Bridging the operational AI gap | MIT Technology Review

SponsoredIn partnership withCeligo The transformational potential of AI is already well established. Enterprise use cases are building momentum and organizations are transitioning from pilot projects to AI in production. Companies are no longer just talking about AI; they are redirecting budgets and resources to make it happen. Many are already experimenting with agentic AI, which promises new levels of automation. Yet, the road to full operational success is still uncertain for many. And, while AI experimentation is everywhere, enterprise-wide adoption remains elusive. Without integrated data and systems, stable automated workflows, and governance models, AI initiatives can get stuck in pilots and struggle to move into production. The rise of agentic AI and increasing model autonomy make a holistic approach to integrating data, applications, and systems more important than ever. Without it, enterprise AI initiatives may fail. Gartner predicts over 40% of agentic AI projects will be cancelled by 2027 due to cost, inaccuracy, and governance challenges. The real issue is not the AI itself, but the missing operational foundation. DOWNLOAD THE REPORT To understand how organizations are structuring their AI operations and how they are deploying successful AI projects, MIT Technology Review Insights surveyed 500 senior IT leaders at mid- to large-size companies in the US, all of which are pursuing AI in some way. The results of the survey, along with a series of expert interview...