A repetitive task is not automatically a good automation candidate. First ask why the task exists, what decision it supports, and where the errors begin.
Factory-floor problem solving teaches a useful lesson here. If the source process produces incomplete information, a faster handoff only moves the defect sooner. The same thing happens when an AI workflow is built on inconsistent files, unclear ownership, or undocumented approval rules.
Map the work before choosing the tool. Identify the trigger, inputs, judgment points, exceptions, owner, output, and measure of success. Then decide which steps should be accelerated and which decisions should remain human.
A strong first automation is narrow enough to evaluate. It might prepare a research brief, organize customer questions, flag reporting changes, or draft a publishing package for review. Each run produces evidence that improves the next one.
The goal is not to remove people from the system. The goal is to remove avoidable friction while making judgment more consistent and visible.