Mapping the Human-AI Collaboration in Strategic Decision-Making: A Bibliometric Review

Background: The increasing integration of artificial intelligence into organizational decision-making has transformed strategic management. It has enabled human expertise and AI-driven analytical capabilities to collaborate in addressing complex problems, evaluating alternatives, and making informed strategic decisions. Objective: This paper examines the intellectual, conceptual, and thematic evolution of research on human-AI collaboration in strategic decision-making and pinpoints emerging research directions. Methods: Based on Scopus database records collected on August 17, 2026. The research involves 1,038 papers published during 2015-2026. Performance analysis of publications and science mapping are conducted using VOSviewer and Biblioshiny in order to explore trends, influential researchers, countries, organizations, collaborations, citation network, keyword co-occurrences, and thematic evolution. The area demonstrates fast growth with an annual growth rate of 51.49%. Results: The literature shows a shift from classical artificial intelligence, machine learning, and decision-support systems to human-AI collaboration, human-in-the-loop systems, explainable AI, trust, and large language models. Decision support systems are still prevalent topics, while human-centered and safety-related themes reflect increased focus on human supervision, transparency, and responsible AI. Combining performance analysis and science mapping allows obtaining a consolidated insight into the intellectual and thematic structure of human-AI collaboration in strategic decision-making. It also points to research gaps regarding theory integration, trust and explain-ability, organizational context, and long-term consequences of generative AI and large language models. The findings support the development of a more integrated socio-technical perspective on human–AI strategic decision-making. Conclusion: The literature reveals a shift from traditional AI and decision-support systems toward human-centered collaboration, explainable AI, trust, and large language models. The findings highlight research gaps in theoretical integration, organizational context, transparency, and the long-term impacts of generative AI, emphasizing the need for an integrated socio-technical approach to strategic decision-making. Novelty: The novelty of this study lies in mapping the intellectual, thematic, and emerging research trends in human–AI collaboration in strategic decision-making while identifying critical research gaps in trust, explainability, organizational context, and the long-term implications of generative AI.