Leveraging AI Technology to Combat Silent Quitting and Enhance Employee Engagement

The modern workplace faces a significant problem with silent quitting, where employees stay physically present but mentally disengaged, undermining productivity, innovation, and employee morale. Traditional turnover contrasts with this form because its hidden nature makes detection more difficult, although it produces similar financial and cultural consequences. Organizations need to develop data-driven proactive strategies to reengage their workforce because hybrid and remote work models have reshaped employee expectations. The chapter demonstrates how artificial intelligence (AI) functions as an essential transformative solution to fight silent quitting through individualized employee experiences that are both responsive and ethically sound. The first section investigates how AI-based adaptive learning systems detect employee skill deficits to create individualized growth plans that fight the stagnation that leads to disengagement. Real-time feedback systems and sentiment analysis tools enable managers to monitor performance continuously to address problems before they worsen. The implementation of AI-driven communication analysis through NLP emotion detection requires non-negotiable ethical considerations that include privacy protections and bias mitigation. The chapter demonstrates how predictive analytics serves as a transformative tool that enables organizations to predict employee engagement patterns to create specific retention programs. The implementation of AI-enhanced human resource (HR) chatbots demonstrates how organizations can use these systems to handle administrative tasks while offering continuous employee support through 24/7 availability. The chapter addresses essential obstacles, including algorithmic bias, technological dependence, and employee surveillance risks. The recommended approach involves human involvement in AI operations to enhance managerial judgment and empathy rather than replace them.