以可信数据扩展 AI Agent

随着 AI Agent 更广泛地嵌入企业运营，突破遗留数据系统限制的需求愈发迫切。如果 Gartner 的预测——到 2027 年，AI Agent 将增强或自动化 50%的商业决策——得到验证，企业就必须消除瓶颈，否则 Agent 将缺少以足够速度作出正确决策所需的数据。

本报告基于对 300 名数据与技术高管的调研，探讨了遗留系统如何在许多组织中限制 AI Agent 的有效性。报告发现，少数组织——即“数据领导者”——在 Agentic AI 方面取得了更大成功，并且受到遗留系统数据限制的影响较小。这些领导者为如何构建适合 Agent 蓬勃发展、并让可信系统得以扩展的数据环境提供了参考。

报告的主要发现包括：

目前仅有少数公司能让 Agentic AI 充分访问企业数据。 在所有受访组织中，AI 平均只能访问公司 45%的数据。在被归类为“数据落后者”的组织中，这一比例降至 30%或以下。然而，少数组织确保 AI 可以访问其超过 70%的数据。这些“数据领导者”在 Agent 应用上比其他组织取得了更大成功。

对 Agent 决策的信任反映了数据就绪程度。 当前，仅约半数受访组织相信其 AI Agent 作出的决策准确且相关。相比之下，100%的数据领导者信任其 Agent 的决策，这强烈表明，可靠的 AI 需要以可靠的数据基础为支撑。

数据领导者更容易实现 Agent 的规模化与速度。 三分之二的数据落后者表示，遗留数据系统限制了 AI Agent 的规模化部署（66%），并妨碍 Agent 快速作出决策（68%）。数据领导者已在很大程度上克服遗留数据限制，基本清除了这些障碍；仅有 8%的领导者报告存在上述任一限制。

推动数据资产做好 Agent 就绪准备的压力正在加大。 两年内，100%的受访者计划使用 Agentic AI，其中 69%预计将广泛使用。如果不消除数据系统限制，Agentic AI 将无法实现其承诺的预期速度和效率。

数据访问与上下文是首要优先事项。 对所有受访者而言，支持规模化的最重要举措，是提升 AI Agent 对结构化和非结构化数据的访问能力。同样位居优先事项前列的是，结合业务上下文强化数据与 AI 治理。数据领导者也高度重视数据管理自动化。

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.

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.