Network operators are embedding AI/ML and generative AI into daily workflows across mobile services, enterprise environments, network management and product development. Tasks that once took months, from compiling information and developing code to planning networks and troubleshooting root causes, can now be completed in hours or minutes, helping organizations achieve outcomes faster and with less effort.
This shift comes as AI adoption drives data center buildouts and network expansion to support GPU compute clusters. Operators must move faster while building more complex networks with fewer resources.
Why AI makes network operational change imperative
Optical networks are becoming the central nervous system connecting compute and cloud platforms. They provide access to GPU resources within data centers and support scaling across GPU clusters between multiple data centers that can be hundreds of kilometers apart.
Building networks at scale and managing their lifecycle across planning, integration, provisioning, monitoring and maintenance has become increasingly complex, making it harder to staff teams with the required domain expertise.
To control costs, train resources, and support evolving business needs, organizations are increasing the use of automation. It reduces repetitive tasks, codifies expertise, accelerates workforce enablement and provides insights that can reduce time-to-repair. Automation also enables more stringent SLAs while preventing SLA violations.
Organizations are also evolving in-house automation toward AI agents that use domain-specific tools and support reasoning to detect network events and explain them as they occur.
How embracing automation and AI empowers network operators
Discussions with global service providers at OFC, ECOC, and Nokia Wavelengths confirm a common reality: network complexity is universal.
Operators want automation benefits beyond daily routines, especially for operations, assurance and service enablement.
Operational use cases are routine-based tasks and include:
- Commissioning a line system between endpoints, then planning, implementing and provisioning optical channels.
- Troubleshooting alarms, alarm storms and root causes raised by network operations teams.
Service enablement use cases are also routine-based and include:
- Intent-based requests that automatically provision wavelengths or spectrum services between endpoints based on service-level requirements.
- Automated assurance reporting that monitors availability, latency and performance margins to show SLA compliance.
AI agents are changing how operators implement AI in situations where deeper domain knowledge is required. Large language models, machine learning, reasoning and natural language interfaces can interpret intents, then analyze KPIs to turn insights into actionable recommendations.
Troubleshooting remains a top use case for network operators. AI agents are well-suited to support alarm detection, anomaly assessment, corrective recommendations and keeping operators informed of high-impact issues.
Moving from basic scripting to closed-loop workflow automation helps operators scale operations and address their service enablement requirements more rapidly and efficiently. Workflows can use rule-based policies or data-driven KPI analysis to trigger actions based on defined intents. Closed-loop automation builds confidence through predefined actions; AI agents add domain-specific reasoning to explain events and accelerate resolution.
For example, AI-assisted closed-loop automation can initiate monthly spectral optimization on selected links, track utilization and improve efficiency. Other workflows can connect planning, inventory, supply chain and readiness pre-checks as new capacity is introduced. AI agents can be used to detect reduced capacity utilization and suggest equipment deployment on highly utilized links.
Coordinated maintenance is another example, automatically rerouting traffic when impairments occur. AI-assisted capabilities using machine learning can generate intent-based triggers from historical and time-series data to enhance current closed-loop operations. AI agents can use this process to recommend actions to proactively reroute traffic before SLAs are affected.
How relevant is AI-assisted automation for network operators?
At in-person events, operators continue to ask for more automation beyond basic network management. Many already use AI in orchestration layers, such as ticketing systems and chatbots, but are still defining how AI-assisted automation applies to the optical domain.
Nokia’s optical automation platforms, WaveSuite and Transcend, support closed-loop operations that help operators adopt AI-assisted automation, from background capabilities to customized workflows.
AI-assisted closed-loop use cases available to explore from Nokia include:
Service enablement and fulfillment to detect capacity constraints and recommend transponder expansion based on historical network insights, reducing delivery time from months to minutes.
Predictive inferencing on incoming router traffic to trigger energy-saving measures. Balancing traffic and placing coherent pluggables and/or transponders into low-power states during reduced demand can improve efficiency by approximately 50% based on global traffic cycle patterns, while other conference sources have shown between 80% to 90% cross-domain power savings.
Optical spectrum optimization through defragmentation combines offline analysis with automated recoloring, pre- and post- validation checks, and the use of natural-language intents and channel constraints to capture requirements before they are implemented across selected network links, including multivendor links. This can reduce OEO conversions, free capacity and shorten recoloring execution time from weeks to a single maintenance window.
Optical network planning that simplifies builds and capacity growth while helping teams scale across design and deployment. AI-assisted planning can reduce configuration and planning effort by more than half (referenced in minutes) to traditional manual approaches.
Software and firmware upgrade automation that deploys upgrades in less than half the time while maintaining successful activations across thousands of elements. These AI-assisted workflows use inventory, migration-specific decision intelligence procedures, health checks and post-activation verification to reduce risk and on-site corrective actions.
Partner with Nokia to maximize the benefits of optical automation
Introducing closed-loop and AI-assisted automation with Nokia WaveSuite, Transcend, and Nokia Optical Engineering Services can help operators accelerate transformation and reduce operational complexity.
From operational efficiency and resiliency to enabling scalable growth, the business value is substantial. Partnering with Nokia at any phase of your network lifecycle with automation helps align technology investments with business objectives while delivering measurable outcomes that deliver long-term success.
Contact Nokia to schedule a demo or trial, or to co-create use cases that meet your specific business needs.