April 22, 2024

AIOps: The Driving Force for Autonomous Networks

The combination of OSS with AI/analytics will lead to significant advancements in the areas of service lifecycle management and service assurance.

Artificial intelligence for IT operations (AIOps) is an essential component of digital transformation as service providers evolve from telcos to techcos. By applying automation and AI technologies to the service operations layer, significant advancements can be achieved in a number of key areas that will benefit telecom service providers.

Using AI Automation for Service Lifecycle Management
There is an increasing need to manage the complexity of the service lifecycle within the network, from service design, zero-touch provisioning and intent-based service orchestration to management of the physical, virtual and cloud-native infrastructure that forms modern networks. Increasingly, within 5G mobile networks we’ll see the introduction of end-to-end network slicing technology to enable highly customized network slices linked to the “intent” or nature of the service being run over them. Service lifecycle management will require the use of real-time systems to curate, configure and monitor the networks and the services that run over them. A good example of this is Swisscom, which has implemented Network Domain Orchestration to automate its multilayer transport networks with real-time federated inventory in its new Terabit IP Transport Aggregation Network (TITAN).

Using AI Automation for Assurance
In an increasingly saturated market, customer experience is the battleground for differentiating your service. When basic performance measures such as speed and coverage are very similar, service providers have to be able to offer differentiated service performance. Combining AI, operational tools and observability enables service providers to create several AIOps use cases that ultimately improve customer experience. This leads to better retention and longer customer lifetime value. Common use cases include problem detection, impact analysis, root-cause analysis and service optimization.

The reality is that most customers only contact customer service when they have a problem. Problem detection means that a huge percentage of issues can be detected and resolved before the customer is even aware. Impact analysis allows proactive communications, telling the customer the nature and duration of the fault. Root-cause-analysis accelerates fault resolution, meaning that downtime is minimized. Finally, intent-based service optimization allows the operator to optimize services based on their importance to the subscriber. Low bandwidth for someone trying to watch a live football game is intensely frustrating, whereas it would be less so for activities that are not as time-sensitive, such as delivering emails. AI-driven assurance enables a substantially better customer experience.

The Impact of Generative AI on Telecom
Generative AI (GenAI) will further improve operational performance by augmenting AI with innovative content generation and natural language interactions. From assisting technicians in the field to creating service designs, or correlating alarms across domains, it will bring a further degree of automation and efficiency.

GenAI will also provide an important trust function to AI automated operations by explaining in natural language every step of the AI analysis and proposed resolution. This level of explanation will help operations experts gain confidence in the technology to further accelerate its adoption across the business.          

Learn more about Netcracker’s AIOps capabilities.



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