This thesis considers the socio-technical infrastructure surrounding AI-enabled Handwritten Text Recognition (HTR), the process of converting images of historical textual materials into computer-readable text. Despite sectoral awareness of the technology, institutional approaches still display gaps between conceptualising and operationalising HTR. The following main research question is answered: What is the impact of HTR on libraries, users, and the wider information environment? Foundational knowledge is provided by exploring HTR's relation to other computational methods and traditional areas of palaeography and papyrology. Through a mixed methods approach, including interviews with National Library of Scotland (NLS) staff, survey methods and thematic analysis, this thesis approaches HTR from multiple vantage points. It critically reflects on HTR’s affordances regarding library audience development by adapting digital engagement strategies, as well as the practicalities in embedding the technology within content-holding institutions. In aligning insights from HTR developers, providers and users, a technical fluency of how to operationalise HTR institutionally is presented. This informs an understanding of HTR’s potential near future implications on the information environment and research. Such analysis results in a set of recommendations for HTR’s future provision, directed thematically at libraries, HTR users and developers. These recommendations include ways to enhance institutional processes, such as digital preservation and audience engagement, in relation to HTR outputs; as well as assessing general HTR capability locally. Other recommendations involve enabling greater collaboration in HTR projects: through dataset sharing principles, standardised metadata and flexible tool usage.