Revolutionizing Workforce Management: The Role of AI and Emotional Intelligence in Enhancing Employee Performance
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The integration of Artificial Intelligence (AI) in workforce management has revolutionized traditional Human Resource Management (HRM) practices, enhancing efficiency, decision-making, and performance evaluation. However, while AI-driven automation optimizes administrative processes, concerns regarding job displacement, employee engagement, and ethical AI usage persist. This study explores the interplay between AI and Emotional Intelligence (EI) in workforce management, emphasizing how EI-driven leadership can mitigate challenges associated with AI adoption. Using a qualitative research approach, this study employs a case study design involving semi-structured interviews with HR managers, employees, and organizational leaders in AI-integrated workplaces. Thematic analysis reveals that AI enhances workforce productivity when implemented as an augmentation tool rather than a full automation substitute. Findings indicate that organizations that integrate EI-driven leadership with AI adoption experience lower employee resistance, greater engagement, and improved workplace morale. The study also identifies key challenges in AI adoption, including workforce skepticism, algorithmic bias, and the need for ethical AI governance. To address these challenges, organizations must prioritize transparent AI implementation, inclusive decision-making, and EI-based leadership strategies. This study contributes to existing literature by providing empirical insights into the AI-EI synergy model, offering a framework for balancing technological efficiency with human-centered workforce management. The findings underscore the importance of aligning AI deployment with ethical and emotional intelligence considerations, ensuring that workforce transformation remains sustainable, inclusive, and ethically sound.
Keywords: Artificial Intelligence, Emotional Intelligence, Workforce Management, AI in HRM, Employee Engagement
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DOI: https://doi.org/10.37531/yum.v8i2.9240
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