We simultaneously modelled ethane, propane, and n-butane oxidation reactions using MT-SISSO with reaction-dependent primary features. This allowed us to obtain universal property-function relationships in the form of analytical expressions. These relationships apply to all three oxidation reactions at the same time, even though they incorporate the response of the catalyst to each specific reaction environment. Indeed, the key physicochemical parameters identified by AI are not only catalyst properties under thermodynamic standard conditions, but also properties of the working catalysts. Therefore, the crystal structure and translational repetitive arrangement of atoms in the surface, used in conventional catalyst design approaches and theoretical models, is insufficient to describe selective oxidation catalysis. The crucial role of the catalyst modulation by the reaction environment highlighted by AI is consistent with the fact that the application of simple test reactions, such as temperature-programmed reduction and oxidation or CO oxidation, does not provide an efficient identification of potential catalysts. The catalyst is in a different chemical state under the conditions of the probe reaction compared to the case of alkane oxidation reaction conditions due to the different chemical potential. Thus, the incorporation of in situ characterization into catalyst design strategies is crucial.