Just a decade ago, AI was virtually unknown in the telecom industry. Few could have imagined its impact on multiple domains such as network configuration, power management, optimization, and customer engagement. The Body of European Regulators for Electronic Communications (BEREC) predicts that AI will become a standard part of operational procedures within the next six to ten years.

#1 Dynamic Resource Allocation

Telecom networks serve diverse customers, each with unique needs for bandwidth, latency, jitter, and other parameters, as well as varied data types like VoIP, video streaming, mission-critical communications, and secure file transfers. Traditionally, these settings were pre-configured and adjusted manually when user requirements changed. With AI, network parameters can now adjust dynamically based on demand, and customer portals can even enable self-service resource allocation. This represents a step beyond cloud computing, where users simply select processing power or storage from a portal.

#2 Energy Optimization

Cellular networks experience fluctuating traffic loads, and their energy costs are rising each year. Historically, networks ran at full capacity to handle peak conditions, with some manual optimization introduced in the past decade. AI now enables automated load sensing and real-time power adjustments, resulting in significant energy savings and more efficient resource utilization.

#3 Predictive Maintenance

Cable cuts are one of the greatest risks for telecom providers, potentially affecting millions of customers if they occur on backbone links. Some submarine cables carry over a petabyte of data per second (1 PB = 1000 TB). Subsea repairs are costly (often exceeding a million dollars) and time-consuming, taking weeks or even months. Terrestrial backbones are similarly critical, with outages leading to heavy penalties due to strict SLAs. AI can correlate alarms—for example, OTDRs detecting sudden signal losses or smart circuit breakers flagging overheating—and provide early warnings to Network Operations Center (NOC) teams, enabling proactive measures to prevent major outages.

#4 Progressive Cybersecurity

Dynamic AI models enable proactive detection of cyber threats and faster response. AI supports real-time anomaly detection, threat prediction, automated mitigation, and fraud detection. Telecom networks generate massive volumes of data that are difficult to analyze with conventional tools. AI enhances cybersecurity by detecting malware, isolating infected nodes, and preventing breaches more effectively.

#5 Future Trends

In addition to these established domains, AI is expected to expand into emerging areas such as Edge AI, Generative AI, and Intent-Driven Networks. These innovations will be closely tied to IoT, augmented and virtual reality (AR/VR), and self-configuring networks that align infrastructure with business intent—similar to how autonomous vehicles respond to changing road conditions.

Summary

AI is no longer optional for telecom networks; it is essential for growth, automation, and security. While its transformative potential is immense, challenges remain, including regulatory hurdles, business model innovation, technical complexities, and policy considerations.