NETSCOUT expanded its data platform on September 7, 2026, to deliver what it calls Smart Data - compact, context-rich operational evidence derived from packet-level observation. The move targets a barrier that blocks enterprise AI adoption: models fail to make reliable operational decisions when fed incomplete, noisy, or context-stripped telemetry. For operations teams, that failure translates directly into slower incident resolution, higher compute costs, and stalled automation initiatives.
The platform observes digital interactions, converts packets into high-fidelity evidence in real time, and curates that evidence at scale. It complements existing observability investments rather than replacing them. The company reported that internal testing showed more than a 25% reduction in AI token consumption compared with using only metrics, events, logs, and traces (MELT data), and more than a 75% reduction in mean time to know (MTTK).
Why MELT data alone falls short
Traditional MELT telemetry remains important, but it often forces AI systems to reconstruct what happened after the fact. Data gets sampled, aggregated, or separated across tools, which increases inference demands, token consumption, and the likelihood of inaccurate recommendations. Gartner predicts that by 2027, organizations prioritizing semantics in AI-ready data will increase agentic AI accuracy by up to 80% and cut costs by up to 60%.
"Unlocking the benefits of AI across the enterprise will not be achieved by adding another model. It will succeed through context engineering: giving AI the right operational context before reasoning begins," said Sanjay Munshi, chief operating officer at NETSCOUT.
Two architectural shifts
NETSCOUT's approach combines two capabilities. First, early semantic extraction derives operational meaning from packets at the point of observation, preserving evidence that conventional datasets can lose. Second, context optimization at source delivers higher-density, relevant context so AI systems spend less of their context window and compute budget on low-value data.
IDC expects 80% of agentic AI use cases will require real-time, contextual, and widely accessible data. The goal is a trusted data environment where AI can reason, decide, and act with appropriate guardrails. NETSCOUT positions its Smart Data layer as that evidence foundation - usable by human operators, analytics platforms, large language models, copilots, and AI agents.
Practical impact across the maturity curve
The platform addresses organizations at different stages of AI for Operations transformation. Teams gain natural-language access to detailed operational evidence, which accelerates investigation and resolution. Reducing the volume of low-value data that AI must process helps manage telemetry, storage, token, and compute costs without sacrificing the context needed to understand service behavior.
For teams moving toward autonomous operations, the platform provides independently observed, explainable evidence that supports recommendations, governance, and auditability. It can expose hidden dependencies, distinguish infrastructure failures from application issues, and clarify the operational impact of events across hybrid environments.
Why this matters for operations professionals
Every minute spent reconstructing context from fragmented telemetry is a minute not spent resolving the incident. NETSCOUT's approach shifts the burden from the AI - and the operator - to the data layer itself. The reported 75% MTTK reduction is not a minor tuning improvement; it represents a structural change in how quickly teams can move from detection to diagnosis. For operations managers building the business case for AI-driven automation, the token-cost savings provide a concrete metric to justify investment in data quality over yet another model. Professionals looking to build these capabilities can explore an AI Learning Path for Operations Managers that addresses process optimization and workflow automation.
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