  Machine learning (ML) is an important component for enabling automation in
Radio Access Networks (RANs). The work on applying ML for RAN has been under
development for many years and is now also drawing attention in 3GPP and
Open-RAN standardization fora. A key component of multiple features, also
highlighted in the recent 3GPP specification work, is the use of mobility,
traffic and radio channel prediction. These types of predictions form the
intelligence enablers to leverage the potentials for ML for RAN, both for
current and future wireless networks. This paper provides an overview with
evaluation results of current applications that utilize such intelligence
enablers, we then discuss how those enablers likely will be a cornerstone for
emerging 6G use cases such as wireless energy transmission.
