Abstract

Collaboration of artificial intelligence (AI) with humans in achieving efficient business processes and decision-making is a rapidly evolving research area. This study explores current research trends in human–AI team collaboration, with a specific focus on its role in enhancing decision-making processes within organizations. Using a systematic literature review of seventy-five articles published between 2018 and 2024 from the Scopus and Web of Science databases, the study employed content analysis to identify themes and subthemes related to human–AI collaboration for organizational decision-making. The analysis revealed twelve key themes, including human–AI collaboration, organizational dynamics, decision-making and problem-solving, trust and reliance on AI, AI effectiveness and evaluation, and ethical considerations in AI, among others. Among these, trust, task delegation, evaluation, and feedback and communication emerged as the most trending topics, with task delegation identified as the highest trending subtheme. The findings underscore that human–AI collaboration extends beyond technological integration to involve structured frameworks, such as division of labor configurations and the “Human–AI Collaboration” (HACO) taxonomy, that facilitate task allocation, emphasize trust, and clarify roles. These frameworks enable organizations to optimize human–AI partnerships, combining AI’s data-driven precision with human creativity and empathy. The critical role of trust calibration, transparency, and reliability in fostering effective collaboration and ensuring user confidence in AI systems is also highlighted. This study lays a foundational understanding of human–AI collaboration, bridging the gap between theory and practice, and identifies opportunities for future research to explore scalable strategies for trust and transparency, the intersection of these themes with organizational dynamics, and empirical validation across diverse industries and contexts.