Photo By: Frames For Your Heart
Telecom networks have relied on people to keep them running for decades. Engineers monitor performance, respond to outages, adjust capacity and decide how traffic should move across the network. Software has automated much of this work, but people have remained firmly in control.
That model is beginning to change. Artificial intelligence is moving deeper into the telecom network, helping operators spot problems, predict traffic, improve performance and automate decisions. In some cases, AI is beginning to take action without waiting for a person to tell it what to do.
This is pushing the industry toward what it calls the “autonomous network.” The idea is straightforward. The network monitors what is happening, AI analyzes the information, the system decides what needs to change and the network takes action.
Telecom companies have used AI and machine learning for years. The technology can predict equipment failures, identify unusual network activity and help operators understand why customers are experiencing poor service. The bigger change is happening now as AI moves from simply providing information to becoming part of the decision-making process.
That shift is becoming increasingly visible across the industry. Conal Higgins, VP/Field CTO, GTM Telecommunications at NewRocket, works at the intersection of telecom operations and emerging technology, an area that is becoming increasingly important as operators look for ways to manage more complex networks with greater automation.
Consider what happens when a major event suddenly brings thousands of people into one area. Mobile network traffic can increase dramatically within minutes. Traditionally, engineers and automated systems would monitor the situation and make adjustments. An AI-driven system could potentially recognize the increase in demand, identify where capacity is needed and adjust network resources automatically.
The goal is not simply to make the network smarter. It is to make it more responsive.
AI agents are adding another dimension to this shift. Traditional software generally follows predefined instructions. An AI agent can be given a goal and determine what steps are needed to achieve it. In telecom, an operator could eventually give an AI system an objective such as maintaining network performance in a particular area. The system could examine information from different parts of the network, determine what needs to change and coordinate those changes.
That capability matters because today’s networks are extremely complex. A mobile service can depend on radio equipment, fiber connections, data centers, cloud systems and software operating across many locations. Managing all of these components manually is becoming increasingly difficult.
One of the key ideas behind autonomous networking is the “closed loop.” The network observes what is happening, AI analyzes the information, the system decides what to do, the network makes a change and the system checks whether the change worked. That process can then repeat continuously.
Mobile networks are an important testing ground because conditions change constantly. People move, traffic rises and falls, and large events can create sudden spikes in demand. AI can analyze these changes quickly and identify patterns that might be difficult for a human team to spot in real time.
The business case is also clear. Networks are becoming more complicated while operators face pressure to control costs and improve the customer experience. AI offers a way to automate some of that complexity without requiring engineers to manually manage every part of the network.
That does not mean telecom engineers are disappearing. Their roles are likely to change. Engineers could spend less time configuring individual pieces of equipment and more time setting policies, defining objectives and establishing the limits within which AI systems can operate.
Greater AI control also creates new risks. An AI system that recommends a change is one thing. A system that automatically makes that change across a large network is something very different. A bad decision could potentially affect thousands or even millions of customers.
Telecom operators will therefore need strong safeguards. They will need to understand why an AI system made a decision, monitor its actions and have ways to intervene when something goes wrong. Clear rules will also be needed around which decisions AI can make independently and which still require human approval.
There is an interesting connection between the growth of AI and the future of telecom. AI systems require enormous amounts of computing power and connectivity, creating greater demand for fast and reliable networks. At the same time, telecom companies are increasingly turning to AI to help operate those networks.
AI needs better networks, while better networks increasingly need AI.
The industry is not yet at the point where AI can run an entire telecom network on its own. Many applications remain focused on specific tasks, and major questions around reliability, security, accountability and human oversight still need to be addressed.
The direction, however, is becoming clear. AI is moving from being a tool that telecom operators use to becoming part of the system that operates the network itself.
The future telecom network may not simply be a network with AI built into it. It may be a network that increasingly relies on AI to decide how it should operate.
