7 Ways AI Is Transforming Telecoms
AI is not just enhancing telecom operations, it’s fundamentally reshaping them. At Fyuz, the Telecom Infra Project’s (TIP) annual event in Dublin, I had the opportunity to moderate a panel discussion on AI’s role in transforming telecom networks. From automation and optimisation to revenue protection and generation, AI is redefining how operators do business in ways that were unimaginable just a few years ago.
TIP is helping operators navigate these changes through its newly launched TelcoAI Project Group. Project participants are defining and prioritising operators’ use cases and through that work, TIP will develop technical requirements and architecture blueprints to enable deployment and operation of AI telecom networks.
But of course, the race is already on to reap the benefits from AI, so here are seven key ways the technology is driving this evolution today.
Monetising IoT Security
With billions of new B2B IoT devices connecting over the next few years, including vehicles, medical devices, and critical infrastructure, operators face an unprecedented security challenge. AI-driven IoT security isn’t just about mitigating threats in real-time though; it can also become an emerging revenue stream. Shield-IoT delivers its AI-powered cybersecurity platform as a zero-touch SaaS solution, allowing CSPs to instantly offer security services without added operational complexity.
Powering Quantum Encryption
AI is accelerating cyber threats by enabling sophisticated DDoS attacks, data poisoning, and vulnerability exploitation at an unprecedented scale. Meanwhile, quantum computing threatens to break traditional encryption methods entirely. Arqit tackles both challenges with its AI-powered, quantum-safe encryption platform, securing mobile networks against future attacks. Its AI-driven automation helps operators scale security, authenticate identities, and create ‘network fingerprints’ that detect abnormal behaviors, ensuring compliance in zero-trust environments.
GPU-Accelerated Data Analytics
Operators generate petabytes of network data daily, but traditional analytics tools struggle with speed and scale. New York-based startup SQream is changing that with GPU-accelerated databases that process massive datasets in minutes instead of hours. This lets operators conduct real-time network optimization, predictive maintenance, and targeted advertising, and turns raw data into actionable insights that reduce churn and unlock new revenue streams.
Chaos Theory-Based Geolocation Models
Traditional geolocation methods struggle in dense urban environments, where GPS signals are weak, and cellular reflections create inaccuracies. Groundhog Technologies, an MIT spin-off, is solving this problem by using AI to analyze chaotic signal patterns, enabling hyper-precise location tracking, even determining which floor of a high-rise building a user is on. For operators, this means improved network planning, proactive troubleshooting, and value generating, enhanced location-based services.
AI-Powered Video Analytics
AI is transforming video surveillance from passive recording to real-time intelligence. AxxonSoft’s Video Surveillance as a Service (VSaaS) allows telecom operators to offer cloud-based AI analytics to enterprise customers. From monitoring construction sites for safety violations to automating security alerts in smart cities, VSaaS presents operators with new revenue generating potential, especially when bundled with private networks.
Improving Operational, Energy, and Spectral Efficiency
The RAN Intelligent Controller (RIC) is one of Open RAN’s most transformative innovations, allowing operators to optimise their networks using AI-powered applications. Aira Technologies is at the forefront of this transformation, leveraging AI for energy-efficient network management, predictive traffic steering, and automated anomaly detection. At Fyuz, Aira introduced ‘Naavik,’ a GenAI platform that translates natural language queries into network-optimising software commands, giving engineers unprecedented control over the RAN.
Making Large Language Models (LLMs) Useful
Large Language Models (LLMs) hold immense promise for telecom automation, but they struggle with dynamic, real-time network data. RG Nets is solving this by introducing ‘moving indexes’, an AI-driven approach that enhances LLMs with live, context-aware data retrieval. LLMs can now provide real-time insights for network diagnostics, customer service, and operational automation, finally making generative AI a practical tool for telecom operators.
In conclusion…
From cybersecurity and encryption to real-time analytics and AI-driven automation, AI is redefining the telecom landscape. As networks become more complex and data-driven, operators that embrace AI will unlock new efficiencies, revenue streams, and customer experiences. The challenge now isn’t whether to adopt AI, but how quickly operators can integrate these technologies into their networks before competitors gain the edge.
