AI-ORIENTED APPROACH TO PROJECT MANAGEMENT

Abstract

The rapid development of artificial intelligence (AI) is transforming project management and revealing the limitations of traditional expert-driven and linear approaches. Although AI tools for forecasting, risk assessment and resource optimization are increasingly available, their use remains fragmented and limited to isolated operational tasks, creating a methodological gap between AI’s technological potential and the structured logic of project management throughout the entire project life cycle. Purpose. To develop a conceptual foundation for an integrated AI-oriented project management model that ensures the systematic incorporation of AI across all project life cycle phases, considering managerial functions and human decision-making. Methods. The research applies analytical, systemic, bibliometric and conceptual modeling methods. Bibliometric analysis of Scopus publications using the keywords “Artificial Intelligence” and “Project Management” was conducted to identify research dynamics and emerging trends. Network analysis via VOSviewer revealed thematic clusters. Comparative analysis of academic literature and project management standards identified methodological gaps. Results. The findings indicate increasing scientific interest in AI-oriented project management and confirm the presence of dominant research clusters, including predictive analytics, decision support, risk management and resource optimization. However, no comprehensive models cover the full project life cycle. A conceptual framework integrating life cycle phases, managerial functions and AI tool classes is proposed based on a human-centered paradigm in which AI enhances rather than replaces managerial judgment. Conclusion. The proposed framework contributes to bridging the gap between AI technologies and project management methodology by enabling systematic integration. It provides a theoretical basis for adaptive, predictive and data-driven project management systems and creates conditions for further empirical research and practical implementation in complex project environments.