Enterprise network operations teams are facing an increasingly uphill battle as the demands placed on them continue to grow. According to a recent Enterprise Management Associates (EMA) benchmarking study, only 31% of IT professionals believe their organization's network operations strategy is completely successful, a sharp decline from 42% just two years ago. This drop signals a growing crisis in network management, driven by a combination of talent shortages, tool sprawl, hybrid and multi-cloud complexity, and the rapid adoption of AI workloads.
The study, titled Network Management Megatrends 2026, surveyed 352 IT professionals across North America and Europe and paints a concerning picture of the state of the network operations center (NOC). Network teams are being asked to do more with less, yet they lack the budget, tools, and automation needed to keep pace. As AI workloads begin to saturate enterprise networks, the stakes have never been higher. CIOs must act now to support their network teams or risk derailing their AI transformation initiatives.
Tool Sprawl Remains a Chronic Problem
One of the most persistent challenges for network operations teams is tool sprawl. The typical IT organization uses between four and ten different 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 number of tools an organization uses and its operational success. In fact, tool sprawl often leads to inefficiency, alert fatigue, and wasted resources.
The data reveals a wide gap between current performance and what is possible. For example, only 58% of network problems are detected proactively before users experience their impact. Additionally, just 37% of alerts generated by monitoring tools are indicative of a real problem, meaning the majority of alerts are noise that distracts teams from critical issues. Manual administrative errors cause 28% of network problems, and the average network professional spends 29% of their day troubleshooting issues. These statistics underscore the need for better tools, improved automation, and a more streamlined approach to network management.
According to Shamus McGillicuddy, EMA's vice president of research for network infrastructure and operations, IT professionals believe that 53% of the network problems they deal with daily could be prevented with better tools. This is reflected in the high rate of tool replacement: 73% of survey respondents said they are at least somewhat likely to replace a network observability or monitoring tool within the next two years. Organizations are looking for AI-driven solutions that can reduce noise, automate routine tasks, and provide deep visibility into network performance.
The Growing Talent Crisis
Talent shortages are compounding the challenges facing network teams. The share of organizations that find it somewhat or very difficult to hire network technology experts has risen from 26% in 2022 to 41% in 2024 and now to 52% today. The shortage is particularly acute at the senior and mid-career levels, where skills in cloud, security, and automation are most needed.
One monitoring architect at a Fortune 500 entertainment company captured the sentiment perfectly: "We're being asked to do more with less. What used to be done by a 25-person team, management now wants us to do with a ten-person team." This pressure is forcing organizations to seek automation solutions that can handle routine work, allowing existing engineers to focus on higher-value tasks. However, the skills gap itself is often the biggest barrier to achieving automation. Network teams cited several top barriers, including skills gaps within the team (46%), tool limitations or lack of integration (36.4%), insufficient data quality or visibility (31.8%), and risk aversion or governance constraints (31.8%). Budget constraints (29.8%) and organizational resistance to change (27.3%) were also significant hurdles, along with a lack of trust in automation (25%).
Automating Day-Two Operations Becomes a Priority
Historically, network automation has focused on provisioning and configuration tasks, often referred to as day-zero and day-one operations. But the new priority is day-two operations: the ongoing detection, triage, diagnosis, and remediation of network problems in production. Seventy-nine percent of respondents rated automating these tasks as a high or very high priority, according to the EMA report.
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. 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 of this automation 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. As McGillicuddy explained, "The MCP access points become like an abstraction layer across your tool sprawl." This approach allows teams to integrate disparate tools without forcing consolidation, enabling a more cohesive and intelligent network management ecosystem.
Hybrid and Multi-Cloud Networks Remain Ungoverned
The complexity of modern network environments is another major challenge. 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 cloud providers, inconsistent telemetry, skills gaps on the network team, and limited end-to-end visibility across cloud and on-premises environments. Even network observability vendors have not achieved full feature parity across the leading cloud providers. As McGillicuddy noted, some vendors excel at collecting and analyzing data from AWS but are far behind on Google Cloud Platform and have not yet considered secondary providers. Organizations that have managed to integrate IP address management and extend network observability tools across hybrid environments report better overall outcomes, but both remain works in progress for most.
AI Networks Are Here, But Tools Are Not Ready
Perhaps the most pressing finding of the EMA study is that AI workloads are already being deployed on enterprise networks, but the tools to manage them are not keeping pace. 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%).
McGillicuddy emphasized that "AI networking, or networks for AI, is going to require some retooling. I recommend you talk to your vendors about whether they're thinking about this. Most of them aren't, probably because they're not hearing from you." The message to network teams is clear: they must advocate for the tools and capabilities needed to support AI workloads, or risk being left behind.
What Successful Teams Are Doing Differently
EMA's research also identified the practices that separate successful organizations from those falling short. Successful teams hold network observability data to a strict accuracy standard, ensuring that the information they rely on is reliable and actionable. They have moved beyond scripts and runbooks to adopt AI-driven and agentic management tools that can automate complex troubleshooting and remediation workflows.
Another key differentiator is that successful teams prioritize integration over consolidation. Instead of trying to reduce the number of tools they use, they focus on creating a unified ecosystem where tools can share data, security insights, and workflow triggers. This approach allows them to leverage the strengths of each tool while minimizing silos. Finally, successful organizations are building unified visibility and security controls that span both on-premises and cloud infrastructure, ensuring consistent governance and observability across all environments.
The EMA study makes it clear that enterprise network teams are at a crossroads. The combination of talent shortages, tool sprawl, hybrid cloud complexity, and the arrival of AI workloads is creating a perfect storm. Without decisive action from CIOs and IT leaders—including increased budget, better tools, and stronger support for automation—network teams will continue to fall behind. The networks that underpin AI transformation projects will make or break those initiatives, and the time to invest in network operations is now.
Source: Network World News