Redefining enterprise intelligence with autonomous AI

用自主人工智能重新定义企业智能

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.

企业人工智能不再是未来的愿景，它已全面投入实际运行。模型能力的进步速度超过了大多数组织的吸收能力，而性能成本却在持续下降。全球范围内，人工智能投资预计将在 2026 年达到 2.5 万亿美元，比上一年增长 44%。

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.

对于许多企业而言，这种投资导致了碎片化。智能往往积聚在信息孤岛中，例如，销售人员可能不知道未解决的支持工单，或者营销系统在进行内容个性化时，无法获知财务部门已掌握的客户信息。每个职能部门或许能独立运作良好，但整个企业却难以从中学习，也缺乏足够的行动依据。

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.

从“人工智能作为工具”向“人工智能作为运营模式”的转变（我们在本报告中称之为“代理化转型”），所要求的不仅仅是更好的模型或更快的架构，而是更本质的变革。它需要实时连接人员、流程和数据，并辅以能够可靠地利用这些智能的治理与控制机制。这意味着必须同时重新思考架构和运营模式。

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.

首先，重建数据基础设施，以可访问性而非数据量为核心。其次，用可组合架构取代固定的技术栈，使其能够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，以及它如何跨越组织和司法管辖区边界进行运作。

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.

主要发现包括：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在脱颖而出。全球人工智能支出急剧上升，模型能力的进步速度超过了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。

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.

那些获得持续回报的公司拥有共同的准则：它们将流程再造视为模型选择之前的工作，为技术的演进进行构建，而不是在部署后才去修补角色和工作流程。对它们而言，代理化转型始于运营模式。

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.

数据就绪度而非数据丰富度，才是人工智能实现复合增长的关键。大多数企业发现得太晚：拥有数据与拥有“人工智能就绪”的数据是两码事。一个主权化、可组合的基础设施——即在数据驻留地进行查询和准备，无需迁移或集中化——能够将原始数据资产转化为人工智能代理可以采取行动的智能。

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.

随着数据驻留法律、多云环境和结构复杂性使得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，正是保持这种适应性的关键。