Beyond Research: Translating AI-Enabled Telecommunications Innovation into National-Scale Implementation

Artificial Intelligence (AI) has presented great opportunities in improving the telecommunication networks in terms of predictive analytics, automated fault management and intelligent resource allocation. Most AI-driven solutions, however, are limited to lab work or pilot projects, which do not have the potential to affect operational networks. The paper will focus on the urgent requirement to commercialize AI telecommunications ideas, the concept of experimental research to practical infrastructure at a national level. We concentrate on the use of Convolutional Neural Networks (CNNs) in extracting high-quality features on complex network traffic and signal data, and the use of Random Forest models in decision-making that is robust and interpretable, and thus can be used in real-time. The framework allows scalable, trustworthy, and interpretable AI functions in geographically split networks by combining these models into a hybrid framework. The planned solution can be used as a feasible roadmap to a nationwide implementation, increase network resiliency, service continuity, and regulatory-compliant operations, improving the modernization and operational intelligence of the U.S. telecommunications ecosystem.