In today's data-driven landscape, understanding user behavior is critical for creating intuitive library services. This research examines all search queries conducted over a single day in Primo VE, utilizing AI tools to extract actionable insights into user search behavior. By analyzing dimensions such as peak search hours, classifications by subject, query types (e.g., author, title, subject/concept), academic disciplines, and search intent, we demonstrate how AI can transform raw search data into meaningful knowledge. Our findings highlight opportunities to enhance discovery services, improve search relevance, and inform strategic decisions in collection development. This session showcases the transformative role of AI in academic libraries and explores how these technologies can be leveraged to better understand and meet user needs.