How AI helps telecom providers simplify optical network operations

This Industry Viewpoint was authored by Pino G. Dicorato, Solution Marketing for Nokia’s Network Infrastructure optical network automation environment portfolio at Nokia.

By: Pino G. Dicorato Optical networks are carrying higher volumes of AI traffic and facing stricter demand around service availability, time-to-market pressure, and operational cost control expectations. Providers are also pushing optical infrastructure deeper into metro locations to support optimized performance and lower latencies for the rich data traffic associated with AI inferencing.

These changes are making optical networks more complex and manual network management methods obsolete. Telecommunication providers are turning to AI for help. For many providers, AI is proving to be a useful tool that helps engineering and operations teams make better judgments about their networks. By converting dispersed network data into timely, intelligent insights, it empowers teams to make decisions that prevent service disruptions and ensure that industries can always rely on their critical communication infrastructure.

The clearest near-term value is showing up in three specific areas.

Predictive maintenance

Optical network impairments can develop gradually, and fixed thresholds may not reveal potential risk until service quality has already declined. That leaves operations teams to interpret performance trends across varying equipment, routes and environmental conditions, often with limited optical networking skills and resources.

AI models can examine time-series KPIs within coherent technology endpoints, open line system inventory, maintenance history or network inventory data to identify abnormal behavior or deteriorating margins. This can produce alerts that help network operations teams prioritize inspection, schedule intervention and prepare remediation before a hard failure occurs.

By combining AI models with automated closed-loop workflows, providers can enable proactive maintenance that reduces avoidable outages, improves operational efficiency and supports more experienced network operators in authorizing higher-risk traffic-impacting configuration changes.

Network planning and optimization

Effective optical capacity planning requires engineering teams to balance forecast demand with route diversity, spectrum, equipment availability, power consumption and deployment constraints. Factors such as fragmented spectrum, interdisciplinary network data handoffs, fiber connection readiness and the state of the coherent technology supply chain can slow implementation, increase time to market and leave networks underused.

AI can combine information on traffic patterns, utilization and topology to predict where congestion could occur and recommend network resource expansion or rebalancing options. It can also support recurring analysis of optical spectrum deployments and performance configurations as network conditions or fiber characteristics evolve. The practical gains for operators include shorter planning cycles, smarter engineering decisions, better use of installed assets and a proactive response to new types of service demands.

Fault management and service restoration

Major incidents can produce thousands of alarms, making it difficult to separate the initiating fault from downstream symptoms. Troubleshooting becomes harder when services cross multiple optical domain layers, when one layer depends on other domains or when inventory and configuration records are incomplete.

AI-assisted automation can correlate alarms, performance changes, topology and prior cases to rank probable causes and affected services. If the provider has implemented operational policy permits and guardrails, AI-assisted automation can recommend the most likely cause of failures. It can also provide instructions for quickly resolving problems or enable closed-loop workflows to initiate protective actions, validate results and escalate exceptions to protect SLAs.

These capabilities can accelerate fault isolation and shorten time to repair while ensuring that humans always approve decisions relating to consequential changes. AI-assisted automation that provides built-in explainability and ingests procedural documentation to improve accuracy reduces barriers to adoption for telecommunication providers.

Turning AI-assisted automation into sustainable operational value

Getting lasting value from AI-assisted optical network automation depends less on isolated models than on the operating foundation around them. To shift from lab trials to live production deployments, telecommunication providers need reliable, structured network data, accurate inventory, governed data access, explainable recommendations and clear boundaries for automated workflows. They also need processes for validating models as networks evolve.

With these elements in place, providers can rely on AI-assisted automation to improve operational efficiency and resilience. They can also ensure that network engineers can access data-driven intelligence that helps them remain accountable for business outcomes. Their pace of adoption will depend on how quickly they can establish the GPU compute infrastructure needed to embed these capabilities across the network management lifecycle.

About the Author

Pino leads Solution Marketing for Nokia’s Network Infrastructure optical network automation environment portfolio, which is paving the path to transition, scale, simplify and digitize the future for the optical transport industry. With over 20 years of experience in the telecom industry, his roles have ranged from business development and consulting within various industry markets to R&D roles in photonic hardware development, ASIC/FPGA design, and software automation for high-scale manufacturing.

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