THE PRACTICAL TAKEAWAY
Write the business outcome first. Give each experiment an owner, a baseline, a review date, and a reason to stop.
Name the constraint
A useful AI plan begins with a constraint: proposals take too long, customer questions sit unanswered, or delivery teams spend too much time preparing updates. Choose one that matters to the business this quarter. If every department has a different priority, start with one department.
Describe the current process in plain language. Name the people who do the work, the information they use, and the next person who depends on the output. This prevents an attractive tool from quietly becoming the strategy.
Give the experiment boundaries
On one page, define the proposed change and what stays under human control. Record the data that may be used, the approved environment, and the person responsible when the process fails. Keep scope tight enough that the owner can explain it in a minute.
Set a review date and two measures: one for the outcome and one for quality. For a proposal-drafting pilot, these might be elapsed drafting time and the number of factual corrections required before approval. Avoid measuring how many prompts employees send; activity is not the same as useful work.
Make the decision visible
Reserve a short weekly review for examples rather than presentations. Look at one strong result, one failure, and one case where the old process was better. Ask whether the underlying workflow needs changing before adding more automation.
At the agreed review date, record the decision and evidence. Expand only when the process meets the team’s quality threshold and someone can maintain it. A one-page plan succeeds when it helps people decide what to do next, including when to stop.
ModelMillionaire publishes AI-assisted editorial guidance. Examples are illustrative unless explicitly identified as documented cases. Our editorial approach.