Process mining (PM) is a big data analytics technology assisting organizations in process optimization by creating insights from event log data available in existing information systems. Although research on PM utilization exists, literature on the adoption phase is scarce. Hence, organizations lack an understanding of how to determine suitable use cases. Accordingly, we followed a design science-based approach and systematically identified twenty criteria, e.g., process variants, processual weaknesses, and analytical skills, to select suitable use cases for PM adoption. The criteria were evaluated with Celonis and Munich Airport and guide PM vendors, organizations, and consultancies through the evaluation process. Hence, we contribute to the early steps of PM diffusion by assisting in determining its consequences and founding the adoption decision. Future research may consider the criteria as a research framework to investigate their effects on the adoption decision.