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, 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.

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.

Source link