As AI footprint expands across telecom networks, older assurance models are emerging a key constraint on performance and service reliability
Telcos are at an inflection point. AI offers a potential opportunity to reorient themselves and grow beyond the revenue plateau that they have been facing as an industry. But it also places new demands on the network, prompting a structural reset of network test, measurement, and assurance.
In a new report, we break down how AI adoption trends are changing the telecom network as we know it. We further put together an AI-ready assurance blueprint based on industry analyses and findings that can help telcos capture new revenue streams in the AI-era, while being a key enabler of AI.
AI is incrementally changing the network
Global AI investment is about to hit a new record again in 2026. According to Nvidia’s survey, 89% of telco executives are set to raise their AI budgets this year.
The enthusiasm is apparent in how the technology is being adopted in myriad use cases like network planning to traffic routing, energy management, predictive maintenance, and code generation. Further to that, the emergence of agentic AI is unlocking fresh waves of automation. Even more adoption will follow as companies increase their AI investments.
But the sweeping adoption also introduces new complexities with AI’s growing traffic footprint in the network and distinguished communication pattern, a kind that service providers never have had to handle before.
As they eye new service level agreement- (SLA) led services such as network slicing and private 5G, and pursue initiatives like edge computing deployments, physical AI, IoT, etc., with 5G, they will need to deliver five-nines uptime on the network, same as the cloud.
Legacy assurance gaps
The dynamic, distributed, cloud-native AI-infused network cannot be managed and assured with reactive tools. As Ross Cassan, senior director of service assurance, Spirent Communications, noted in a recent webinar, “Legacy assurance frameworks are disconnected from the speed and complexity of the modern networks.”
The older frameworks have multiple gaps and blindspots. Key among them is that they are fragmented, and do not involve assimilating distributed data silos into unified sources of truth. As a result, 360-degree visibility and holistic awareness that are essential for AI-native network assurance are a myth with those frameworks.
Another major shortcoming of legacy assurance models is that they are reactive, and therefore best-effort. Troubleshooting often happens after an issue has occurred and the users have felt its impact.
The fundamentals of AI-ready assurance
AI workloads are extremely unforgiving. Even a second of jitters and latency issues can undo days of model training or cause KPIs to drop below thresholds, causing SLA breach. With the network expected to support millions of AI applications and SLA-backed services, these interruptions can amount to huge financial losses.
The network requires a nuanced and mere intent-driven approach, that is intelligent and powered by AI. The question becomes not where to apply AI in assurance, but how to redesign the framework such that it is optimized for the AI-native network.
According to experts, the best way to assure modern AI-native networks is with assurance models that provide continuous and automated assurance. They must offer correlated visibility and AI-powered analytics, and automated operation and faster root cause analysis. Additionally, they must deliver predictive, context-aware assurance that enable service providers to maintain higher service quality goals and deliver more high-value services to customers.
The foundation of the AI-native network is not always-on connectivity. It is intelligent and predictive assurance powered by real-time, context-rich data and AI.
Read the full report, Test, Measurement, and Assurance in the AI-era for a deeper look.
