A network engineer is a tech professional responsible for designing, implementing, and maintaining computer networks that enable organizational communication and data exchange.

These networks might range from small, local systems to large-scale, global infrastructures connecting multiple locations.

Network engineers commonly ensure the stability, security, and efficiency of network operations, which can involve configuring hardware such as routers, switches, and firewalls, optimizing network performance, troubleshooting connectivity issues, and managing network infrastructure upgrades.

Network engineers are also key players in setting up wireless networks, virtual private networks (VPNs), and cloud connectivity solutions.

While companies differ in their networking needs, a network engineer not only solves immediate issues but also plans for the future by building resilient, scalable networks that support business growth and innovation. This is a tech arena that will evolve rapidly in coming years thanks to artificial intelligence (AI), with sizable impacts on network engineers’ daily workflows.

How AI Can Help

David Deitch, board chair and executive director of the Network Professional Association (NPA), a trade association of and for IT/Networking professionals, explained AI is revolutionizing the way network engineers work.

“For example, AI-powered tools can monitor network traffic in real time, detecting and responding to anomalies or potential issues before they become major problems,” he said. “This means less downtime and faster resolutions.”

Another key area for network evolution is automation, with AI called upon to handle routine tasks such as configuring devices or managing privacy policies. This not only ensures that standards are maintained, but can also reduce errors and free up network engineers to focus on more complex challenges.

As Deitch explained, AI can also be used to dynamically optimize network performance, adjusting bandwidth or routing traffic more efficiently.

On the security side, AI can play a critical role in identifying and responding to cyber threats by analyzing vast amounts of data to spot unusual activity and even take automated actions to mitigate risks. “Overall, AI can shift the role of the network engineer from putting out fires to strategic planning and innovation, which ultimately results in smarter, more resilient networks,” Deitch said.

Essential Understanding

While AI offers promising tools for network engineers, its strengths and limitations must be carefully understood to avoid missteps, said Dr. James Stanger, CompTIA chief technology evangelist.

AI excels at automating manual tasks and supporting routine processes, but it cannot replace human oversight or deliver a complete understanding of complex networks. “AI can generate scripts and automate tasks using tools like Ansible or Puppet,” Stanger said. “It’s particularly good at analyzing data and making suggestions that can improve network performance.”

This ability to streamline tasks such as configuration or troubleshooting offers significant time savings for network professionals.

But Stanger also emphasized AI’s limitations, particularly when it comes to accuracy and contextual understanding. “If you don’t train the AI properly, it will generate generic or irrelevant suggestions,” he said. “AI requires a well-curated database to perform effectively. Without it, you’ll get results that are too vague or outright incorrect.”

Another critical drawback lies in AI’s inability to create a holistic view of the network. “AI can’t deliver a systematic or complete understanding of a network on its own,” Stanger said. “That still requires human expertise. People with deep knowledge of the network must validate and refine AI’s outputs to ensure they make sense in context.”

Ultimately, while AI can enhance automation and provide valuable insights, it remains a tool—not a replacement. “AI is great at improving existing processes,” Stanger concluded. “But it’s not going to build or manage the network without human input.”

AI Training: Where to Go

Udemy offers an “AI for Network Engineers” course, providing an in-depth exploration of reinforcement learning and other AI-centric topics, especially in the context of practical applications in networking and systems administration.

Suitable for network engineers of all experience levels, it equips participants with the knowledge to optimize network operations and address complex challenges using AI-driven solutions.

Upon completion, participants will be able to design, implement, and manage AI-powered solutions to improve networking and security systems.

Basic networking knowledge is recommended, while familiarity with Python is beneficial but not required.

Cisco’s CCDE AI Infrastructure certification is designed for expert-level network engineers and design professionals at the forefront of network automation and AI integration. It focuses on leveraging infrastructure as code and NetDevOps, all while bridging current architectures with future technologies.

The elective focuses on designing and optimizing networks for AI workloads. Key areas include GPU optimization and building high-performance generative AI network fabrics.

Written by Dutch IT expert Jeroen Erne, The Artificial Intelligence handbook for Network Engineers boasts more than one thousand practical ChatGPT prompts for network troubleshooting, security, wireless optimization, and disaster recovery, and includes one month of complimentary access to the Complete AI Training platform for network engineers, including training videos and other resources.

Securing Executive Buy-In for Upskilling

From Deitch’s perspective, securing executive buy-in for AI upskilling starts with aligning the need for new skills with the company’s strategic goals.

“A network engineer should demonstrate how enhancing their skills will directly impact business outcomes, whether it’s improving network performance, reducing downtime, or strengthening cybersecurity,” he said.

For example, if the company is pursuing cloud adoption, Deitch suggested showing how AI tools can streamline the migration process, making a stronger case for training investments. “Data is key,” he said. “Metrics that demonstrate potential cost savings, productivity improvements, or risk reductions are critical for building the argument.”

Deitch pointed out that studies often highlight how AI reduces manual configuration errors, leading to fewer outages and operational savings: “IT departments are often seen as cost centers, but reducing those costs through upskilling can positively impact profits.”

Positioning upskilling as a strategy for future-proofing the organization is another persuasive angle. “Technology evolves quickly,” Deitch said. “Staying ahead of trends like AI integration, software-defined networking, or IoT ensures the company remains competitive. Falling behind can be just as costly as not advancing.”

Deitch also emphasized the role of upskilling in employee retention, noting investing in professional development shows the company values its talent.

“Framing upskilling as a win for both the company’s innovation goals and the network engineer’s ability to deliver value makes executive buy-in much easier to secure,” he said.