  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.
