Enterprise network operations teams are struggling to keep pace with the demands placed on them, and the challenge is growing as enterprises prepare their networks and observability tools for AI workloads. According to a recent benchmarking study by Enterprise Management Associates (EMA), only 31% of IT professionals surveyed said their organization's network operations strategy is completely successful, a significant drop from 42% just two years ago. This decline underscores the mounting pressures network teams face in an era of rapid technological change.

The EMA Network Management Megatrends 2026 report, based on a survey of 352 IT professionals across North America and Europe, identifies several key factors contributing to this struggle. Talent shortages, tool sprawl, hybrid and multi-cloud complexity, and the arrival of AI workloads on networks not designed for them are all creating a perfect storm of challenges. As Shamus McGillicuddy, EMA's vice president of research for network infrastructure and operations, noted, network operators know they need to improve but lack the support from leadership.

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 the EMA research found no significant correlation between the size of a toolset and operational success. This suggests that having more tools does not automatically lead to better outcomes; instead, the quality and integration of tools matter more.

Key performance metrics from the report highlight the scope for improvement. Only 58% of network problems are detected proactively before users experience their impact. A mere 37% of alerts generated by monitoring tools indicate a real problem, meaning the majority are noise. Manual administrative errors cause 28% of network problems, and the average network professional spends 29% of their day troubleshooting. These figures paint a picture of reactive teams overwhelmed by inefficient tools.

McGillicuddy explained that IT pros believe 53% of the network problems they deal with daily could be prevented with better tools. This explains why tool replacement is widespread—73% of respondents 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 Is Getting Worse

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

The talent gap is also driving urgency around automation. Short-staffed teams need tools that handle routine work automatically so that existing engineers can focus on higher-value tasks. However, the skills gap itself becomes a barrier to automation, with teams lacking people who know how to build and maintain automation pipelines. The top barriers cited by network teams include skills gaps (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 historically focused on day-zero (provisioning) and day-one (configuration) tasks. But the new priority is day-two operations: ongoing detection, triage, diagnosis, and remediation of network problems in production environments. The EMA report found that 79% of respondents rate automating these tasks as a high or very high priority.

Organizations are increasingly looking for AI-driven, agentic automation—tools 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 vendor. 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%).

An emerging enabler is the 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 to tools. McGillicuddy described MCP access points as an abstraction layer across tool sprawl.

Megatrend 3: Hybrid and Multi-Cloud Networks Remain Ungoverned

Nearly seven in ten surveyed organizations (69%) operate hybrid cloud environments, and 66% are multi-cloud. 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.

McGillicuddy noted that many network observability vendors still lack feature parity across the three major cloud providers. They may excel at collecting data from AWS but lag behind on Google Cloud Platform and have not even considered secondary providers. Organizations that have successfully integrated IP address management and extended 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, and 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 specific to AI infrastructure include 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 to close the gap 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%).

What Successful Teams Are Doing Differently

The EMA research also identified practices that separate successful organizations from those falling short. Successful teams 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. Additionally, successful organizations build unified visibility and security controls that span both on-premises and cloud infrastructure. As McGillicuddy advised, AI networking will require retooling, and network teams should talk to vendors about their plans, as many are not yet thinking about it because they haven't heard from customers.

Source: Network World News