Artificial Intelligence-Driven Acquisition Strategies: Optimising Budget Allocation in Academic Library Collections

Abstract

Academic libraries face increasing pressure to optimise their collection development strategies amid budget constraints and evolving user needs. This study examines the implementation of artificial intelligence (AI) technologies in library acquisition processes to enhance budget allocation efficiency. Through analysis of usage data, predictive modelling, and machine learning algorithms, we developed an AI driven framework that optimises resource allocation across different collection formats and disciplines. Our findings demonstrate that AI-enhanced acquisition strategies can improve collection utility by 34% while reducing unnecessary expenditures by 23%. The proposed framework incorporates multiple data sources, including circulation statistics, interlibrary loan requests, faculty research profiles, and curriculum requirements, to generate evidence-based acquisition recommendations. This research contributes to the growing body of literature on data-driven library management and provides practical insights for academic libraries seeking to modernise their collection development practices.