Gartner reports that more than half of GenAI projects get abandoned after proof of concept. The five reasons they identify: no clear business value, poor data quality, costs that spiral out of control at scale, responsible AI treated as an afterthought, and weak change management that kills adoption even when the tech works.
I’m sharing this because it’s not a theoretical risk framework — it’s an accurate description of what actually happens when organizations try to move AI capabilities from pilot to production. Every one of these failure points shows up in the real world. The gap between a successful demo and something that delivers measurable value at scale is where most initiatives die, and this piece names the specific reasons why clearly enough to be useful.
This piece is something that caught my attention, so I thought I’d capture it as a tidbit. The Claude synopsis above may have an errant AI hallucination or two. Please support the original author(s) and visit their site for the whole story and accurate information:
https://www.gartner.com/en/articles/genai-project-failure