vestnykepsВестник экономики, права и социологииThe Review of Economy, the Law and Sociology1998-5533Общество с ограниченной ответственностью «Эксперт 16»10.24412/1998-5533-2025-4-426-430vestnykeps-368Research ArticleПрактический опытОт пилота к масштабу: как гибридные архитектуры и модели управления определяют успех внедрения искусственного интеллекта в бизнесеFrom Pilots to Scale: How Hybrid Architectures and Governance Models Shape the Success of AI Adoption in BusinessПавловаА. В.PavlovaA. V.

Павлова Аделия Вадимовна – доктор экономических наук, профессор кафедры сервиса и туризма, проректор по учебной работе и цифровой трансформации 

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930895@list.ruЯгудинаЕ. В.YagudinaE. V.

Ягудина Елена Валерьевна – доктор экономических наука, профессор, заведующая кафедрой управления человеческими ресурсами 

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efahr@mail.ruПоволжский государственный университет физической культуры, спорта и туризмаРоссияThe Volga State University of Physical Culture, Sports and TourismRussian FederationКазанский (Приволжский) федеральный университетРоссияKazan (Volga Region) Federal UniversityRussian Federation20252908202604426430Copyright © Павлова А.В., Ягудина Е.В., 20262026Павлова А.В., Ягудина Е.В.Pavlova A.V., Yagudina E.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.vestnykeps.ru/jour/article/view/368

В статье рассматриваются актуальные аспекты практического внедрения искусственного интеллекта в бизнес-процессы современных организаций. Основное внимание уделяется анализу эффективности различных технологических архитектур и организационных моделей управления ИИ-проектами.На основе исследования более 40 практических кейсов внедрения ИИ в российских и зарубежных компаниях авторы выявляют ключевые факторы успеха проектов. Особое внимание уделяется сравнению трёх основных моделей развёртывания ИИ: облачной, локальной и гибридной.Доказано, что гибридная архитектура обеспечивает оптимальный баланс между масштабируемостью, безопасностью и экономической эффективностью. В работе также анализируются три основные модели управления ИИ-инициативами: централизованная, децентрализованная и гибридная «хаб-спиц». Показано, что последняя модель является наиболее эффективной для масштабирования ИИ-решений в крупных организациях.Практическая значимость исследования заключается в разработке эволюционной траектории внедрения ИИ – от централизованной модели на начальных этапах к гибридной по мере роста зрелости организации. Авторы подчёркивают важную роль мультидисциплинарного Центра компетенций по ИИ как стратегического органа управления процессом цифровой трансформации.Результаты исследования могут быть использованы руководителями компаний и специалистами по цифровой трансформации при планировании и реализации проектов внедрения искусственного интеллекта.

The article examines current aspects of practical implementation of artificial intelligence in business processes of modern organizations. The main focus is on analyzing the effectiveness of various technological architectures and organizational management models for AI projects.Based on a study of over 40 practical cases of AI implementation in Russian and international companies, the authors identify key success factors for projects. Particular attention is paid to comparing three main AI deployment models: cloud, on-premises, and hybrid. It is proven that hybrid architecture provides an optimal balance between scalability, security, and cost-effectiveness.The paper also analyzes three main models of AI initiative management: centralized, decentralized, and hybrid hub-and-spoke. It is shown that the latter model is the most effective for scaling AI solutions in large organizations.The practical significance of the research lies in developing an evolutionary trajectory for AI implementation — from a centralized model at initial stages to a hybrid model as the organization matures. The authors emphasize the important role of a multidisciplinary AI Center of Excellence as a strategic body managing the digital transformation process.The research findings can be used by company executives and digital transformation specialists when planning and implementing AI implementation projects.

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The authors declare that there are no conflicts of interest present.