Telecom networks are among the most complex machines humans have built: millions of antennas, cables and switches carrying voice, video and data for billions of people. Operators have used automation for years to keep them running. AI is now central to managing their growing complexity, reducing energy costs and fighting fraud — and networks themselves are being redesigned to carry AI traffic.

Network planning and optimization

Mobile networks must balance coverage, capacity and interference across thousands of cells. Self-organizing networks (SON), standardized in the mobile industry since the 4G era, automate configuration, optimization and healing. Machine learning extends this:

  • predicting traffic by location and time to allocate capacity;
  • optimizing antenna tilt, power and handovers between cells;
  • planning where to build new sites using demand forecasts and terrain data.

Energy savings

Energy is one of operators’ largest costs, and radio access networks consume most of it. AI predicts when traffic will be low — typically at night — and switches off parts of base stations or puts them into sleep modes without noticeably affecting service. Operators and equipment vendors report significant energy reductions from these features.

Predictive maintenance and operations

AI analyzes alarms and performance data to detect faults early, find root causes among thousands of correlated alarms and dispatch technicians efficiently. Operators aim for increasingly autonomous networks that detect and fix many problems without human intervention.

Open RAN and AI

The O-RAN Alliance promotes open, interoperable radio access networks, including standardized interfaces where third-party applications can control network functions. These “xApps” and “rApps” create a platform for AI-based optimization from different vendors.

AI-RAN and the AI era

Industry initiatives explore running radio network functions and AI workloads on shared accelerated computing at the network edge. The idea: the same infrastructure could improve radio performance and host AI inference close to users, creating new business opportunities for operators. Research on 6G envisions AI built into the air interface itself.

Fraud, spam and security

  • detecting SIM-swap fraud, subscription fraud and international revenue share fraud;
  • identifying spam and scam calls and messages, increasingly including AI-generated voices;
  • protecting networks against cyberattacks.

AI-voiced robocalls became a policy issue in the US in 2024, when a robocall using an AI-generated imitation of President Joe Biden’s voice targeted voters ahead of the New Hampshire primary. The Federal Communications Commission subsequently ruled that AI-generated voices in robocalls fall under existing restrictions on artificial voices in the Telephone Consumer Protection Act.

Customer care

Operators handle huge volumes of customer contacts about bills, plans and technical issues. AI assistants answer questions, troubleshoot connectivity remotely and help agents. Personalization models recommend plans and predict which customers are likely to leave.

Challenges

  • integrating AI across legacy systems and multiple vendors;
  • ensuring reliability for critical communications, including emergency calls;
  • data privacy for location and usage data;
  • the skills needed to combine network engineering and data science.

What it means for customers

For customers, AI in networks is mostly invisible: fewer outages, faster fixes, better coverage where demand grows and more scam calls labelled or blocked. In customer care, expect faster answers for simple questions and remote troubleshooting of home broadband. Privacy matters, because operators hold sensitive location and usage data; check your operator’s settings for marketing and data sharing.

What to watch next

  • AI-RAN deployments combining radio and AI computing at the edge.
  • 6G standards and the role of AI in the air interface.
  • Network-level defenses against AI voice scams.
  • Progress toward autonomous network operations.

Key takeaways

  • Self-organizing networks and machine learning optimize capacity, coverage and handovers.
  • AI reduces energy use by putting network equipment to sleep during low traffic.
  • Predictive operations aim for autonomous networks that fix many issues themselves.
  • Open RAN enables third-party AI applications in radio networks.
  • The FCC ruled in 2024 that AI-generated voices in robocalls fall under existing TCPA restrictions.

Frequently asked questions

Does AI make my mobile connection better?

Indirectly: operators use AI to manage capacity, reduce interference and fix faults, which can improve reliability and speeds.

How do operators block scam calls?

They analyze call patterns, use caller authentication standards and apply AI to identify likely spam, labelling or blocking calls.

What is AI-RAN?

An industry concept of running radio access network functions and AI applications on shared accelerated computing infrastructure.