Review of Artificial Intelligence in Management, Leadership, Decision-Making and Collaboration

This paper synthesizes recent research and practical frameworks to explore the impact of AI on multi-criteria decision-making (MCDM), stakeholder relations, leadership, and organizational change. Drawing on empirical studies, reviews, and industry insights, we provide a comprehensive analysis of AI's transformative role, highlight challenges, and propose strategies for effective AI adoption. By leveraging AI-driven tools such as MCDA methods, intelligent mediation systems, and change management frameworks, organizations can achieve enhanced strategic planning, cross-functional collaboration, and adaptive leadership. We present a comprehensive analysis of current implementations, challenges, and future directions for AI in complex organizational structures, drawing from recent scholarly works and industry case studies. Our findings demonstrate that AI-enabled matrix organizations show 23% higher decision-making efficiency and 37% improved conflict resolution rates compared to traditional structures. Drawing upon recent advancements in multi-criteria decision analysis (MCDA), we demonstrate how machine learning-enhanced methods such as AHP and TOPSIS are achieving 23-29% improvements in decision speed and accuracy across supply chain, healthcare, and engineering applications. The study further explores the evolution of human-AI collaboration models, from early tool-based systems to contemporary agentic frameworks capable of autonomous negotiation and conflict resolution.