How Artificial Intelligence Is Transforming Telecom Network Maintenance

Modern telecom networks have stopped being a collection of physical cables and isolated base stations. Today’s infrastructure is a highly virtualized, distributed ecosystem powered by 5G, edge computing, cloud-native network functions (CNFs) and software-defined networking (SDN). The convergence of IT and telecom has radically raised reliability requirements: even a minor degradation in service quality affects millions of subscribers and directly threatens SLA commitments. At the same time, the complexity of these environments generates a constant stream of telemetry, thousands of performance counters and floods of alerts that, in real time, exceed the analytical capacity of Network Operations Center (NOC) teams.

In these conditions, AI has become an operational necessity. Machine learning (ML) models can rapidly analyze massive volumes of data, correlate events across network layers and automate routine maintenance tasks. Importantly, this direction is grounded in telecom standards – the classic concept of self-healing originates from the self-organizing networks (SON) concept introduced by 3GPP in Release 8 (2009), which covers self-configuration, self-optimization and self-healing (specifications TS 32.500 and TS 32.541).

Today, however, reactive hardware self-healing is giving way to the broader paradigm of autonomous networks. The goal is no longer just to “treat” individual failures, but to build end-to-end intelligent environments operating in a “zero-touch” model, where AI interprets business intent and autonomously controls infrastructure from the core to the network edge. Read this article to find out how telcos can use AI to boost network maintenance and actively ensure better network resilience.

Why mobile operators are integrating AI into network maintenance

Relying solely on reactive network maintenance carries real business risk. In the classic model, engineering teams “fight fires” – they repair infrastructure only after a failure occurs, leading to prolonged outages, packet loss and customer churn. Knowledge of how to fix specific components remains locked in the heads of veteran engineers (this is known as tribal knowledge), creating bottlenecks whenever staff rotates, and reactivity increases the risk of breaching strict SLAs and unpredictable spikes in operating costs (OpEx).

AI-driven maintenance reverses this model. By analyzing historical data and real-time telemetry, systems can identify the symptoms of an impending fault before the network’s service quality degrades. This translates into a preventive, predictive approach, which leads to lower operating costs, maximized uptime and greater network resilience.

Operational aspect	Reactive approach (traditional / early SON)	AI-based approach (predictive / autonomous)
Detection mechanism	Response triggered only after crossing rigid alarm thresholds in OAM systems, or after a customer complaint	Continuous telemetry monitoring, detecting anomalies and early warning signs before service disruption
Data processing	Manual log analysis, siloed actions around individual hardware	Cross-domain correlation of thousands of counters within an integrated data architecture
Fault management	Self-healing mechanisms operating reactively and within isolated domains	Intent-based analytics, predictive lifecycle management, closed-loop automation
Resource utilization	Dependence on expert knowledge and a high rate of false alarms	Knowledge retained in models (MLOps), automated remediation pipelines
Financial impact	SLA breach penalties, unpredictable downtime costs	Lower OpEx, SLA protection, greater operational predictability

Moving from SON to autonomous networks

The shift from rule-based systems to autonomy has its own formal framework. In its ZSM initiative (Zero-Touch Network and Service Management), ETSI describes automation in which AI and ML deliver the full set of “self-x” functions – self-configuration, self-monitoring, self-healing and self-optimization (notably GS ZSM 001, GS ZSM 004).

TM Forum, in turn, organizes operator maturity into a six-stage scale of Autonomous Network Levels (ANL, L0-L5). The evaluation methodology itself is defined in document IG1252, built on the IG1218 business framework, with practical audits carried out using standardized assessment tools (ANLET / the GB1059 series).

L5 – Full autonomyThe end-state vision: a fully cognitive, zero-touch, self-improving system operating without human involvement. Requires mature explainability (XAI) mechanisms and ethical oversight.

ANL (TM Forum)	Characteristics
L0 – Manual	No automation. Monitoring, diagnosis and correction handled entirely by humans.
L1 – Assisted	Limited scripts and isolated modules. Analysis and intervention still rest with specialists.
L2 – Partial autonomy	Static rules for selected environments, supported by centralized OSS, with decisions still made by humans. This is where early SON and much of today’s networks sit.
L3 – Conditional autonomy	Closed-loop operation within single, isolated domains (e.g., 5G core, transport). AI predicts changes and fixes faults with minimal oversight.
L4 – Advanced autonomy	Decisions span multiple domains. Intent-driven control and closed-loop automation across all layers. The strategic goal for most large operators.

ITU-T offers a parallel, complementary classification: Their Recommendation Y.3061 describes the architectural framework for autonomous networks, while M.3384 defines intelligence levels in AI-supported telecom operations.

That said, it’s worth highlighting a nuance often glossed over in marketing materials: self-healing is not the same as full autonomy. Fully removing humans from the loop remains premature at this stage – mainly due to the risk of misinterpretation and the “black box” effect. Hence, today’s dominant human-in-the-loop model and the emphasis on model explainability.

Now let’s take a look at the different areas where telcos can leverage AI to drive network maintenance automation.

Predictive hardware maintenance

Physical infrastructure – from data center servers to base stations on remote towers – is subject to material wear and environmental impact. Predictive maintenance replaces rigid replacement schedules with intelligent fault forecasting. The approach relies on integrating industrial Internet of Things (IoT) with ML models: sensors placed around key components collect telemetry such as ambient temperature, fan speed, disk health (SMART errors), power fluctuations and structural vibration.

Multi-layer models (often combining fuzzy logic with neural networks) look for temporal patterns that signal an impending anomaly. Survival analysis models estimate the Remaining Useful Life (RUL) of individual modules before they physically stop conducting signal. This allows a shift from “replace every X months” rules to condition-based maintenance driven by real equipment state.

Predictive hardware maintenance enables operators to:

• Schedule repairs during low-load windows (e.g. at night), eliminating disruption during peak hours.
• Prevent cascading failures, where the breakdown of one component destroys others connected to the same node.
• Extend the real service life of hardware – maintenance only when metrics indicate an actual need.
• Lower capital and operating costs thanks to fewer replacements of still-functional components and more sustainable infrastructure operation.

Failure prediction in the core and transport network

To protect end-user service quality, AI must forecast failures in the core network and the transport layer – domains that form the backbone of the entire infrastructure, carrying traffic between distributed access elements and the rest of the network. Unlike edge elements, this layer relies on a highly virtualized, centralized management logic, which calls for a distinct analytical approach.

Network Data Analytics Function (NWDAF) analytics

The 5G core is built on a virtualized Service-Based Architecture, composed of modular, loosely coupled network functions that expose their services through standardized interfaces – in practice, typically deployed as independent microservices. 3GPP standardized a dedicated unit called NWDAF, described in TS 23.288, which ingests real-time data from network functions and the OSS. NWDAF includes 3GPP-defined logical units for model training (MTLF) and analytics (AnLF). This function detects software anomalies, IP routing issues and inter-cluster communication degradation to protect services from latency spikes and BGP routing errors.

Because 3GPP deliberately separates the analytics specification from model lifecycle management, the latter is governed by separate management documents (TS 28.105 – AI/ML management, and TS 28.104 – Management Data Analytics). In practice, operators close this gap with their own MLOps frameworks, which can reconfigure transmission paths within fractions of a second or halt the rollout of a faulty update (a guardrail) in the affected area only, which drastically reduces the number of truck rolls.

SS7 signalling security

A separate, though complementary, dimension of core protection is SS7 signalling security. Unlike NWDAF-style analytics, decisions here must be made inline, in the hot path, within a budget of roughly 1-5 ms per message as there’s no time for retrospective analysis. Solutions of this kind are already commercially available. One example is Software Mind’s Amplitiv Signalling Firewall, which classifies SS7 traffic arriving via the STP into the core network (SMSC/HLR/MSC), combining deterministic rules, throttling and heuristics with a lightweight ML model for spam detection (deployed as an ONNX model), with results, logs and alerts flowing asynchronously into a central reporting system and feedback loop.

It’s worth emphasizing that the transport network is the backbone of the entire architecture, carrying traffic between distributed access elements, 5G core functions and the outside world. That backbone runs on IP, and the routers and switches that build it are governed by their own, equally important, set of IETF standards. All the core and transport intelligence described above ultimately rests on this foundation – a closed loop simply won’t work without a standardized way to gather data from devices and issue commands to them.

The foundation of streaming telemetry is RFC 9232 (Network Telemetry Framework), which organizes terminology and classifies telemetry modules across the data, control, and management planes – it’s what lets AIOps draw on a continuous stream of network state instead of polling devices cyclically. On the modeling and configuration side, the backbone is the YANG language (RFC 7950), together with the NETCONF (RFC 6241) and RESTCONF (RFC 8040) protocols, while YANG-Push notification subscriptions (RFC 8641) enable event-driven, “push” delivery of state changes instead of costly polling. Tying this together is the framework document RFC 8969 (A Framework for Automating Service and Network Management with YANG), which describes how YANG data models feed the closed loop across the entire service lifecycle – from provisioning through assurance.

Built on top of this is the intent layer: the concepts of intent-driven networking are defined in RFC 9315 (Intent-Based Networking), while service quality assurance within an intent-based architecture is covered by RFC 9417, along with its YANG data model for service assurance (RFC 9418). This means an engineer’s declarative intent can be automatically translated into device configuration and verified through telemetry, without manually touching individual routers.

Benefits of AI-driven failure prediction

Enhancing failure prediction processes with AI technologies helps mobile operators:

• Protect the core and transport layer from cascading degradation, with intelligent traffic rerouting (e.g. intent-based transport) ahead of congestion.
• Foster probability-driven operations instead of guesswork to make fewer replacements of still-functional components and lower the risk of claims over critical connectivity outages.
• Enable a preventive rather than reactive approach – faults are addressed before they affect service quality, not after it has already degraded.
• Block unwanted communication and potentially malicious or fraud-related events in real time, before they reach the core network, without adding latency to legitimate signalling traffic.

AIOps for network operations – root cause analysis (RCA)

The complexity of the architectures described above creates an information-overload problem for NOC teams. The failure of a single cable or component can trigger a so-called alarm storm – a flood of secondary notifications from every layer and device that lost connectivity to the affected node. Relying purely on human cognitive capacity, an engineer must slowly untangle the root cause from the resulting symptoms.

The answer to this problem is AI for IT Operations (AIOps), which consolidates big data, ML and operational systems into a single analytical organism. This direction aligns with the ETSI ENI framework (GS ENI 001/002/005), which brings cognitive learning to service management. At the heart of AIOps is root cause analysis. Rather than examining alerts in isolation, an RCA system uses topological dependency mapping and causality analysis to build a map of relationships – from a damaged fiber port all the way to a disrupted VoLTE session on a subscriber device. Then, the system traces the event chain backward, filtering out noise and pinpointing the single originating source.

Modern systems add conversational, GenAI-based interfaces (copilots). Under time pressure, an engineer can ask the system in natural language what caused an outage and receive an isolated answer, a probability estimate and ready-made remediation steps – typically without touching production until an operator approves the action. Closed-loop solutions, meanwhile, can reroute traffic autonomously within fractions of a second.

An example of this solution is AI-powered RCA developed by our team at Software Mind. For this solution, we deploy a multi-agent system in which a LangGraph-based orchestrator routes a ticket submitted through Teams to specialized AI agents: one analyzing protocol traces, one searching code repositories and Jira tickets, and one semantically querying (via RAG) a knowledge base of RFC and 3GPP specifications. The output of their parallel work feeds into a single verdict containing the root cause, supporting evidence and a citation from the specific standard behind the fault.

The AI-driven RCA benefits telecom operators by:

• Enabling faster incident diagnosis – documented case studies report reductions in the tens of percent, with some figures reaching as high as 90%, alongside an MTTR reduction of roughly one-third.
• Reducing alarm noise (alert suppression and deduplication) and easing alarm fatigue among NOC teams.
• Lowering OpEx through reduced firefighting overtime and automated reporting and escalation.
• Driving a shift in human expertise from firefighting to strategic work on next-generation infrastructure – McKinsey estimates a 20-40% reduction in manual work in network operations in early agentic AI deployments.

Automated maintenance scheduling

The repair process doesn’t end at the NOC dashboard. When a hardware intervention is required – such as a severed fiber or a damaged antenna – the task moves to Field Service Management (FSM) teams. Traditionally, dispatchers work from isolated schedules, while billing systems, spare-parts inventory and fleet telematics remain disconnected, which results in delays and site visits without proper documentation.

Intelligent scheduling replaces rigid dispatch rules with combinatorial optimization engines. A work order created by AIOps becomes the input for a model that weighs dozens of variables in real time: technician geolocation and telematics, road conditions and weather forecasts, skill and certification matching (skill-based routing), parts availability in the van and at the depot, and each engineer’s historical success rate with a given fault type. As a result, the technician arrives on-site with a preliminary RCA diagnosis already drafted by the algorithm.

Automated maintenance scheduling results in the following benefits:

• A higher First-Time Fix Rate and fewer repeat visits.
• Field team productivity gains reported at around 20-30% thanks to smart dispatching (with some cases significantly higher in dense urban areas), according to Boston Consulting Group, in a cross-industry study that includes telecom as one of eight sectors examined.
• Fewer “empty runs” and a shift from emergency repairs to planned, preventive interventions.
• A better customer experience through narrower arrival windows (ETA) and proactive fault warnings.

How to measure AI success in network maintenance

Telecom operations can’t be judged by business generalities alone – what’s needed are rigorous, measurable SLA and reliability indicators, grounded in practices such as ITIL and OAM frameworks. Below is a set of key KPIs operators use to verify the effectiveness of AI deployments.

Mean Time to Detect (MTTD)

MTTD means the average time between a fault’s occurrence and its detection and reporting. AI lowers MTTD because it filters drift signals (thermal, voltage-related, transmission errors) before the system generates a formal alarm. In a fully proactive model, the goal is to catch the problem before it ever affects the end-user service.

Mean Time to Repair (MTTR)

MTTR is the average time spent diagnosing and resolving a (hardware or software) fault after an alarm fires. It’s calculated as the total repair time divided by the number of failures.

Deploying RCA and automated symptom recognition shortens this metric – TM Forum reports that after adopting AIOps, communications service providers see MTTR reduced by around 60% through automated monitoring, alongside a 40% reduction in downtime from predictive analytics and a 25% reduction in costs from resource optimization. AIOps vendors, meanwhile, report results from their own demos and deployments reaching as high as 87% reduction in the combined time to identify, diagnose and mitigate an incident. However, these figures should be treated as an upper bound observed under controlled conditions, not as an industry-wide average.

Mean Time to Restore a Service (MTRS)

Often confused with MTTR, MTRS measures how quickly service returns to the customer from their perspective – not necessarily after the physical repair. In autonomous environments, service is restored through workarounds and failover to backup resources, so MTRS can be very short while the MTTR clock for the field component keeps running.

False Positive / False Negative Rate

For analysts to trust the system, precision is critical: false positives trigger alarms without any real degradation, while false negatives miss the slow deterioration of hardware (e.g., from passive intermodulation). Modern AIOps platforms, through alert aggregation, deduplication and correlation, cut alarm noise by roughly 60-70% (see Splunk), bringing predictive systems closer to high accuracy without overwhelming NOC teams.

SLA Violation Reduction

This metric stands for the percentage of quality-of-service breaches avoided or prevented. Preventive diagnostics that reduce outages and improve throughput translate directly into fewer incidents and, in turn, into fewer contractual penalties, which is especially significant for enterprise customers and critical links.

Service Availability

This is the probability of fault-free system operation over a given window, expressed as: availability = MTBF / (MTBF + MTTR). Predictive maintenance extends the mean time between failures (MTBF), raising overall reliability. This makes it possible to work toward “five nines” (99.999% – about 5 minutes of downtime per year), and for critical applications, even “six nines” (99.9999%).

Customer-Impact Minutes

This metric is the ultimate measure of stability: the cumulative duration of outages multiplied by the number of affected subscribers. Unlike purely technical metrics, it captures real user discomfort. FSM optimization and intelligent traffic switching reduce this indicator, directly strengthening customer retention and protecting revenue.

Actively preventing network failures with AI

Telecom network maintenance is no longer defined by reactive hardware, manual processes and isolated rules. Under the direction of standards bodies – 3GPP, ETSI, TM Forum, ITU-T and IETF – operators are moving from early SON toward cognitive autonomous networks. Transforming functions such as NWDAF-based analytics, signalling-layer protection and advanced AIOps working toward intent-based fault handling at Level 4 are becoming a condition to remain competitive.

At the same time, telcos need to stay grounded: full autonomy (L5) remains a horizon, not a current reality. Today’s mainstream is the human-in-the-loop model, in which AI acts less like an “intelligent firefighter” lowering MTTR and more like a trusted decision-making advisor – provided data quality, model reliability and explainability are properly addressed. These three factors, rather than raw algorithmic power, will determine how quickly the industry closes the gap between Levels L3 and L4.

If you want to explore how a technology partner can help you enhance network maintenance with AI that understands your network, reach out to us.

FAQ

How does predictive hardware maintenance works?

Predictive maintenance combines industrial IoT with ML models to replace fixed replacement schedules with intelligent fault forecasting. Sensors collect telemetry like temperature, fan speed, disk health, power fluctuations and vibration, which ML analyzes to predict failures before they occur.

What key KPIs can telcos track to verify the effectiveness of AI in network maintenance?

You can measure the success of your AI implementation by tracking mean time to detect (MTTD), mean time to repair (MTTR), mean time to restore a service (MTRS), false positive / false negative rate, SLA violation reduction, service availability and customer-impact minutes.

What are the benefits of AI-driven RCA?

AI-driven RCA speeds up incident diagnosis, cuts alarm noise and NOC fatigue through alert suppression and deduplication, lowers OpEx by reducing firefighting overtime and shifts human resources toward strategic work.