streamlining integration with various other automation tools. At the same time, advanced network controllers feature modular software architectures and have also been designed around standard data models and open APIs, facilitating seamless communication both southbound into the network and northbound into diverse automation environments.

Additionally, the rise of streaming telemetry has transformed how operators access and use network data. Instead of relying on static network snapshots and a limited set of measurements, teams can now tap into real-time performance metrics originating from devices across the network. And if this influx of data becomes overwhelming, artificial intelligence and machine learning techniques can be employed to help analyze massive datasets, detect anomalies, and even predict behaviors before they occur.

The building blocks for automation are available and proven. Operators now have automation tools they can use for both simple and complex tasks. Network automation has moved beyond theory and labs to deliver real results in live environments.

Network automation in action

Operators are deploying automation to tackle use cases that improve efficiency and reliability and enhance service delivery. Here are some popular examples:

Optical automation initiatives often begin with comprehensive, systematic network performance monitoring, network health analysis, and consolidated network inventory and resource lifecycle management. These foundational use cases facilitate compliance with committed service level agreements and allow for better network utilization.

On-demand capacity fulfillment is another automation use case with clear benefits. Traditionally, delivering new capacity in an optical network requires lengthy manual analysis and processes, often leading to over-provisioning ahead of needs or the inability to respond in time. With automation tools, operators can benefit from uncluttered, dynamic visibility of their network, can easily assess resource utilization and traffic patterns to recognize trends and identify, allocate, and provision resources in real time, activating services and ensuring that capacity matches demand without delay or waste.

Fault localization and troubleshooting are areas where automation also helps. In complex networks, identifying the source of a failure can be time-consuming and disruptive. Automation can correlate events, pinpoint issues, and even suggest corrective actions, dramatically reducing mean time to repair (MTTR) and improving overall network availability.

Many networks have under-utilized or stranded spectrum, due to the accumulation of services that were never removed or inefficient spectral allocation, possibly made worse by the co-existence in the same link of several transponder generations with different spectral widths. Spectrum management and optimization are becoming more important as transponders move to higher baud-rates and need larger slices of contiguous spectrum. Automation solutions that help identify unused services and support defragmentation (reallocating existing demands in a way that frees up blocks of contiguous spectrum) unlock gains without new hardware investments.

Next: AI-powered network operations

The use of artificial intelligence expands network automation potential even further, driving smarter, more adaptive network operations.

Generative AI-powered conversational assistants are emerging as valuable tools for operations teams that are also easy to adopt. These virtual operational assistants can respond to natural language queries, guide troubleshooting, search through relevant documentation, and even suggest configuration changes, making previously complex tasks more accessible, resulting in shorter resolution times.

AI-enhanced fiber sensing is another promising area. By interpreting data that would normally be too noisy for humans, including measurements on the optical signals’