In this study, the initial problem is the capacity of the human mind to set up a conceptual model. The novelty of this article is that we show that defined concepts created by the human mind can be passed on to an artificial intelligence-based expert system. The expert system helps the human mind to settle the logical connections between the defined terms and the conceptual model thus the created model will be better than what man could have set up without an expert system. The appearance and role of employee turnover has become a more and more important factor in the daily business of corporate life. Scientific journals have examined the positive and negative effects of it, which has also provided useful knowledge for practitioners. These articles have examined the impact of employee turnover in countless aspects, but one aspect was the same in all literature: the impact of turnover on a company is not negligible and cannot be ignored in terms of neither material nor moral aspects. As a starting point for our research, we systematically reviewed the literature on employee turnover and selected six concepts that are bilaterally related to our phenomenon. Based on the terms and the correlation of it, we created a conceptual model that was examined with the help of an artificial intelligence-based system. To select a system, we reviewed the classifications of the artificial intelligence-based systems which can model human decision making and can help our research. Relying on the processing of the literature review articles, we selected and briefly characterized a rule-based reasoning system, and investigated the rule constellations of it, which can model the turnover cases as the topic of our study. Based on our experience in observing, consulting, and working with decision-makers, we examined the aspects of employee turnover phenomenon in the analysis and we constructed a three-level model that found logical relationships between each subcategory and was able to realistically reflect certain behavioral patterns of the physical workforce of a manufacturing company. The analysis was performed using a rule-based system, which used logical rules and found classical “if-then” connections in the employee behavior cases. According to our examination, our outcomes can provide credible results for further research activities as well as for practitioners.