Features and Emerging Challenges of the “Researcher + AI” Tandem in Qualitative Sociological and Marketing Research

In recent years, qualitative sociological and marketing research has faced a paradox: the demand for in-depth interpretative analysis of large data arrays coincides with the exponential growth of information volumes and shrinking project timelines, making traditional analysis methods inefficient in terms of time and resource costs. The integration of artificial intelligence (AI) tools into qualitative analysis of sociological and marketing data helps address some technical challenges but generates fundamental methodological issues, primarily concerning the preservation of researcher reflexivity as the central element of validity in qualitative research.The article examines three key challenges: the problem of delegating interpretation to AI-based algorithms, the risk of algorithmic inertia and AI hallucination, and the question of methodological limits of AI application in sociology and marketing. The author proposes conceptualizing the "researcher + AI" tandem as a specific configuration of analytical practice where human reflexivity remains central, and AI serves as an auxiliary tool. Based on the author's research experience analyzing 86 in-depth interviews using the AI platform exeDrive, the article demonstrates the possibility of significantly reducing analysis time without compromising the quality and validity of findings, provided systematic control and methodological discipline by the researcher are maintained.The article proposes four procedures for working in the "researcher + AI" tandem: preparation and development of analysis architecture, iterative feedback system, two-level validation, and protocol for detecting systemic biases. The author also formulates additional criteria for AI-supported validity of qualitative research (algorithm transparency, human-involved validation, bias audit, result interpretability) and offers methodological solutions to minimize algorithmic bias. The article concludes on the need to transform the professional competencies of qualitative sociologists and introduce specialized AI methodology courses into academic curricula.