事實脈絡
解讀與影響
可信数据成为 AI 智能体规模化落地的关键瓶颈
报道开篇即点明，商业与技术领导者无需被说服相信智能体时代的到来——真正的问题在于，当智能体开始自主执行任务、做出决策时，它们所依赖的数据质量与可信度能否匹配这种自主性。原文摘要在此处中断，未提供后续关于具体数据治理方案或企业案例的细节，但标题本身已将核心命题交代清楚：规模化扩展 AI 智能体的前提，是建立可信的数据基础。
这一议题的背景值得放在更长的技术演进脉络中理解。过去两年，生成式 AI 的讨论重心已从模型能力本身，逐渐转向智能体如何在真实业务环境中可靠运行。智能体不同于传统软件或聊天机器人，它们需要持续访问企业内部数据、调用工具、执行多步骤任务。一旦底层数据存在偏差、过期或来源不明，错误会在自主执行链条中被放大。因此，数据可信度不再只是数据团队的内部议题，而直接关系到智能体部署的成败与安全边界。
值得注意的是，同一时期发布的另一篇 MIT Technology Review 报道从完全不同的角度切入 AI 议题——采访儿童对人工智能的真实看法，而 Ars Technica 则报道了 Chrome 采用设备绑定会话凭证来防范账户接管攻击。这些看似分散的报道共同勾勒出一个趋势：AI 的讨论正在从“能做什么”转向“如何安全、可靠地做”，而数据可信度正是其中绕不开的一环。
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來源原文
Business and technology leaders need no convincing that the time of agentic AI is here. Organizations are rapidly adopting agents, and few executives doubt the technology’s potential to transform work. But many organizations find that realizing the desired return on investment (ROI) from AI hinges on having the right foundation, with inadequate infrastructure and data being major blockers.
Agentic AI places considerable new demands on enterprise data systems. The shift from answering questions to taking actions means AI agents need data from across the enterprise, in all its structured and unstructured forms, and with the right business context. To make decisions and act in real time, agents also need frictionless access to the organization’s operational systems—for example, those storing its supply chain, point-of-sale, or human resources data. Legacy data systems, even those updated just a few years ago, struggle to meet these demands.
[图片](https://wp.technologyreview.com/wp-content/uploads/2026/08/Google-Report-2026-cover.png)
As AI agents become embedded more widely in enterprise operations, the need to overcome the restrictions of legacy data systems grows more urgent. If Gartner’s prediction that AI agents will augment or automate 50% of business decisions by 2027 proves correct, organizations must eliminate bottlenecks or risk depriving agents of the data they need to make the right decisions at speed.
This report, based on a survey of 300 data and technology executives, explores how legacy systems are limiting the effectiveness of AI agents in many organizations. It finds that a handful of organizations—the data leaders—are having greater success with agentic AI and experiencing fewer data limitations as a result of legacy systems. These leaders offer a guide to creating the right data environment for agents to flourish and trusted systems to scale.
[图片](https://wp.technologyreview.com/wp-content/uploads/2026/08/Google-Social-Card-2-1.png)
Key findings from the report include:
Few companies currently provide agentic AI with ample access to enterprise data. Across all the surveyed organizations, AI only has access to an average of 45% of company data. That number falls to 30% or less in organizations categorized as “data laggards”. A select group, however, ensures access to over 70% of their data. These “data leaders” are having greater success with their agents than the rest.
Trust in agent decisions is a reflection of data readiness. Today, only around half of surveyed organizations trust that the decisions their AI agents make are accurate and relevant. By contrast, 100% of the data leaders trust their agents’ decisions, a strong indicator that reliable AI requires a reliable data foundation.
Data leaders find it easier to achieve agent scale and speed. Two-thirds of data laggards say legacy data systems limit AI agent scaling (66%) and prevent agents from making decisions at speed (68%). Having largely overcome legacy data constraints, the leaders have mostly cleared these roadblocks, with just 8% reporting either constraint.
The pressure is on to make data estates agent-ready. Within two years, 100% of respondents plan to be using agentic AI, with 69% expecting to use it widely. Without removing data system constraints, agentic AI will fail to deliver the desired speed and efficiencies it promises.
Data access and context are top priorities. The most important initiative to enable scaling among all respondents is improving access to structured and unstructured data for AI agents. Also high on the list is enhancing data and AI governance with business context. Data leaders are also focusing heavily on the automation of data management.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.