  Digital Twins (DTs) are set to become a key enabling technology in future
wireless networks, with their use in network management increasing
significantly. We developed a DT framework that leverages the heterogeneity of
network access technologies as a resource for enhanced network performance and
management, enabling smart data handling in the physical network. Tested in a
Campus Area Network environment, our framework integrates diverse data sources
to provide real-time, holistic insights into network performance and
environmental sensing. We also envision that traditional analytics will evolve
to rely on emerging AI models, such as Generative AI (GenAI), while leveraging
current analytics capabilities. This capacity can simplify analytics processes
through advanced ML models, enabling descriptive, diagnostic, predictive, and
prescriptive analytics in a unified fashion. Finally, we present specific
research opportunities concerning interoperability aspects and envision
aligning advancements in DT technology with evolved AI integration.
