Research in material science requires to investigate huge parameter spaces. The number of new chemical compounds with potentially attractive properties or chemical compounds that are candidates to fulfill targeted material properties are to numerous to be counted. This requires a paradigm change in the way that we are operating our labs. We are heading to automated and autonomeous laboratories that can investigate a large number of compositions in a short time or solve complex material problems guided by machine learning and artifical intelligence. We are providing hard- and software solutions for the labs of the future.