Last year, analyst Forrester reported that while IT departments manage billion-dollar portfolios, their internal operations lag in automation, coordination and visibility. The complexity of managing a modern IT architecture means network management must evolve. This is not something that is entirely new.
Automation is part of the functionality available in modern network management tools. Big data analysis of network log files is used in security information and event management (SIEM), and machine learning (ML) is helping network administrators identify potential issues before they affect the business.
Phil Huang, business development and field application manager at D-Link, explains: “We have been offering a pure cloud management platform for networks for a number of years and the AI [artificial intelligence] assistance behind such network management gives us the ability to monitor in real time and also proactively try to alert of any potential problems that may arise.”
Advances in tooling potentially reduce the complexity of network management. Matt Stava, CEO and chairman of third-party support firm Spinnaker Support, says this changes the role of IT administrators and programmers. Looking specifically at network skills, he says: “The need for a Cisco-certified expert is getting less and less right now.”
Modern networking skills
Modern IT infrastructure means that having an industry-certified network specialist is becoming less relevant. In a March 2026 blog post, Amit Katz, vice-president of ethernet switch at Nvidia, highlights the shifts occurring in network management.
In the post, Katz points out that while the value of a new network administrator may have previously been measured by their level of expertise in a particular networking command line interface (CLI), the advent of hybrid cloud and DevOps means there is a growing shift towards application programming interfaces (APIs).
“Skills in Ansible, Salt [the open source automation framework] and Python now have more value than a Cisco certification,” he says.
Now, Katz believes the tasks network administrators need to do are very different from the way they used to monitor and manage networks.
Skills in Ansible, Salt and Python now have more value than a Cisco certification Amit Katz, Nvidia
“You’ve moved from tools that polled devices across the datacentre using SNMP [Simple Network Management Protocol] and NetFlow [which monitors IP traffic] to new switch-based telemetry models where the switches proactively stream flow-based diagnostic details,” he notes in the blog post.
And according to Katz, while network administrators have a lot of experience introducing new workloads into datacentres – some of which have unique networking requirements – building an AI cluster is actually very different.
He writes: “It is tempting to think that AI is just a bigger and faster big data application. But AI is different, and AI can be hard without the right tools.”
AI also has a role to play in helping network administrators manage this complexity more easily. Information Services Group (ISG), a research and advisory firm, says organisations are taking advantage of the enhanced capabilities of AI and ML to automate configuration changes and optimisation across the network.
In an ISG article about how AI is transforming network operations, Marc Herren, a director at ISG, notes that AI can analyse network data and identify patterns to automatically generate configurations that optimise performance.
He says Cisco and Juniper Networks, the latter now being part of Hewlett Packard Enterprise, are developing intent-based networking products that use AI to understand an administrator’s intent and automatically configure the network accordingly. Such technology is essential to keep on top of ever-more-complex network management.
Network complexity
In a presentation at Microsoft Build 2025, Phil Gervasi, director of technical evangelism at Kentik, spoke about how networks are growing in complexity. They now span different clouds, datacentres, edge computing and hybrid IT infrastructure, all of which introduce new challenges for network management.
“The volume of telemetry, events and logs has exploded beyond human capacity to analyse in real time,” he told attendees. At the same time, as Gervasi noted, network teams are under pressure to improve the mean time to resolution of an issue, and maintain uptime without expanding headcount.
The volume of telemetry, events and logs has exploded beyond human capacity to analyse in real time Phil Gervasi, Kentik
“What AI offers is not magic, but a better way to correlate data, forecast performance and understand network behaviour in context. So, in short, AI helps operators move from reacting to predicting,” he added.
While ML is being used in networking for capacity planning, anomaly detection and baselining, Gervasi said that large language models (LLMs) offer a different approach to network management. “Unlike classical data models, which rely on structured data, LLMs operate on unstructured information like documentation, configuration files and tickets,” he told Build 2025 delegates. However, LLMs are probabilistic, which means they can produce inconsistent and different answers to the same prompts.
They also hallucinate. To get around these limitations, Gervasi stressed the need to ensure quality of training data, proper evaluation and controlled model behaviour. These are key to keeping LLM responses honest.
Privacy and regulation are also issues for LLMs, especially when handling network data that could contain sensitive information. Some IT operations challenges are inherent to AI use. For Gervasi, IT decision-makers need to be aware of the difficulties that may arise when integrating real-time telemetry, dealing with diverse data types, and managing compute costs for AI workloads.
But, despite these caveats, Gervasi believes the real power of LLMs lies in their ability to synthesise vast volumes of data into information that can then be used by people to make better decisions.
The starting point in using AI for network management is collecting network telemetry logs, helpdesk ticket and configuration files. Those then need to be cleaned up and stored in a format that can be accessed by the AI system.
Gervasi told delegates that one of the most effective ways to use this information is through retrieval augmented generation (RAG). As an example, he said when a user submits a query, the system converts the question into a mathematical representation, which searches a vector database for semantically related data, such as telemetry, past incidents or documentation.
“The LLM then synthesises an answer, using both its general knowledge and the retrieved context,” he explained.
Another use for LLMs is in text-to-structured query language (SQL), which, as Gervasi noted, enables network engineers to use natural language, where their queries are converted by the LLM into an SQL query and then, where relevant, provide a graphical representation of the data.
Once the data is in a format the AI model can process, agentic AI is a natural progression. “An LLM doesn’t just respond to prompts, but acts kind of like the brain, coordinating multiple tools,” he says.
During the presentation, Gervasi spoke about how with agentic AI powering network management, an agent could run a trace route, collect network telemetry, consult a knowledge base, and then generate a remediation plan, all autonomously, but with human oversight.
This is something that is likely to provide autonomous operations behind commercial network provider services. Analyst Gartner expects that AI will be embedded into managed network services (MNS) by 2028, to increase and enhance operational efficiency and enable more informed decision-making.
According to Gartner, AI will be used to ensure that networks are robust and agile enough to adapt to changing demands and traffic patterns. “Looking ahead three to five years from now, we anticipate significant transformation in MNS due to extensive use of AI and automation,” the analyst firm stated in its AI will transform managed network services in the next three years report.
For Stava and other industry watchers, the hot skill is agentic AI and the ability to integrate AI agents into workflows to achieve a business outcome. And these outcomes are increasingly IT-focused, especially as IT teams are being asked to do more with fewer resources and being put under increasing strain to support companies’ appetites for all things relating to AI.
But AI also has a big role to play in making networks more manageable. As network management becomes more automated and networks become self-healing, network engineers will need to learn how to integrate the latest tooling with agentic technology to provide the data stream for AI-powered network management.
