Artificial intelligence in Finnish art museums : current applications, implementation conditions and future directions

Abstract

This thesis explores how Artificial Intelligence (AI) is currently being used in Finnish art museums and analyses the factors shaping its implementation, with particular attention to associated needs and challenges. Conducted between August 2025 and March 2026, the research uses a qualitative, exploratory multiple-case study design involving museum professionals from seven major Finnish art museums: EMMA – Espoo Museum of Modern Art, Finnish National Gallery, Helsinki Art Museum, Lahti Museum of Visual Arts Malva, Sara Hildén Art Museum, Tampere Art Museum and Turku Art Museum.

The research combines a review of current AI applications through publicly available materials with interviews and email correspondence with museum professionals. The data were analysed thematically, forming the basis for an empirically grounded framework that helps explain current approaches to AI adoption in the art museum sector.

The findings indicate that AI adoption in Finnish art museums is still at an early stage and is mainly oriented towards operational uses. In most cases, AI is being used through informal, staff-led experimentation rather than being coordinated through an institutional strategy. This reflects not only the absence of formal internal AI governance structures, but also constraints related to resources, expertise and the broader social and policy context surrounding AI.

At the same time, this thesis argues that art museums are not only well placed to engage critically with AI as a force changing culture and society but also have a responsibility to develop opportunities for critical engagement with its implications through exhibitions, public programmes, and participatory initiatives.

Overall, this thesis offers an empirical overview of how AI is currently being adopted in Finnish art museums and provides a conceptual framework for understanding how it may be approached responsibly within the sector.