Begin with the workflow
A useful first AI project starts with an activity people already perform. Follow that activity from its initial input to its final decision. Note where someone searches for information, copies data between systems or waits for a specialist. These observations reveal opportunities that a list of fashionable technologies will miss. Interview the people doing the work and look at representative examples before deciding what to automate.
Establish a baseline
Measure the current process before building a replacement. For a document review workflow, this might include review time, correction rates and the number of cases requiring escalation. Keep the measurement practical enough to repeat. A faster first draft does not necessarily reduce total work if reviewers spend more time checking it. Define success around the complete task, including the effort needed to catch and correct mistakes.
Choose a manageable boundary
A bounded internal workflow can offer a useful starting point when the inputs are available and a reviewer can check the output. For example, drafting a summary from approved project documents is easier to evaluate when the source material and intended audience are explicit. Agree on what the system must decline, what information it may access and which actions remain with a person. These boundaries belong in the project scope.
Make the first decision reversible
Set a review point before committing to broader rollout. Compare the assisted workflow with the baseline using representative tasks, including awkward cases. Ask whether the benefit survives integration costs, review effort and ongoing operation. It is reasonable to narrow the scope or stop if the evidence is weak. A useful discovery project leaves behind a clearer decision, even when that decision is to improve the existing process without AI.
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