Bridging the operational AI gap
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.
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 interviews, all conducted in December 2025, show that a strong integration foundation aligns with more advanced AI implementations, conducive to enterprise-wide initiatives. As AI technologies and applications evolve and proliferate, an integration platform can help organizations avoid duplication and silos, and have clear oversight as they navigate the growing autonomy of workflows.
Key findings from the report include the following:
Some organizations are making progress with AI. In recent years, study after study has exposed a lack of tangible AI success. Yet, our research finds three in four (76%) surveyed companies have at least one department with an AI workflow fully in production.
AI succeeds most frequently with well-defined, established processes. Nearly half (43%) of organizations are finding success with AI implementations applied to well-defined and automated processes. A quarter are succeeding with new processes. And one-third (32%) are applying AI to various processes.
Two-thirds of organizations lack dedicated AI teams. Only one in three (34%) organizations have a team specifically for 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, the responsibility is spread out.
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 integration platforms also have more multi-departmental implementation of AI, more autonomy in AI workflows, and more confidence in assigning autonomy in the future.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.