Enterprise network operations teams are increasingly falling behind as the demands of artificial intelligence workloads compound existing pressures. According to a new benchmarking study from Enterprise Management Associates (EMA), only 31% of IT professionals describe their organization's network operations strategy as completely successful. That figure has dropped from 42% in 2024, signaling a troubling trend for NetOps teams already stretched thin.
The report, based on a survey of 352 IT professionals across North America and Europe, identifies four major megatrends driving this decline: a deepening talent crisis, persistent tool sprawl, the difficulty of managing hybrid and multi-cloud networks, and the sudden arrival of AI workloads on infrastructure not designed for them. EMA vice president Shamus McGillicuddy noted that network operators clearly know they need to improve but lack the budget, tools, and organizational support to do so.
The state of the NOC: tool sprawl and operational gaps
Tool sprawl remains a chronic issue. The typical IT organization uses between four and ten monitoring and troubleshooting tools to manage its network, a figure that has barely budged in over a decade. However, EMA found no meaningful correlation between the size of a toolset and operational success. What matters far more is how well those tools are integrated and whether they provide actionable insights.
Key metrics from the report illustrate the scope of the problem. Only 58% of network problems are detected proactively before users notice any impact. Alarmingly, just 37% of alerts generated by monitoring tools actually indicate a real problem, meaning teams waste significant time on false positives. Manual administrative errors cause 28% of network issues, and the average network professional spends 29% of their day on troubleshooting. According to respondents, 53% of daily network problems could be prevented with better tools. This dissatisfaction is driving a wave of tool replacement: 73% of those surveyed said they are at least somewhat likely to replace a network observability or monitoring tool within the next two years.
Megatrend 1: The talent crisis deepens
Hiring qualified network professionals has become increasingly difficult. The share of organizations reporting difficulty finding network technology experts has jumped from 26% in 2022 to 41% in 2024 and now 52% in 2026. The shortage is most acute at senior and mid-career levels, where skills in cloud, security, and automation are in highest demand. A monitoring architect at a Fortune 500 entertainment company noted that a 25-person team has been asked to do the work that used to be done by a 25-person team with only ten people.
The talent gap is accelerating the push for automation. Understaffed teams need tools that can handle routine tasks automatically, freeing engineers to work on higher-value activities. Yet the skills gap itself is the biggest barrier to achieving that automation: 46% of teams cite lack of internal skills as a top obstacle. Other barriers include tool limitations (36.4%), insufficient data quality (31.8%), risk aversion (31.8%), budget constraints (29.8%), organizational resistance (27.3%), and lack of trust in automation (25%).
Megatrend 2: Automating day-two operations becomes a priority
Network automation has traditionally focused on provisioning and configuration—so-called day-zero and day-one tasks. The focus is now shifting to day-two operations: the ongoing detection, triage, diagnosis, and remediation of problems in production. Seventy-nine percent of respondents rate automating these tasks as a high or very high priority. Organizations are looking for AI-driven, agentic automation—tools that can reason about network conditions and take autonomous or semi-autonomous action. The report found that 55% of respondents say AI features are a requirement when evaluating new tools, and AI-driven insights and automation is the top reason for replacing an incumbent.
The day-two tasks most desired for automation include security response and containment (54.3%), capacity and performance optimization (49.7%), incident remediation and self-healing (44.3%), configuration optimization (40.3%), event correlation/alert noise reduction (37.5%), and change validation and rollback (26.4%). An emerging enabler is Model Context Protocol (MCP) support, which gives AI agents a standard interface to interact with multiple management tools. Successful organizations are more likely to prioritize MCP support for agentic AI access, according to EMA.
Megatrend 3: Hybrid and multi-cloud networks remain ungoverned
Nearly 70% of surveyed organizations operate hybrid cloud environments, and 66% run multi-cloud architectures. Yet only 36% say they are completely effective at managing their cloud networks. This gap reflects both technical complexity and cultural friction between network teams and cloud engineering groups. Core challenges include proprietary networking constructs that vary across providers, inconsistent telemetry, skills gaps on the network team, and limited end-to-end visibility across cloud and on-premises environments.
Many network observability vendors still lack feature parity across the three major cloud providers. For instance, they may handle AWS well but lag on Google Cloud Platform and secondary providers. Organizations that have integrated IP address management (IPAM) and extended observability tools across hybrid environments report better outcomes, but both remain works in progress for most enterprises.
Megatrend 4: AI networks need managing—and few tools are ready
Nearly half of respondents (47.7%) report that AI training or inference workloads are already deployed on their networks, and most of the rest expect to deploy within two years. However, only 35% say their current network observability tools are completely ready to manage those workloads. Performance concerns specific to AI infrastructure include isolating problems across networks, applications, and GPU clusters; managing inference tail latency; and gaining visibility into GPU utilization as a network signal.
The tool enhancements teams most want include AI-powered troubleshooting and remediation (51.3%), proactive alerting for AI-related performance risks (49.3%), AI workload awareness via real-time packet analysis (46.9%), real-time streaming telemetry to replace polling intervals (40.2%), and correlation of GPU, application, and network performance metrics (34.3%). As McGillicuddy noted, AI networking will require retooling, and vendors are unlikely to invest unless they hear demand from customers.
What successful teams are doing differently
EMA's research also identified the practices that separate successful teams from those falling behind. Top-performing organizations hold network observability data to a strict accuracy standard. They have moved beyond scripts and runbooks to AI-driven and agentic management tools. They prioritize integration over consolidation, focusing on security insights, workflow integration, and data sharing across their toolset rather than trying to reduce its size. And they are building unified visibility and security controls that span both on-premises and cloud infrastructure.
For enterprise network teams, the message is clear: the stakes have never been higher. Without investment in modern tools, automation, and talent development, organizations risk being unable to support the AI workloads that will define the next decade of business operations. The networks of the future must be built today, and the teams managing them need every advantage they can get.
Source: Network World News