Automated coding applies machine learning and natural language processing to qualitative research data, identifying recurring themes, sentiments, and patterns across large volumes of interview transcripts, open-ended survey responses, or video session recordings. In traditional qualitative research, manual coding is one of the most time-intensive steps, often requiring researchers to read, tag, and reconcile codes across hundreds of responses before analysis can begin. Within AI-moderated research workflows, automated coding compresses this process significantly, allowing insights teams to move from raw data to structured thematic output in hours rather than days. The result is a traceable, auditable coding structure that experienced researchers can review, refine, and build on, rather than build from scratch.