  User demand on network communication infrastructure has never been greater
with applications such as extended reality, holographic telepresence, and
wireless brain-computer interfaces challenging current networking capabilities.
Open RAN (O-RAN) is critical to supporting new and anticipated uses of 6G and
beyond. It promotes openness and standardisation, increased flexibility through
the disaggregation of Radio Access Network (RAN) components, supports
programmability, flexibility, and scalability with technologies such as
Software-Defined Networking (SDN), Network Function Virtualization (NFV), and
cloud, and brings automation through the RAN Intelligent Controller (RIC).
Furthermore, the use of xApps, rApps, and Artificial Intelligence/Machine
Learning (AI/ML) within the RIC enables efficient management of complex RAN
operations. However, due to the open nature of O-RAN and its support for
heterogeneous systems, the possibility of misconfiguration problems becomes
critical. In this paper, we present a thorough analysis of the potential
misconfiguration issues in O-RAN with respect to integration and operation, the
use of SDN and NFV, and, specifically, the use of AI/ML. The opportunity for
AI/ML to be used to identify these misconfigurations is investigated. A case
study is presented to illustrate the direct impact on the end user of
conflicting policies amongst xApps along with a potential AI/ML-based solution
to this problem. This research presents a first analysis of the impact of AI/ML
on misconfiguration challenges in O-RAN.
