NETSCOUT Systems, Inc. has now made its Omnis AI Insights platform available to communications service providers [CSPs]. It aims at one of the biggest limits in telecom, which is data readiness to agentic AI.
Telcos are hastening artificial intelligence investments. In a recent survey of C-level executives conducted by McKinsey and Company. 64 percent of the operators are ramping AI programs with AI agents coming into the limelight as a central focus. However, 45% detected data as the primary impediment to improvement. Most networks continue to produce fragmented and high volume telemetry, which is operationally challenging.
NETSCOUT addresses this gap. It claims that it is able to transform unstructured, multi-domain network traffic into structured, AI-ready datasets. The goal is to assist CSPs in the deployment of AI agents to predictive maintenance, automated customer experience and assurance optimization without the need to restructure the core infrastructure.
Omnis AI Develops AI-Ready Data on 5G and Core Networks
Omnis AI Sensor Service Provider provides curated data of real-time in 5G, RAN, Core, MEC and transport environments in the form of smart data. It integrates activity both in fixed and world domains to form a single operational perspective.
This is important because the networks are becoming more distributed and virtualised. The operators should be able to connect technical performance and subscriber experience on near real time basis. The platform attempts to decrease human input and minimise AI mistakes by normalising and aligning events on the network. It is possible to increase the accuracy of models using high-fidelity datasets, and minimize the probability of so-called hallucinations.
In the case of operations teams, speed is the advantage. Outage resolution time is reduced by increasing the speed of root-cause analysis and providing a better understanding of the impact of services. Minutes are important in competitive markets.
Converting Network Telemetry into Actionable Intelligence
The sensor layer is complimented by the Omnis AI Streamer. It removes, collects and names high-value signals using large telemetry streams. Using a programmable approach in the form of a playbook, operators have the ability to form data feeds to service assurance, analytics or outside AI agents.
Selective feeds can have optional machine-learning enrichment, such as outlier detection and contextual classification. The outcome is reduced faster data flows that can be absorbed directly by external platforms, making it possible to achieve closed-loop automation.
According to the arguments of Richard Fulwiler, a senior director of product management at NETSCOUT, the usefulness of the AI agents is merely as good as the intelligence that drives them. There is a decrease in infrastructure cost and operational risk by reducing the volume and complexity of data. To achieve the aim of CSPs under pressure defending margins, that change may convert the customer care into a cost centre rather than a revenue protection and retention strategic lever.
