As a follow on to my article on whether AI can be used in Networking, this post dives in deeper to a question of will humans still be involved? And if so, how do we upskill that human expertise?
To successfully integrate AI into network automation while ensuring human expertise is maintained, organizations and IT professionals need to adopt a strategic approach. Here’s how:
1. AI’s Role in Network Automation
AI can handle several aspects of network configuration and management, including:
- Automated Provisioning – AI-powered tools can configure routers, switches, and firewalls based on predefined templates.
- Predictive Analytics – AI can anticipate network failures or congestion by analyzing patterns and recommending preventive actions.
- Self-Healing Networks – AI can detect anomalies and automatically adjust configurations or reroute traffic to maintain performance.
- Security & Compliance Automation – AI can scan configurations for security risks and enforce compliance with policies like NIST or CIS standards.
However, while AI can execute these tasks, it still requires human input for strategic decisions, validation, and oversight.
2. Maintaining Human Expertise in an AI-Driven Network
To prevent skill degradation among engineers and ensure they remain valuable, organizations should focus on:
A. Upskilling & Training
- Encourage Continuous Learning – Engineers should be trained not just in networking, but also in AI, scripting, and automation frameworks (e.g., Python, Ansible, Terraform).
- Hands-on Labs – Organizations should offer simulation environments where engineers manually configure devices, troubleshoot issues, and experiment with AI-driven automation.
- Certifications & Courses – Encouraging certifications like Cisco’s DevNet, CCNP Enterprise with automation focus, or Juniper’s Automation & DevOps certification can keep skills sharp.
B. Human-in-the-Loop AI Model
- Implement a system where AI suggests configurations, but a human validates and applies them before deployment.
- Engineers should regularly review AI-generated configurations to understand the logic behind them and detect potential errors.
C. Hybrid Approach to Automation
Rather than fully automating everything, a hybrid model can balance automation with manual oversight:
- Low-risk tasks (e.g., device onboarding, VLAN assignments) → Fully automated
- Moderate-risk tasks (e.g., firewall rule changes, routing adjustments) → AI suggests, human validates
- High-risk tasks (e.g., major network redesign, security policies) → Fully human-controlled with AI insights
3. AI’s Limitations & Why Human Expertise is Still Needed
While AI is powerful, it has limitations that make human expertise crucial:
- Context Awareness – AI can’t always understand the business or security implications of a network change.
- Troubleshooting Complex Issues – When an AI-driven automation fails, a network engineer needs to step in and fix the issue.
- Security Risks – AI-generated configurations could be exploited if not properly reviewed.
- Customization & Strategy – AI follows patterns, but humans drive innovation, architecture decisions, and long-term network evolution.
4. Future of Networking Jobs in an AI Era
Instead of eliminating jobs, AI is shifting networking roles:
- From Configuration to Automation Engineers – Network engineers will need to know scripting (Python, Bash, Ansible) and automation platforms.
- AI Operations (AIOps) Specialists – New roles will emerge that focus on monitoring and optimizing AI-driven network operations.
- Security & Compliance Experts – Engineers will focus more on securing AI-driven automation and ensuring compliance.
Recommended Tools & Training for AI-Driven Network Automation
To stay ahead in the AI-driven networking landscape, network engineers should familiarize themselves with the right tools and training programs. Below are some of the best options categorized by automation, AI-driven network management, and learning resources.
1. Tools for AI-Driven Network Automation
A. Network Automation Frameworks
- Ansible – A widely used open-source tool for automating network configurations (good for multi-vendor environments).
- Terraform – Useful for managing infrastructure as code (IaC), including networking automation.
- Python + Netmiko/NAPALM – Python libraries specifically designed for automating network devices (Cisco, Juniper, Arista, etc.).
- Cisco DNA Center – AI-powered intent-based networking platform that automates and optimizes networks.
- Juniper Apstra – Uses intent-based automation for data center networks.
- Arista CloudVision – AI-driven automation and analytics for managing network infrastructure.
B. AI-Powered Network Monitoring & Management
- Cisco AI Network Analytics – Uses AI to detect network anomalies, optimize performance, and suggest fixes.
- Arista CloudVision AI – Provides AI-driven insights and automation for network operations.
- Juniper Mist AI – Uses AI-driven automation to enhance wireless and wired networking operations.
- NetBrain – AI-powered network automation platform focused on troubleshooting and self-healing networks.
- ThousandEyes (Cisco-owned) – AI-based network visibility tool that provides real-time analytics on performance and security.
C. Security & Compliance Automation
- Tufin – Automates network security policy changes and compliance enforcement.
- Palo Alto Networks Cortex XSOAR – AI-powered security automation and incident response.
- RedSeal – Uses AI-driven analytics to assess network vulnerabilities and compliance.
2. Training & Certification Programs
All the following information is current as of the publishing of this post in February 2025. I have written previously about an example of AI Training in this post.
There is a free course offered by Cisco (free until April 2025 I believe) – you can read more about that here.
To ensure that network engineers stay up-to-date with AI and automation, the following certifications and courses are recommended:
A. Vendor-Specific Certifications
- Cisco DevNet Associate/Professional – Focuses on network automation and APIs, including Python, Ansible, and REST APIs.
- Cisco Certified Network Professional (CCNP) Enterprise with Automation Focus – Covers automation and programmability for Cisco networks.
- Juniper Networks Automation & DevOps (JNCIA-DevOps, JNCIS-DevOps) – Teaches automation strategies using Junos OS and Python.
- Arista Cloud Engineer (ACE) Certification – Covers Arista’s AI-driven network automation and cloud networking.
- VMware NSX-T Data Center Certification – Focuses on software-defined networking (SDN) and network automation.
B. General Network Automation & AI Courses
- Python for Network Engineers (Kirk Byers on Udemy) – A solid introduction to using Python for network automation.
- Ansible for Network Automation (Red Hat Training or Udemy) – Hands-on course on automating network tasks.
- Introduction to AI for IT & Network Engineers (Coursera by Stanford or MIT) – Covers AI applications in networking.
- AI for Network Operations (Cisco Learning Network) – Explores AI-based network optimization and automation.
- Networking with Terraform & Infrastructure as Code (Pluralsight) – A course focused on using Terraform for automating networks.
C. Free Learning Resources
- Cisco DevNet Sandbox – A free, hands-on lab environment to practice automation with Cisco APIs.
- Juniper vLabs – Provides free virtual lab access to Juniper automation tools.
- Red Hat Ansible Free Labs – Hands-on training for network automation using Ansible.
- NAPALM (Python Library) Documentation – Free tutorials on using Python for multi-vendor network automation.
3. Recommended Learning Path for AI-Driven Network Engineers
If you’re just getting started or looking to advance, here’s a suggested learning path:
Beginner Level: Build Automation Basics
- Learn Python (focus on networking libraries: Netmiko, NAPALM)
- Study Ansible and Terraform for network automation
- Take classes to upskill such as Cisco DevNet Associate or equivalent vendor-agnostic automation course
- Experiment in virtual lab environments such as Cisco DevNet Sandbox or Juniper vLabs
Intermediate Level: Implement AI & Advanced Automation
- Gain hands-on experience with AI-driven network analytics tools like Juniper Mist AI or Cisco DNA Center
- Learn machine learning basics (for AI-based networking insights)
- Get certified in CCNP with automation focus or JNCIS-DevOps
- Practice security automation with Tufin, Palo Alto Cortex XSOAR, or RedSeal
Advanced Level: Lead AI-Powered Networking
- Master AI-driven network monitoring (Arista CloudVision, Cisco AI Network Analytics)
- Get experience with AIOps platforms (AI for IT operations)
- Develop custom AI models for network automation (using Python, TensorFlow, or OpenAI APIs)
- Move towards an Architect or AI-driven NOC (Network Operations Center) role
Some Conclusions and Final Thoughts
AI in network automation should be seen as an enhancement, not a replacement for humans. Organizations need to invest in upskilling, implementing a human-in-the-loop approach, and maintaining a balance between automation and manual control. The goal is not just efficiency but also resilience, security, and adaptability in an evolving tech landscape. AI-driven network automation is not about replacing engineers but enhancing their capabilities. By embracing AI tools and continuously upskilling, network engineers can transition into higher-value roles focused on architecture, strategy, and security.
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