By: Akhil Gokul
The networks that powered digital connectivity over the past two decades were built to scale. As mobile broadband demand surged, service providers raced to expand infrastructure to deliver
consistent, reliable performance. We’ve now entered a new era — one that demands more than capacity. Today, network transformation is not just about scaling technology — it’s about scaling
purpose.
We are at a critical inflection point. Networks are evolving to become more open, intelligent, and programmable. Their role has expanded beyond connecting people to enabling smart cities,
autonomous factories, and immersive digital experiences. At the same time, the expectations placed on networks have never been higher. They must offer improved performance, more flexibility,
lower energy use, and greater inclusivity — simultaneously.
This paradigm shift requires more than incremental upgrades. It calls for a systematic reimagining of how networks are designed, operated, and monetized. The transformation ahead is
architectural, operational, and philosophical – driven by five core forces: AI, openness, programmability, sustainability, and equity.
To ensure the future network is trusted as critical infrastructure, the transformation integrates security by design across all five core forces. This holistic approach strengthens resilience and
trust while supporting the progression from network infrastructure to critical infrastructure.
To realize this potential, networks must be designed as AI-native — embedding data collection, model training, and continuous feedback across the architecture. This enables faster service
delivery, lower operational costs, and more sustainable performance.
In addition to this, AI-native network nodes with embedded AI will enable an enhancement of RAN (radio access network) performance. For example, AI predicts how to best
handle the radio channel conditions traffic patterns and user demands, allowing the network to dynamically adjust radio link configurations achieving approximately 12 percent improved
throughput for heavy users measured in field by CSP. AI predicts traffic patterns and cell usage. Based on these predictions, the network can activate or deactivate MIMO (multiple input, multiple
output) functionalities in cells, optimizing energy consumption without compromising performance. Up to 14 percent of reduced energy consumption per radio site has been estimated.