This study proposes a method to be used in the strategic decision-making process, taking into account the identification and prioritization of the potential risks of Management of Change (MOC) in an industrial environment. The analytical hierarchy process (AHP) and the Bayesian Belief Networks (BBN) were used to assess the risks that could affect the regular operations, in order to generate data for an effective decision-making process, in addition, concepts of machine learning and artificial intelligence (AI) were introduced so that the analyzes can be done in a more automated way, generating reports that can assist in decision making. No previous work was found dealing with analysis to prioritize risks arising from MOC in any type of industry. As a result of this study, a global risk matrix was proposed. The factors that most impact are Lack of Stakeholder involvement, Lack of risk assessment in the MOC process and Lack of knowledge of employees involved in the MOC process. Culminating 12 steps were created to implement a risk-free MOC process. The study provides a method to be used by professionals, engineers and decision makers to identify risk factors that could affect companies operations.