The geography of artificial intelligence in European regions: innovation, exposure and use
This paper maps the geography of artificial intelligence (AI) innovation, exposure and use across European Union (EU) NUTS-2 regions using six complementary indicators. Findings reveal a spatial paradox: while AI exposure and worker-level use are relatively widespread, innovation and firm-level adoption remain concentrated in high gross domestic product (GDP), human capital-intensive hubs. A persistent implementation gap separates structural exposure from productive use, driven by institutional, organisational and capability-related frictions. Income and human capital are strongly associated with AI activity, while trade openness shows a negative relationship. Results point to the need for place-sensitive policies combining support for leading regions with targeted capability-building in lagging ones.
Authors
- Pablo Casas (ORCID: https://orcid.org/0000-0003-0997-1666)
- Bernardo Buarque (ORCID: https://orcid.org/0000-0001-6370-7503)
- Dieter F. Kogler (ORCID: https://orcid.org/0000-0002-6744-5632)
- Ryan M. Hynes (ORCID: https://orcid.org/0000-0003-3023-955X)
- Simone Salotti (ORCID: https://orcid.org/0000-0003-1898-6683)
- Enrique Fernández‐Macías (ORCID: https://orcid.org/0000-0002-1087-2369)
- Ignacio González-Vázquez (ORCID: https://orcid.org/0009-0004-7066-9284)
Institutions
- University College Dublin (IE)
- University of Auckland (NZ)
Publication Details
- Journal
- Regional Studies
- Published
- 2026-06-25
- DOI
- https://doi.org/10.1080/00343404.2026.2681659
- Primary Topic
- Digital Economy and Work Transformation
- Type
- article
- Field-Weighted Citation Impact
- 0.00