Organizational Determinants of Successful Artificial Intelligence Implementation: a Comparative Analysis of Russian and International Practices

In the current context of accelerating digital transformation and intensifying global competition, the adoption of artificial intelligence is becoming a key driver of organizational competitiveness and sustainable development, while empirical evidence indicates that organizational and managerial, rather than purely technological, parameters play the decisive role.
The purpose of this study is to identify and systematize the organizational determinants of successful artificial intelligence adoption and the barriers to its scaling, as well as to specify the distinctive features of Russian practices in comparison with those of the United States, the European Union, and China. Within this research, the authors generalize empirical data and AI implementation cases, single out the key success and failure factors, develop an original model of determinants and barriers, and formulate managerial recommendations for companies that regard AI as a lever of strategic transformation.
The scientific contribution and practical relevance of the study lie in integrating fragmented approaches to the analysis of AI adoption into a unified classification of success factors and barriers, grouped into five core dimensions: strategic leadership, organizational structure and culture, human capital, technological and data infrastructure, and business orientation.
The proposed model enables managers to build a holistic AI governance system by aligning technological solutions with change management, capability development, and data architecture, thereby increasing the likelihood of achieving sustainable business impact. The main findings demonstrate that successful AI implementation projects are characterized by a clear strategy and roadmap, functioning centers of excellence, a strong culture of collaboration and innovation, investment in the development of employees’ digital skills, as well as data readiness and scalable infrastructure. The key barriers include a shortage of qualified personnel, low quality and fragmentation of data, the absence of a coherent strategy and employee resistance, as well as institutional constraints and specific features of the regulatory environment, which are particularly pronounced in the Russian context. The results underscore that the long-term success of AI adoption is determined not by the level of technology, but by the maturity of organizational culture, the quality of strategic leadership, and the ability of firms to establish systematic change management

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