Bengaluru: NETSCOUT Systems, Inc. has highlighted the role of real-time, high-quality data in enabling AI-driven network operations during the TM Forum’s NeuroNOC Catalyst project at DTW Ignite 2025. The project brought together telecom operators and technology partners to explore how AI and automation could support self-healing networks, with a focus on faster fault detection and resolution.
The NeuroNOC Catalyst project, developed by TM Forum alongside technology and telecom partners including Amazon Web Services, Accenture, Symphonica, and Sand Technologies, examined how closed-loop automation and AI agents could improve network reliability. Supported by telecom carriers such as BT Group, Telecom Argentina, Omantel, Turknet, Axian Telecom, and Safaricom, the project simulated real-world network challenges to test AI-driven responses.
NETSCOUT contributed its Omnis AI Insights Solution, which includes its AI Sensor and AI Streamer tools, to generate high-fidelity network telemetry essential for these AI operations. Using deep packet inspection technology and real-time analytics, the system helped network operations teams identify subscriber registration problems, trace root causes with the help of a large language model (LLM), and carry out corrective measures with limited human intervention.
One of the primary takeaways from the project was the central role of data quality in achieving reliable AI-driven network automation. Participants stressed that without accurate, curated data, AI models cannot deliver meaningful results. The project also highlighted practical benefits for telecom service providers, including an estimated 80% reduction in manual troubleshooting and up to 50% lower operational costs. Additionally, the initiative demonstrated that streamlining data tokenization through AI models like AWS Bedrock could cut data use by as much as 80%.
“Curated real-time data is essential for intelligent network operations,” said Richard Fulwiler, Senior Director of Product Management at NETSCOUT. He noted that while fully autonomous networks remain a long-term goal, the project offered evidence that AI agents can already assist in faster and more precise problem-solving when backed by quality data.
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