Enterprise network operations teams face mounting pressures as artificial intelligence workloads begin to deploy on corporate networks. A broad industry study reveals that only 31% of IT professionals consider their organization's network operations strategy completely successful—a sharp decline from 42% just two years ago. The findings underscore a growing crisis: network teams are expected to support increasingly complex, AI-heavy environments with insufficient staffing, outdated tools, and fragmented visibility across hybrid and multi-cloud architectures.

The research, which surveyed hundreds of IT professionals across North America and Europe, identifies four major megatrends driving this strain: a worsening talent shortage, the push to automate day-two operations, the persistently ungoverned nature of hybrid and multi-cloud networks, and the unpreparedness of existing management tools for AI workloads. Together, these challenges threaten to derail digital transformation initiatives unless organizations take decisive action to retool their network operations.

The state of the network operations center

Tool sprawl remains a chronic problem for network teams. The typical IT organization uses between four and ten monitoring and troubleshooting tools to manage its network, a number that has barely changed in over a decade. Yet there is no significant correlation between the size of a toolset and operational success. This suggests that integration and intelligence matter far more than the sheer number of tools.

The data reveals considerable room for improvement across the board. For instance, 58% of network problems are detected proactively before users experience their impact, but that leaves over 40% that are not. Only 37% of alerts generated by network monitoring tools are indicative of a real problem, meaning teams waste time sifting through noise. Manual administrative errors cause 28% of network problems, and the average network professional spends 29% of their day troubleshooting. These metrics highlight the inefficiencies that hold teams back.

IT professionals believe that 53% of the network problems they face daily could be prevented with better tools. This explains why tool replacement is widespread: 73% of surveyed organizations say they are at least somewhat likely to replace a network observability or monitoring tool within the next two years. The desire for more intelligent, automated solutions is driving a wave of vendor evaluation and churn.

Megatrend 1: The talent crisis worsens

The difficulty of hiring network technology experts has escalated dramatically. The share of organizations that find it somewhat or very difficult to hire has risen from 26% in 2022 to 41% in 2024 and now to 52%. The shortage is most acute at senior and mid-career levels, where cloud, security, and automation skills are most needed. Many teams report being asked to do more with less—one monitoring architect noted that management expects a ten-person team to accomplish what a 25-person team used to handle.

This talent gap is accelerating the urgency to deploy automation. Short-staffed teams need tools that can handle routine tasks automatically, allowing existing engineers to operate at a higher, more strategic level. However, the skills gap itself becomes a barrier to achieving automation. Teams often lack the expertise to build and maintain automation pipelines. The 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%).

The industry is responding with new training programs and certification paths aimed at closing these gaps. Yet the pace of technology change may outstrip the ability of traditional education to keep up. Organizations must invest in continuous learning and cross-training to build resilient teams.

Megatrend 2: The push to automate day-two operations

Network automation has traditionally focused on provisioning and configuration—so-called day-zero and day-one work. Now the priority is shifting to day-two operations: the ongoing detection, triage, diagnosis, and remediation of network problems in production. A full 79% 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 study 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 solution. The day-two tasks most desired for automation 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 provides AI agents with a standard interface to interact with multiple network management tools. Successful organizations are already prioritizing MCP support for agentic AI access. MCP acts as an abstraction layer across tool sprawl, allowing AI agents to orchestrate actions without requiring deep integration with each individual tool.

Megatrend 3: Hybrid and multi-cloud networks remain ungoverned

Nearly seven in ten 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. While they may excel at collecting data from one platform, they often lag on others. This forces teams to piece together partial views.

Organizations that have managed to integrate IP address management and extend network observability tools across hybrid environments report better outcomes. However, both remain works in progress for most. The need for unified visibility and security controls spanning on-premises and cloud infrastructure is becoming critical as workloads continue to migrate.

Megatrend 4: AI networks need managing, but few tools are ready

Nearly half of respondents (47.7%) say AI training or inference workloads are already deployed on their networks, and most of the rest expect to deploy within the next two years. Yet only 35% say their current network observability tools are completely ready to manage those workloads. This preparation gap is alarming given the performance requirements of AI infrastructure.

Specific 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. Tool enhancements most desired 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%).

As AI workloads grow, network teams must demand that their vendors prioritize these capabilities. Experts recommend that organizations start conversations with tool vendors now to ensure their roadmaps align with AI networking requirements.

What successful teams are doing differently

The study 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. And they build unified visibility and security controls that span both on-premises and cloud infrastructure.

These practices enable teams to detect problems earlier, reduce noise, and automate remediation. They also help organizations justify investments in new tools and training by demonstrating clear operational improvements. As the industry evolves, the ability to adapt will separate the leaders from the laggards in an increasingly AI-driven world.

Source: Network World News