The Silent Failure of AI Projects: Why Promising Pilots Rarely Make It to Real-World Use

A 2026 Harvard Business Review analysis finds that despite internal disagreements over AI’s cost and value, companies are pushing forward with implementation, suggesting that the decision is more about how quickly they can adopt AI than whether they should.

And yet, behind the surge in pilots and proofs of concept, most AI projects never make it to meaningful, scaled use.

A report from Solvd also reveals that up to 80% of enterprise technology leaders blame a lack of oversight and visibility for the failure of their AI initiatives. Tech executives report that corporate boards are actively questioning the scale of AI spending, and several organizations admit they will likely terminate underperforming AI pilots this year. 

To mitigate these risks, many enterprises run multiple concurrent AI experiments with the expectation that only a few will succeed. And yet, behind the surge in pilots and proofs of concept, most AI projects never make it to meaningful, scaled use.

In controlled environments, AI performs remarkably well. Demos are polished. Outputs are impressive. Stakeholders are convinced. The technology appears to work.

But once these pilots are pushed into production, cracks begin to show.

“The mistake is that organizations rush to adopt the technology as a ‘shiny object’ rather than treating it as a specific tool to solve a problem,” says Mariano Jurich, Senior AI Product Leader at Making Sense, a digital transformation company for mid-market businesses and private equity portfolios.