Enterprise network operations teams are falling behind as the demands placed on them escalate, with a new study revealing that only 31% of IT professionals report a completely successful network operations strategy—a steep decline from 42% just two years ago. The findings come from an Enterprise Management Associates (EMA) benchmarking study, Network Management Megatrends 2026, which surveyed 352 IT professionals across North America and Europe. The report identifies four major pressures: a deepening talent crisis, persistent tool sprawl, hybrid and multi-cloud complexity, and the sudden arrival of AI workloads on networks not built to handle them.

According to Shamus McGillicuddy, EMA’s vice president of research for network infrastructure and operations, network operators clearly know they need to improve, but they lack the support necessary to do so. They require budget to fill open positions, better tools, more automation, and greater influence over modern architectures like hybrid and multi-cloud networks. As CIOs push forward with AI transformations, the network will make or break those projects.

The state of the NOC

Tool sprawl remains a chronic issue for network operations teams. The typical IT organization uses between four and ten monitoring and troubleshooting tools to manage its network—a number that has barely budged in over a decade. Yet EMA found no significant correlation between the size of a toolset and operational success. Data shows that 58% of network problems are detected proactively before users experience impact, only 37% of alerts generated by monitoring tools indicate a real problem, manual administrative errors cause 28% of network issues, and the average network professional spends 29% of their day troubleshooting. These numbers highlight significant room for improvement irrespective of tool count.

IT professionals believe that 53% of the network problems they deal with daily could be prevented with better tools. This explains why only 31% feel completely successful. Tool replacement is widespread: 73% of respondents said they are likely to replace a network observability or monitoring tool within the next two years.

Megatrend 1: The talent crisis is getting worse

The share of organizations finding it somewhat or very difficult to hire network technology experts has risen sharply—from 26% in 2022 to 41% in 2024 to 52% today. The shortage is most acute at senior and mid-career levels, where cloud, security, and automation skills are in highest demand. One monitoring architect at a Fortune 500 entertainment company noted that teams are being asked to do more with less, with management expecting a 10-person team to handle work that used to require 25 people.

The talent gap is driving urgency to deploy automation. Short-staffed teams need tools that handle routine work automatically, freeing engineers to operate at higher levels. However, the skills gap itself becomes a barrier to achieving automation. Teams often lack people who know how to build and maintain automation pipelines. Top barriers to automation include skills gaps within the team (46%), tool limitations or lack of integration (36.4%), insufficient data quality or visibility (31.8%), risk aversion or governance constraints (31.8%), budget constraints (29.8%), organizational resistance to change (27.3%), and lack of trust in automation (25%).

Megatrend 2: The push to automate day-two operations

Network automation has traditionally focused on provisioning and configuration (day-zero and day-one work). The new priority is day-two operations—the ongoing detection, triage, diagnosis, and remediation of network 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 capable of reasoning about network conditions and taking 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 they would replace an incumbent.

The day-two tasks organizations most want to automate 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 and alert noise reduction (37.5%), and change validation and rollback (26.4%). EMA highlighted that an emerging enabler is Model Context Protocol (MCP) support, which gives AI agents a standard interface to interact with multiple network management tools. Successful NetOps organizations were more likely to prioritize MCP support for agentic AI access.

Megatrend 3: Hybrid and multi-cloud networks remain ungoverned

Nearly seven in ten surveyed organizations operate hybrid cloud environments, and 66% are multi-cloud. Yet only 36% say they are completely effective at managing their cloud networks—a gap reflecting 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.

McGillicuddy noted that many network observability vendors still lack feature parity across the three big cloud providers; some are good at AWS but behind on Google Cloud Platform and others. Organizations that have managed to integrate IP address management and extend network observability tools across hybrid environments report better outcomes, but both remain works in progress for most.

Megatrend 4: AI networks need managing, and few tools are ready

Nearly half of respondents (47.7%) said AI training or inference workloads are already deployed on their networks. Most of the rest expect to deploy within the next two years. However, only 35% say their current network observability tools are completely ready to manage those workloads. Performance concerns are specific to AI infrastructure: isolating problems across networks, applications, and GPU clusters simultaneously; 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%). McGillicuddy advised network teams to talk to vendors about AI networking retooling, noting that many vendors aren't thinking about it because they aren't hearing from customers.

What successful teams are doing differently

EMA's research also identified practices that separate successful organizations from those falling short. Successful teams hold network observability data to strict accuracy standards. 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. Additionally, successful organizations build unified visibility and security controls that span both on-premises and cloud infrastructure. By adopting these practices, they are better positioned to handle the escalating demands of modern enterprise networks and the impending wave of AI workloads.

Source:Network World News