Microkinetic models (MKMs) derived from density functional theory (DFT) are a powerful tool in the field of heterogeneous catalysis, linking DFT-predicted kinetics to experimental observables such as reactant conversions and product selectivities. These models have been successfully leveraged for optimizing reactor conditions, elucidating rate determining steps, and high-throughput catalyst screening. However, as advanced experiments provide more detailed insights into these complex systems, such as site-specific kinetics and the coupling of surface and gas-phase reactions, there is a growing demand for models that can better capture the complexity of real catalytic processes. Traditional approaches in the construction of DFT/MKMs are manually intensive and computationally expensive, limiting their comprehensiveness and necessitating the use of approximations which reduces their accuracy. Coupling automation with strategies to reduce computational expense provides a pathway to address these challenges and accelerate MKM development.