Artificial intelligence (AI) is currently one of the most vividly discussed topics in innovation research and practice. One possible definition of AI is that it involves computer systems to perform tasks that would otherwise require human intelligence. In recent years, technological advances have enabled new applications of AI such as self-driving cars and intelligent chatbots, sparking hopes that it would soon also bring about improvements to the functioning of public organizations. Oftentimes, machine learning algorithms (supervised, unsupervised and reinforcement learning algorithms) are used to implement these applications. In this research focus, we study the possibilities, opportunities and risks of AI applications with a specific focus on public organizations.
In many areas of the public sector, there are both high expectations and numerous concerns regarding the use of AI. However, to date, the number of functional AI applications in public organizations is still very limited and it remains unclear which mechanisms lead to successful AI adoption. To improve our understanding of these mechanisms, this project empirically investigates the different approaches that public organizations take to implement AI projects. Specifically, the project explores AI adoption in different Swiss public organizations, focusing on organizations that have already started working on at least one AI project. In qualitative interviews, various possible factors influencing the success of AI adoption are examined, such as organizational, environmental or technological factors. The findings of the study are supposed to both advance the scientific debate and help public organizations to better understand the success factors of implementing AI projects.
Project led by: Oliver Neumann, Katharina Guirguis, Reto Steiner
