Artificial Intelligence (AI) profoundly reshapes research methodologies, offering opportunities and challenges. This paper critically examines the algorithmic reconfiguration of qualitative inquiry, focusing on the tension between AI-driven efficiency and interpretive richness. It explores how AI capabilities in large-scale data processing, pattern recognition, multimodal analysis, and cross-lingual understanding alter qualitative research. While AI enhances speed, cost reduction, and overcomes language/survey barriers, these efficiencies risk superficiality, algorithmic bias, and ethical dilemmas. The paper discusses strategies for navigating this tension, evolving researcher skills, and the imperative for human-centricity to ensure AI genuinely augments insights, particularly within Service Science and digital transformation.