Autonomous Networks by Capgemini

An autonomous network is defined as a network that can manage, configure, optimize and heal itself with minimal or no human intervention by using automation, AI and closed-loop control systems.

According to analysis from the Capgemini Research Institute, autonomous networks are projected to deliver an estimated ROI of 1.7x–3.4x and a payback period of 2.9 to 1.5 years in conservative and optimistic scenarios, respectively. Returns are driven by operational efficiency gains, faster service rollout, reduced downtime, and optimized resource utilization.

According to research from the Capgemini Research Institute, autonomous networks can deliver potential OPEX savings of $150–300 million over five years per operator. Autonomous networks reduce OPEX by automating configuration, optimization, fault detection, and remediation processes. By minimizing manual intervention and enabling predictive maintenance, operators can lower operational costs, improve efficiency, and reduce network downtime and energy consumption.

Autonomous networks are essential for managing the complexity of 5G environments. They use AI, automation, and closed-loop control to dynamically optimize performance, manage traffic, and self-heal issues in real time—enabling scalable, resilient 5G operations.

Telecom organizations need to modernize legacy networks because traditional architectures were not designed for real-time data processing, AI integration, or closed-loop automation. Legacy systems also often operate in silos, rely on manual processes, and lack the cloud-native flexibility required to support intelligent orchestration and self-optimizing capabilities. By adopting open, cloud-native architectures, operators can enable scalable AI-driven automation, improve network resilience, reduce operational costs, and accelerate the rollout of new services.

The TM Forum Network Autonomy maturity model defines five levels of autonomous network maturity—from Assisted Operations (L1) to Full Autonomy (L5). Today, 84% of operators remain in Levels 1 or 2, where human-led operations are supported by automation. Moving to Levels 3 and 4 requires shifting to AI-driven, closed-loop systems where networks make conditional and increasingly autonomous decisions with minimal human intervention.

AI Autonomous Networks is a comprehensive service offering from Capgemini that enables telecom operators to transform at the network level—leveraging network automation, AI, and closed-loop control to build next-gen networks that self-configure, self-optimize, and self-heal.

AI enables self-configuring and self-healing networks by continuously analyzing performance data, detecting anomalies, and triggering automated corrective actions without human intervention. Closed-loop systems allow the network to sense, decide, and act in real time, improving reliability and service assurance.

Key AI tools for telecom operations include Network AIOps platforms, RAN Intelligent Controllers (RIC), Network Data Analytics Functions (NWDAF), High Performance Computing (HPC) environments, and observability platforms. These tools enable predictive analytics, fault detection, automated remediation, performance optimization, and intelligent orchestration.