Advancing Decision-Making through AI-Human Collaboration: A Systematic Review and Conceptual Framework

Abstract The interplay between humans and artificial intelligence (AI) in decision-making has become increasingly intricate and significant. Despite rapid advancements, the literature remains fragmented, with limited integrative frameworks to explain how AI-human dynamics and decision-making typologies shape outcomes. This study addresses this critical gap by conducting a systematic review and bibliometric analysis of 1,004 articles, culminating in a novel conceptual framework. The framework identifies two critical dimensions, AI-human dynamics and decision typologies, that shape decision outcomes and introduces four distinct paradigms of AI-human collaborative decision-making: autonomous execution, guided resolution, collaborative exploration, and augmented discovery. By synthesizing these paradigms, this research advances the theoretical understanding of hybrid decision-making systems and provides actionable insights for organizations navigating complex and AI-driven environments. By elucidating the mechanisms and trade-offs inherent in AI-human collaboration, this work lays a robust foundation for future research on adaptive decision systems in an era marked by accelerating technological change.