Luca BertonTelecom AI: Where Milliseconds and Regulations Collide
Telecommunications providers are among the most data-rich enterprises in the world. Call detail records, network telemetry, subscriber behaviour, geolocation data — the raw material for AI is everywhere. But telecom AI operates under unique constraints: real-time performance requirements, massive scale, and critical infrastructure regulations.
AI Use Cases in Telecom
5G Network Optimisation
- Network slicing — AI dynamically allocates network slices based on demand, SLA requirements, and traffic patterns
- Beam management — ML optimises massive MIMO beam steering in real-time for 5G mmWave
- Self-organising networks (SON) — AI automates cell configuration, handover optimisation, and interference management
- Predictive capacity planning — Forecast network load by cell, time, and event to prevent congestion
- Energy optimisation — AI reduces base station energy consumption by 15-30% through intelligent sleep modes
Revenue Protection & Fraud Detection
- SIM swap fraud detection — Real-time ML scoring of SIM swap requests to prevent account takeover
- International revenue share fraud (IRSF) — Pattern detection on call routing anomalies
- Subscription fraud — Identity verification and credit risk scoring at onboarding
- Roaming fraud — Anomaly detection on roaming usage patterns
Customer Experience
- Churn prediction — Identify at-risk subscribers 30-60 days before they leave
- Next-best-action — Personalised offers and retention strategies based on usage patterns
- Network experience scoring — Per-subscriber quality of experience measurement using AI
- Automated customer support — LLM-powered customer service for billing, technical support, and plan changes
NIS2 Compliance for Telecom AI
Telecom providers are essential entities under NIS2 (Annex I, Sector 1). AI infrastructure must comply with:
- Risk management (Art. 21) — All 10 minimum measures applied to AI systems that manage or monitor network infrastructure
- Incident reporting (Art. 23) — AI-related security incidents affecting network availability or integrity: 24h/72h/1 month reporting
- Supply chain security (Art. 21(2)(d)) — AI model providers, cloud infrastructure for AI training, and network equipment vendors with AI components
- Cross-border coordination — Telecom providers operating across EU member states must coordinate with multiple CSIRTs
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Watch on Skillshare →Infrastructure Architecture for Telecom AI
- Edge AI — Network optimisation AI must run at the edge (near base stations) for real-time decisions. Latency to cloud is too high for beam management and traffic steering.
- Distributed training — Training on network telemetry across regions while respecting data localisation requirements
- Real-time inference at scale — Fraud detection processing millions of CDRs per second with sub-100ms decision latency
- Multi-vendor integration — Telecom AI must work across Ericsson, Nokia, Huawei, Samsung RAN equipment (Open RAN helps)
telecom
5G
AI infrastructure
network optimization
NIS2
fraud detection
compliance
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