In recent years, AI-algorithms, namely deep learning-based algorithms, have improved auto-segmentation drastically. It is generally believed that the use of such tools will lead to lowered inter-observer variation and time savings for clinical staff. A wide palette of commercial deep learning-based auto-segmentation solutions are emerging with the promise of leveraging the aforementioned benefits. The selection and contouring of target volumes and organs-at-risk (OARs) has become a key step in modern radiation oncology. Concepts and terms for definition of gross tumor volume, clinical target volume and OARs have been continuously evolving (e.g. through ICRU reports 50, 62, 78, 83) and have become widely disseminated and accepted by the European and international radiation oncology community. From previous research is clear that instructor-led guideline workshops are effective in reducing the inter-observer variation, however, it is unknown if and how the introduction the artificial intelligence based auto-segmentation modifies this causation.