From Predictive Maintenance to Network Automation: How AI Is Reshaping Telecom Operations

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Maria Golitsyna also highlighted predictive maintenance, traffic optimisation and faster issue resolution as areas where AI is already delivering tangible benefits while consumers may never directly interact with these systems, they often benefit from improved network reliability and fewer service disruptions.

Together, these observations point to a common conclusion AI’s current success in telecom is less about visible customer features and more about making networks smarter behind the scenes.

Predictive Maintenance Is Emerging as a Leading Use Case

Among the applications mentioned by experts, predictive maintenance emerged as one of the clearest examples of AI delivering real-world value.

Sayali Patil, AI Reliability Researcher and former Senior Network Consulting Engineer at Cisco, pointed to anomaly detection and predictive maintenance as areas where AI is already producing measurable results. Telecom networks generate enormous volumes of telemetry data every day. AI systems can analyse this information continuously, identifying patterns that may indicate equipment degradation, abnormal behaviour or impending failures.

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Instead of waiting for a network problem to occur, operators can intervene earlier and reduce the likelihood of service disruptions as telecom networks become increasingly complex with the expansion of 5G, fibre broadband and enterprise connectivity services, the ability to anticipate and prevent problems before they impact customers is becoming increasingly valuable.

Patil also emphasised the importance of reliable telemetry and data infrastructure. In her view, the long-term competitive advantage may depend less on the AI models themselves and more on the quality and completeness of the data feeding them.

Network Automation Is Becoming a Strategic Priority

Another major theme emerging from the expert responses is network automation telecom operators manage vast infrastructures that require constant monitoring and optimisation. Traditionally, many of these tasks relied heavily on manual intervention and specialised engineering expertise.

AI is beginning to change that.

Zhang believes the industry is steadily moving towards greater levels of automation, where AI assists with fault identification, resource allocation and operational decision-making. While human expertise remains essential, AI is helping reduce complexity and improve efficiency across network operations.

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Eshaan Jain, Senior Manager and Product Leader at Mphasis, sees growing opportunities for Agentic AI and Retrieval-Augmented Generation technologies within telecom environments. He noted that AI systems can increasingly connect information across network operations, business support systems and customer service platforms, helping operators diagnose issues faster and make more informed decisions.

The long-term vision for many operators is the development of more autonomous networks capable of detecting, diagnosing and resolving certain issues with minimal human intervention.

Why Experts Still View AI as an Efficiency Tool

While experts generally agree that AI is delivering measurable value, many do not yet see it as a major competitive differentiator. Instead, they view AI primarily as an efficiency and optimisation technology.

Several respondents noted that operators across the industry are pursuing similar AI strategies. As a result, AI is becoming an operational necessity rather than a unique competitive advantage.

Patil offered one of the most thought-provoking observations she suggested that operators positioning AI as a key differentiator are often doing so more in their communications than in their architecture. In practice, successful AI deployments depend heavily on strong telemetry, reliable data collection and operational readiness.

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This highlights a broader industry reality access to AI alone is unlikely to create a lasting advantage. The operators that derive the greatest value may be those with the strongest data foundations and the ability to integrate AI effectively into everyday operations.

Real-World Business Benefits Are Emerging

Experts also pointed to tangible business outcomes from AI deployments.

A contributor from Totogi highlighted how AI can help operators reduce fault-finding times and identify potential revenue leakage. These use cases demonstrate that AI’s value extends beyond technical optimisation and can directly influence business performance other experts pointed to lower operational costs, improved uptime and faster issue resolution as examples of measurable benefits already being realised across the industry.

While these improvements may not always generate headlines, they can significantly impact customer satisfaction and operational efficiency.

The Future of AI-Driven Network Operations

Although today’s most successful AI deployments are focused on network operations, experts believe the technology’s role in telecom will continue to evolve future applications could include proactive customer support, intelligent capacity management, self-healing networks and deeper integration between network intelligence and business systems.

As operators gain experience and improve their data infrastructure, AI may gradually move beyond efficiency gains and become a more visible driver of differentiation and innovation.

Nemanja Prekovic, Head of Monetization and Digital Experience at Avenga, shares a similar view. According to Prekovic, AI is already helping telecom operators improve early problem detection, capacity planning and operational efficiency. However, he believes AI has not yet become a major competitive differentiator for most operators.

Prekovic argues that while operators are seeing real gains in automation, detection and planning, these improvements are largely operational in nature. The shift towards competitive differentiation may come through closed-loop operations, where AI-driven insights are connected directly to decision-making and remediation. In such environments, AI moves beyond identifying issues and begins actively helping operators improve service quality and network performance.

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The Emerging Reality of AI in Telecom

The experts who spoke with TelecomTalk delivered a remarkably consistent message AI is already making a meaningful difference in telecom, but much of that impact is happening behind the scenes predictive maintenance, anomaly detection, fault management and network automation emerged as some of the most mature and valuable use cases identified by industry professionals.

While AI may not yet be transforming the telecom business overnight, it is helping operators build networks that are smarter, more resilient and more efficient. As networks continue to grow in complexity, those capabilities are likely to become increasingly important in the years ahead.