The transformational potential of AI is already well established. Enterprise use cases are constructing momentum and organizations are transitioning from pilot projects to AI in production. Firms are not any longer just talking about AI; they’re redirecting budgets and resources to make it occur. Many are already experimenting with agentic AI, which guarantees latest levels of automation. Yet, the road to full operational success remains to be uncertain for a lot of. And, while AI experimentation is in all places, enterprise-wide adoption stays elusive.
Without integrated data and systems, stable automated workflows, and governance models, AI initiatives can get stuck in pilots and struggle to maneuver into production. The rise of agentic AI and increasing model autonomy make a holistic approach to integrating data, applications, and systems more vital than ever. Without it, enterprise AI initiatives may fail. Gartner predicts over 40% of agentic AI projects shall be cancelled by 2027 as a result of cost, inaccuracy, and governance challenges. The actual issue isn’t the AI itself, however the missing operational foundation.
To grasp how organizations are structuring their AI operations and the way they’re deploying successful AI projects, MIT Technology Review Insights surveyed 500 senior IT leaders at mid- to large-size corporations within the US, all of that are pursuing AI not directly.
The outcomes of the survey, together with a series of expert interviews, all conducted in December 2025, show that a powerful integration foundation aligns with more advanced AI implementations, conducive to enterprise-wide initiatives. As AI technologies and applications evolve and proliferate, an integration platform may also 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 next:
Some organizations are making progress with AI. Lately, study after study has exposed an absence of tangible AI success. Yet, our research finds three in 4 (76%) surveyed corporations have at the least one department with an AI workflow fully in production.
AI succeeds most incessantly with well-defined, established processes. Nearly half (43%) of organizations are finding success with AI implementations applied to well-defined and automatic processes. 1 / 4 are succeeding with latest processes. And one-third (32%) are applying AI to numerous processes.
Two-thirds of organizations lack dedicated AI teams. Just one in three (34%) organizations have a team specifically for maintaining AI workflows. One in five (21%) say central IT is liable for ongoing AI maintenance, and 25% say the responsibility lies with departmental operations. For 19% of organizations, the responsibility is opened up.
Enterprise-wide integration platforms result in more robust implementation of AI. Firms with enterprise-wide integration platforms are five times more more likely to use more diverse data sources in AI workflows. Six in 10 (59%) employ five or more data sources, in comparison with only 11% of organizations using integration for specific workflows, or 0% of those not using an integration platform. Organizations using integration platforms even have more multi-departmental implementation of AI, more autonomy in AI workflows, and more confidence in assigning autonomy in the longer term.
