People are unreliable at predicting their future behavior. Instead of asking if they *would* use a new feature, ask for a specific instance in the last month where it *would have been* useful. If they can't recall one, it's a major red flag for adoption.
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When a prospect asks for a free pilot, treat it as a sign that you failed to build enough confidence in the outcome. Instead of agreeing, diagnose their uncertainty by asking what they still need help predicting. This shifts the conversation back to value and avoids deploying your best resources on your least committed customers.
Don't just collect feedback from all users equally. Identify and listen closely to the few "visionary users" who intuitively grasp what's next. Their detailed feedback can serve as a powerful validation and even a blueprint for your long-term product strategy.
To discover high-value AI use cases, reframe the problem. Instead of thinking about features, ask, "If my user had a human assistant for this workflow, what tasks would they delegate?" This simple question uncovers powerful opportunities where agents can perform valuable jobs, shifting focus from technology to user value.
Users aren't product designers; they can only identify problems and create workarounds with the tools they have. Their feature requests represent these workarounds, not the optimal solution. A researcher's job is to uncover the deeper, underlying problem.
True problem agreement isn't a prospect's excitement; it's their explicit acknowledgment of an issue that matters to the organization. Move beyond sentiment by using data, process audits, or reports to quantify the problem's existence and scale, turning a vague feeling into an undeniable business case.
Intentionally create open-ended, flexible products. Observe how power users "abuse" them for unintended purposes. This "latent demand" reveals valuable, pre-validated opportunities for new features or products, as seen with Facebook's Marketplace and Dating features.
When handed a specific solution to build, don't just execute. Reverse-engineer the intended customer behavior and outcome. This creates an opportunity to define better success metrics, pressure-test the underlying problem, and potentially propose more effective solutions in the future.
Figma learned that removing issues preventing users from adopting the product was as important as adding new features. They systematically tackled these blockers—often table stakes features—and saw a direct, measurable improvement in retention and activation after fixing each one.
To truly validate their idea, Moonshot AI's founders deliberately sought negative feedback. This approach of "trying to get the no's" ensures honest market signals, helping them avoid the trap of false positive validation from contacts who are just being polite.
