Implementation of Artificial Intelligence in Diagnosing Employees Attrition and Elevation: A Case Study on the IBM Employee Dataset

Abstract

Need of the Study: Artificial intelligence (AI) can be regarded as a big leap in the case of technological advancement. Developments in AI have profound implications for economic sectors and on the societal level. In contemporary times, AI is applied widely in assisting organisations in informing managerial decisions, organisational goals, and business strategies. One can very well witness the interest of human resource (HR) professionals in the implementation of AI for the formulation of HR policies and future frameworks. In the past few years, various research works have been carried out on how these two critical branches can be combined for bringing out the best in human resource management (HRM). The fundamental explanation for this is found in every organisation’s most important management aim is employee retention and elevation.

Purpose: In this direction, this chapter will try to analyse the probability of employees leaving the company, the key drivers behind it, recommendations or strategies that can be implemented in improving employee retention, elevation predictions with the help of different features of machine learning, and the possibility of some other techniques other than key performance indicators (KPI), and rating and training score in this field.

Methodology: The goal will be achieved with the help of implementing machine learning-based classification tools and an ensemble learning approach to the data set of the corporate sector.

Findings: Machine learning techniques can be utilised to develop reliable models to find different factors for elevation and employee attrition.

Citation

Sharma, S. and Bhat, R. (2023), "Implementation of Artificial Intelligence in Diagnosing Employees Attrition and Elevation: A Case Study on the IBM Employee Dataset", Tyagi, P., Chilamkurti, N., Grima, S., Sood, K. and Balusamy, B. (Ed.) The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part A (Emerald Studies in Finance, Insurance, and Risk Management), Emerald Publishing Limited, Leeds, pp. 197-213. https://doi.org/10.1108/978-1-80382-027-920231010