Think of the mobile network as a huge city full of roads (radio links) and traffic (data). AI in 5G/6G is like a smart traffic control system that constantly watches congestion, predicts where it will build up, and automatically opens new lanes, changes traffic lights, and reroutes cars so everything flows faster and more reliably without humans having to tweak every detail.
Traditional telecom networks are too complex and dynamic to be efficiently managed by manual rules and human operators. AI helps automate and optimise radio resource allocation, fault detection, energy usage, and service quality across thousands of cells and devices, enabling higher performance, lower operating costs, and support for new 5G/6G use cases (IoT, ultra-low latency, massive connectivity).
Potential moats come from proprietary network data (traffic patterns, RF measurements, faults), deeply integrated OSS/BSS and RAN tooling, and long-term optimisation know‑how embedded in models and workflows. Vendors that can combine AI with existing telecom-grade reliability and standards compliance gain strong stickiness with operators.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Real-time inference latency and throughput for large-scale RAN optimisation, plus data privacy and governance across distributed network elements.
Early Majority
Compared with generic AI platforms, AI for 5G/6G must integrate tightly with RAN, core, and OSS/BSS, operate under strict latency/reliability constraints, and comply with telecom standards. Differentiation typically lies in telco-grade robustness, use of proprietary network data, and pre-built models for tasks like traffic prediction, anomaly detection, self-organising networks, and energy optimisation.