The rapid progress of digital chemistry has profoundly transformed chemical research. Despite this evolution, there are implementation gaps that hinder the widespread adoption of such digital protocols among a significant portion of the chemistry community. For example, technologies such as computational chemistry and machine learning often present steep learning curves that discourage potential users who could otherwise benefit from them. This review focuses on classical and recent advances in the automation and generalization of digital chemistry, examining the evolution of the field while highlighting popular cheminformatics tools.We elaborate on efforts from different groups in automating quantum chemistry and machine learning workflows, with the goal of bridging implementation gaps and making these technologies accessible to the broader chemistry community.