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 the Enterprise Management Associates (EMA) Network Management Megatrends 2026 report, based on a survey of 352 IT professionals across North America and Europe, only 31% of organizations report that their network operations strategy is completely successful—a significant drop from 42% just two years ago.

The report confirms that network teams today face multiple pressures: a talent shortage, tool sprawl, hybrid and multi-cloud complexity, and AI workloads on networks that weren't built to manage them. Shamus McGillicuddy, EMA's vice president of research for network infrastructure and operations, stated that network operators clearly know they need to improve but are not receiving the necessary support. He emphasized that CIOs must step up to give network operators the budget, tools, automation, and influence they need, especially as AI transformation projects depend heavily on network performance.

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 EMA says has barely moved in more than a decade. Yet EMA found no significant correlation between the size of a toolset and operational success. The data shows room for improvement regardless of tool count: 58% of network problems are detected proactively before users experience their impact; only 37% of alerts are indicative of a real problem; manual administrative errors cause 28% of problems; and 29% of the average network professional's day is spent troubleshooting. IT pros believe that 53% of network problems could be prevented with better tools, which explains why 73% of respondents 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 that find it somewhat or very difficult to hire network technology experts has risen from 26% in 2022 to 41% in 2024 to 52% today. According to EMA, the shortage is most apparent at senior and mid-career levels, where cloud, security, and automation skills are needed most. A monitoring architect at a Fortune 500 entertainment company said: "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." The talent gap is also driving urgency to deploy automation. Short-staffed teams need tools that handle routine work automatically, but the skills gap itself can be the biggest barrier to achieving automation. 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: 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/alert noise reduction (37.5%), and change validation and rollback (26.4%). EMA found 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 to tools.

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—a gap that reflects both technical complexity and cultural friction between network teams and cloud engineering groups. The core challenges are familiar: 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 have not achieved feature parity across the big three cloud 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.

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 two years. However, only 35% say their current network observability tools are completely ready to manage those workloads. Performance concerns 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 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

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, have moved beyond scripts and runbooks to AI-driven and agentic management tools, and prioritize integration over consolidation—focusing on security insights, workflow integration, and data sharing across their toolset rather than trying to reduce its size. They also build unified visibility and security controls spanning both on-premises and cloud infrastructure. McGillicuddy advised that AI networking will require retooling and recommended that teams talk to their vendors about AI readiness, noting that many vendors are not yet thinking about it because they are not hearing from customers.

Source: Network World News