Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementArtificial intelligence became increasingly useful since the 1990s, trying to imitate the human brain with its thinking, reasoning, and learning using the key concepts of machine learning, deep learning, and artificial neural networks. Case-based reasoning (CBR), another form of artificial intelligence, stores and retrieves past cases that can be adapted to find a solution to a current problem. The new solution can then be retained and made available to solve other future problems. Business Process Management (BPM) analyzes and optimizes business processes to make them more effective and efficient for an organization’s strategy to ultimately increasing shareholder value. CBR can help to support BPM, making better decisions with existing knowledge when solving process problems. This study investigates effectively store, retrieve, and adapt Business Process Management Notation (BPMN) solutions that best fit the underlying BPM problem using case-based reasoning as a tool. Therefore, a theoretical model was proposed, containing each CBR live cycle phase with different possible tools applied to BPMN diagrams, which was validated by expert interviews. This study concludes that a whole CBR life cycle can be applied to BPMN diagram problems with the need for human intervention. This work did not have the objective to solve the whole problem but to contribute to a possible solution by using CBR through a theoretical model
