Abstract

The telecommunications sector in Uganda is undergoing a transformation driven by digitalization, increasing data 
traffic, and the adoption of next-generation technologies such as 5G and Open RAN. Artificial Intelligence (AI) and 
Machine Learning (ML) have emerged as central enablers of network automation, enabling predictive maintenance, 
fault detection, traffic optimization, and dynamic resource allocation with minimal human intervention. This review 
synthesizes the current state of AI/ML adoption in Uganda’s telecommunications sector, highlights local 
deployments and pilot initiatives, evaluates technical, economic, and regulatory challenges, and offers strategic 
insights for policymakers, network operators, and researchers. The study emphasizes that while Uganda has made 
early progress through operator engagements with the Telecom Infra Project (TIP), 5G rollouts, and network
sharing agreements, the full potential of AI-driven network automation is constrained by limited local expertise, 
infrastructure gaps, data availability issues, and evolving regulatory frameworks. Strategic interventions, including 
capacity building, investment in open and interoperable architectures, and robust data governance policies, are 
critical to achieving sustainable and scalable network automation.