AI-Driven Decision-Making in Leadership: Balancing Automation and Human Judgment for Optimal Organizational Performance

This study examines how artificial intelligence (AI)-driven decision-making can be effectively balanced with human judgment to enhance organizational performance and ethical leadership. The research adopts a systematic literature review covering studies from 2015 to 2025, retrieved from Scopus, Web of Science, IEEE Xplore, and Google Scholar. Through thematic analysis, the review identifies three core dimensions: (1) the impact of AI on strategic accuracy and operational efficiency, (2) the mediating role of human judgment in fostering innovation and trust, and (3) the influence of ethical AI governance on employee well-being and accountability. The findings reveal that organizations integrating AI’s analytical precision with leaders’ contextual awareness achieve greater adaptability and stakeholder trust. However, challenges such as algorithmic bias, overreliance on automation, and insufficient AI literacy persist. The study recommends a hybrid leadership framework in which AI acts as an enabler supporting but not replacing human decision-making. Practical implications include developing AI literacy programs for leaders, establishing fairness audits, and embedding transparent governance mechanisms to ensure responsible AI adoption. Future research should empirically test this hybrid leadership model across industries and cultural contexts.