<![CDATA[The increasing adoption of Large Language Models (LLMs) has transformed the way organizations manage, access, and utilize knowledge in industrial environments. As industries continue to generate vast amounts of information, LLMs have emerged as powerful tools for enhancing knowledge management processes through advanced natural language understanding, information retrieval, and intelligent decision support capabilities. This study aims to analyze the effectiveness of LLMs in supporting industrial knowledge management and to evaluate their contributions, benefits, and challenges across organizational contexts. A Systematic Literature Review (SLR) approach was employed to examine relevant studies published between 2020 and 2026, with data collected from major scientific databases, including Scopus, Web of Science, IEEE Xplore, and ScienceDirect. The selected literature was analyzed using thematic, content, and comparative analysis techniques to identify patterns, applications, and implementation outcomes. The findings indicate that LLMs significantly enhance knowledge creation through automated report generation, technical documentation support, and lessons-learned extraction. Furthermore, LLM adoption contributes to increased organizational productivity by reducing information search time, supporting decision-making, and improving employee access to critical knowledge resources. However, several challenges remain, including hallucination, data inconsistency, model bias, integration complexity, security and privacy concerns, and issues related to transparency, accountability, and explainability. To maximize their benefits, organizations should implement robust AI governance frameworks, adopt secure knowledge retrieval architectures, and invest in employee AI literacy and training programs. Future research should focus on real-world industrial evaluations, comparative analyses of LLM platforms, and long-term assessments of organizational impacts.]]>
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