Connecting AI agents to enterprise knowledge将 AI 智能体连接到企业知识

The purpose of this report, which is based on a survey of 300 data, AI, and other technology executives, is threefold. First, it seeks to gauge organizations’ agentic knowledge capabilities (i.e., their ability to give AI agents a full contextual understanding of the data they ingest) across semantic knowledge, episodic memory, and procedural knowledge. 本报告基于对 300 名数据、AI 及其他技术高管的调查，其目的有三个。第一，报告试图评估组织的智能体知识能力（即其让 AI 智能体完整理解所摄取数据的语境的能力），涵盖语义知识、情景记忆和程序性知识。

Second, the report probes the challenges organizations face in improving access to knowledge and ultimately to getting more agent use cases into production. Third, it explores the measures organizations are taking to overcome these challenges. 第二，报告探讨了组织在改善知识获取以及最终让更多智能体用例投入生产方面所面临的挑战。第三，报告考察了组织为克服这些挑战正在采取的措施。

The key findings include the following: 主要发现包括：

Data and knowledge weaknesses consistently stall AI agent progress. On average, only around a third (34%) of organizations’ agentic AI projects make it into production. Even high-tech firms struggle with this. Legacy data systems, security and privacy concerns, and a lack of knowledge and context are the key points of failure. 数据与知识方面的薄弱环节持续阻碍 AI 智能体取得进展。平均而言，组织只有约三分之一（34%）的智能体 AI 项目能够投入生产。即便是高科技公司也难以做到这一点。遗留数据系统、安全与隐私顾虑，以及知识和上下文的缺乏，是关键失败点。

Strong knowledge capabilities correlate with agent success. A small group of production leaders (organizations where on average 61% of agentic projects advance beyond pilot) have stronger knowledge capabilities than the rest, especially when it comes to semantics. This advantage tracks closely with their higher production rate. 强大的知识能力与智能体的成功相关。一小群投产领先者（其平均 61% 的智能体项目能推进到试点之后）拥有比其他组织更强的知识能力，尤其是在语义方面。这一优势与其更高的投产率密切相关。

Fragmented data hugely complicates knowledge access. Data fragmentation (the inadequate sharing of data across systems) was most commonly cited as a top challenge to expanding agents’ access to knowledge (cited by 55%). Production leaders, by contrast, are more likely to see security and privacy concerns as a major concern (cited by 72% of this group).碎片化数据极大增加了知识获取的难度。数据碎片化（系统之间数据共享不足）最常被列为扩大智能体对知识获取的首要挑战（55% 的受访者提及）。相比之下，投产领先者更可能将安全与隐私顾虑视为主要问题（该群体中有 72% 提及）。

Most firms aim to strengthen the link between data and agents. Among steps that can yield higher quality agent decisions, executives expect the biggest impact to come from strengthening the structural foundation between the organization’s data and its AI agents. The experts we interviewed see a knowledge layer as a prime way to achieve this.大多数公司都希望加强数据与智能体之间的联系。在可带来更高质量智能体决策的举措中，高管们预计最大影响将来自加强组织数据与其 AI 智能体之间的结构基础。我们采访的专家认为，知识层是实现这一目标的主要方式。

Investment priorities to boost knowledge range from pipelines to knowledge graphs. To expand agent access to knowledge, organizations will prioritize investments in retrieval technologies, like ingestion pipelines, AI-ready APIs, and retrieval-augmented generation (RAG); in AI evaluation agents; and in knowledge graphs.为增强知识而优先投资的方向涵盖从管道到知识图谱。为扩大智能体对知识的获取，组织将优先投资检索技术，如数据摄取管道、AI 就绪 API 和检索增强生成（RAG）；投资 AI 评估智能体；以及投资知识图谱。

Download the report下载报告

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. 本内容由 MIT Technology Review 的定制内容部门 Insights 制作，而非其编辑团队。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的制作流程。