Generative artificial intelligence (GenAI) can now perform many activities traditionally associated with management case analysis, including summarizing cases, identifying problems, applying analytical frameworks, generating alternatives, and drafting recommendations. This creates a practical challenge for case teaching because plausible outputs do not necessarily reflect students’ engagement in the reasoning and managerial judgment that case-based learning is intended to develop. This paper examines the implications of GenAI for graduate management case teaching and proposes practical adjustments across three stages of the teaching process. Before class, students are encouraged to develop an initial problem formulation before consulting GenAI. During class, AI-generated outputs can be used as material for evaluating evidence, questioning assumptions, comparing alternatives, and defending decisions. After class, written assessment can be supplemented with information about students’ reasoning and use of GenAI. The paper argues that the objective should not be to exclude GenAI from case teaching, but to design learning activities that keep students actively engaged in problem formulation, critical evaluation, and managerial judgment.