Employee Performance Evaluation and Succession Planning through the Impact of Predictive Analytics in Jordanian Institutions

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This study examines how companies can find talent, assess performance, and plan for future leadership needs by leveraging predictive analytics to evaluate employee performance and plan for leadership succession. Predictive analytics uses data, statistical techniques, and machine learning to identify patterns and make educated guesses about the future. It has evolved into a critical tool for enhancing decision-making in HR management in Jordan. By examining its impact on succession planning and employee performance reviews, this study focuses on how predictive analytics can enhance accuracy, speed, and fairness in organizational processes. Traditional methods of evaluating an employee’s work, such as annual reviews and self-evaluations, may not necessarily provide a complete and unbiased picture of their abilities. Predictive analytics, a more data-driven approach, uses vast amounts of data from multiple sources, including individual workflows, peer and manager feedback, and past performance records. This gives companies additional insight into their employees’ performance and helps them make decisions about training needs, rewards, and raises. Since predictive analytics identifies an organization’s future leaders, it is also essential for succession planning. Models can predict future employee performance and readiness for leadership roles by leveraging performance data, skill sets, behavioral patterns, and even external factors such as market trends and industry changes. By doing so, companies can reduce risk, fill skill gaps, and prepare future leaders. With changeable leadership comes predictive analytics. Applying predictive analytics also improves the fairness and clarity of decision-making. Companies can ensure that succession planning and performance evaluations are fairer by eliminating human biases and relying on data-driven insights.