The growing aviation industry asks for innovative solutions to be able to handle the increasing amount of baggage. An example of such an innovative solution is Computer Vision Technology (CVT), which uses cameras to identify bags using data and artificial intelligence. The value of CVT for the baggage handling ecosystem is currently unknown. Besides this practical knowledge gap, a scientific gap is found as well. The majority of the literature on digital transformations has an organizational viewpoint and does not incorporate established ecosystem perspectives. It is unknown how a value proposition needs to be designed for a data driven technology to lead to a digital transformation of an established ecosystem. A DSR approach is executed in a situated setting at Schiphol Airport to capture the value proposition of CVT for the baggage handling ecosystem by the identification of use cases. . The results show that the implementation of CVT provides value for Schiphol Airport, the baggage handling system provider, airlines, handlers, passengers, and society. The value proposition of CVT is the automated identification of bags based on visual images that provides thirteen use cases applicable throughout the whole baggage handling process, which leads to more autonomous processes, process improvement, the generation of more (types of) valuable data compared to the current identification techniques and can contribute to the achievement of sustainable goals if it replaces the current identification techniques. The results not only contribute to the aviation industry, but the insights gained during the research are also valuable for future digital transformations within other established ecosystems. During the research, a lack of ecosystems’ support for the digital transformation was identified, caused by two factors. It was found that certain process choices had a positive influence on these two factors, which inspired the formulation of process guidelines. These guidelines contribute to the digital transformation knowledge base as they provide insights into how to enhance ecosystems support for digital transformations. In this way, it guides future digital transformation processes within established ecosystems. Furthermore, the research provides an approach to get a grip on a complex established ecosystem and a tool to specify data-driven use cases in combination with its implications for the established ecosystem. No tool existed to accommodate that. Therefore, a tool was constructed and used, which provided guidance on the use cases’ specification and could be valuable within future ideation processes of data-driven use cases for established ecosystems.