You do not need to transform your entire business to explore AI.
Start with one practical question:
Where could AI help your team do useful work with less unnecessary effort?
Maybe your team repeatedly writes product descriptions, prepares answers to common customer questions, or turns training notes into step-by-step instructions.
Choose a task your people already understand. When you know what a good result looks like, you have a clear way to judge whether AI is helping.
You also do not need to make this decision based on what everyone else appears to be doing. In a May 2026 analysis, the U.S. Census Bureau reported that business AI use ranged from 17% to 20% across survey periods from December 2025 through early May 2026, with adoption varying by company size and industry. Those figures describe that survey window, not a current adoption rate.
For your business, the starting point is purpose. What do you want to improve, and how will you know whether the change is worthwhile?
1. Choose one task worth improving
Ask the people doing the work:
“What do you repeat every week that takes more effort than it should?”
Look for a task that happens often enough to make a difference, has a clear expected result, and can be reviewed by someone who understands the work.
Possible starting points include:
- Drafting product descriptions from approved specifications.
- Organizing existing training material into a usable first draft.
- Preparing draft responses to common customer questions.
- Turning approved meeting notes into an action list for review.
Keep the first test manageable. Choose work your team can confidently check before it reaches a customer or influences an important decision.
Tasks involving hiring, safety, contractual commitments, or sensitive information require a more careful risk assessment and appropriate technical or professional guidance. NIST’s AI framework identifies safety, reliability, privacy, accountability, and the management of harmful bias as important considerations when evaluating AI use.
2. Understand the process before adding a tool
Follow one piece of work from beginning to end.
Where does the information come from? Which source is current? Who prepares the work, checks it, and approves the finished result?
Consider this hypothetical example.
A distributor wants to use AI to draft descriptions for its online catalog. The team has current, approved specification sheets, but older descriptions are scattered across several shared folders.
Before testing AI, the team agrees on which source to use and who is responsible for keeping it current. It also decides that the tool should flag missing information rather than fill in gaps.
Now the test has a clear purpose: turn verified product facts into a useful first draft.
This review is also an opportunity to question the process itself. Is a step necessary? Is a handoff clear? Does someone have to search several places for the same information?
Decide what the process should look like before asking a tool to support it.
3. Set boundaries people can follow
Before the test begins, agree on:
- What information the tool may receive.
- What it may produce.
- What it must not decide or promise.
- Who will review the output.
- What requires approval before use.
For the hypothetical distributor, that means checking measurements, materials, and compatibility statements against approved specifications before publishing. A description is not ready simply because it reads well.
Review the tool’s data-handling, retention, and access settings. Keep confidential customer, employee, and business information out of the test unless that use has been specifically reviewed and authorized.
NIST’s voluntary AI Risk Management Framework provides a basis for considering reliability, privacy, accountability, and other risks. These safeguards are practical starting points, not certification that a tool or use is safe for your business.
Give the reviewer enough time, knowledge, and authority to do the job. “Someone will check it” is not a complete review plan.
4. Measure the whole job, not just the first draft
Before introducing AI, record how the task works today.
How long does it take? How often does it need correction? What effort goes into checking, approving, and handing it off?
Then run a small pilot with a named owner and a review date.
Compare the finished work against the same quality expectations. Include:
- Preparation and drafting time.
- Review and correction time.
- Approval and handoff time.
- Subscription costs.
- Time spent learning and maintaining the process.
If drafting becomes faster but checking takes longer, investigate why. The instructions may need work, the source material may be incomplete, or the task may not be a good fit.
If the pilot saves time while meeting your standards, decide how you will use that capacity.
Will your team respond to customers sooner? Keep training material current? Finish a project that has repeatedly been pushed aside?
Connect the improvement to a business priority. That is how you decide whether the tool is worth keeping.
5. Involve the people doing the work
Explain what you are testing and why.
Ask your team to point out mistakes, challenge assumptions, and suggest improvements. Make it acceptable to say, “This is not helping,” without treating that feedback as resistance.
Allow learning time within the workload. If you propose changing responsibilities, discuss those changes openly. Only make commitments about future roles that you can support.
At the review date, choose one of three paths:
- Continue if the process meets your standards and provides worthwhile value.
- Adjust if there is a specific problem worth addressing.
- Stop if the results do not justify the effort, cost, or risk.
A pilot that leads you to stop can still be useful. You have learned something before making a larger commitment.
Choose your first step
Pick one recurring task. Talk with the person who knows it best. Agree on what improvement would look like, what boundaries are needed, and when you will review the results.
Build from there, using evidence and experience rather than pressure to adopt the latest tool.
You’ve worked hard to build your business. Now make it work better for you.
At Creating Greatness, we help you connect strategy, financial clarity, operations, and leadership so you can make better decisions and put them into practice.