How network engineers can prepare for the future with AI

A new ally

Security operations center (SOC) teams sometimes rely on manual processes and analysis for threat hunting. Integrating AI and automation into these processes limits time-consuming tasks, simplifying operations so teams can focus on critical responsibilities that might evade AI tools. GenAI can also provide suggestions to the team before they make decisions. Integrating AI into threat detection can be a good experience for teams who are hesitant to work with AI.

AI agents are set to become true agents of agility, revolutionizing network intelligence. They'll automate critical tasks like traffic analysis, anomaly detection and threat prevention. By harnessing historical data, AI agents will predict and resolve network issues faster, reduce human error and significantly improve efficiency -- shifting operations from reactive to proactive. The following are some well-known AI agents:

• Oracle Miracle Agent.
• OpenAI Operator.
• Nvidia Eureka.

AI can also augment some network engineering tasks using automated processes overseen by professionals. Engineers should consider using AI to accomplish the following goals:

• Augment decisions.
• Gain time to work on other tasks.
• Focus on what humans can do better than AI.

AI isn't a replacement for the workforce. As professionals work with AI and provide feedback, AI companies will improve their applications, and the technology will evolve. Working with AI will benefit network engineers as they gain an understanding of the technology as it evolves, not after.

Prerequisites for AI

Adaptability is key in the AI era, since the technology's versatility enables teams to work more closely than ever before. Working with different teams requires network engineers to understand many applications and programs.

Network engineers should consider fluency in the following areas if they intend to move to AI:

• REST APIs. Knowing the basics is important as most services on the internet use APIs for software communication. Network engineers and developers use APIs to manage network devices and sometimes build innovative services around them.
• Python. Automation is a stepping stone to AI, and Python is the de facto language of automation. Fluency in Python ensures engineers can easily work on many tasks.
• Data formats. Servers and clients use YAML, XML and JSON to exchange data. Knowing the basics of these formats is a must for anyone wishing to work with network and development teams.
• Cloud platforms. Multi-cloud environments permit enterprise customers to choose a platform based on their needs. AWS, Azure and Google Cloud are the major cloud service platforms, and AI now powers some of these services.
• AI tools. Use Amazon CodeWhisperer, IBM Watsonx and Tabnine for code autocompletion and Darktrace to detect and respond to threats in real time.

Critical skills to invest in

Large language models (LLMs), prompt engineering and machine learning are just a few of the important skills network engineers need to learn to stay relevant. Network engineers should familiarize themselves with the following technology areas:

• Machine learning. Machine learning empowers network engineers to analyze massive amounts of network data in real time, proactively detect potential issues, optimize performance, predict failures before they happen and automate routine tasks. The result? Enhanced network reliability, reduced downtime and improved efficiency -- all with minimal human intervention.
• Data analysis. Data analysis forms the foundation for AI systems to analyze network behavior as well as identify patterns and anomalies within vast amounts of network data. Analytics enables predictive maintenance, proactive troubleshooting and optimized performance.
• AI models. Google's Gemini, Meta's Llama and OpenAI's ChatGPT are some of the most commonly known and used AI models. Learning how they work helps engineers explore the potential of AI in their networks and software development. Harnessing this potential will lead to more innovative services.
• AI agents. AI agents boost operational efficiency, minimize downtime and improve system reliability, enabling IT teams to concentrate on strategic, high-impact initiatives.
• Business strategies. Networking's role is expanding beyond traditional infrastructure management into areas that affect organizational goals. Aligning technology with business objectives, bridging the gap between IT and leadership, and preparing for industry disruption will be crucial for the new AI economy.

AI agents: An artificially intelligent network engineer

While some network engineers view AI as mere hype, real-world use cases from industry leaders and practitioners demonstrate its transformative potential. Beyond the excitement, AI drives innovation, delivers agility and significantly reduces human errors.

In the emerging AI economy, we're seeing massive investments in data centers, with major tech companies seizing opportunities to innovate and adapt. This raises a critical question: Can we continue working the old way as networks grow increasingly complex to manage?

John Capobianco, a recognized leader in network automation and AI, recently showcased an exclusive demo of an AI agent he built capable of performing tasks handled by network engineers. This demonstration highlights the future of network management, where AI complements human expertise to enhance efficiency and scalability.

As the industry evolves, staying ahead means embracing AI and its potential to revolutionize how networks are designed and managed. Are you ready for the shift?

Certifications

Certifications are a great way to prove AI knowledge as it integrates into more applications and services. The following entry-level certifications are a good starting point for those interested in AI:

• Cisco Certified DevNet Associate. Implementing network automation is the first step to integrating AI. This certification focuses on network automation and teaches skills in network engineering, software development and application deployment. These skills are invaluable when it comes to cultivating AI skills.
• Cisco Certified Cybersecurity Associate. This certification focuses on the technical skills SOCs need on their teams. Knowing how to work with AI tools and models helps improve workflows with threat detection and response.
• Nvidia-Certified Associate: Generative AI LLMs. GenAI and LLM development, integration and maintenance are more important than ever as AI becomes more widely available. NCA-GENL validates these foundational concepts.
• AWS Certified Cloud Practitioner. This certification is a good investment for beginners looking to become fluent in cloud services. It's useful for understanding the basics of AWS cloud services. Upon completion, practitioners can move to higher levels of certifications and expand their AI knowledge.

As AI becomes increasingly unavoidable, network engineers must adapt their networks or risk their technology becoming obsolete.

Editor's note: This article was updated in February 2025 to reflect the latest developments in network engineering and AI automation.

Verlaine Muhungu is a self-taught freelance network technician. He was recognized as a Cisco top talent in sub-Saharan Africa during the 2016 NetRiders IT Skills Competition