Connecting AI agents to enterprise knowledge

Enterprise AI agents often struggle not because they lack data, but because they lack the organizational knowledge needed to interpret that data correctly. A sponsored report from MIT Technology Review in partnership with Neo4j argues that this gap can limit how reliably agents make decisions and take actions in business settings. Based on a survey of 300 data, AI, and technology executives, the report looks at how organizations are building the contextual layers agents need. It breaks knowledge capabilities into areas such as semantics, memory, and procedures, and says weak data systems, privacy concerns, and fragmented information are major barriers to moving AI projects from pilot to production. The report says only about 34% of agentic AI projects reach production on average, while a smaller group of leaders sees much better results. It also finds that firms plan to invest in tools such as ingestion pipelines, AIready APIs, retrievalaugmented generation, evaluation agents, and knowledge graphs to improve the link between enterprise data and AI agents.