CNI Spring 2026 Membership Meeting

Academic libraries are under increasing pressure to deliver fast, high‑quality virtual reference services amid rising demand, staffing constraints, and expectations for always‑available support. Generative artificial intelligence (AI) is frequently marketed as a scalable solution, often without sufficient evidence about where automation might genuinely help or where it may introduce new burdens or risks of suboptimal service. This project briefing details a multi-institutional study of nearly 35,000 anonymized transcripts using a structured natural language processing (NLP) framework. It shares lessons learned from methodological evolution, testing a spectrum of approaches from off-the-shelf interfaces (Microsoft Copilot, Gemini) to a custom-engineered API pipeline. By pairing human expertise with an auditable codebook, the framework generates AI-reasoning trails for every assigned code, allowing researchers to see exactly where model logic aligns with or diverges from professional judgment. The results highlight promising opportunities for responsible automation but also document the extensive iterative cleaning and error-correction cycles necessary to achieve reproducible results. By foregrounding these insights and the often-invisible labor required, the session will provide library leaders with a realistic roadmap for responsible AI integration, moving beyond theoretical promises to offer evidence-based guidance on staffing, workflow design, and long-term strategy in scaling AI-assisted virtual reference.