Empirical AI Transformation Research: A Systematic Mapping Study and Future Agenda

Background: Intelligent software is a significant societal change agent. Recent research indicates that organizations must change to reap the full benefits of AI. We refer to this change as AI transformation (AIT). The key challenge is to determine how to change and which are the consequences of increased AI use. Aim: The aim of this study is to aggregate the body of knowledge on AIT research. Method: We perform an systematic mapping study (SMS) and follow Kitchenham’s procedure. We identify 52 studies from Scopus, IEEE, and Science Direct (2010–2020). We use the Mixed-Methods Appraisal Tool (MMAT) to critically assess empirical work. Results Work on AIT is mainly qualitative and originates from various disciplines. We are unable to identify any useful definition of AIT. To our knowledge, this is the first SMS that focuses on empirical AIT research. Only a few empirical studies were found in the sample we identified. Conclusions We define AIT and propose a research agenda. Despite the popularity and attention related to AI and its effects on organizations, our study reveals that a significant amount of publications on the topic lack proper methodology or empirical data.