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. The purpose of this report, based on a survey of 300 data, AI, and other technology executives, is threefold: to gauge organizations’ agentic knowledge capabilities, to probe challenges in improving access to knowledge, and to explore measures to overcome these challenges.
Key findings include: Data and knowledge weaknesses consistently stall AI agent progress. On average, only 34% of organizations’ agentic AI projects make it into production. Strong knowledge capabilities correlate with agent success; production leaders (where 61% of projects advance beyond pilot) have stronger knowledge capabilities. Fragmented data hugely complicates knowledge access, cited by 55% as a top challenge. Most firms aim to strengthen the link between data and agents, with investment priorities including retrieval technologies, AI evaluation agents, and knowledge graphs.
Source: MIT Technology Review AI · Summarized by HeadlinesBriefing