Telefónica Targets Level 4 Network Autonomy by 2030

Telefónica Targets Level 4 Network Autonomy by 2030

Telefónica has set an ambitious timeline for transforming its telecommunications infrastructure into self-managing systems capable of anticipating problems and making autonomous decisions. The company aims to reach Level 3.75 autonomy by 2028 and Level 4 by 2030, according to the TM Forum framework that defines network autonomy stages.

The effort centers on the Autonomous Network Journey (ANJ), a strategic program launched in 2021 that applies artificial intelligence and advanced automation across network operations. Telefónica currently operates more than 500 AI use cases in production environments, with 15 already functioning at Level 4 autonomy—meaning they can self-configure, self-optimize, and self-heal without human intervention.

Why it matters

The shift from reactive to autonomous network management addresses a fundamental scaling problem for telecom operators. As networks grow more complex—spanning radio, core, transport, and IT domains with multi-vendor equipment and cloud technologies—manual and semi-automatic processes cannot keep pace with demand for dynamic configurations and near-instant response times. Autonomous networks promise to manage this complexity through intelligence rather than increased operational headcount, directly impacting efficiency, energy consumption, and capital allocation.

From instructions to intent

Traditional network management relies on explicit commands and step-by-step configurations. Level 4 autonomy replaces this with intent-based operations, where teams define objectives—improve customer experience, reduce latency, optimize resource usage—and the system determines how to achieve them.

This approach already shows results in Telefónica's operations. In Brazil, AI automatically generates network designs for the IP domain, reducing both time and errors. Spanish fiber deployments use AI to optimize routes and costs. German operations employ three-dimensional digital twins that simulate the network with urban infrastructure, links, and real-time traffic patterns before implementing changes.

Continuous planning and deployment

Autonomous networks eliminate the lengthy planning cycles that often produce outdated decisions by implementation time. Instead, the network continuously analyzes demand patterns, user behavior, and resource usage to adjust in real time. Digital twin technology enables simulation of thousands of scenarios in virtual environments before any physical changes occur.

Deployment processes have similarly transformed. Rather than coordinating multiple teams and scheduling maintenance windows, Level 4 systems accept an intent—such as deploying a new software version—then plan, execute, and validate autonomously. The network monitors itself during changes and triggers automatic rollback if anomalies appear. Telefónica already conducts 5G core updates without service interruptions using these autonomous workflows.

Anticipating failures before they occur

Incident management shifts from reactive alarm response to predictive problem detection. The network analyzes millions of metrics in real time, identifies anomalous patterns, and anticipates failures. When a fiber cut occurs, for example, the system identifies the problem, assesses impact, reroutes traffic, and mobilizes resources automatically while operators monitor the process.

Telefónica recently received the TM Forum "Catalyst Innovator: Voyager 2026" award recognizing its leadership in autonomous network development. The company emphasizes that artificial intelligence amplifies rather than replaces human expertise—engineers now define intent, set rules, and oversee system behavior rather than executing manual tasks.

These details were first reported by Telefónica in a company blog post on autonomous networks.