Optimizing Managerial Selection Parameters using AI Approach

Managerial level employee selection process are becoming a big challenge for the HR department. Selecting employees based on their potential with the mapping to the organization human resource needs poses several issues to the recruiter’s. Always the selection is based on various parameter related to knowledge and skill. This research explores the application of machine learning techniques to evaluate managerial skills, aiming to enhance leadership performance. Traditional methods of skill assessment, such as performance reviews and psychometric tests, often suffer from subjectivity and limited scalability. By leveraging machine learning, this study seeks to provide a more objective, data-driven approach to skill evaluation. Using machine learning algorithms, we developed models to predict key managerial skills such as leadership, communication, and strategic thinking. The results demonstrate that machine learning models can effectively predict managerial skills parameters with high accuracy, as compare to outperforming traditional evaluation methods. Furthermore, the study highlights the importance of feature selection, showing that certain performance indicators and behavioral traits are strong predictors of managerial effectiveness. Our findings suggest that incorporating machine learning into the skill evaluation process can provide more reliable and actionable insights, aiding to the development of tailored training programs and better-informed managerial promotions. This research contributes to the growing field of data-driven human resource management and offers practical approach for organizations seeking to enhance their leadership capabilities. Future research would focus on refining these models and exploring their application across different cultural and organizational specific need-based contexts.