At a time when competition between telecom providers is at an all-time high, advances in artificial intelligence (AI) offer companies the ability to recognize and rectify dropped calls, delayed setup times, and policy mismatches that degrade the customer experience and cause provider switching. By leveraging AI to optimize signaling first, rather than immediately focusing on churn prediction, companies can address the technical causes of poor service before tackling customer retention. This approach helps ensure a reliable and efficient network, which immediately improves the user experience and makes any subsequent churn prediction more meaningful and actionable.
In the long term, optimized core signaling becomes the foundation for customer retention, network efficiency, and service-level agreement (SLA) enforcement. AI consistently analyzes real-time network telemetry, learns signaling patterns, and makes predictive adjustments to reduce congestion and improve stability. It also prepares operators for future 6G-era demands, when adaptive, intent-based networks will rely on AI to deliver user-specific performance in milliseconds.
Better service starts with optimized signaling
The effect of telecoms optimizing signaling before predicting churn is comparable to a struggling restaurant improving its dining experience for customers. Optimizing signaling is like the restaurant revamping its kitchen to ensure timely and high-quality orders. Predicting churn is similar to the restaurant noticing that customers are leaving because of slow service. Fixing the kitchen prevents the problem, whereas predicting churn is reacting after the problem has occurred.
Telecoms wanting to “fix their kitchen” by improving their foundation or core infrastructure can do so by applying advanced AI in several powerful ways:
- Creating self-optimizing networks (SONs). In these networks, AI algorithms continuously monitor traffic patterns, signal quality, and resource usage. They then auto-adjust in real time to resolve issues and maintain higher service quality.
- Performing predictive maintenance. Telecoms apply AI to analyze data from sensors, logs, and historical outages to predict equipment failures before they occur and cause expensive downtime.
- Managing traffic intelligently. AI dynamically prioritizes network slices (dedicated bandwidth allocations) based on real-time demand to handle traffic surges more effectively. Companies can now go one step further and create backup sites to route traffic to when demand is too high. For instance, if traffic in the Seattle market spikes suddenly, it could be automatically directed to a backup site in Las Vegas so customers never experience degraded service quality.
- Enhancing security and threat detection. Telecom networks are prime targets for cyberattacks. To prevent these attacks, companies can use AI-based anomaly detection systems to identify unusual traffic flow, suspicious login attempts, or distributed denial of service (DDoS) activity in real time and then respond automatically to isolate threats before they cause significant damage.
Additionally, telecoms can use AI to improve energy efficiency and sustainability, reduce latency and make networks more responsive through edge intelligence, automate network design and planning, and provide end-to-end visibility into the customer experience to reduce churn and improve satisfaction.
The key to faster innovation is addressing AI signaling challenges
Signaling is one of the most complex and resource-intensive aspects of telecom networks, and rule-based optimization is often ineffective for improving efficiency. AI excels at addressing issues that are high-volume, highly dynamic, and unpredictably patterned. British multinational telecommunications company Vodafone recently adopted AI predictive maintenance across its European network and saw an approximate 30% reduction in network outages and an approximate 25% reduction in maintenance spending.
Results like that are why T-Mobile US’s CEO Mike Sievert told Time that future connectivity “will be powered by AI technology: networks will optimize themselves in real-time to get you the very best possible signal, ensuring you’re connected everywhere you go. … In our core business, AI-RAN (already) has the potential to allow the network to self-correct in real time.”
Making AI an integral part of the operation
It is crucial for companies that want to innovate faster and scale smarter with AI to be prepared to address the challenges that frequently arise when optimizing core signaling. Those challenges include signaling storms and volume spikes, handover complexity in 5G/6G, paging and idle mode inefficiency, multivendor and interoperability issues, security threats and malicious signaling, energy consumption of the control plane, and scalability for future use cases. AI-driven strategies that consider both signaling and resource management are the solutions to overcoming these challenges.
Effective strategies include predictive load management, which utilizes machine learning (ML) to forecast traffic surges and smooth out spikes, and reinforcement learning, which improves handover decisions to reduce dropped calls. Using supervised models to enhance paging efficiency by predicting device activity and leveraging of cross-domain AI to harmonize signaling across multi-vendor environments are additional tactics. Telecoms can use unsupervised learning to strengthen security by detecting anomalous behaviors early. Energy-aware optimization saves power through smarter Radio Resource Control (RRC) state transitions. Finally, dynamic slice-aware orchestration ensures service-level priorities are maintained across the Internet of Things (IoT), ultra-reliable low-latency communications (URLLC), and enhanced mobile broadband (eMBB).
Companies can then ensure their AI efforts continue to produce positive results by monitoring a blend of key performance indicators (KPIs) that address:
- Network layer. Key metrics include throughput, latency, efficiency, signaling load, and energy use.
- Operations. Performance can be measured by automation percentage, mean time to detect/mean time to respond (MTTD/MTTR)
- Service assurance. Standards focus on slice SLA compliance and achieving five nines availability.
- User experience. Indicators include quality of experience (QoE), net promoter score (NPS), churn, and customer complaints.
- Security. KPIs include attack prevention and detection accuracy.
These strategies and metrics provide telecoms with a practical framework for embedding AI into daily operations while ensuring reliability, efficiency, and scalability.
Unleashing the many benefits of using AI in 5G networks
Telecom companies that embrace AI use in their 5G networks realize a variety of short- and long-term benefits. AI delivers fast value through radio access network (RAN) optimization, energy savings, predictive maintenance, customer experience management, and security. Long-term payoffs include a smoother, easier transition into future autonomous 6G AI-native networks that are likely to feature ultra-low latency services, advanced IoT and edge intelligence, and increased sustainability. The bottom line is that companies position themselves to stay ahead of the competition tomorrow by being proactive in their AI use today.
About the Author:Shon Lonkar is a senior engineer of systems architecture and technology at a wireless communications provider, leading 4G/5G technical requirements for existing and imminent network products. He’s a strategic technology leader with more than 16 years of expertise in mobility network architecture, specializing in wireless voice and data access with a proven ability to lead and deliver innovative network cloud solutions. He holds a Bachelor of Engineering, Electronics and Telecommunications. Connect with him on LinkedIn.