ARTIFICIAL INTELLIGENCE IN MANAGEMENT: THEORETICAL LANDSCAPE AND EVOLUTION OF RESEARCH

Abstract

The article examines the transformation of the thematic landscape of artificial intelligence (AI) in management and identifies its structural changes. The empirical basis of the study comprises metadata from scientific articles indexed in the Scopus database and selected using the PRISMA methodology. The methodological framework combines bibliometric analysis, keyword co-occurrence analysis, network visualization, thematic clustering, and a comparison of the absolute and relative representation of research clusters across the periods 2019–2022 and 2023–2025. The findings reveal an acceleration in research on AI in management, accompanied by a broader thematic scope and an expansion of international research collaboration. Network analysis identified four interconnected thematic clusters covering technological and analytical support for managerial decision-making; human resource, knowledge, and organizational process management; operations, project, and risk management; and responsible AI governance, ethics, and regulation. Strong interconnections between these clusters indicate the gradual emergence of an integrated approach to studying AI in management. The comparison between the two periods reveals a structural reconfiguration of research priorities: while all thematic areas demonstrate absolute growth, the relative dominance of technology-oriented research has declined, whereas attention to organizational, human, ethical, and regulatory dimensions has increased. Particularly dynamic growth is observed in research related to generative AI, human–AI interaction, innovation, digital transformation, sustainability, and responsible management. The findings demonstrate an evolutionary shift in research focus from the technological and operational application of AI toward its managerial and organizational integration, followed by the emergence of responsible and human-centered approaches. The results provide a foundation for further research on managerial capabilities for AI integration, models of interaction between humans and intelligent systems, and mechanisms for the responsible use of AI.