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. A new benchmarking study from Enterprise Management Associates (EMA) reveals that only 31% of IT professionals believe their organization's network operations strategy is completely successful, a sharp drop from 42% two years ago. The report, based on a survey of 352 IT professionals across North America and Europe, confirms that network teams today face multiple pressures: a persistent talent shortage, tool sprawl, hybrid and multi-cloud complexity, and the arrival of AI workloads on networks that were not built to manage them.
Network operators clearly know they need to do better, but they are not getting the support they need, according to Shamus McGillicuddy, EMA's vice president of research for network infrastructure and operations. They require budget to fill empty seats, better tools, more automation, and greater influence over modern architectures like hybrid and multi-cloud networks. CIOs need to step up and give network operators the support they deserve, especially if they want to succeed with AI transformation. Networks will make or break those projects.
The state of the NOC
Tool sprawl remains a chronic condition for network operations teams. The typical IT organization uses four to ten monitoring and troubleshooting tools to manage its network, a number that has barely moved in more than a decade. Yet the EMA research found no significant correlation between the size of a toolset and operational success. This suggests that the quality and integration of tools matter far more than raw quantity.
The data shows how much room for improvement exists, regardless of how many tools a team has. Only 58% of network problems are detected proactively before users experience their impact. Just 37% of alerts generated by network monitoring tools are indicative of a real problem, meaning that nearly two-thirds of alerts are noise. Manual administrative errors cause 28% of network problems, and the average network professional spends 29% of their day troubleshooting. IT professionals believe that 53% of the network problems they deal with daily could be prevented with better tools. This explains why only 31% of respondents felt completely successful with their network operations strategy. Tool replacement is widespread, with 73% of respondents 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 dramatically, from 26% in 2022 to 41% in 2024 and 52% today. The shortage is most acute at the senior and mid-career levels, where cloud, security, and automation skills are most needed. One monitoring architect at a Fortune 500 entertainment company noted that what used to be done by a 25-person team, management now expects to be done with a ten-person team.
The talent gap is driving urgency to deploy automation successfully. Short-staffed teams need tools that handle routine work automatically, freeing engineers to operate at a higher level. However, the skills gap itself can be the biggest barrier to achieving automation. Teams often lack people who know how to build and maintain automation pipelines. The top barriers to automation identified by network teams 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
Historically, network automation has focused on provisioning and configuration, known as day-zero and day-one work. The new priority is day-two operations: 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 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 platform.
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 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. MCP access points can act as an abstraction layer across a fragmented toolset, helping teams overcome integration challenges.
Megatrend 3: Hybrid and multi-cloud networks remain ungoverned
Nearly seven in ten (69%) surveyed organizations 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. Many network observability vendors still lack feature parity across the three major cloud providers, meaning that even when tools are deployed, they often fail to provide a unified view.
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. As cloud adoption continues to accelerate, network teams must find ways to govern these environments without sacrificing agility or security.
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, with most of the rest expecting to deploy within the next two years. However, only 35% say their current network observability tools are completely ready to manage those workloads. The 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%). These requirements underscore the need for a new generation of observability tools designed to handle the unique demands of AI workloads.
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 a strict accuracy standard. They have moved beyond scripts and runbooks to adopt 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 are building unified visibility and security controls that span both on-premises and cloud infrastructure. As one researcher noted, AI networking will require retooling, and network teams should engage vendors early to ensure their tools evolve to meet these demands.
The EMA findings paint a clear picture: enterprise network teams are under immense pressure, and the stakes are rising with the adoption of AI. Without significant investment in talent, tools, and automation, organizations risk falling further behind. Network operations must become a strategic priority for CIOs who seek to enable AI transformation.
Source: Network World News