Employee attrition is a critical challenge for organizations, impacting productivity, operational costs, and workforce stability. Traditional approaches to managing attrition rely on reactive strategies, often failing to provide predictive insights. The advent of Artificial Intelligence (AI) has transformed attrition management by enabling data-driven decision-making, predictive analytics, and proactive employee engagement.This research explores the role of AI in predicting and managing employee attrition through machine learning algorithms, natural language processing (NLP), and AI-driven sentiment analysis. AI models analyze vast datasets, including employee performance metrics, engagement surveys, and organizational culture indicators, to identify early warning signs of attrition. Predictive analytics empowers HR professionals to implement targeted retention strategies, enhance employee experience, and reduce voluntary turnover.Furthermore, AI-driven chatbots and virtual HR assistants contribute to employee satisfaction by offering personalized career development suggestions, real-time feedback, and mental well-being support. Explainable AI (XAI) frameworks ensure transparency in AI-driven decisions, fostering trust between employees and organizations. Despite AI’s potential, ethical concerns, data privacy, and algorithmic biases remain key challenges that require robust governance frameworks.This study provides a comprehensive analysis of AI applications in attrition management, highlighting case studies from multinational corporations that have successfully integrated AI for workforce retention. The findings underscore AI's transformative potential in HRM, enabling organizations to shift from reactive to proactive attrition management strategies. The paper concludes with future research directions on AI’s evolving role in predictive HR analytics and its integration with emerging technologies like blockchain and the metaverse for enhanced workforce planning.
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