Exploring the dynamica virtus of Machine Learning (ML) in Human Resource Management - A Critical Analysis of IT industry

Authors

  • Malathi Sriram Shri Dharmasthala Manjunatheshwara Institute for Management Development (SDMIMD), Mysore, India
  • L Gandhi Shri Dharmasthala Manjunatheshwara Institute for Management Development (SDMIMD), Mysore, India

DOI:

https://doi.org/10.26438/ijcse/v5i12.173180

Keywords:

Machine Learning (ML), Replacing HR with ML, AI and HRM

Abstract

“A breakthrough in machine learning would be worth ten Microsofts”- -Bill Gates, Chairman, Microsoft The fact that humans have been progressing to reach a point where machine does most, if not all the mechanical labor is not new. What is new is the use of machines that is intelligent enough to replace humans in analyzing situations, portraying situations and scenarios, and then taking the (presumably) right decisions. This paper focuses on the use of machine learning that has replaced certain functions related to Human Resources Management, specifically in the IT industry. The paper is sectioned into five parts. In the first section the topic on hand is introduced, evolution of Machine Learning, introducing congruence of Machine Learning with HR functions such as Recruitment, Performance Management, Training & Development, Managing Attrition, Compensation Management etc. Section two, does a literature review that outlines the work previous written in this area i.e review of literature is done on this aspect. Section three takes a case study approach to highlight select IT companies that are using AI/ML for their HR functions. The fourth section attempts to design a simple Model from the literature review done. The fifth section presents the authors’ conclusion of the findings, and draws a futuristic picture.

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Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v5i12.173180
Published: 2025-11-12

How to Cite

[1]
M. Sriram and L. Gandhi, “Exploring the dynamica virtus of Machine Learning (ML) in Human Resource Management - A Critical Analysis of IT industry”, Int. J. Comp. Sci. Eng., vol. 5, no. 12, pp. 173–180, Nov. 2025.