以自主AI重新定义企业智能
内容总结：
企业人工智能已进入全面落地阶段，但多数企业面临结构性扩张难题。全球AI投资预计2026年将达2.5万亿美元，同比增长44%，模型能力提升速度远超企业消化能力。然而，大量投资导致智能分散于各业务孤岛，销售、客服、营销和财务等系统各自为政，整体学习与协同能力不足。
报告指出，从“AI工具”向“AI运营模式”的转变——即“智能体转型”——需要从根本上打通人员、流程与数据，并建立可靠的治理与控制机制。这要求企业同步重构架构与运营模式：一是重建数据基础设施，重在可访问性而非数据量；二是以可组合架构替代固定技术栈，适应模型与工具的快速演进；三是解决AI主权问题，明确智能运行位置、控制权归属及跨组织与司法辖区的运作方式。
核心发现包括：企业AI的扩展问题是结构性的，以流程为先的企业正拉开差距；持续获得回报的企业将流程重塑置于模型选择之前，围绕技术演进方向进行建设，而非部署后再调整岗位与工作流。数据就绪而非数据丰富，才是AI产生复利效应的关键。多数企业事后才发现，拥有数据与拥有AI就绪数据截然不同。主权化、可组合的基础架构能够在数据所在位置进行查询与准备，无需迁移或集中化，从而将原始数据资产转化为AI智能体可操作的智能。随着数据驻留法规、多云环境和结构复杂性使集中化愈发不切实际，对模型运行位置和数据存储位置的主权控制，是保持适应能力的关键。
中文翻译：
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以自主AI重新定义企业智能
可组合的基础设施、主权数据与跨职能协同，能够让智能流动起来，让AI越用越聪明。
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企业AI已不再是未来的愿景，而是正在全面落地运行。模型能力的进步速度远超大多数组织的消化能力，而性能成本却在持续下降。全球范围内，AI投资预计将在2026年达到2.5万亿美元，较上一年增长44%。
对许多企业而言，这笔投资带来的却是碎片化。智能可能在各处孤岛中各自积累，以至于销售代理不知道有未关闭的支持工单，或者营销系统在进行内容个性化时，看不到财务部门已经掌握的客户信息。每个职能部门单独来看可能表现不错，但企业整体却学不到多少东西，可据以行动的信息也更少。
AI从工具向运营模式的转变——我们在本报告中称之为“智能体转型”——所需要的，不只是更好的模型或更快的基础设施，而是更根本的东西。它要求实时连接人员、流程和数据，并具备治理与控制能力，以可靠地依据这些智能采取行动。
这意味着要同时重新思考架构和运营模式。第一，重建数据基础设施，重点在于可访问性而非数据量。第二，用可组合架构取代固定技术栈，使其能够随着模型和工具的变化而演进。最后，解决AI主权问题，包括智能在哪里运行、由谁控制，以及它如何跨越组织和司法管辖边界运作。
主要发现如下：
企业AI的规模化问题是结构性的。以流程为先的企业正在拉开差距。全球AI支出正在急剧上升，模型能力的进步速度远超大多数组织的整合能力。然而，大多数企业仍未通过AI实现收入增长，也没有从根本上重新思考自身的运营方式。能够持续获得回报的公司都有一个共同的自律：它们把流程重新设计视为模型选型之前的工作，围绕技术将如何演进进行建设，而不是在部署之后再改造角色和工作流程。对它们来说，智能体转型始于运营模式。
让AI产生复利效应的关键不是数据充裕，而是数据就绪。大多数企业发现得太晚：拥有数据和拥有AI就绪的数据是两回事。一个主权化、可组合的基础——能够在数据所在之处进行查询和准备，无需迁移或集中化——可以将原始数据资产转化为AI智能体可据以行动的智能。随着数据驻留法律、多云环境和结构性复杂性使集中化越来越不切实际，对模型运行位置和数据存放位置的主权控制，才是保持这种适应能力的关键。
本内容由MIT Technology Review的定制内容部门Insights制作，并非由其编辑团队撰写。内容由人工研究和撰写，可能使用的任何AI工具仅限于在人工监督下参与制作流程。
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Redefining enterprise intelligence with autonomous AI
Composable infrastructure, sovereign data, and cross-functional coordination can enable intelligence to flow and AI to grow smarter.
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Enterprise AI is no longer a future ambition. It is in full operational flight. Model capabilities are advancing faster than most organizations can absorb, while the cost of performance continues to fall. Globally, AI investment is set to reach $2.5 trillion in 2026, up 44% from the previous year.
For many enterprises, this investment has produced fragmentation. Intelligence can accumulate in silos so that sales agents are unaware of open support tickets, for instance, or marketing systems are personalizing content without visibility into what finance already knows about a customer. Each function may perform well in isolation, but the enterprise as a whole learns little and has less information to act upon.
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.
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.
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