AI-driven network operations push telecom to predictive mode

What do firefighters ask Santa Claus? Ok, they are perhaps too grown-up for that, but if they were to make a wistful wish, what might they go for? No traffic jams? Possibly. Faster trucks? Certainly. No cats on the ledges? Definitely. Bigger hoses and buckets? Why not. Tougher blankets? Never hurts.

The answer, however, lies not in the smoke or the flames—but in the calm before the siren. It begins far ahead, even before the alarm bell rings or the call reaches the fire station. If firefighters could ask for something miraculous, it might be something deceptively simple yet notoriously hard to get—what if humans could detect a fire before the smoke spirals out of control?

This is precisely the sort of pre-emptive thinking now emerging in network operations rooms—across telecom operators, cloud providers, and data infrastructure teams. The question they are asking is no longer “What went wrong?” but “Can we know before it goes wrong?” The hope is that Artificial Intelligence (AI) may finally offer the predictive capability that decades of reactive network management could not.

AI Intelligence: An Operator’s Fantasy

The intelligence and real-time footwork that AI brings in can mean a whopping impact when translated into speed, precision, and analytics in the realm of networks.

In the past, AI’s role was mainly limited to analytics, decision support, and tasks such as fault prediction or capacity optimisation. But as Arvind Khurana, Regional Vice President and Country Head – Cloud and Network Services, Nokia India, captures it, today, AI is evolving to operate in real time, enabling autonomous, on-the-fly decision-making, powering capabilities like dynamic resource allocation, self-optimisation, and preventive assurance. “AI is essential for telecom operators as it forms the foundation of truly autonomous networks.”

For telecom companies, the impact of this AI-driven approach is vital and extends beyond the network itself, concurs Jophy Varghese, APAC Head – System Integration and Country Manager – India Enterprise, Verizon. “Internally, it improves reliability and reduces operational costs. For customers, the same AI foundation enhances their experience through virtual assistants that troubleshoot issues and intelligent systems that route calls based on predicted complexity to resolve problems faster.”

In fact, AI is rewriting the rulebook for networking, as Hon Kit Lam, Vice President – Hybrid Connectivity Services at Tata Communications, sees it. “It is turning static infrastructures into ever-smarter, self-adapting ecosystems. It automates complex processes, enhances real-time monitoring, and pre-emptively detects faults before they even reach our customers. The use of these technologies is helping shift from reactive fire-fighting to proactive, self-healing operations, particularly as isolating root causes and launching corrective measures is done without human intervention.”

AI can proactively detect problems with impact assessment and provide automated root-cause analysis for immediate, decisive remediation, explains Manish Gangey, Executive President – Product Line Management, HFCL. “AI-based systems continuously learn from current and historical trends, enabling them to flag issues before they impact users.”

Andrew Lerner, Vice President Analyst at Gartner, observes that AI is having, and will continue to have, a major impact on network infrastructure. “Their impacts range from short-term and cute to long-term and disruptive.”

Let us expand how, and where exactly, AI is helping in preventing all the fires that networks were caught up in.

AI Siren: Faster and Smarter Alerts

AI can be used to quickly detect network issues, personalise customer service and boost efficiency by automating support workflows, explains Lam. “For fault diagnosis, AI-powered programmes automatically analyse and correlate alarms across networks—including third-party networks—and even at the user end.”

“This means that up to 85% of critical faults are automatically diagnosed, significantly reducing Mean Time To Recovery (MTTR) and ensuring a seamless customer experience. AI also enables predictive maintenance by identifying patterns that precede network failures, helping anticipate and address issues before they escalate,” he says.

No wonder then, the power of AI—in context to networks—lies in more than one strength that it brings to the table. As Lerner breaks it down: “Typically, as it relates to network infrastructure, AI is packaged into a few different technologies: AI assistants, Digital Twins, and Agentic NetOps.”

He unravels it further. “Network AI assistants are interactive digital tools backed by Generative AI (GenAI) and Machine Learning (ML) technologies that allow human users to communicate via conversational, natural language chat-interfaces. When built into network management consoles, network AI assistants provide actionable network insights and help with network operational tasks, improving administrator user experience, configuration, and operational efficiency. Today, these assistants are mostly ‘cute’ (i.e., nice to have), but they are evolving and eventually will become much more critical.”

Varghese also points out that AI helps customer service agents by using generative AI to provide real-time recommendations. “By mastering AI for their own operations, these companies are better positioned to provide the robust, low-latency connectivity that other industries need for their own AI transformations, driving new revenue and future growth.”

On the customer-service front, AI is powering advanced virtual assistants and sentiment analysis that personalise every interaction, avers Lam. “By drawing on account history, open incidents and behavioural data, these systems can greet users by name, provide tailored updates and guide them through self-help workflows—all before a ticket is created. Additionally, speech analytics helps gauge customer sentiment in real time, enabling proactive experience recovery when needed.”

Digital Twins: Network’s Second Sight

Consider how a twin changes the scenario completely—by seeing and alerting to things faster and better than ever before.

A network digital twin is a model of the behaviour of campus, Wide Area Network (WAN), or data centre network components, elaborates Lerner. “It is usually delivered as software and provides a model that can be used for validating the configuration, policies, or operations of a single network component or the entire network. It automatically synchronises with the production network. A network digital twin allows enterprises to validate configuration and security policies, as well as individual component operations or aggregation of components into a network.”

Lerner contends that for IT leaders, a network digital twin allows faster testing and subsequent delivery of network changes, requiring fewer personnel resources and incurring lower costs by reducing system testing equipment needs. “We believe a network digital twin can improve delivery times for requests by 20% across the network.”

With unified data, machine learning models analyse everything from traffic patterns to equipment health to forecast and prevent failures before they occur, Varghese adds. “This allows telecom companies to dynamically optimise network coverage, detect fraudulent activity, manage energy use and power tools that can answer plain-language questions about network performance.”

There is more to AI than these ultra-powerful eyes and ears. AI also helps with new feet. And that is where we come to NetOps.

Agentic NetOps: Autonomous Agility

NetOps can work as an entirely different kind of internal agility source—when combined with the astronomic speed and processing leaps of AI.

Agentic NetOps leverages goal-driven autonomous AI agents with capabilities such as memory, planning, sensing, tooling, and policy guardrails that have been granted rights by the organisation to operate network tasks and processes independently, Lerner indicates.

“AI agents work as a system to communicate autonomously with other AI agents to manage network infrastructure life cycle management with minimal to no human involvement. Agentic NetOps operates autonomously by minimising network operations personnel from being in the loop.”

Lam adds that ultimately, this technology dynamically divides networks to control and contain threats and allocate resources most efficiently. “It is a combination of intelligence and agility that not only improves operational efficiency but also elevates the overall customer experience, making networks truly future-ready.”

The impact translates into real-ground gains for operators. Lerner cites how AI agents can autonomously query other systems, tools, and other agents, make network changes, monitor network traffic, and use synthetic traffic injection to continuously analyse the network environment in real time and respond proactively to address issues. “They can improve network performance, efficiency, and response times by gathering data from multiple systems to make rapid decisions that cannot reasonably be achieved through traditional network operations.”

Workflow automation is another area where AI delivers substantial value, Lam chimes in. “By having AI handle repetitive tasks, organisations can reduce response times from tens of minutes to near-instant, freeing expert teams to focus on strategic initiatives and innovation.”

Predictive AI: Fewer Network Fires

All this predictive acuity means a great deal to telcos—as networks are the backbone of everything they do—and also of every place they fail.

Since Telcos operate at scale, a minor degradation can affect thousands of customers, Gangey underscores. “Predictive AI helps proactively identify user-impacting issues and bring down the mean-time-to-resolution. In particular, AI enables early detection of network anomalies by identifying patterns, deviations, or behavioural shifts, proactive coverage and capacity augmentation before user experience starts to degrade, and transformation of operational teams from first responders to informed, ahead-of-time decision-makers.”

Varghese sums up that advanced AI is transforming predictive network maintenance from a reactive to a highly proactive discipline. However, AI is only as good as the data it is built on, and we are on a journey to consolidate all our data into common platforms, he argues. “This will provide both cost savings because we will spend fewer resources moving and translating data; as well as operational benefits in having data from multiple domains in the same place, updating in near real time.”

Manas R, Associate Vice President – Digital Engineering Expert, Aditi Consulting, underscored the challenge. “AI requires vast amounts of accurate, integrated data. Organisations need systems that can continuously collect, analyse, and act on this data. This demands not only the right tools but also the right talent and ethical frameworks.”

He added that AI deployment often requires updating legacy systems, re-skilling staff, and developing new governance frameworks. “Without quality data and skilled people, even the smartest AI will fall short.”

So, while the metaphorical cat may still climb the network ledge, operators are now better prepared—not to chase it, but to prevent it from climbing at all.