Artificial intelligence (AI) methods are currently experiencing rapid development and are also being used more and more frequently in the context of environmental data. However, this use is often in the context of isolated solutions. The systematic use of modern AI methods is not yet established in environmental and Earth system sciences. In particular, there is often a discrepancy between the requirements of a solid and technically sound environmental data analysis and the applicability of modern AI methods such as deep learning for researchers. The KI:STE project (AI strategy for Earth system data) closes this gap with a sophisticated strategy that combines the development of diverse AI applications on different socially relevant aspects of environmental and Earth system research with a strong training and network concept. It creates the technical prerequisites to make high-performance AI applications on environmental data portable for future users and to establish environmental AI as a key technology.