The widespread integration of Artificial Intelligence has redefined traditional library automation by converting it into intelligent knowledge-driven systems. This study adopts a qualitative research approach to assess the performance of AI-based library automation in selected university libraries of Rajasthan. Secondary sources, including library usage records and institutional documentation, are utilized to examine key parameters such as operational effectiveness, user satisfaction, and managerial decision-support functions. The analysis applies advanced data-handling methods, including descriptive and comparative techniques along with aspect-oriented evaluation. The findings demonstrate that AI-supported library systems offer substantial improvements in search precision, circulation management, and customized user services when compared with conventional automated platforms. However, issues related to technological infrastructure, staff competency, and data protection remain significant constraints. These results offer meaningful guidance for policymakers and academic authorities seeking to develop efficient, secure, and intelligent library systems.
Katiyar et al. (Sun,) studied this question.