The Impact of Innovative Artificial Intelligence Technologies in Talent Acquisition

Human resources management plays a crucial role in the success of an organization by acting as a link between employees and employers. Its policies and decisions shape the dynamics of the workforce, aiming to optimize their utilization and productivity. This includes strategic planning for recruitment, retention, and training and continuous assessment of employee performance. The study on performance prediction in human resources management begins by acknowledging the crucial role HRM plays in the success of an organization, acting as a vital link between employees and employers. It involves a wide range of strategic decisions and policies aimed at optimizing the utilization and productivity of the workforce. Workforce planning, a key HRM component, entails developing recruitment, retention, and training strategies while continuously assessing employee performance to ensure alignment with organizational goals and objectives. The quality of the workforce is a crucial determinant of organizational achievement, as employees are the most asset for development. Accurately evaluating employee performance is essential for fostering growth and progress, although traditional methods may be subjective and inefficient. However, introducing machine learning algorithms offers promising opportunities to enhance accuracy and objectivity in this process. At the core of this research is the understanding that the quality of the workforce significantly impacts organizational outcomes, with employees being the most valuable asset for organizational growth. However, accurately evaluating employee performance can be challenging due to subjective biases and inefficiencies inherent in traditional evaluation methods. In this context, machine learning algorithms offer a promising approach to enhance performance evaluation processes' accuracy, objectivity, and efficiency. This research focuses on evaluating and improving existing machine learning classifiers for predicting performance in human resources management.