Maximizing value capture from AI digital solutions : A case study of a startup in the wind energy industry

Sammanfattning: Purpose  The purpose of this study is to extend current literature on the concept of value capture for AI start-ups, focusing on the challenges they face and how to maximize value capture. By investigating relational and economical value capture dimensions, this study aims to identify opportunities for start-ups to extract value from their AI digital solutions. The study further aims to contribute valuable insights to the literature, by building on the link between digital revenue models and value capture.  Method  To fulfill the stated purpose, this study has adopted a qualitative, abductive single case study approach with a focus on an AI start-up in the wind energy industry. The analysis was based on 20 semi-structured interviews which were conducted with different companies active in the wind energy industry. All data was analyzed through a 5-step thematic analysis process.  Findings  Two main challenges a start-up may face were identified which were “Difficulties getting access to partnering companies” and “Difficulties selling as a start-up”. Additionally, it was found that relational value capture can be maximized using pilot studies, which is possible by building trust and close relationships. Regarding economical value capture, this study showcases the importance of adapting the choice of revenue model to the customer where the perceived risk of the investment, in the customers’ point of view, plays a big role.  Theoretical contributions  Previous literature has mainly established a connection between the concept of value capture and revenue models. However, this study further bridges the two, and more in depth displays how revenue models could affect the captured value regarding AI start-ups. Additionally, this study further elaborates on the literature regarding relational value capture, showcasing how it can differ for a start-up and the challenges that arise when AI is involved.  Practical contributions  This study contributes with concrete examples of what challenges a start-up needs to consider when trying to capture value from their product. Additionally, the study contributes with a practical understanding on how a start-up can maximize value capture, by showcasing important factors to consider, both when it comes to relational and economical value capturing. Moreover, a decision tree has been formed, which can support AI start-ups when choosing a suitable revenue model.  Limitations and future research  Firstly, the study's findings may not be applicable to other industries, highlighting the need for multi-industry case studies for generalization and cross-industry comparisons. Secondly, more in-depth research is needed to explore the specific steps and strategies for building relationships, especially in the context of start-ups. Thirdly, this study primarily focuses on the revenue model aspect of value capture, overlooking the concept of value proposition which limits the depth of the findings and contributions and would be of interest to further investigate.