Connecting AI agents to enterprise knowledge

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.

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.

The purpose of this report, which is based on a survey of 300 data, AI, and other technology executives, …