Systemic challenges in AI adoption in public social and health organizations in Finland: a technology-organisation-environment perspective

Abstract

Purpose – The aim is to identify the key technological, organizational, and environmental challenges affecting the adoption of artificial intelligence (AI) in public social and health organizations. The Technology-Organization-Environment (TOE) theory was used as a framework for the study. As AI is increasingly utilized, research is needed to support organizational management and development work. Design/methodology/approach – We employed a mixed-methods research design, utilizing a web-based survey that included both quantitative, structured questions and qualitative, open-ended questions. The data included answers from experts within the Finnish social and healthcare AI innovation ecosystem (n = 82), representing public, private, and third-sector organizations. A theory-driven content analysis was conducted for the qualitative data, and descriptive statistical analysis was performed for the quantitative data. Findings – The challenges of AI adoption form a systemic whole where factors are strongly interdependent. All 46 challenges were rated at least somewhat significant (mean ≥ 1.6, scale 0–3), with an overall mean score of 2.05. Organizational challenges emerged as the most critical, notably limited financial resources, insufficient AI competence, and inadequate change management. Among the environmental challenges, ambiguity in legislative interpretation and national funding shortfalls were particularly notable. Experts with prior involvement in AI projects rated challenges statistically less substantial than those with less experience. Originality/value – This study provides the first national-level analysis examining AI adoption challenges across all three TOE theory dimensions in public social and healthcare organizations, empirically demonstrating their systemic interdependencies through multi-stakeholder perspectives. Previous research has primarily focused on specific AI applications or individual organizational factors.