Why AI breaks traditional service assurance

By Sulagna Saha March 28, 2026

Collected at: https://www.rcrwireless.com/20260328/test-measurement/why-ai-breaks-traditional-service-assurance

Reactive assurance models are pushed to the limit as AI-driven traffic rips through the network

Networks are increasingly carrying cutting-edge AI-related workloads, and that is straining traditional network testing approaches in unexpected ways.

On one hand, new AI functions and applications are being loaded into the network to operationalize AI, on another, new AI-related use cases are mushrooming at the user end, demanding faster, more reliable connectivity.

A common misconception is that the existing ways of testing and measuring network health and performance could be carried over to these AI-enhanced or AI-native networks. But increasingly the older model is cracking under the weight of AI’s enormous scale and specific traffic profiles.

“Legacy assurance is often too disconnected from the speed and complexity that we’re seeing in modern networks,” said Ross Cassan, senior director of assurance strategy at Spirent during a recent RCR webinar on AI-era network test, measurement, and assurance.

Cassan argues that assurance frameworks built on top of older models are doomed to fail in the AI-era. Here’s why:

Fragmented data

Data in telecommunications is spread across legacy systems, cloud platforms, and disparate departments. This fragmentation obstructs a 360-degree view of user experience – a must for network assurance – not to mention, takes a heavy toll on operational efficiency. This could lead to a series of unfavorable outcomes, including service delays, security risks, increased OpEx, and ultimately poor customer experience resulting from blind spots in the network. 

The fragmentation is not only confined to data, but runs across tools, workflows, teams, and departments, impacting how they interact with each other. Current assurance processes are domain-specific, periodic, and stuck in silos like the ecosystem they assure. As a result, they are inadequate to prevent service-level agreement (SLA) breaches in dynamic, ever-expanding network fabrics.

Network Intelligence

Organizations need data delivered as insights in order to act on them. That’s 101 of assurance. While it is the primary goal of all service assurance frameworks to bring insights to the fingertips of engineers as fast as possible, current root cause analysis processes are slow, and that leads to longer outages, revenue leakage, and customer churn. 

In order to obtain real-time intelligence, operators require smart AI and machine learning algorithms that can crunch through pools of data and correlate data points in minutes, delivering that holistic visibility that allows for deeper understanding of what to upgrade in the network, how to ensure better quality of experience (QoE), how to prevent service degradation, etc. in a predictive manner. 

Once again, this requires agentic AI workflows embedded in modern assurance frameworks that are designed to enable swift root cause analysis and instant insights.

Reactive processes

The biggest reason why service assurance needs to change in the AI era is that today it is largely reactive, meaning it finds issues and failures, implements corrective actions, and remedies the situation. “It tells you what happened after a problem occurred, but not always fast enough to prevent a customer impact,” noted Cassan.

AI-driven applications are less forgiving to such performance disruptions. The smallest hiccup can lead to slow response times, inconsistent outputs, and even application failures in mission-critical scenarios. That leaves very little room for performance issues. 

Operators need to identify risks and put controls in place before the failures occur so that issues can be resolved before they reach users and hurt their experience.

Watch the webinar for free here.