What network engineers need to know about AI in 2025

AI is an ally, not a threat

Although artificial intelligence has existed for several years, generative AI tools have rapidly entered the industry thanks to the launch of numerous global products. Competition drives innovation, enabling the creation of new solutions based on APIs. AI covers many fields, but not everyone is ready to embrace it. Some fear that technology will replace employees if they fail to adapt.

2025 will be a turning point for AI thanks to initiatives such as the Stargate Project, which is investing $500 billion in building OpenAI infrastructure, and the growing number of AI agents. For network engineers, skill development becomes a key condition for success in these dynamic conditions.

Despite fears of automation, practice shows that adaptation and leveraging existing knowledge open up new perspectives. AI is no longer just hype — it is already changing the future.

Network engineers have long been accustomed to technological changes, moving from command-line interfaces to APIs and AI. To cope with growing workloads, they must understand the capabilities of artificial intelligence and meet new requirements.

A new assistant

Security operations teams often rely on manual processes when identifying threats. Integrating AI and automation reduces the burden on employees, allowing them to focus on critical tasks. Generative AI can provide recommendations before decisions are made, and its integration into monitoring systems simplifies the work of specialists who are still skeptical about technology.

AI agents are transforming network analysis by automating key processes — traffic monitoring, anomaly detection, and threat prevention. Using historical data, AI predicts and resolves issues faster, reduces human error, and increases efficiency, shifting network management to a proactive mode. Among the most well-known AI agents are Oracle Miracle Agent, OpenAI Operator, and Nvidia Eureka.

AI also enhances the capabilities of network engineers, helping them make more informed decisions, free up time for other tasks, and focus on what humans still perform better.

AI does not replace specialists, but those who start working with these technologies now will be able to adapt to changes more quickly.

What an engineer needs to know to work with AI

Flexibility and interdisciplinary knowledge are becoming important in the AI era, as technology brings teams together. Network engineers should develop skills in the following areas:

— REST API — most services use APIs for program interaction, and engineers use them to manage network devices
— Python — the primary language for automation and a basic tool for working with AI
— Data formats YAML, XML, JSON — necessary for working with server and client applications
— AWS, Azure, Google Cloud — the largest cloud service providers are actively implementing AI
— AI tools. Amazon CodeWhisperer, IBM Watsonx, Tabnine for code completion, and Darktrace for real-time threat detection.

Key skills for the future

To remain in demand, network engineers need to understand machine learning, data analytics, AI models, AI agents, and business strategies.

— Machine learning helps analyze network data in real time, predict failures, and automate tasks
— Data analytics allows identifying patterns in large volumes of information and engaging in predictive maintenance
— Understanding how AI models such as Gemini, Llama, and ChatGPT work helps effectively apply these technologies in networks
— AI agents automate network infrastructure operations and increase system reliability
— Knowledge of business strategies is important for aligning technology with business goals and preparing for industry changes.

AI agents: artificial intelligence in network engineering

Some engineers still consider AI merely a trendy topic, but practical experience demonstrates its real impact. AI reduces risks, increases work speed, and helps companies adapt to growing data volumes.

Large-scale investments in data centers and the development of cloud technologies prove that traditional network management methods are becoming ineffective. For example, John Capobianco, a network automation expert, presented a demo of an AI agent capable of performing engineers' tasks. This demonstrates how artificial intelligence complements human expertise, increasing scalability and work efficiency.

Certifications for network engineers

Elevating qualifications and proving knowledge in the field of AI can be achieved through certifications:

— Cisco Certified DevNet Associate. Foundation for network automation and AI integration
— Cisco Certified Cybersecurity Associate. Skills in using AI tools in the field of cybersecurity
— Nvidia-Certified Associate: Generative AI LLMs. Principles of working with GenAI models
— AWS Certified Cloud Practitioner. Understanding cloud services and their integration with AI.

AI is an integral part of the future, and it is important for engineers not to fall behind, otherwise their technology will become obsolete.

The article was translated from the TechTarget website.