Indirect bonding today represents the gold standard of precision in orthodontic bracket placement, reducing the errors typical of the direct technique and optimizing clinical chair time. However, the stages of dental segmentation on digital models and virtual set-up planning often remain operator-dependent and time-consuming. This presentation offers an overview of the most recent applications of Artificial Intelligence in automatic dental arch segmentation and in the digital indirect bonding workflow, illustrating the working principles of neural network-based segmentation algorithms, their impact on accuracy and clinical chair time compared to traditional manual segmentation, and the clinical implications for transferring bracket positioning via digital transfer trays. The advantages, current limitations, and future perspectives of these technologies will also be discussed, with the aim of providing clinicians with practical tools to navigate the integration of AI into everyday orthodontic practice.