Digital Twins (DTs) create fully-synchronized virtual representations of real-world systems, which can serve as interactive counterparts for artificial intelligence (AI) and machine learning (ML) algorithms, and hold significant importance for the upcoming 6G mobile networks. In this paper, we argue that DTs can improve all phases of the intelligent networks' workflow, due to their adaptability and scalability properties that would allow them to transparently integrate new AI/ML algorithms faster, more scalably, and more precisely. Our contribution is two-fold: first, we propose three specific application scenarios of DT-enhanced network architectures in the context of 6G. Second, using open-source tools, we implement and evaluate in detail one of them. Our results demonstrate that our DT reflects the characteristics of the physical object, successfully and scalably twinning it, and adapting to changing contextual conditions.