Anomaly detection at machine speed: catch problems before customers do.
Technology infrastructures are more deeply interconnected than ever, and subscribers are increasingly hyper-connected. The pressure on operators to resolve problems before they impact users, is intense. What was once a competitive edge is now a baseline expectation.
STL Partners identifies three critical AI use cases that are transforming telco operations: fault detection and resolution, network optimisation, and network planning. Each use case demonstrates how AI can shift operations from reactive to proactive, enabling telcos to deliver more reliable services while reducing costs and improving customer satisfaction.
1. Fault detection, prediction and resolution
AI is revolutionising fault management by enabling telcos to predict and resolve service impairments before they escalate. Machine learning models trained on historical data such as trouble tickets, firmware logs, and customer interactions, can identify root causes faster and more accurately than traditional methods. This reduces mean time to repair (MTTR), lowers labour costs, and improves customer experience.
Automation is also evolving. Instead of relying on static policies, AI can recommend and even execute fixes autonomously, provided the data is rich and detailed enough. This shift toward machine-led resolution means fewer outages, faster recovery, and less reliance on manual intervention - especially for common faults.
2. Network optimisation
AI-powered optimisation helps telcos balance traffic loads and maximise infrastructure efficiency. Self-organising networks (SONs) use real-time data to detect congestion, consult policy engines, and dynamically reconfigure network assets to maintain service quality. For example, Elisa’s deployment of a self-organising RAN led to a 20% reduction in customer complaints and a 2% improvement in CAPEX efficiency.
Sub-use cases include prioritising traffic to avoid SLA breaches, scheduling firmware updates during low-impact windows, and reallocating cloud resources to prevent virtual network function (VNF) bottlenecks. These capabilities allow telcos to deliver consistent service even during demand spikes or disruptions.
3. Network planning and upgrades
AI is also reshaping how telcos plan and expand their networks. With the rollout of 5G, operators are using AI to identify underserved areas, optimise base station placement, and intelligently provision resources for dynamic network slicing. Telefónica, for instance, used AI image recognition on satellite data to locate rural populations lacking coverage, then deployed networks accordingly.
As networks become more cloud-native and software-defined, AI will play a growing role in strategic planning, helping operators scale efficiently while maintaining SLAs and improving margins.
Numo Data’s perspective:
By investing in AI-driven fault prediction, real-time optimisation, and smart planning, telcos can catch problems before customers notice, reduce costs, and future-proof their networks. But enabling these capabilities starts with one critical component: high-quality, contextualised, AI-ready data.
