In many organizations, employee turnover represents a significant challenge impacting their success. Every employee is a resource trained and gained skills over the years, and his departure will constitute a loss for the company. To address this problem, machine learning (ML) algorithms are used to predict turnover rates and cases. This prediction can help organizations develop their human resources strategies by proposing recommendations that could be extracted. In this work, we aim to highlight the effectiveness of two robust Explainable AI (XAI) models, Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME), in elucidating the key factors influencing employee turnover. By providing clear insights from the data, these models offer valuable information that can help management develop human resources strategies to mitigate the risk of employee turnover.