The transformative potential of AI is already well established. Enterprise use cases are accelerating and organizations are moving from pilot projects to AI in production. Companies aren’t just talking about AI anymore. They are redirecting budgets and resources to do this. Many are already experimenting with agent AI, which promises new levels of automation. Yet, the path to full operational success is still uncertain for many. And, while AI experimentation is ubiquitous, enterprise-wide adoption is still 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 increased model autonomy make it more important than ever to integrate data, applications and systems. Without it, enterprise AI initiatives can fail. Gartner predicts that more than 40% of agentive AI projects will be canceled by 2027 due to cost, error and governance challenges. The real problem is not the AI ​​itself, but the missing operational foundation.
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-sized companies in the U.S., all of whom are pursuing AI in one way or another.
The survey results, along with a series of expert interviews conducted in December 2025, show that a strong integration foundation is compatible with more advanced AI implementations, suitable for enterprise-wide initiatives. As AI technologies and applications evolve and proliferate, an integration platform can help organizations avoid duplication and silos, and provide clear oversight while navigating increased workflow autonomy.
Key findings of the report include the following:
Some organizations are thriving with AI. In recent years, study after study has exposed the lack of concrete AI success. Yet, our research shows that three out of four (76%) surveyed companies have at least one department where AI workflows are fully in production.
AI often succeeds with well-defined, established processes. Nearly half (43%) of organizations are finding success with the implementation of AI applied to well-defined and automated processes. A quarter are succeeding with the new process. And one-third (32%) are applying AI in various processes.
Two-thirds of organizations lack dedicated AI teams. Only one in three (34%) organizations have a team dedicated to maintaining AI workflows. One in five (21%) say central IT is responsible for ongoing AI maintenance, and 25% say the responsibility falls to departmental operations. Responsibility for 19% of organizations 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%) employ five or more data sources, compared to only 11% of organizations using integration for specific workflows, or 0% of those not using an integration platform. Organizations using an integration platform have more multidisciplinary implementation of AI, more autonomy in AI workflows, and more confidence in delegating autonomy in the future.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by the MIT Technology Review editorial staff. It is researched, designed, and written by human authors, editors, analysts, and illustrators. This includes survey writing and data collection for the survey. The AI ​​tools that might have been used were limited to secondary production processes that underwent thorough human review.