AI
Experience in execution also matters: the MIT study showed that Gen AI project success rates double when external experts are involved.
To navigate these hurdles, enterprise change initiatives must be guided by a deep understanding of the business context and a clear strategy for how technology can drive meaningful outcomes. This principle holds true for AI today.
“Enterprise transformation is rarely straightforward”
Alan Jacobson, Chief Data & Analytics Officer, Alteryx
Looking beyond headlines to adapt AI’ s rollout The worst response enterprises could have to the MIT finding on project failures is to pull back from Gen AI or scale down investments. The real opportunity lies in the insights the report provides.
A key takeaway is that stalled Gen AI projects often stem less from the technology itself and more from workforce readiness-employees’ understanding of how and where to apply LLMs to improve workflows and outputs. The report also highlights the challenge of inherent variability in model outputs, which can make achieving consistent results difficult.
The first point demonstrates how Gen AI rollout amounts to much more than putting the right technology into the right hands. In my experience, effective initiatives are those that are complemented by education for the workforce on the fundamentals of working with data and the inner workings of LLMs. This doesn’ t mean upskilling every employee to become a data scientist – but it does require
76 April 2026