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Large datasets or 'big data' corpora are typically the domain of quantitative scholars, who work with computational tools to derive numerical and descriptive insights. However, recent work asks how computational tools and other technologies, such as AI, can support qualitative scholars in developing deep and complex insights from large amounts of data. Addressing this question, Jiang et al. found that qualitative scholars are generally opposed to incorporating AI in their practices of data analysis. In this paper, we provide nuance to these earlier findings, showing that the stage of qualitative analysis matters for how scholars believe AI can and should be used. Through interviews with 15 CSCW and HCI qualitative researchers, we explore how AI can be included throughout different stages of qualitative analysis. We find that qualitative scholars are amenable to working with AI in diverse ways, such as for data exploration and coding, as long as it assists rather than automates their analytic work practice. Based on our analysis, we discuss how incorporating AI into qualitative research can shift some analytic practices, and how designing for human-AI collaboration in qualitative analysis necessitates considering tradeoffs in scale, abstraction, and task delegation.