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An AI coding assistant can make an idea feel real surprisingly quickly. Screens and features appear, and it becomes tempting to keep building. Validation asks a different question: does a particular group of people have a problem important enough to change their behavior for your solution?
Imagine an idea for a tool that checks project handover folders for missing documents. The first task is to find people who actually prepare those folders. Your assumptions about their work may be less accurate than the interface you can generate in an afternoon.
Learn about the last time the problem happened
Ask a prospective user to describe a recent handover. What information was missing? Who discovered it? What did the delay cause? Ask to see a redacted example or a blank template if they can share one safely.
Avoid leading with your feature list. A person may agree that automation sounds useful while having no reason to adopt it. Look for an existing workaround: a spreadsheet, a checklist, a paid contractor, or repeated manual checks. An established workaround gives you something specific to improve.
Speak with several people in the same type of role before expanding to unrelated audiences. A design agency, a construction firm, and an accounting practice may all use folders, but their requirements can produce three very different products.
Test the smallest useful outcome
You do not need a complete application to evaluate the workflow. Prepare a mock result using invented sample files, or manually run the proposed check with permission. Show the missing-document report and ask the user what they would do next.
Separate usability questions from demand questions. Someone understanding a demonstration does not establish willingness to pay. Useful next steps include agreeing to a supervised pilot, supplying a representative template, or introducing you to the person responsible for purchasing. Treat expressions of interest as signals with different strengths, not interchangeable proof.
Define a decision before the experiment
Write down what you need to learn and what would change your plan. For example: can users identify a recurring handover failure, can the report correctly detect it, and will a suitable pilot customer commit time to trying the workflow?
Record contrary evidence. If every interview reveals a different document structure and no shared purchasing process, narrowing the audience may be wiser than adding a universal configuration engine. If the problem happens once a year, a subscription might be a poor match for that customer’s buying habits.
Keep the experiment honest
- Label mockups and prototypes clearly.
- Use synthetic or properly authorized sample data.
- Explain what a pilot does and does not include.
- Record observed actions separately from positive comments.
- Avoid presenting a small interview sample as market-wide demand.
You can use AI to organize interview notes and explore possible workflows, provided your handling of the information is appropriate. Read the original notes and retain uncertainty; a polished summary should not turn an ambiguous conversation into a commitment.
Turn what you learned into a build brief
Write a one-page summary naming the user, recurring task, current workaround, proposed outcome, and evidence still missing. That brief gives development a useful boundary. Continue with writing a specification for AI-assisted development when you can describe a real job the first version should complete.