Using Artificial Intelligence and Social Network Analysis for Building Diverse and Balanced Corporate Teams

Abstract

Diverse and inclusive teams are increasingly recognized as critical drivers of innovation, productivity, and organizational resilience in corporate environments. However, forming balanced and effective teams remains a persistent challenge for human resource managers and project leaders. Traditional approaches—such as self-selection, managerial assignment, or random allocation—often fail to adequately account for the complex interplay of employees’ skills, social ties, and organizational goals. To address this gap, we present a novel, AI-driven system that automates the formation of heterogeneous corporate teams by integrating genetic algorithms with social network analysis. The system enables managers to define project requirements, specify required skills, collect employees’ self-assessments, and visualize workplace social networks through an intuitive interface. The hybrid algorithm optimizes three critical objectives simultaneously: maximizing skill diversity, enhancing workplace cohesion through social ties, and maintaining balanced team sizes. Using simulated organizational networks of varying scales, we demonstrate the system’s reliability, robustness, and adaptability in producing inclusive and balanced teams. This approach has strong potential to reduce biases in team formation and empower organizations to align workforce composition with strategic objectives.