JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT 2782-4586 2949-1851 104429 10.26118/2782-4586.2025.36.39.009 Научные статьи SCIENTIFIC ARTICLES Научные статьи The role of large language models in optimizing business processes and knowledge management in corporate structures Роль больших языковых моделей в оптимизации бизнес-процессов и управлении знаниями в корпоративных структурах Булаев Ярослав Андреевич Bulaev Yaroslav Andreevich Бурцев Даниил Сергеевич Burcev Daniil Sergeevich

кандидат сельскохозяйственных наук;

candidate of agricultural sciences;

 Санкт-Петербургский национальный исследовательский университет информационных технологий, механики и оптики Saint-Petersburg National Research University of Information Technologies, Mechanics and Optics 11 10 2025 11 10 2025 7 72 80 21 09 2025 https://zhpi.ru/en/nauka/article/104429/view

В современных условиях цифровой трансформации эффективное управление знаниями становится ключевым фактором конкурентоспособности бизнеса. Большие языковые модели (LLM) открывают новые возможности для автоматизации процессов поиска, структурирования и передачи знаний внутри организации. В данной статье анализируется влияние LLM на управление корпоративными знаниями, включая ускорение доступа к информации, снижение когнитивной нагрузки на сотрудников и повышение точности принятия решений. Особое внимание уделено экономическим аспектам внедрения LLM: исследуются потенциальные выгоды от сокращения временных затрат на поиск информации, оптимизации адаптации сотрудников и снижения операционных расходов за счет автоматизации рутинных задач. Систематизированы ключевые вызовы, такие как высокая стоимость разработки, риски утечек конфиденциальных данных. На основе анализа существующих исследований и кейсов предложены критерии оценки эффективности LLM в бизнес-среде. Статья содержит обзор современных подходов к интеграции технологии, а также рекомендации по адаптации LLM с учетом специфики организационных процессов. Результаты исследования позволяют сформировать гибкую систему оценки, которая может быть адаптирована для различных отраслей и масштабов бизнеса, а также подчеркивает важность сбалансированного подхода к внедрению, учитывающего как преимущества, так и риски технологии.

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.

 большие языковые модели LLM управление знаниями экономическая эффективность цифровая трансформация генеративный искусственный интеллект автоматизация риски внедрения искусственного интеллекта large language models LLM knowledge management economic efficiency digital transformation generative artificial intelligence automation AI implementation risks

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