Why most AI products fail before the first user interaction

"Most AI products fail before the first user interaction because they don't solve a real user problem. That may sound dramatic, but I keep hearing the same sentence in executive rooms: "We need an AI feature. Our competitor just launched one." And just like that, features are built out of fear of being left behind rather than from a clear understanding of what users actually need."

"Recently, I came across a report titled The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed by RAND. The researchers interviewed 65 experienced data scientists and engineers who have spent years building AI and machine learning systems. Their goal was simple: understand why so many AI initiatives fail and what differentiates the few that succeed."

"One of their core conclusions is striking. The most successful AI projects focus relentlessly on the problem they are meant to solve, not on the technology used to solve it. That sentence could have come straight out of an UX strategy deck. Designers have been trained for decades to understand users and their problems before building solutions. What if AI projects are struggling not because of the models, but because design thinking was missing from the room?"

Most AI products fail before first use because they do not solve a real user problem. Companies often build AI features out of competitive fear rather than from clear user needs. Sixty-five experienced data scientists and engineers were interviewed to identify why many AI initiatives fail and what differentiates successful projects. The most successful AI efforts focus relentlessly on the problem to be solved, not on the technology. Design thinking and user-centered research are critical. Many agentic AI projects face cancellation risks due to escalating costs, unclear business value, and inadequate risk controls.

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