Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review

TL;DR

This systematic review examines how AI models are applied across all stages of employee lifecycle management, highlighting prevalent algorithms and emphasizing the need for further research in this emerging field.

Contribution

It provides a comprehensive overview of AI applications in employee lifecycle management and identifies key algorithms used, filling a gap in existing literature.

Findings

AI models are used in all employee lifecycle stages

Random Forest, SVM, AdaBoost, Decision Tree, and Neural Networks are most common

Research in this area is still in early stages

Abstract

Background/Purpose: The use of artificial intelligence (AI) models for data-driven decision-making in different stages of employee lifecycle (EL) management is increasing. However, there is no comprehensive study that addresses contributions of AI in EL management. Therefore, the main goal of this study was to address this theoretical gap and determine the contribution of AI models to EL. Methods: This study applied the PRISMA method, a systematic literature review model, to ensure that the maximum number of publications related to the subject can be accessed. The output of the PRISMA model led to the identification of 23 related articles, and the findings of this study were presented based on the analysis of these articles. Results: The findings revealed that AL algorithms were used in all stages of EL management (i.e., recruitment, on-boarding, employability and benefits, retention, and off-boarding). It was also disclosed that Random Forest, Support Vector Machines, Adaptive Boosting, Decision Tree, and Artificial Neural Network algorithms outperform other algorithms and were the most used in the literature. Conclusion: Although the use of AI models in solving EL problems is increasing, research on this topic is still in its infancy stage, and more research on this topic is necessary.