Decoding Leadership: Leveraging Machine Learning For Enhanced Talent Management

Abstract

Artificial Intelligence and Machine learning are finding their way into different functional business areas because of their immense popularity and applicability. As businesses accumulate data in volumes, they want to explore Artificial Intelligence technologies to leverage their data assets to make strategic decisions. These decisions cut across various departments of the organization, including business administration. One of the critical functional areas is human resource management, where policies and rules are in place to get the best from the employed workforce. Business leaders and managers are exploring the application of Artificial intelligence and machine learning to bring out the best in their talent pool. The proposed work focuses on developing a data science and machine learning approach for identifying the leadership style one possesses. This work used a detailed questionnaire, and exploratory data analysis (EDA) was carried out to determine the correlations and features from the collected responses. Later, Machine learning models were developed to predict the leadership styles of an employee. Out of the experiments conducted, the support vector machine (SVM) model has been identified as one of the potential models with an 86% accuracy for this purpose. The outcome of the work can be used to shortlist potential employees for leadership roles and groom them with the necessary training and skills.