The growing availability and popularity of AI tools, especially large language models, has become a source of sharply divergent opinions – ranging from enthusiastic to deeply concerned. There is no doubt that involving machine learning in solving our everyday problems not only affects how quickly we address them but also changes the way we approach these problems. In the proposed long paper, we would like to present two AI-driven automation solutions that have been successfully integrated into our workflow for a collaborative digital scholarly edition. We emphasize the process of developing ML models and our experience with tools and programming libraries which can be used by humanists without formal ML training (Transkribus, Hugging Face: Wolf et al. 2020). As Cugliana and her colleagues (2024) pointed out, many scholars suggest that current digital scholarly editing has not yet taken advantage of the opportunities emerging from the AI revolution. We believe that this transition should be encouraged as it is getting easier.