Beyond Retrieval Accuracy: Evaluating Enterprise AI Knowledge Retrieval Systems Across Organisational Knowledge Environments

This paper presents a four-stage empirical benchmark evaluating contemporary enterprise AI knowledge retrieval systems across progressively more demanding organisational knowledge environments. Rather than comparing language models in isolation, the study evaluates complete AI-supported retrieval systems operating on real organisational repositories. Four complementary benchmark studies investigate the effects of retrieval architecture, corpus organisation, knowledge domain and repository scale. The findings demonstrate that modern native enterprise AI systems consistently achieve high retrieval accuracy, strong evidence grounding and robust resistance to hallucinations. As retrieval capabilities mature, operational characteristics—including retrieval efficiency, response latency, workflow stability and execution cost—emerge as the principal differentiators. The paper proposes a multidimensional evaluation framework for assessing enterprise AI knowledge retrieval systems under realistic organisational conditions.