Artificial intelligence in managerial decision-making: A bibliometric analysis

Abstract

The study analyzed the evolution, structure, and dynamics of research on artificial intelligence applied to managerial decision-making between 2020 and 2025, with the aim of understanding how this field was shaped and what patterns characterized its recent development. To this end, a longitudinal bibliometric approach based on documents indexed in Scopus was adopted, combining scientific performance analysis, conceptual mapping, and international collaboration networks. The procedure made it possible to reconstruct publication trends, identify thematic clusters, and visualize the geographical and institutional distribution of knowledge. The results showed sustained growth in scientific production accompanied by increasingly interconnected cooperation networks between countries and institutions. At the same time, there was a marked concentration of academic leadership in a small group of countries and institutions, suggesting significant progress but also persistent inequalities in the generation and circulation of knowledge. The thematic analysis revealed clusters centered on data analytics, decision support systems, and organizational management, showing a progressive integration between technical and managerial approaches. Overall, the work provided an overview of the field, offered systematic evidence on its current configuration, and pointed to the need to strengthen collaboration with less represented regions, as well as to deepen interdisciplinary studies that link artificial intelligence with strategic and social problems in business management.