Qualitative research is essential for shaping person-centered public health policy and practices, particularly within complex care systems like transplant care, which involve multidisciplinary teams managing clinically complex patients through coordinated, multi‑step care amid variation in practices, policies, and partnerships across centers. However, its time and resource demands limit scalability and timeliness. Generative artificial intelligence (AI) technologies offer unique opportunities to transform and accelerate qualitative research; yet strengths and limitations of its use in transplant research remain unclear. Two case studies compared AI-Media’s LEXI Recorded (January-July 2024 version) and Microsoft Copilot (July 2025 version) against human researchers for transcription and thematic analysis, respectively. Fourteen semi-structured interviews with dialysis and transplant center leaders (January-July 2024) were transcribed using LEXI Recorded, with quality assurance conducted by researchers. Comparisons between initial AI-generated transcripts and final transcripts focused on time and labor costs, document formatting, speaker identification, flow and coherence, content accuracy, and redaction of identifiers. Transcription accuracy was evaluated using character error rate (CER) relative to final transcripts. For thematic analysis, two qualitative researchers manually coded 616 open-ended survey responses from dialysis staff; subsequently, an analyst applied the same methods using Microsoft Copilot. Comparisons focused on thematic overlap, efficiency, and dimensions of Lincoln and Guba’s trustworthiness framework, including credibility, transferability, dependability, and confirmability. AI transcription produced complete transcript drafts with an average CER of 1.77% (range: 0.82%-2.87%), demonstrating strong but variable accuracy and an estimated savings of four hours and 26 per transcript). However, AI-generated transcripts performed worse in document formatting, flow and coherence, speaker identification, accuracy, and redaction of identifiers. While thematic analysis comparisons revealed overlapping themes, notable differences emerged across trustworthiness dimensions. Manual analysis offered context-sensitive insights, clear attribution of perspectives, and synthesis across social and systemic factors, incorporating emotional resonance and theoretical frameworks to inform policy and practice. AI-generated themes were compartmentalized, with limited connection to broader implications, often flattening complex human experiences. Contemporary AI tools offer significant efficiency gains in transcription and analysis but fall short in contextual richness. Findings support using structured, researcher-led hybrid human-generative AI workflows to ensure ethical, rigorous qualitative research using AI.