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Your Customers Do Not Want Another AI Feature
How do I know whether an AI feature solves a real customer problem? A concrete business example, a practical experiment, and the tradeoffs worth measuring.
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How do I know whether an AI feature solves a real customer problem? A concrete business example, a practical experiment, and the tradeoffs worth measuring.
How do I know whether an AI feature solves a real customer problem?
Start with the job customers are already trying to complete, including the workaround they use today. “An AI assistant for our platform” describes a technology choice, not a customer problem. A more useful statement identifies a delay, error, cost, or uncertainty that customers currently experience and care enough to change.
Ask about recent behavior instead of hypothetical enthusiasm. A customer may like the idea of automatic reports while never opening the reports they already receive. Find out when the problem last occurred, what they did, who was involved, and what consequence followed. Specific examples reveal whether the proposed feature addresses an actual constraint.
Consider whether AI is necessary for the first version. A better default, clearer interface, ordinary search, or a simple rule may solve much of the problem with less uncertainty. The point is not to avoid AI; it is to choose a mechanism that earns its complexity through an observable improvement.
Define success before building. A useful outcome might be completing a comparison in ten minutes instead of an hour, with no increase in important errors. A vague goal such as “more engagement” leaves too much room to celebrate curiosity clicks even if the feature does not improve the customer's work.
What is the cheapest useful demand test?
Offer a small, clearly described pilot to a few relevant users and perform part of the workflow manually behind the scenes when appropriate. Be honest about the service and its limitations. The test should establish whether the outcome is valuable before you spend time automating every step needed to deliver it.
For a proposed document-comparison feature, ask users to bring two real but appropriately shareable documents and identify the differences they need. Deliver a structured comparison, then observe which findings they use and which they independently verify. Their behavior is more informative than a broad statement that the result looks impressive.
Measure the whole experience: preparation, waiting, checking, corrections, and the next decision. If users spend as long validating the answer as they previously spent doing the task, the current design has not solved the problem. You may need better evidence links, narrower scope, or a different output format rather than a more elaborate model.
Use the result to make one decision: continue, narrow, or stop. Record the evidence supporting it and the uncertainty that remains. A cheap demand test is successful when it prevents an expensive wrong build as well as when it identifies a promising feature, so do not require every experiment to justify your original idea.
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