AI Agents for Organizational Knowledge Retrieval and Sharing: A Systematic Literature Review - Sri Rosa Anjelia; Dana Indra Sensuse; Sofian Lusa (Indonesian Scientific Journal, 2025)
As complexity and volume of data continue to increase, studies have found that traditional knowledge management systems are unable to keep up. Distributed teams, which are increasingly adopted by organizations as ways of working, have significantly transformed how employees manage their knowledge within these organizations. Artificial intelligence (AI), especially AI agents, is increasingly being used by organizations as a solution to enhance knowledge retrieval and sharing. However, it remains fragmented, with little awareness of its effective capabilities, limitations, and implications for organizational knowledge processes. The objective of this study was to systematically evaluate and synthesize recent research on AI agents for organizational knowledge retrieval and sharing. A PRISMA-based Systematic Literature Review (SLR) was carried out on studies published between 2021 and 2025. A total of 28 studies were analyzed to classify AI agent capabilities, supporting technologies, and key challenges across diverse domains and regions. The results revealed five key capabilities of AI agents, such as user-centered interaction, semantic knowledge extraction & retrieval, intelligent reasoning & decision support, automation & workflow management, and explainability & traceability. These capabilities were supported by technologies such as large language models, machine learning, natural language processing, knowledge graphs, ontologies, and other prominent technologies. Adoption challenges primarily included data quality & semantic alignment, system interoperability, trust & adoption issues, and ethical & governance concerns. This review concludes that AI agents hold strong potential to improve organizational knowledge processes, but it requires strategic integration, strong data governance, and human-centered design principles.