Small Business AI 101 lesson
Lesson 7: Measure a small experiment honestly
Learn from a bounded trial without inventing a savings claim.
Write a small hypothesis
Describe the task, the person doing it, the type of output, and what you hope to learn. For example: can a structured outline make our weekly internal update easier to start while a manager still checks the final version?
Do not begin with a promise that the tool will save a certain number of hours or transform the business. A small experiment is for observation, not proof of a marketing claim.
Capture useful evidence
Notice the amount of human effort, the quality of the first draft, the number of corrections, the uncertainty created, the time needed for review, and whether the team would use the method again. Keep examples only when doing so is appropriate and approved.
A business may decide the tool is not useful for a task, even if the output looks impressive. That is a valid result. The right outcome is a clearer decision about the work.
Choose the next move
At the end of the trial, keep the practice, improve the prompt or review rule, choose another low-risk task, or stop. Name one owner and one next date so the learning does not become another unfinished experiment.
Expand only after the business understands the information boundary, review effort, actual quality, and responsibility for the output. Scale should follow evidence, not curiosity alone.