By: Karthikeyan Nathillvar

As modern networks become increasingly complex and dynamic, traditional manual approaches to network management are no longer sufficient. The rapid growth of devices, applications, and data traffic puts immense strain on network operations teams to ensure optimal performance, security, and reliability. Put simply, these teams are being overwhelmed in the increasing digital chaos of integrating multiple solutions together, when most CIOs would prefer to have their teams focus on projects that move their businesses forward.

This is where artificial intelligence (AI) comes into play. By leveraging AI techniques, IT teams can give themselves superpowers to rapidly evolve their network management practices, enabling automated, intelligent, and proactive management of their infrastructure.

For example, instead of being a tool for point problems and temporary solutions, network management solutions need to evolve into systems that don’t just use AI for reporting but leverage this intelligence to continue to improve themselves over time. This also means solutions performing network management duties rather than simply suggesting solutions. 

It’s essential to distinguish between “AIOps,” which is primarily limited to “Day 2” activities such as device offline issues, user connectivity issues, and configuration errors, from the real potential for AI and the network. We often talk about a “Shift Left” strategy with our customers, where they see a role for AI from Day 0 (design and implementation) to Day “N” activities, where AI is responsible for continuously optimizing the network for performance guarantees such as availability, capacity, and coverage.

The biggest benefit of leveraging AI is saving IT time wasted on ongoing management and time-consuming troubleshooting tasks. AI will serve as a copilot that will enable IT to solve problems more quickly and provide better performance while having to do less work. Most AI solutions for network management today simply identify problems for IT to fix. We should be using it to proactively address issues without human intervention. No IT person looks forward to grunt work like addressing minor connectivity issues or other day-to-day drudgery of managing a network. AI can and should eliminate that wasted effort and allow teams to focus on activities that generate real value for their companies.

With these goals in mind, let’s explore five key principles that underpin effective AI-powered network management solutions.

1. Closed Loop Automation

One of the fundamental principles of AI-powered network management is closed loop automation. In this approach, the AI system continuously monitors the network, gathering data on performance metrics, traffic patterns, and device behavior. It then analyzes this data in real-time, identifying potential issues or anomalies. Based on predefined policies and machine learning models, the system automatically takes corrective actions to mitigate problems and optimize network performance. This closed loop process operates autonomously, without the need for human intervention, enabling faster issue resolution and reducing the workload on network administrators.

A good example of closed loop automation is the ability to automatically detect anomalies in the network element resources (e.g., sudden spike in CPU utilization) or the connecting links (e.g., sudden spike in cabling errors), isolate the points of failure, reroute traffic, and restore the network to the original behavior when the condition is cleared. All of these happen in the background without the users or IT administrators knowing about it.

2. Continuous Optimization

Networks are dynamic environments with constantly evolving user behavior, application requirements, and device landscapes. AI-powered network management solutions embrace the principle of continuous optimization to adapt to these changes. By leveraging an integrated data model and a deterministic system design, AI algorithms can continuously analyze customer environment, network performance, and make intelligent decisions to optimize resource allocation, traffic routing, and quality of service. This proactive optimization ensures that the network remains agile and responsive to changing demands, delivering consistent performance and user