Most asked
Why do AI projects fail even when the technology works?
Short answer
Because most AI failures are not AI problems. They are the software engineering failures the industry has understood for decades: poor requirements, unresolved technical debt, and misaligned expectations between leadership and the teams doing the work. The technology changed. The failure patterns did not.
The AI can be flawless and the result can still be a disaster, because nobody asked the right questions before the build began.
When leaders skip the diagnostic, they end up solving the wrong problem with the right technology. The project technically works and delivers no measurable value. Or worse, it surfaces risks nobody mapped before the tools went live.
The most common specific version of this is a data problem wearing a technology problem's costume. The model performs exactly as advertised, and the information it was pointed at was fragmented, inconsistently formatted, or scattered across systems that define the same thing three different ways. The pilot did not fail. It surfaced a condition that was already there, invisible because people had been compensating for it with effort.
The problems are not new. The speed is. In my experience, what used to take 18 months to go wrong now takes closer to three. And speed without a baseline is how well-intentioned initiatives become the line item nobody wants to explain to the board.
The specific failure has a name worth using with your team: solving the wrong problem with the right technology. The system performs exactly as specified and nothing improves. We have seen it up close. A client was certain they needed generative AI to speed up a complicated workflow, and discovery showed that most of the time was going to locating and matching information they already had. The right answer was a structured database with limited semantic search. Had they gone straight into development, they would have received an elegant application that cost more, carried more risk, and solved the problem less well.
The sequence that prevents it is not complicated. Audit before you build. Align before you automate. We audit, we analyze, we align, and only then do we automate. It sounds slow. It is dramatically faster than doing it twice.