Enterprise AI has moved from concept to daily operation. Global spending on the technology is projected to reach $2.5 trillion in 2026, a rise of 44% from the prior year, while model performance improves faster than most firms can keep pace.
Rapid adoption has produced fragmented intelligence. Sales teams may miss open support tickets, and marketing engines personalize content without insight into finance’s customer data, creating isolated pockets of knowledge that limit the organization’s overall ability to act on information.
The report labels the transition from a tool-based approach to an operating model as the agentic shift. It calls for real-time links among people, processes and data, plus governance structures that ensure the resulting intelligence can be trusted and acted upon consistently.
Achieving that vision requires rethinking architecture. Companies must rebuild data layers for easy access rather than sheer volume, replace rigid tech stacks with composable systems that evolve with new models, and address AI sovereignty—deciding where models run, who controls them, and how cross-border rules apply.
Organizations that prioritize process redesign before selecting models tend to capture value faster than those that retrofit after deployment. This discipline aligns technology with existing workflows, reducing friction when new capabilities emerge. Consequently, the shift toward autonomous agents reflects a strategic emphasis on adaptable operations.
Findings show the scaling challenge is structural. Firms that place processes first are pulling ahead, yet most enterprises still do not see revenue growth from AI nor fundamentally alter their operating methods. Investment continues to surge, outpacing integration capacity.
Successful firms treat process engineering as a prerequisite to model choice, building infrastructure that can accommodate future tools instead of reshaping roles after a model is live. For them, the agentic shift begins with how work is organized, not with the technology itself.
Readiness of data, not its sheer amount, determines AI’s compound effect. A sovereign, composable foundation that queries data in place, without migration, turns raw estates into actionable intelligence. Multicloud setups and residency regulations make such decentralized control increasingly vital.
The report, produced by Insights, the custom content arm of MIT Technology Review, is available for download. Its authors note that all research and writing were performed by humans, with any AI tools limited to production under human oversight.
