Connecting AI agents to enterprise knowledge

Enterprise AI agents often struggle because they lack knowledge, not just data. While AI systems collect and analyze vast amounts of information, knowledge refers to the ability to understand what that data means within the specific context of an organization. For example, an AI agent in a retail company needs to know not just sales numbers but also how those numbers relate to customer behavior, inventory levels, and seasonal trends. Without this contextual understanding, AI agents make flawed decisions. The article highlights that 34% of organizations’ AI agent projects fail to reach production, often due to this knowledge gap. High-tech firms also face this challenge, with issues like outdated data systems and privacy concerns compounding the problem.

The article reveals that organizations with strong knowledge capabilities are far more successful in deploying AI agents. A survey of 300 executives found that production leaders—organizations where 61% of AI agent projects advance beyond pilot stages—have significantly better knowledge systems than others. These leaders excel particularly in semantic knowledge, which involves understanding the meaning of data in context. For instance, they can distinguish between a customer complaint about a product defect and one about delivery delays. The report emphasizes that improving knowledge access is essential for scaling AI agents and capturing efficiency gains promised by the technology.

A major obstacle to AI agent success is data fragmentation, where data is scattered across incompatible systems and cannot be easily shared or accessed. The survey found that 55% of organizations cite this as their top challenge in expanding AI agents’ knowledge access. Fragmented data makes it difficult for AI to connect the dots between different pieces of information, such as linking a customer’s purchase history with their support tickets. Production leaders, however, are more likely to prioritize security and privacy concerns (72%) over fragmentation, suggesting they have already addressed data-sharing issues. Addressing fragmentation often requires investing in unified data platforms or knowledge graphs, which organize data into meaningful relationships.

To bridge the gap between data and AI agents, experts recommend creating a knowledge layer—a structural foundation that connects an organization’s data to its AI systems. This layer acts as a translator, ensuring that raw data is transformed into actionable knowledge. For example, it could link a customer’s transaction history with their preferences and past interactions, allowing an AI agent to provide personalized recommendations. The article notes that executives expect the biggest impact on AI decision quality to come from strengthening this structural foundation. Investments in technologies like retrieval-augmented generation (RAG), which combines AI models with real-time data retrieval, are seen as key to building this layer.