Which Scenario Should Your First AI Workflow Target?

Not every step is a good starting point. Screen candidates on frequency, rule clarity and verifiability so your first pilot produces a result you can defend.

Many teams pick the most painful and most complex process for their first attempt. The rules turn out to be unclear, the outcome is hard to describe, and the pilot quietly ends. The goal of scenario selection is not to solve the biggest problem, but to secure a first result that holds up.

Three screening criteria

Frequency: it should happen several times a week, or the time saved is not noticeable. Rule clarity: you can state the decision criteria in a few sentences; if you cannot, map the process before adding tools. Verifiability: there is a clear way to tell whether the output is right.

Good starting points

Aggregation (collecting scattered inputs into a fixed format), classification and routing (tagging incoming requests and assigning them), and draft generation (producing a reviewable draft from existing material). All three are frequent, rule-based and easy to judge.

Scenarios to defer

Anything involving external commitments, monetary judgment, personnel evaluation, or cross-team negotiation of wording. These are not off limits, but they are better attempted once the team understands where the tooling stops.

Defining pilot success

Before starting, write one testable sentence, such as "nobody needs to compile this by hand again." Whether that sentence holds at the end is more convincing than any percentage.

Summary

The value of a first scenario is the confidence and judgment the team gains. Finishing one small, clear process beats running five half-built ones.