From hype to practice: Leveraging artificial intelligence to enhance archival discovery and accessibility

Abstract

Archives and cultural heritage institutions face a mounting backlog of uncatalogued or sparsely described materials that constrain public access and scholarly research. This paper examines the deployment of artificial intelligence (AI) as a practical, strategic tool to alleviate these backlogs, enhance collection discoverability, and advance the mission of democratic access to historical records. Case studies showcase large language models and computer-vision techniques applied to handwritten prisoner-of-war index cards, administrative records and extensive photographic and video archives. Each case study outlines the workflow for extracting structured metadata at scale or the enabling of novel visual and semantic search capabilities. Results demonstrate that AI can process photographs, video footage and handwritten index cards with remarkable speed and accuracy. Structured metadata derived from these methods unlocks new genealogical and historical research pathways by transforming unstructured content into searchable datasets. While acknowledging ongoing challenges, including transcription errors, algorithmic bias and seamless integration into existing archival workflows, the findings affirm that AI has matured into an accessible, scalable solution for reducing persistent cataloguing backlogs across institutions of all sizes, augmenting, rather than replacing, the indispensable expertise of professional archivists. The paper presents a straightforward framework to help archival organisations launch their own AI initiatives to accelerate backlog reduction, elevate institutional visibility, unlock fresh avenues for research and ultimately broaden public engagement with cultural heritage collections. This article is also included in The Business & Management Collection which can be accessed at https:// hstalks.com/business/.