Enterprise network teams are falling behind as AI raises the stakes

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.

Roughly 31% of IT professionals surveyed for an Enterprise Management Associates (EMA) benchmarking study said their organization’s network operations strategy is completely successful, a figure that decreased from 42% two years ago. That is one of the findings of EMA’s Network Management Megatrends 2026 report, based on a survey of 352 IT professionals across North America and Europe. 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.

“Network operators clearly know they need to do better, but they aren’t getting the support they need,” said Shamus McGillicuddy, EMA’s vice president of research for network infrastructure and operations, in a statement. “They need budget to fill empty seats on their teams. They need better tools. They need more automation. They need more 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 those CIOs 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 EMA said 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 how much room for improvement exists, regardless of how many tools a team has:

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

“IT pros believe that 53% of the network problems that they are dealing with on a day-to-day basis could be prevented with better tools, so that gives you some color around why only 31% of the people we surveyed felt like they are completely successful with network operations strategy,” McGillicuddy explained on a webinar about the study results. “Tool replacement is widespread. Seventy-three percent of the people we surveyed said they are likely to replace, at least somewhat likely to replace, a network observability or network 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 the senior and mid-career levels, where cloud, security, and automation skills are most needed.

“We’re being asked to do more with less,” a monitoring architect with a Fortune 500 entertainment company said in the EMA report. “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 the urgency to successfully deploy automation. Short-staffed teams need tools that handle more routine work automatically, so that the engineers they do have on staff can operate at a higher level, according to EMA. The skills gap itself can be the biggest barrier to achieving that automation, according to EMA. Teams often lack the people who know how to build and maintain the automation pipelines. Network teams shared their top barriers to automation, including:

• 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%
• Lack of trust in automation: 25%

Megatrend #2: The push to automate day-two operations

Network automation has meant provisioning and configuration, considered 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, according to the EMA report.

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:

• 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%
• 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, according to EMA.

“The MCP access points become like an abstraction layer across your tool sprawl,” McGillicuddy said.

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.

EMA found 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.

“I still talk to network observability vendors that haven’t got feature parity across the big three cloud providers yet,” McGillicuddy said. “They might be good at collecting and analyzing data from AWS, but they’re still kind of far behind on things with Google Cloud Platform, and they haven’t even thought about some of the secondary ones yet.”

Organizations that have managed to integrate IP address management and extend network observability tools across hybrid environments report better overall outcomes, EMA said, 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. Most of the rest expect to deploy within the next two years. But only 35% say their current network observability tools are completely ready to manage those workloads.

The performance concerns are specific to AI infrastructure: 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:

• 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%
• Correlation of GPU, application, and network performance metrics: 34.3%

What successful teams are doing differently

EMA’s research also identified the practices separating successful organizations from those falling short.

The research firm found that successful teams hold network observability data to a strict accuracy standard. They have moved beyond scripts and runbooks to AI-driven and agentic management tools, and 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 the successful organizations are building unified visibility and security controls that span both on-premises and cloud infrastructure, according to EMA.

“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,” McGillicuddy said.