Optimizing Sustainable Human Resource Management through AI-Driven Predictive Analytics and Data-Driven Decision Models

An area in Human Resource Management (HRM) that is very important in organizational success is often challenged in adapting to new requirements of a diverse and dynamic workforce. Therefore, as needed in improving HR practices, the preference for Artificial Intelligence (AI, in particular, Deep Learning and Natural Language Processing (NLP)) has been integrative. In the scope of this paper, the presented enhanced AI driven framework for Sustainable Human Resource Management (SHRM) leverages the use of the Deep Learning to benchmark employee performance and predict employee turnover, achieving over 90% accuracy in turnover prediction. NLP analyses employee feedback with regard to organizational sustainability, demonstrating a precision rate of 91% in sentiment analysis. These AI techniques process and analyse the data generated from internal organizational sources and environmental impact assessments. Data preprocessing techniques improve prediction reliability by 25%, while integrated AI models use multi-source inputs, including real-time data streams, performance reviews, and social media sentiment. Deep Learning is used in the model for predictive analytics around employee retention and turnover tendencies, as well as for NLP for sentiment analysis on employee surveys and feedback. This study evaluates the effectiveness of these AI techniques for optimization HR strategies. Using key performance indicators like turnover prediction accuracy, employee engagement and sustainability alignment, the impact of the proposed framework is measured, and results are presented numerically to give a complete understanding about use of AI in HRM. This paper brings to light the effectiveness of AI as a catalyst to make SHRM a dynamic, data driven, adaptive system for the organizations in the current world.