NETSCOUT Expands Its Data Platform to Strengthen Enterprise AI

In September 2026, NETSCOUT recently expanded its data platform for enterprise AI, with aims to improve the operational context available to AI systems. The announcement centers on a platform that observes digital interactions and converts network packets into what NETSCOUT calls contextualized operational evidence in real time. The information is then curated for uses including observability, service assurance, cybersecurity and AI-driven operations.

NETSCOUT argues that many enterprise AI projects struggle because the data supplied to models is incomplete or lacks context. In network and IT operations, organizations often rely on metrics, events, logs and traces, known as MELT data. This can force AI systems to reconstruct incidents after information has already been sampled, aggregated, or split across different tools. According to the company, this increases inference and compute demands, as well as token consumption and the risk of inaccurate recommendations. The company is seen positioning its platform as an alternative source of operational evidence drawn directly from network traffic.

At the core of the approach is deep packet inspection, which is used to derive meaning from packets at the point of observation. NETSCOUT also stated that this preserves evidence that can be lost in more conventional datasets and gives AI systems denser, more relevant context from the start. The platform is designed to sit alongside existing observability tools rather than replace them. NETSCOUT further said that the data can also be integrated into broader enterprise data and AI workflows, including analytics platforms, large language models, copilots and AI agents.

This also reflects a broader market shift as companies look beyond model choice and focus more on the quality of the data fed into those systems. Industry analysts have increasingly argued that AI tools need access to real-time, contextual, and broadly accessible data to support operational decision-making reliably.

Impact on the AI Market

The platform is aimed at several operational groups, including network operations, security operations, development operations, site reliability engineering and service teams. The company says these users could gain natural-language access to detailed operational evidence to speed up investigations and problem resolution. Another part of the pitch is cost control. By increasing the density of relevant information, NETSCOUT says customers can reduce the volume of low-value telemetry that AI systems must process, cutting storage, token and compute costs without losing the context needed to understand service behavior.

The company is also framing the platform as a step towards more automated IT operations. It says independently observed and explainable evidence can help support governance and audit requirements as organizations move from AI-assisted recommendations to more autonomous actions. This matters because many large organizations are experimenting with AI agents in infrastructure, security, and service management, but remain cautious about letting those systems act without human oversight. In that context, the quality and traceability of underlying operational data becomes a key issue.

Impact on the AI in Enterprise Operations Market

The global AI in enterprise operations market is driven by the rise of AI-powered agents that adapt, learn, and act within enterprise ecosystems. The market is growing rapidly, driven by digital transformation, rising demand for autonomous decision-making in business activities, and deeper integration of AI with enterprise software.

According to Precedence Research, the market is establishing a foothold as enterprises evolve from predictive systems to autonomous systems, in search of more efficient, cost-effective, and adaptive intelligence. Deeper integration with enterprise software and the speed at which AI is developing mean that agentic AI will cause a paradigm shift within enterprise operations. Organizations are moving toward autonomous systems that limit human decision-making. This movement helps spur the adoption of agentic AI because AI can find the right sequence of actions and minimize human involvement in the enterprise workflow.

Data is being produced quickly and is getting bigger and more unstructured, and organizations need intelligent systems to manage that data. Agentic AI agents can autonomously examine data and extract meaning from it, and act upon insights to speed up decision-making and improve accuracy in different departments.

Organizations all over the world are under pressure to reduce costs, reduce mistakes in processes, and improve return on investment. Agentic AI automates repetitive, resource-consuming tasks with greater accuracy and intelligence that improve efficiencies and free up employees to do the strategic portions of their jobs.

Impact on the IT Operations Analytics Market

The global IT operations analytics market size is calculated at USD 22.47 billion in 2025 and is predicted to increase from USD 29.15 billion in 2026 to approximately USD 278.70 billion by 2035, expanding at a CAGR of 28.63% from 2026 to 2035.

According to Precedence Research, the market has grown significantly as a result of many major factors, including increasing business use of ITOA solutions and services to decrease operating costs and improve infrastructure. Organizations are looking for effective solutions to monitor and manage their operations as contemporary IT systems become more sophisticated. ITOA solutions give useful insights into system performance, detect possible obstacles, and fix issues proactively, resulting in better operational efficiency and cost savings.

Strong momentum in the IT operations analytics market is being driven by the increasing complexity of IT infrastructure, the use of hybrid IT environments, and the demand for real-time insights into performance. Enterprises are relying more heavily on predictive analytics to lower the likelihood of downtime and enhance the efficiency of operations. The global expansion of IT operations analytics is also being driven by multinational companies implementing standardization of IT visibility across different geographical locations. Cloud-native analytics platforms also support centralized monitoring, enable fast incident resolution, and maintain consistent levels of service by providing visibility into globally distributed IT operations.

Expert Opinion

Sanjay Munshi, Chief Operating Officer, NETSCOUT, outlined the company's case for the approach. "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," he said.