student
employee
In the current context of digital transformation, effective knowledge management has become a key factor in maintaining business competitiveness. Large Language Models (LLM) offer new opportunities to automate the processes of knowledge retrieval, structuring, and transfer within organizations. This article analyzes the impact of LLMs on corporate knowledge management, including accelerated access to information, reduced cognitive load on employees, and improved decision-making accuracy. Special attention is given to the economic aspects of implementing LLMs: the potential benefits of reducing time spent on information search, optimizing employee onboarding, and lowering operational costs through the automation of routine tasks are explored. Key challenges are systematized, such as the high cost of development and the risks of confidential data leakage. Based on the analysis of existing research and case studies, the paper proposes criteria for assessing the effectiveness of LLMs in a business environment. It provides an overview of current approaches to technological integration and practical recommendations for adapting LLMs to organizational processes. The findings support the development of a flexible evaluation framework adaptable across industries and business scales, while emphasizing the importance of a balanced implementation strategy that considers both the benefits and risks of the technology.
large language models, LLM, knowledge management, economic efficiency, digital transformation, generative artificial intelligence, automation, AI implementation risks
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