Enterprise network operations teams are falling behind as the demands on them increase, and the challenge is intensifying as organizations prepare their networks for AI workloads. According to a recent benchmarking study from Enterprise Management Associates (EMA), the percentage of IT professionals who consider their organization's network operations strategy completely successful has dropped from 42% two years ago to just 31% today.

The report, based on a survey of 352 IT professionals across North America and Europe, identifies several key pressures: a persistent talent shortage, tool sprawl, hybrid and multi-cloud complexity, and the introduction of AI workloads onto networks that were not originally designed to handle them. EMA Vice President Shamus McGillicuddy noted that network operators know they need to improve but lack the necessary support, including budget, better tools, automation, and influence over modern network architectures.

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 changed over the past decade. Despite the size of the toolset, EMA found no significant correlation between the number of tools and operational success. Key findings include:

• 58% of network problems are detected proactively before users experience their impact.
• Only 37% of alerts generated by monitoring tools indicate a real problem.
• Manual administrative errors cause 28% of network problems.
• 29% of the average network professional's day is spent troubleshooting.

McGillicuddy explained that 53% of daily network problems could be prevented with better tools, which is why tool replacement is widespread. Seventy-three percent 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 difficulty in hiring network technology experts has risen dramatically. The share of organizations finding it somewhat or very difficult to hire has increased from 26% in 2022 to 41% in 2024 and now 52% today. The shortage is most acute at senior and mid-career levels, particularly for roles requiring cloud, security, and automation skills. One monitoring architect at a Fortune 500 entertainment company noted that what used to be done by a 25-person team is now expected of a ten-person team.

The talent gap is driving urgency around automation. Short-staffed teams need tools that can handle routine work automatically, allowing engineers to operate at a higher level. However, the skills gap itself is the biggest barrier to achieving automation. Teams often lack people who can build and maintain automation pipelines. The top barriers to automation reported 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 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 — 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%). 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. 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 (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. McGillicuddy noted that some vendors have not achieved feature parity across the three major cloud providers. Organizations that have integrated IP address management and extended network observability tools across hybrid environments report better outcomes, but most are still a work in progress.

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; 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 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. Successful organizations also build unified visibility and security controls that span both on-premises and cloud infrastructure. McGillicuddy emphasized that AI networking will require retooling and recommended that teams engage with their vendors about their AI readiness plans.

Source: Network World News