Kobelev S.V. —
Conceptual model of AI-transformation strategy
// Finance and Management. – 2024. – ¹ 4.
– P. 61 - 78.
DOI: 10.25136/2409-7802.2024.4.72461
URL: https://en.e-notabene.ru/flc/article_72461.html
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Abstract: The research subject is the problem of the lack of a systematic approach to developing and implementing AI transformation strategies for business. The author examines differences between digital and AI transformation processes, specific requirements for data, technologies, and personnel competencies in artificial intelligence implementation, as well as ethical aspects of AI use in business processes. Special attention is paid to systematizing existing approaches to AI transformation and identifying their limitations in modern conditions. The paper explores key success factors of AI implementation, including the necessity of a systematic approach to data management, formation of specialized teams, and development of relevant competencies. The paper addresses issues of AI solutions integration into existing organizational structure and business processes, as well as mechanisms for evaluating effectiveness and adjusting AI transformation strategy. The research methodology is based on a comparative analysis of existing digital and AI transformation methodologies, followed by a synthesis of best practices. The comparison used criteria of AI transformation orientation, presence of a step-by-step action plan, ethical aspects, and mechanisms for AI integration into business processes. The main findings of the study are the development of a comprehensive 15-stage AI transformation strategy model and identification of critical success factors for its implementation. The scientific novelty lies in systematizing existing approaches to AI transformation and creating a holistic methodological framework that considers specific requirements for data, technologies, ethical aspects, and personnel competencies. The author's special contribution is the structuring of the AI transformation process into logically connected blocks (preparatory, technological, implementation, and transformational), which provides a systematic approach to AI implementation. The practical significance of the research lies in creating a methodological toolkit for company leaders, allowing them to minimize risks and optimize resource utilization when implementing artificial intelligence technologies.