By: Sandeep Raina

As Communications Service Providers (CSPs) roll out their latest 5G and Fiber Broadband services, the need for automating operations and reducing costs has become a necessity.The complexity of such disaggregated networks introduces errors, only worsened by manual operations. With operational automation based on Assurance and AI insights, not only can high amounts of data be processed efficiently, but operational errors can be significantly reduced. Leveraging AI as a predictive tool is critical to the evolution of Autonomous Networks which will speed up troubleshooting, prioritize resolution, and automate the next best actions for remediation.

In a recent Fiber Broadband survey conducted by Analysys Mason, 34% CSPs reported that they were already working towards autonomous fault resolution with assurance-driven closed-loop automation. Another 34 percent reported that they had started implementing a real-time monitoring solution and some automated fault resolution. When asked about their approach to closed-loop automation of NOC and SOC, 48 percent said that they wanted closed-loop automation only for network and service assurance, and 24 percent said that they wanted closed-loop automation for most of their network and service operation processes.

It's evident that the need for automation is high, yet the progress towards fully autonomous networks is piecemeal and taking time.

Autonomous Networks: How Do We Get There?

The question that arises is what will drive CSPs to automating their networks and to achieve a fully Autonomous Networks status faster? If costly manpower, expensive human mistakes, and low quality of services can be reduced through automation, CSPs will put their money on Automation.

Other than operational efficiency and for cost reasons, CSPs identify business objectives that drive them to Automation. These could include introduction of network slicing for enterprise services, a new fixed broadband service, or entering a new enterprise market.

Simply put, CSPs would invest in technologies that offer higher revenue at lower costs. Automated operations and Autonomous Networks promise both.

Let’s break this down. CSPs need to do the following to remain competitive as new services consume higher volumes of data and require faster data rates:

1. Deliver high performance and high reliability services.
2. Reduce manual operations.
3. Simplify problem investigation in complex networks.
4. Increase workforce productivity.
5. Meet high speed demands of enterprise services.
6. Respond dynamically to changing network conditions.
7. React faster to customer requirements.

The key to achieving some of the targets above lies not in acquiring new tools, but in the data generated in the CSP networks that carries within it the intelligence required for automation. Systems that leverage this data can offer a solid foundation for automation. Let’s explore what the network data offers:

• Assurance (Performance, faults, and QoS) metrics.
• Network data when modelled correctly, can unleash AI/GenAI insights.
• Assurance and AI insights together create a powerful combination, underpinning the foundation of Automation.

Network data is already collected by the CSPs for Assurance purposes. However, if this data is manipulated and turned into intelligent AI insights, it can drive the evolution of Autonomous Networks. Only intelligent data in a closed loop feedback system will self-correct the network errors, reduce service delays, and eliminate human errors.

Assurance, AI and Automation: Can They Bring Automated Diagnosis and Remediation?

TM Forum has defined the TMF Autonomous Network Maturity Levels (0-5) with definitions, processes and sample use cases. A recent (2024) Omdia survey shows how CSP autonomous levels are distributed: 20 percent L1 (Assisted Operation and