Expanding AI adoption in public sector organizations: perspectives on management practices

Abstract

Purpose

The purpose of this study is to enhance the understanding of management practices that expand artificial intelligence (AI) adoption in public organizations. Design/methodology/approach

The research approach is an exploratory study. Research data were collected from informants representing various organizations working for public or government services in Finland. Findings

The findings of this study indicate that large-scale AI adoption is a complex process in the public sector. This study identified three main practices of AI adoption: technological design practices, AI project design and management practices and networking practices. Additionally, this study shows that several value drivers and barriers to AI adoption are related to technological, organizational and environmental dimensions. This study emphasizes the importance of developing cross-functional AI capabilities and resources, which are crucial for expanding AI initiatives across organizations and networks. Practical implications

Expanding and scaling AI adoption across organizational boundaries requires new management practices and multidisciplinary teamwork, from technological skills to AI governance practices and change management. Originality/value

This study contributes to the emerging research on management practices involved in AI adoption in the public sector.