AI-Driven Emotional Quotient Analytics for Enhancing Workforce Efficiency and Organizational Performance: A Sustainable Human Capital Development Framework

Abstract

In the contemporary business environment, organizations increasingly recognize that employee emotions and interpersonal competencies play a significant role in achieving sustainable performance outcomes. While technical expertise and operational skills remain essential, emotional quotient (EQ) has emerged as a critical factor influencing employee productivity, collaboration, adaptability, and workplace well-being. This study proposes an AI-driven Emotional Quotient Analytics Framework designed to examine the relationship between emotional competencies, workforce efficiency, and organizational performance. The framework integrates emotional intelligence indicators with workforce analytics techniques to generate actionable insights for human resource decision-making. A quantitative research approach is adopted to evaluate the influence of self-awareness, emotional regulation, empathy, motivation, and social skills on employee effectiveness and organizational outcomes. The proposed model further explores how artificial intelligence can support the continuous assessment and prediction of workforce performance patterns. The expected findings suggest that organizations that strategically incorporate emotional quotient analytics into their human resource practices may experience improved productivity, stronger employee engagement, enhanced teamwork, and superior organizational performance. The study contributes to the growing body of knowledge on intelligent human capital management by presenting a sustainable and data-informed approach to workforce development in the digital era.