Connecting AI agents to enterprise knowledge (www.technologyreview.com)

A recent report highlights a critical challenge facing enterprise AI agents: the lack of contextual knowledge that limits their decision-making capabilities. Conducted in partnership with Neo4j, the research surveyed 300 technology executives and found that only 34% of AI projects successfully progress to production, primarily due to insufficient knowledge and data fragmentation. This shortcoming is particularly concerning as competitive pressure mounts, urging organizations to maximize their AI investments and outpace rivals who effectively deploy their agents. The study underscores the importance of strong knowledge capabilities, which correlate significantly with successful AI project outcomes. Organizations that excel in providing semantic, episodic, and procedural knowledge are more likely to see their agentic projects thrive. Key strategies for overcoming knowledge barriers include enhancing data integration, investing in technologies such as knowledge graphs and retrieval-augmented generation (RAG), and strengthening the structural links between their data and AI agents. By addressing these issues, companies can better leverage AI’s potential for increased efficiency and innovative decision-making.