Connecting AI agents to enterprise knowledge

A report based on a survey of 300 data, AI, and other technology executives finds that data and knowledge weaknesses consistently stall AI agent progress. On average, only about 34% of organizations' agentic AI projects make it into production, and high-tech firms struggle with this as well. Legacy data systems, security and privacy concerns, and a lack of knowledge and context are the key points of failure.

The report, produced by MIT Technology Review's Insights custom content arm in partnership with Neo4j, defines agentic knowledge capabilities as the ability to give AI agents a full contextual understanding of the data they ingest, spanning semantic knowledge, episodic memory, and procedural knowledge.

A small group of production leaders—organizations where an average of 61% of agentic AI projects advance beyond pilot—have stronger knowledge capabilities than the rest, especially in semantics. This advantage tracks closely with their higher production rate.

Data fragmentation, described as the inadequate sharing of data across systems, was the most commonly cited top challenge to expanding agents' access to knowledge, at 55%. Production leaders were more likely to see security and privacy concerns as a major concern, cited by 72% of this group.

Among steps that can yield higher quality agent decisions, executives expect the biggest impact to come from strengthening the structural foundation between the organization's data and its AI agents. Experts interviewed for the report see a knowledge layer as a prime way to achieve this.

To expand agent access to knowledge, organizations will prioritize investments in retrieval technologies such as ingestion pipelines, AI-ready APIs, and retrieval-augmented generation (RAG); in AI evaluation agents; and in knowledge graphs.