从将人工智能视为工具到将其作为运营模式——我们在本报告中称之为“代理式转变”（agentic shift）——这要求比更好的模型或更快的基础设施更根本的变革。它需要实时连接人员、流程和数据，并具备可靠的治理与控制能力，以基于这些智能采取行动。

这意味着必须同时重新思考架构和运营模式。首先，重建以可访问性而非数据量为重点的数据基础设施。其次，用可组合架构取代固定的技术栈，以便随着模型和工具的变化而演进。最后，解决人工智能主权问题，包括智能运行的位置、控制权归属以及如何在组织和司法管辖区边界内运作。

主要发现如下：

企业人工智能的扩展问题是结构性的。以流程为先的公司正拉开差距。全球人工智能支出急剧上升，模型能力的提升速度快于大多数组织的整合能力。然而，大多数企业仍未通过人工智能实现收入增长，也未从根本上重新思考其运营方式。那些能够产生持续回报的公司拥有一种共同的纪律。它们将流程重构视为模型选择之前的工作，着眼于技术的演进进行构建，而不是在部署后对角色和工作流进行修补。对它们而言，代理式转变始于运营模式。

数据就绪而非数据丰富才是使人工智能具备复利效应的关键。大多数企业发现得太晚，拥有数据和拥有适用于人工智能的数据是两回事。一个主权可控、可组合的基础设施——能够在数据原位查询和准备数据，无需迁移或集中化——可以将原始数据资产转化为人工智能代理可以执行操作的智能。随着数据驻留法规、多云环境和结构性复杂性使集中化变得越来越不切实际，对模型运行位置和数据存储位置的主权控制是保持这种适应性的关键。

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本内容由《麻省理工科技评论》旗下定制内容部门 Insights 制作，而非其编辑团队。本文由人类研究并撰写，其中可能使用的任何人工智能工具仅限于在人类监督下的生产流程。

The shift from AI as a tool to AI as an operating model—what we call the “agentic shift” in this report—demands something more fundamental than better models or faster infrastructure. It requires connecting people, processes, and data in real time, along with the governance and control to act on that intelligence reliably.

This means rethinking both architecture and operating models simultaneously. First, rebuilding data infrastructure for accessibility rather than volume. Second, replacing fixed tech stacks with composable architectures that can evolve as models and tools change. And, lastly, resolving questions of AI sovereignty, including where intelligence runs, who controls it, and how it operates across organizational and jurisdictional boundaries.

Key findings include the following:

Enterprise AI’s scaling problem is structural. Process-first companies are pulling ahead. Global AI spending is rising sharply and model capabilities are advancing faster than most organizations can integrate them. Yet the majority of enterprises are still not growing revenue through AI or fundamentally rethinking how they operate. The companies generating sustained returns share a common discipline. They treat process redesign as the work that precedes model selection, building for how the technology will evolve rather than retrofitting roles and workflows after deployment. For them, the agentic shift begins with the operating model.

Data readiness, not data abundance, is what makes AI compoundable. Most enterprises discover too late that having data and having AI-ready data are very different things. A sovereign, composable foundation—one that queries and prepares data where it resides, without migration or centralization—can convert raw data estates into intelligence that AI agents can act upon. As data residency laws, multicloud environments, and structural complexity make centralization increasingly impractical, sovereign control over where models run and data lives is what keeps that adaptability intact.

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This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.