The slickest demo in the world cannot tell you whether an AI tool belongs in your business. Start with a real task, a real input, and a clear definition of a good result. Then decide what the software is allowed to see and what a person must still check. That gives you a buying brief before a sales pitch gives you one.
Name the task, not the technology
“We need AI” is too broad to test. “Turn a weekly support-ticket export into a draft summary of recurring issues” is specific. Write down who does the task now, where the input comes from, what the output looks like, how often it happens, and who uses the result. If the underlying process changes every week, document that first. An AI tool will not settle an argument about what the team is trying to accomplish.
A small task map can fit in five lines: input → steps → output → reviewer → decision. For a marketing report, the input might be campaign data, the steps might include checking date ranges and calculating changes, the output is a narrative summary, the reviewer is the marketing lead, and the decision is whether to investigate or adjust spend. This also shows which steps can be assisted and which need judgment.
Scenario 1: the social video that usually eats an afternoon. Imagine a small business has a 90-second founder interview, a few product clips, and a brand style guide. The goal is a 30-second vertical video that tells one story: the problem, the product in use, and the payoff. Give the tool the actual footage, the intended sequence, a reference cut, and the specific elements that must survive, including the founder’s original voice and room sound. Ask it to trim pauses, time the three beats, add captions and two short callouts in the supplied font, and export a version for human review.
A useful test is not whether it produces a slick-looking video. Does it put each callout on the right shot, preserve the meaning of the founder’s words, keep the product demonstration intact, and avoid replacing the original sound or visual style unless instructed? Try one difficult edit, such as an outfit or background change, on a few seconds of footage. Inspect moving edges, lighting, continuity, and whether the result still looks like the same person and product. Count the minutes spent correcting its work. If the editor must rebuild the timing, captions, and transitions by hand, the AI has added an extra step rather than streamlined one.
Scenario 2: the sales pattern you think you see but cannot yet prove. A regional sales director notices that a few representatives consistently close more deals. The team wants to know whether their results relate to territory, lead source, response time, call timing, or something they do during successful calls. Give an analytics tool a defined period of CRM opportunities, rep assignments, qualified-lead dates, stages, won revenue, and marketing-source fields. Add call metadata or reviewed transcripts only if permitted and available. Ask for a report that compares reps on a fair basis, such as win rate and revenue per qualified opportunity, alongside volume, territory, and tenure.
A good output would show the KPI definitions, sample sizes, missing fields, and a path from source to opportunity to closed sale. It might find that one region’s stronger results coincide with faster follow-up or a particular mix of paid search and referral leads. It should not pronounce the highest-revenue rep “best” without accounting for lead quality or territory, invent call-quality indicators when there are no transcripts, or treat a correlation as proof that changing call time will increase sales. If cost-per-lead and campaign data can be joined reliably, the marketing team can examine which sources produce qualified pipeline and profitable outcomes. If those joins are weak, the useful result is a clear data gap to fix before scaling spend.
Sort the data before you paste anything
List the information the task requires. Separate public material, internal but ordinary information, confidential business data, and personal or customer information. Ask whether you can test with a synthetic or redacted example first. If sensitive information is essential, check the actual vendor terms, access controls, retention settings, and your organization’s requirements before uploading it. Product defaults and plan features can change.
For a fuller discussion of confidential prompts, see our guide to using ChatGPT with business data. The same habit applies when you evaluate any vendor: decide what may enter the tool before a convenient workflow normalizes sharing everything.
Define the human job
An AI-generated draft can be useful without being ready to send. Assign a person to verify facts, tone, calculations, citations, and customer-facing commitments. Write down what that reviewer will compare against. If an incorrect answer could create a costly or hard-to-reverse consequence, move the human check earlier and make it more rigorous. “A human is in the loop” means little until someone knows what to check and has time to do it.
Make three test cases before comparing vendors
Use one ordinary example, one messy example, and one example where the tool should decline or flag uncertainty. For the support-summary task, that could mean a normal week, a week with duplicate or incomplete tickets, and a ticket containing a request the tool should not answer. Keep the prompts and expected outcomes consistent across products. A polished vendor sample is not a substitute for your own inputs.
- Useful output: Does it answer the task as defined, without inventing facts?
- Review burden: How much checking and rewriting does it create?
- Workflow fit: Can the right person access, review, and export the result?
- Data fit: Are the available controls appropriate for the information involved?
- Total effort: Include setup, training, review, and maintenance, not just a subscription price.
Write a one-page buying brief
State the task, acceptable inputs, prohibited inputs, required output, reviewer, test cases, and criteria for proceeding. Then compare tools. You may discover that your present software already solves the problem, that a simple template is enough, or that an AI tool earns its place. All three are useful decisions.
GeekyHuman already covers affordable AI tools for small businesses. Use that kind of shortlist after you know what you need the tool to do.
Further reading
The NIST AI Risk Management Framework Core is a useful reference for defining use context, measuring performance and risk, and assigning human oversight. It is a framework, not a vendor recommendation.
Leave a Reply