An Analysis of Artificial Intelligence (AI) Capability in Libraries and Archives

This paper seeks to evaluate the AI capability of libraries and archives using a qualitative content analysis of 54 case studies of AI uses published between 2018 and 2024. It is framed by the model of AI capability proposed by Mikalef and Gupta (Patrick Mikalef and Manjul Gupta, 'Artificial Intelligence Capability: Conceptualization, Measurement Calibration, and Empirical Study on Its Impact on Organizational Creativity and Firm Performance', Information & Management 58, no. 3 (2021): 103434.). The findings of the analysis largely confirm the model, but suggest that there are many gaps in library and archive AI capability, especially in areas such as infrastructure and technical resources, data issues arising from metadata inconsistencies, and financial resources.