Evaluating Artificial Intelligence Based Predictive Analytics for Improving Performance Management and Employee Satisfaction in Organizations

This article analyses the contribution of Artificial Intelligence (AI)-based predictive analytics to both the performance management systems and employee satisfaction in the organization. Subjective performance assessment and regular reviews have been ineffective in the traditional performance management as they could not give timely and accurate performances of employees and their satisfaction. The use of AI models, based on the machine learning algorithms, is a potential game changer by providing real-time, datagenerating efforts that match organizational objectives with individual performance. This paper is a predictive analytics model derived with data of the organization, including past performance history, the training attendance history, and the employee engagement measurement. The findings prove that predictive analytics are much more effective in terms of accuracy of performance forecasting and even enables providing feedback to employees in a personalized manner. In addition, the integration of these predictive models in performance management processes can be found to have a positive impact on employee satisfaction and offer them more customized growth opportunities and less dissatisfaction. These findings stress the opportunities available to AI to enhance the operation of performance management systems and create an increasingly engaged and more satisfied workforce. Moral aspects of data privacy and fairness of algorithms are another issue. The next generation of research could be focused on examining the long-term effects of AI-based interventions on the performance of the organization, such as retention and productivity of employees.