The transformative potential of AI is already well established. Enterprise use cases are gaining momentum and organizations are transitioning to AI from pilot projects into production. Companies are no longer just talking about AI; To do this they are redirecting budgets and resources. Many are already experimenting with agentic AI, which promises new levels of automation. Yet, for many, the path to full operational success is still uncertain. 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 go 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 estimates that more than 40% of agentic AI projects will be canceled by 2027 due to cost, inaccuracy, and governance challenges. The real issue is not AI, but the missing operational base.
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 from medium to large-sized companies in the US, all of whom are pursuing AI in some way.
The results of the survey, along with a series of expert interviews conducted in December 2025, show that a stronger integration foundation aligns with more advanced AI implementation, which is conducive to enterprise-wide initiatives. As AI technologies and applications evolve and spread, an integration platform can help organizations avoid duplication and silos, and have clear oversight in navigating the increasing autonomy of workflows.
Key findings of the report include the following:
Some organizations are making progress with AI. In recent years, study after study has exposed the lack of concrete AI breakthroughs. Yet, our research shows that three out of four (76%) companies surveyed have at least one department with AI workflows fully in production.
AI often succeeds with well-defined, established processes. Nearly half (43%) of organizations are finding success with AI implementation applied to well-defined and automated processes. A quarter are having success with new procedures. And a third (32%) are implementing AI in various processes.
Two-thirds of organizations lack dedicated AI teams. Only one in three (34%) organizations have a team specifically dedicated to maintaining AI workflows. One in five (21%) say central IT is responsible for ongoing AI maintenance, and 25% say the responsibility lies with departmental operations. For 19% of organizations, responsibility is diffuse.
Enterprise-wide integration platforms lead to more robust implementation of AI. Companies with enterprise-wide integration platforms are five times more likely to use more diverse data sources in AI workflows. Six in 10 (59%) use five or more data sources, while only 11% of organizations use integration for specific workflows, or 0% of organizations do not use an integration platform. Organizations that use integration platforms have more multi-departmental implementation of AI, more autonomy in AI workflows, and more confidence in providing autonomy in the future.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes writing surveys and collecting data for the surveys. The AI tools that may have been used were limited to secondary production processes that underwent thorough human review.
