AI and Network Engineers: What's Actually Happening and What to Do

The honest assessment

Network engineering sits in an interesting position in the AI landscape. The profession is being changed by AI in meaningful ways, but the combination of physical infrastructure, complex troubleshooting, and the critical nature of network availability means the role isn't going anywhere fast. It is, however, evolving in ways that matter.

AI is already embedded in modern networking. Cisco's AI Network Analytics uses machine learning to detect anomalies, predict failures, and suggest optimisations. Juniper's Mist AI platform provides intent-based networking with AI-driven troubleshooting. Aruba's AIOps analyses network data to identify issues before they impact users. HPE, Palo Alto Networks, and Fortinet all have AI-driven network management capabilities. These tools can monitor thousands of network devices, correlate events across the infrastructure, and identify the root cause of issues faster than any human could by manually checking logs.

What AI handles well in networking: monitoring and alerting, configuration compliance checking, traffic pattern analysis, capacity planning based on usage trends, basic configuration generation from templates, and automated response to known issue patterns. An AI system can detect that a switch port is flapping, correlate it with similar historical events, and either fix it automatically or escalate it to a human with a diagnosis already prepared. That's genuinely useful and it's eliminating a lot of the routine monitoring and first-line troubleshooting work.

What AI doesn't handle well: designing a network for a new building that has specific physical constraints and business requirements. Troubleshooting an intermittent issue that doesn't match any documented pattern and turns out to be caused by electromagnetic interference from a new piece of equipment installed in the floor below. Migrating a legacy network infrastructure to a new architecture while maintaining zero downtime for a 24/7 operation. Managing vendor relationships and negotiating contracts. Explaining to a non-technical CFO why the organisation needs to spend £500,000 on network infrastructure. These are the human elements that AI assists with but doesn't replace.

The physical dimension is particularly important. Networks run on physical kit. Switches, routers, access points, firewalls, cabling, data centre equipment. Someone needs to rack and stack, cable, configure, and maintain this hardware. Someone needs to do site surveys, plan wireless coverage around physical obstacles, and troubleshoot the physical layer when things go wrong. AI can't crawl through a ceiling void to trace a faulty cable.

Your exposure level: Medium

Medium. The monitoring and routine management aspects of the role are being significantly automated, but the design, implementation, and complex troubleshooting aspects remain firmly human.

Network engineering has a natural protection that pure software roles don't: physical infrastructure. As long as organisations have offices, data centres, and physical locations, they need people who can design, install, and maintain network infrastructure in those physical spaces. The shift to cloud has changed the balance — less on-premises infrastructure in some organisations — but it hasn't eliminated the need for network expertise. It's shifted it. Cloud networking, SD-WAN, and hybrid infrastructure require network engineering skills applied in new contexts.

The demand picture is also supportive. The proliferation of IoT devices, the growth of edge computing, the expansion of wireless networks, and the ongoing migration to cloud infrastructure all create demand for network engineering skills. Cisco's Annual Internet Report consistently projects massive growth in connected devices and network traffic. More devices and more traffic means more network complexity, which means more need for skilled engineers.

Where the medium exposure comes from is the operational monitoring and management layer. The network operations centre (NOC) analyst role — watching dashboards, responding to alerts, making routine changes — is being significantly automated by AIOps platforms. If that's your primary function, your exposure is higher than medium. If you're designing networks, implementing complex solutions, and solving novel problems, your exposure is lower.

The 90-day action plan

1. This week: explore the AI features in your network management platform. Whether you use Cisco DNA Center, Juniper Mist, Meraki, or anything else, there are AI-powered features you're probably not fully utilising. Predictive analytics. Anomaly detection. Automated root cause analysis. Understanding these capabilities is the first step to working with them effectively.

2. Week two: use AI for configuration management. Try using ChatGPT or Claude to generate network configurations. "Write a Cisco IOS configuration for a VLAN with these parameters and this routing setup." Review what it produces. Correct the errors (there will be some). This workflow — AI drafts, you review and refine — is the future of network configuration, and learning to work this way makes you faster.

3. By day 30: develop or deepen your cloud networking skills. If you haven't already, get hands-on with AWS VPC, Azure Virtual Networks, or Google Cloud VPC. Cloud networking is where growth is happening. The concepts are the same — subnets, routing, firewalls, load balancing — but the implementation is different. A network engineer who's fluent in both physical and cloud networking is extremely valuable.

4. By day 45: learn network automation and infrastructure as code. Ansible, Terraform, Python with Netmiko or NAPALM. The ability to manage network infrastructure programmatically is becoming essential. It's also a skill that makes you more resilient to AI disruption, because you're moving up from manual configuration to automated infrastructure management. Use AI tools to help you learn — they're excellent at explaining code and writing automation scripts.

5. By day 60: strengthen your security knowledge. Networks and security are inseparable. Understanding firewall policy, network segmentation, zero trust architecture, and threat detection makes you a more complete network engineer and opens up career paths into the rapidly growing cybersecurity field. Study for a security certification alongside your networking credentials.

6. By day 75: develop your design and architecture skills. Practice designing network architectures for different scenarios. High-availability data centre networks. Campus networks for large buildings. SD-WAN solutions for distributed organisations. The design phase is the most human-dependent part of network engineering and the hardest to automate. Build a portfolio of designs, even if they're theoretical exercises.

7. By day 90: position yourself for the evolved role. The future network engineer is less about CLI configuration and more about architecture, automation, security, and cloud. Talk to your manager about your development: "I've been expanding my skills into cloud networking, automation, and security. I'd like to take on more of the design and architecture work and help the team adopt infrastructure-as-code practices."

AI tools you should be using this week

• ChatGPT for Work — Generate network configurations, troubleshoot issues by describing symptoms, explain complex networking concepts, and draft network documentation. "Explain BGP route reflectors to a junior engineer" or "What could cause asymmetric routing in this topology?" are the kinds of prompts that make ChatGPT useful for daily network engineering work. Also good for generating Python scripts for network automation.

• Claude for Work — Strong for analysing complex network issues methodically. Describe a multi-layered problem — intermittent connectivity affecting specific users on specific VLANs at specific times — and Claude will help you work through the diagnostic process step by step. Also useful for reviewing firewall rules and access control lists for security gaps.

• Microsoft Copilot for Work — Useful for the documentation and reporting side of network engineering. Summarise change requests, draft post-incident reports, create network documentation, and prepare presentations for management about infrastructure projects and investments. The administrative overhead of network engineering is real, and Copilot helps manage it.

• Perplexity for Research — When you encounter a new technology, a vendor-specific issue, or need to evaluate a networking product, Perplexity can quickly research current information and provide referenced answers. Useful for staying current with rapidly evolving technologies like SD-WAN, SASE, and Wi-Fi 7 without spending hours reading vendor whitepapers.

What to say in meetings

When management discusses AIOps and automated network management: "These tools are excellent for monitoring and responding to known issue patterns. What they don't replace is network design, complex troubleshooting, and the strategic decisions about infrastructure investment. I'd recommend we adopt AIOps for operational efficiency and redirect our team's expertise toward architecture and security improvements that AI can't handle."

If colleagues worry about network automation replacing engineers: "Automation handles the routine configuration work faster and more consistently. That's a good thing — it reduces human error and frees us up for the interesting problems. The design, the architecture, the complex troubleshooting, the physical infrastructure — that still needs engineers. We need to be the engineers who can also write the automation, not the ones who are replaced by it."

In performance reviews: "I've been developing skills in cloud networking, infrastructure-as-code, and security alongside my core network engineering work. I've also been using AI tools to improve efficiency on configuration and troubleshooting tasks. I'd like to take on more responsibility for network architecture and design."

If the worst happens

If your specific network engineering role is restructured, your skills are in strong demand across the IT industry. Network knowledge is foundational to cloud engineering, cybersecurity, DevOps, and systems architecture. You understand how systems communicate, which is relevant to virtually every area of technology.

Adjacent roles to consider: cloud engineer, security engineer, DevOps engineer, solutions architect, pre-sales engineer at a network vendor, technical project manager, or network consultant. Many network engineers also move into management roles within IT operations. The combination of technical depth and operational experience is valued in IT leadership positions.

The honest truth about network engineering is this. The profession is changing from "person who configures network devices" to "person who designs, automates, and secures network infrastructure." If you make that transition — from operator to architect, from manual configuration to automation, from networking-only to networking-plus-security-plus-cloud — your career is not just secure but growing. The skills gap in network engineering isn't shrinking. It's shifting. Move with it.