[Deliverable] D3.2 Digital Transformation and ICT v.2.0

National Libraries are beginning to explore the use of AI (artificial intelligence) tools to enhance their digital transformation efforts. Previous digital innovations have included augmented and virtual reality tools, eBooks instead of physical lending, and the use of social media to engage patrons. However, AI presents new risks and opportunities for transforming library services.
Libraries play a crucial role in preserving information for future generations and democratizing knowledge access. They promote digital literacy among patrons and library staff. Despite this, the use of AI in libraries has yet to be thoroughly studied. This book and its chapters aim to understand how libraries integrate AI into their services, the main drivers of this adoption, and the barriers they face.

This deliverable contains the findings of year 2 of the EU LibrarIn work package 3 task 3.1 “Digital transformation and ICT v02”. The deliverable includes a short overview of the methods that were used to a) identify the relevant case studies, b) a short case illustration of each case using a shared case illustration template, and c) a summative overview of the findings that will serve as a starting point for the cross-case analysis that is planned for year 3.

Based on a shared interview guide, the partners in this task conducted case interviews with national libraries in Europe to understand their adoption and use of artificial intelligence (AI) tools to fulfill their mission.

The participating partners include:

• UKON, Ines Mergel, Anna-Lea Schumann, Justus Kühler, Carsten Schmidt, University of Konstanz, Germany
• ULILLE, Antoine Henry, Université de Lille, France
• LC, Francesco Molinari, The Lisbon Council for Economic Competitiveness ASLB, Belgium
• UAH, Alberto Peralta, Universidad da Alcala, Spain
• RUC, Ada Scupola, Roskilde Universitet, Denmark
• LIBER, Barbara van der Vaart, Stichting Liber, Netherlands

As a group, we collected 13 case studies on the implementation of artificial intelligence in national libraries. The case studies were extracted from expert interviews with the executive directors of the international library associations (see, deliverable 01).

Using criteria derived from an extensive systematic literature on digital transformation in libraries as well as expert interviews, we identified 13 National Libraries for our case studies. Each library had at least one AI project underway or developed. We conducted case interviews with project leaders, information or cataloging experts, and users, with librarians often being the primary users of AI services. In an exploratory interpretative research design, we conducted expert and case study interviews in the following National Libraries:


1. Koninklijke Bibliotheek van België, BE
2. Deutsche Nationalbibliothek, DE
3. Det Kongelige Bibliotek, DK
4. Eesti Rahvusraamatukogu, EE
5. Biblioteca Nacional de España, ES
6. Kansalliskirjasto, FI
7. Bibliothèque nationale de France, FR
8. Koninklijke Bibliotheek, NL
9. Nasjonalbiblioteket, NO
10. Kungliga biblioteket, SE
11. Narodna in univerzitetna knjižnica, SI
12. British Library, UK
13. Library of Congress, US

We found that Northern European libraries are the most advanced in the adoption of AI and started earlier than their European counterparts to experiment with AI in cataloging, tagging, and conversion of historic text into digital text documents. They are followed by the Benelux countries and very rare Southern European National libraries. This peculiarity was already pointed out to us in the preceding interviews with the executive directors of the international library associations. Nevertheless, we made every effort to also include Eastern and Southern European cases.

Next, we will present our shared research design that was in large parts similar to tasks 3.2 and 3.3 in the same work package but overall focused on different tasks and technologies that National libraries use to digitally transform themselves. We will then present our 13 case illustrations and end the report with an overview of the main findings.

At this point in the project, we won’t be providing recommendations as deliverable 3.1 v02 was designed as an intermediary step on the way to the presentation of the cross-case analyses (deliverable v03 in year 3).