Technical analysis of AI-driven solutions for aligning HR policies with employee satisfaction - Камрун Нахар; Омар Фарук; Захир Райхан (Информатика. Экономика. Управление)
This technical analysis examines the application of AI and advanced technologies in addressing the misalignment between HR policies and employee satisfaction. By leveraging predictive analytics, sentiment analysis, ML models, organizations can create dynamic, data-driven HR policies that enhance employee engagement and satisfaction. The paper delves into the technical architecture, algorithms, and implementation challenges of AI-driven HR solutions, providing a roadmap for organizations to adopt these technologies effectively. Addressing the discrepancy between HR policies and employee satisfaction is a critical challenge for organizations. Misalignment between policies and employee needs can lead to dissatisfaction, reduced productivity, and higher turnover rates. Technology, particularly AI, offers innovative solutions to bridge this gap. The alignment of HR policies with employee satisfaction is a critical factor in organizational success. However, discrepancies often arise due to rigid policies, lack of personalization, and insufficient feedback mechanisms. This paper explores how technology, particularly AI, can address these challenges. By leveraging predictive analytics, sentiment analysis, and personalized employee experiences, organizations can create dynamic, responsive HR policies that enhance employee satisfaction. The paper also discusses ethical considerations and best practices for implementing AI-driven solutions in HR.