Connecting AI agents to enterprise knowledge

将 AI 智能体与企业知识相连接

For all the data that AI systems continually amass and analyze, enterprise AI agents often suffer from a curious shortcoming: a lack of knowledge. More than data, knowledge is the understanding of what the data means in the context of individual organizations. AI agents need this understanding to reason about situations, make decisions, and ultimately take actions. Without sufficient knowledge, agents are prone to making flawed and unreliable decisions.

尽管 AI 系统不断积累和分析海量数据，但企业级 AI 智能体往往存在一个令人好奇的短板：缺乏知识。知识不仅仅是数据，更是对数据在特定组织背景下所代表含义的理解。AI 智能体需要这种理解来分析情境、做出决策并最终采取行动。如果没有足够的知识，智能体很容易做出错误且不可靠的决策。

A lack of knowledge, our research finds, is a major reason agentic AI use cases never make it to production. Competitive pressure is making it urgent to address this. Organizations need to deploy and scale more of their agentic projects to capture the efficiency gains AI promises. Falling short risks wasting the investment already sunk into these projects, and it cedes ground to rivals already putting their agents to work more effectively.

我们的研究发现，缺乏知识是智能体 AI 用例无法投入生产的主要原因。竞争压力使得解决这一问题变得刻不容缓。企业需要部署并扩展更多的智能体项目，以获取 AI 所承诺的效率提升。如果做不到这一点，不仅可能浪费已投入的资金，还会将市场份额拱手让给那些已经能更有效地利用智能体的竞争对手。

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.

本报告基于对 300 位数据、AI 及其他技术高管的调查，其目的有三：首先，旨在评估组织在语义知识、情景记忆和程序性知识方面的智能体知识能力（即赋予 AI 智能体对其摄入数据进行全面情境化理解的能力）。其次，探讨组织在改善知识获取途径以及最终将更多智能体用例投入生产时所面临的挑战。第三，探索组织为克服这些挑战所采取的措施。

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.

数据和知识的薄弱环节持续阻碍 AI 智能体的进展。 平均而言，只有约三分之一（34%）的企业智能体 AI 项目能够投入生产。即使是高科技公司也面临这一困境。遗留数据系统、安全与隐私顾虑，以及知识与背景信息的匮乏，是导致失败的关键点。

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密切相关。 一小部分生产领先企业（平均有 61% 的智能体项目超越试点阶段）比其他企业拥有更强的知识能力，尤其是在语义方面。这种优势与它们更高的生产率紧密相关。

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).

数据碎片化极大地增加了知识获取的难度。 数据碎片化（跨系统数据共享不足）被普遍认为是扩大智能体知识获取范围的最大挑战（55% 的受访者提及）。相比之下，生产领先企业更倾向于将安全和隐私问题视为主要顾虑（该群体中 72% 的人提及）。

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.

大多数企业旨在加强数据与智能体之间的联系。 在能够产生更高质量智能体决策的举措中，高管们认为，加强组织数据与 AI 智能体之间的结构性基础将产生最大的影响。我们采访的专家认为，构建“知识层”是实现这一目标的最佳途径。

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.

提升知识能力的投资重点涵盖从数据管道到知识图谱的各个方面。 为了扩大智能体对知识的获取，企业将优先投资于检索技术（如摄取管道、AI 就绪 API 和检索增强生成 (RAG)）、AI 评估智能体以及知识图谱。