(untitled) (PILAR, 2025)
Digital transformation in the world of libraries requires innovation in collection management, especially in the aspect of cataloguing that affects the quality of information retrieval. The manual cataloging process is often considered time-consuming, competent experts, and prone to human error. Artificial Intelligence (AI) is emerging as a technology that offers solutions in cataloging automation through the ability to extract metadata, automatic subject classification, and align entry headers based on specific standards. This study analyzes the implementation of AI in cataloging automation in digital libraries with a case study approach. Data is collected through system observations, interviews with librarians, and analysis of the metadata output generated by the AI system. The results show that AI is able to improve time efficiency by 40–60%, strengthen classification consistency, and reduce human error. However, there are obstacles in the form of limited local datasets, the need for HR training, and reliance on the quality of digital documents. These findings are expected to be the basis for libraries to develop AI integration strategies to improve the quality of information services.