Generative Artificial Intelligence (GenAI) is reshaping qualitative marketing research, and new questions now surround how human-machine collaboration can amplify rather than replace researchers’ insight and creativity. This chapter examines four touchpoints where that shift is already visible. First, synthetic participants offer instant pilot testing and what-if scenario exploration. Second, AI interviewers scale conversational depth to hundreds of sessions running in parallel. Third, automatic transcription removes clerical delays, delivering searchable text moments after each interview. Fourth, large language-model coding proposes preliminary thematic maps that provide researchers with a structured starting point rather than a blank page. Yet, these same systems inherit biases from their training corpora, exhibit output drift, and raise concerns about data protection. The chapter proposes a staged guideline that positions generative AI as an audited assistant under continuous human supervision. Key safeguards include systematic bias checks, locked model versions, on-premises processing for sensitive materials, and transparent logging of every parameter choice. The guidelines may aid marketers and researchers in aligning machine consistency with contextual judgment. The future research agenda proposed highlights the need for cross-cultural validation, longitudinal replication, explainable interfaces, and privacy-preserving engineering.