A “market gap” is only useful if people have a problem that existing options do not solve well enough. An AI-generated list of untapped opportunities does not establish that such customers exist, can be reached, or will pay for another solution.
You can use AI to organize research and challenge an idea without treating its predictions as evidence. Begin with a situation you understand from work, a hobby, or everyday life. Experience gives you questions to investigate; it does not remove the need to listen to prospective customers.
Describe a problem you could disprove
Replace “busy people need smarter services” with a specific hypothesis. For example: “Independent workshop organizers spend too long turning attendee questions into a follow-up resource sheet.” This is an illustrative research idea, not a claim that this market has been tested.
Write down who experiences the problem, when it occurs, what they do now, and what an improvement would look like. Add what would make you abandon the idea: perhaps the task takes only five minutes, happens once a year, or is already handled well by existing software.
The SBA’s market-research resources distinguish learning about customers from studying competitors. They include questions about demand, alternatives, location, and pricing, and describe both existing research and direct customer research. Use those questions to keep your investigation grounded.
Create an evidence notebook
Collect a small set of relevant sources and observations. For each, record the date, original source, specific claim, and limits. Keep separate columns for what you observed, your interpretation, and what remains unknown.
A supplier’s sales page tells you what the supplier advertises. A customer interview describes one person’s experience. A complaint shows a problem for its author but does not reveal how common it is. Ten articles repeating one press release are not ten independent confirmations.
Ask AI to organize material you are allowed to use: “Group these observations by problem. Cite the source label for every summary. Separate direct evidence from assumptions. Identify contradictions and unanswered questions. Do not estimate market size or willingness to pay unless the supplied evidence supports it.”
Open the original sources and check the model’s summary. If it cannot provide a source for a statistic or quote, remove that claim from your decision sheet rather than adding it to a persuasive pitch.
Talk about the last real experience
Interview a few people who actually perform the task, with their permission. Use open questions about behavior:
- When did this problem last occur?
- What did you do to solve it, and what was inconvenient?
- Which tools or services did you try?
- What did the solution cost in time or money?
- Who decides whether to adopt or pay for a different approach?
Avoid leading with “Wouldn’t an AI tool make this better?” It invites agreement with your solution before you understand the problem. Ask what works well in the current approach too. The replacement may need to preserve something you have overlooked.
Use anonymized summaries when asking AI to compare interviews. Do not upload recordings, names, private business records, or sensitive personal details without appropriate permission and safeguards. A small sample supplies useful leads, not a reliable percentage of an entire market.
Include the ordinary alternatives
List what customers can do instead of buying your idea: use a template, delegate the task, accept the inconvenience, or use an existing product. Check competitors’ current pages directly for features and pricing rather than trusting a model’s memory.
Compare the same task across options. Record setup effort, recurring work, support, data requirements, and the parts each option leaves to the user. If your concept saves ten minutes but takes an hour of preparation, the apparent gap may disappear.
Ask AI to argue against your preferred idea using the evidence. Then verify its objections just as carefully as its enthusiasm. A convincing tone is not a substitute for facts in either direction.
Test a small promise before building software
Offer a clearly described manual sample or prototype to a small group. Explain what it does, what it does not do, what data it needs, and whether there is a charge. Do not advertise a finished automated service that does not exist.
Choose the evidence you need before the test. Can people complete the task? Do they use the result? Do they return or ask to continue at a stated price? Likes, polite compliments, and email signups can be encouraging without demonstrating sustained demand.
Set limits on time and spending. Compare actual delivery effort with the benefit users receive. If the idea only works when you provide extensive unpaid support, include that work in the business calculation rather than hiding it behind “AI-powered.”
Finish with a decision: investigate a missing fact, narrow the customer group, change the solution, run another bounded test, or stop. If the evidence supports an app, the next step is testing a small AI app before building it. The value of this research is a better-informed decision, including the decision not to launch.
AI disclosure: This article was written and revised with AI assistance. Sources checked September 25, 2026. See our disclaimer.
Illustrative photograph by Kelly Sikkema on Unsplash.
