The rapid rise of generative artificial intelligence (GAI) is changing qualitative inquiry. Tools such as ChatGPT and Claude offer unprecedented accessibility and analytical capacity, but their use introduces epistemic and ethical risks that threaten the interpretive core of qualitative research. This article demonstrates that while GAI promises efficiency, it systematically risks interpretive conservatism, i.e., favoring predictable and descriptive interpretations, and interpretive depletion, i.e., excluding researchers’ contextual and reflective judgment. Drawing on an empirical study of teachers’ discursive practices in a Swedish digital professional development project, we compare three analytical approaches: humanistic discourse analysis, text mining, and GAI-assisted thematic analysis. The comparison reveals both the power and the limitations of GAI: while it reliably reproduces instrumental and descriptive patterns, it fails to capture subtle, contextually grounded discourses, such as teachers’ negotiation of digitalization as a socio-cultural boundary object. We propose a strategy of epistemic risk management based on complementarity rather than coherence: each method compensates for the epistemic vulnerabilities of the others. Human interpretation mitigates GAI’s conservatism and interpretive depletion, text mining counterbalances human bias, and GAI manages statistical artifacts. This integrative design preserves interpretive depth while embracing computational breadth, ensuring qualitative research remains rigorous, reflexive, and contextually sensitive in the age of GAI.