Scaling AI agents with trustworthy data

利用可信数据扩展 AI 智能体

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. 商业和技术领袖们无需多言便已意识到，智能体 AI（Agentic AI）的时代已经到来。各组织正在迅速采用智能体，几乎没有高管怀疑这项技术改变工作方式的潜力。然而，许多组织发现，要实现预期的 AI 投资回报率（ROI），关键在于拥有正确的基础，而基础设施和数据的不足是主要的阻碍。

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. 智能体 AI 对企业数据系统提出了巨大的新要求。从“回答问题”到“采取行动”的转变意味着 AI 智能体需要获取企业各处的数据，包括所有结构化和非结构化的形式，并具备正确的业务背景。为了实时做出决策并采取行动，智能体还需要无缝访问组织的运营系统——例如存储供应链、销售点或人力资源数据的系统。遗留数据系统，即使是几年前才更新过的系统，也难以满足这些需求。

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. 随着 AI 智能体更广泛地嵌入企业运营，克服遗留数据系统限制的需求变得愈发紧迫。如果 Gartner 关于“到 2027 年，AI 智能体将增强或自动化 50% 的商业决策”的预测准确，那么各组织必须消除瓶颈，否则将面临智能体因缺乏必要数据而无法快速做出正确决策的风险。

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. 本报告基于对 300 位数据和技术高管的调查，探讨了遗留系统如何在许多组织中限制 AI 智能体的效能。报告发现，少数组织（即“数据领导者”）在智能体 AI 方面取得了更大的成功，并且因遗留系统导致的数据限制较少。这些领导者为创建适合智能体蓬勃发展的数据环境以及扩展可信系统提供了指南。

Key findings from the report include:

报告的主要发现包括：

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. 目前很少有公司为智能体 AI 提供充足的企业数据访问权限。 在所有受访组织中，AI 平均只能访问 45% 的公司数据。在被归类为“数据落后者”的组织中，这一比例降至 30% 或更低。然而，少数群体确保了超过 70% 的数据访问权限。这些“数据领导者”在智能体应用方面比其他组织取得了更大的成功。
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. 对智能体决策的信任反映了数据就绪程度。 如今，只有约一半的受访组织信任其 AI 智能体所做决策的准确性和相关性。相比之下，100% 的数据领导者信任其智能体的决策，这有力地证明了可靠的 AI 需要可靠的数据基础。
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. 数据领导者更容易实现智能体的规模化和速度。 三分之二的数据落后者表示，遗留数据系统限制了 AI 智能体的扩展（66%），并阻碍了智能体快速做出决策（68%）。由于在很大程度上克服了遗留数据的限制，领导者们基本扫清了这些障碍，只有 8% 的人报告存在上述任一限制。
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. 使数据资产具备智能体就绪能力的压力与日俱增。 在两年内，100% 的受访者计划使用智能体 AI，其中 69% 预计将广泛使用。如果不消除数据系统的限制，智能体 AI 将无法实现其承诺的预期速度和效率。
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. 数据访问和背景信息是重中之重。 所有受访者实现扩展的最重要举措是改善 AI 智能体对结构化和非结构化数据的访问。同样排在首位的是通过业务背景增强数据和 AI 治理。数据领导者也正高度关注数据管理的自动化。