should look for a stable pattern: the system handles the promised case, exceptions return to the named reviewer, and the review burden does not erase the intended benefit. If missing inputs remain the dominant delay, reject or reshape the use case. That is a useful result because it stops investment in the wrong layer.
Translate the surviving workflow change into business terms only after the test. The payback-period guide contributes a sequence from the job to the metric, cost of inaction, accountable people, and an acceptable payback window. Use actual internal observations as inputs. Unknown implementation effort stays unknown rather than becoming an invented return.
Frequently Asked Questions
Workflow pain is an observed problem such as delay or rework. A use case names the trigger, allowed evidence, bounded output, reviewer, exclusions, and a testable change.
Reject or reshape it when missing inputs remain the main cause of delay, or when reviewing the proposed output recreates the original reporting work.
The named process reviewer should inspect the source evidence, accept or correct the output, record the reason, and return novel cases to the existing procedure.
The final artifact is not a chatbot demo. It is the approved contract, the case set, the review log, the exclusions, and a decision to stop, reshape, or advance. If you want that evidence assembled into a prioritized opportunity brief, request an AI Opportunity Report.