Connecting AI agents to enterprise knowledge

Only about 34% of organizations' agentic AI projects reach production on average, according to a survey of 300 data, AI, and other technology executives conducted by MIT Technology Review Insights in partnership with Neo4j. The report attributes the failures chiefly to legacy data systems, security and privacy concerns, and a lack of knowledge and context, and finds that production leaders — organizations where 61% of agentic projects advance beyond pilot — have stronger semantic knowledge capabilities. Data fragmentation was the most commonly cited challenge to expanding agents' access to knowledge, named by 55% of respondents, while 72% of production leaders flagged security and privacy concerns as a major issue.

Sponsored Connecting AI agents to enterprise knowledge A strong structural foundation that links data and agents is key for context-rich agentic AI that scales. In partnership with Neo4j https://neo4j.com/ 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. The key findings include the following: 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. Even high-tech firms struggle with this. Legacy data systems, security and privacy concerns, and a lack of knowledge and context are the key points of failure. Strong knowledge capabilities correlate with agent success. A small group of production leaders organizations where on average 61% of agentic projects advance beyond pilot have stronger knowledge capabilities than the rest, especially when it comes to semantics. This advantage tracks closely with their higher production rate. Fragmented data hugely complicates knowledge access. Data fragmentation the inadequate sharing of data across systems was most commonly cited as a top challenge to expanding agents’ access to knowledge cited by 55% . Production leaders, by contrast, are more likely to see security and privacy concerns as a major concern cited by 72% of this group . Most firms aim to strengthen the link between data and agents. 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. The experts we interviewed see a knowledge layer as a prime way to achieve this. Investment priorities to boost knowledge range from pipelines to knowledge graphs. To expand agent access to knowledge, organizations will prioritize investments in retrieval technologies, like ingestion pipelines, AI-ready APIs, and retrieval-augmented generation RAG ; in AI evaluation agents; and in knowledge graphs. This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight. Deep Dive Artificial intelligence AI’s recursive self-improvement might not come so quickly after all AI agents are not yet creative enough to carry out genuinely innovative open-ended AI research, it seems. Don’t be fooled by this summer of AI hype Breathless claims about AGI and new capabilities fall apart pretty quickly under scrutiny. These startups are chasing the next big thing in LLMs Meet the new kids nipping at the heels of the AI giants. Bill Gates says we’ve passed AI’s danger thresholds. Now what? In a new interview, the billionaire philanthropist sounds an alarm on the urgency of getting our AI policies in order. Stay connected Get the latest updates from MIT Technology Review Discover special offers, top stories, upcoming events, and more.