连接 AI Agent 与企业知识

本报告基于对 300 名数据、AI 及其他技术高管的调查，目的有三项。首先，评估各组织的 Agent 知识能力（即让 AI Agent 完整理解其所摄取数据在特定组织语境中含义的能力），涵盖语义知识、情景记忆和程序性知识。

其次，报告探讨组织在改善知识访问、并最终推动更多 Agent 用例进入生产环境时所面临的挑战。第三，报告研究组织为克服这些挑战而采取的措施。

主要发现如下：

数据与知识能力不足持续阻碍 AI Agent 的发展。 平均而言，组织的 Agentic AI 项目中仅约三分之一（34%）能够进入生产环境。即使是高科技企业也面临这一难题。遗留数据系统、安全与隐私担忧，以及知识和上下文的缺失，是主要的失败原因。

强大的知识能力与 Agent 成功密切相关。 一小部分生产化领先者（其 Agentic 项目平均有 61% 能够跨越试点阶段的组织）比其他组织拥有更强的知识能力，尤其是在语义能力方面。这一优势与其更高的生产化率高度一致。

数据碎片化极大增加了知识访问的复杂性。 数据碎片化（即数据无法在不同系统之间充分共享）是最常被列为扩大 Agent 知识访问能力首要挑战的因素，占比为 55%。相比之下，生产化领先者更倾向于将安全和隐私问题视为重大关切，这一群体中有 72% 的受访者如此认为。

多数企业旨在加强数据与 Agent 之间的连接。 在有助于提升 Agent 决策质量的举措中，高管预计，最具影响力的措施将是强化组织数据与其 AI Agent 之间的结构性基础。接受采访的专家认为，知识层是实现这一目标的重要方式。

提升知识能力的投资重点涵盖从管道到知识图谱的多个领域。 为扩大 Agent 对知识的访问，组织将优先投资于检索技术，例如数据摄取管道、面向 AI 的 API 和检索增强生成（RAG）；同时也将投资于 AI 评估 Agent 和知识图谱。

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本内容由《麻省理工科技评论》的定制内容部门 Insights 制作，并非由其编辑团队制作。内容由人类研究和撰写；如使用任何 AI 工具，其用途也仅限于在人类监督下的制作流程。

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.

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.

The key findings include the following:

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.

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.

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).

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.

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.

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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.