AI-Powered NetDevOps: The Future of Network Automation

How AI-Powered NetDevOps Is Reshaping Network Automation

The Future of Network Automation Is AI-Driven

Enterprise networking is undergoing a seismic shift as AI-driven automation and NetDevOps reshape how networks are built, managed, and optimized. The Gartner® 2025 Strategic Roadmap for Enterprise Networking underscores this transformation, predicting that “by 2027, generative AI (GenAI) will account for 25% of initial network configurations, up from less than 3% in 2024.”

At Network to Code, we’ve been at the forefront of network automation innovation, recognized by Gartner multiple times over the last 5 years, including multiple mentions in the Hype Cycle for I&O Automation, Market Guide for Network Automation Platforms, and as a Cool Vendor in Enterprise Networking. As the industry evolves toward AI-assisted NetDevOps, it’s clear that manual networking approaches can no longer keep up with the scale and complexity of modern infrastructures.

But what does AI-driven NetDevOps actually look like in practice? And how can IT leaders leverage AI, a Network Source of Truth (NSoT), and intent-based networking to drive efficiency, reliability, and automation maturity? Let’s dive in.

The Evolution of Network Automation: From Scripts to AI-Driven Intelligence

For years, network automation was script-heavy, requiring engineers to develop and maintain custom Python scripts, Ansible playbooks, and Terraform templates to automate tasks. While these efforts helped reduce manual work, they often led to inconsistent automation, difficult troubleshooting, and a lack of scalability.

The next step in automation maturity was the adoption of Intent-Based Networking (IBN) and NSoT, where automation is data-driven, version-controlled, and validated in real time. These advancements led to robust automations that kept networks compliant, reliable, and secure. However, achieving this required deep expertise in both networking and code development—a rare but essential skill set that many companies must access via third-party providers or cultivate through targeted training and upskilling efforts.

Now, AI and GenAI are redefining network automation by adding intelligence, adaptability, and predictive capabilities. According to Gartner, “AI-enabled/GenAI-enabled networking assistants will dynamically interact with network teams, simplifying design, troubleshooting, and deployment.” This shift is crucial for organizations aiming to improve efficiency, reduce human error, and accelerate innovation.

How AI Is Enhancing NetDevOps: Key Innovations

AI-driven automation isn’t just about replacing manual management tasks—it’s about making networks more intelligent, adaptable, and resilient. Here’s how:

1. AI-Assisted Code Development 

AI is revolutionizing how engineers develop automation scripts by providing:

• AI-powered copilots that suggest optimized configurations and best practices.
• Error detection, reducing misconfigurations before deployment.
• Auto-generated workflows, allowing engineers to build automations faster and with greater accuracy.

As Gartner notes, “Organizations will increasingly turn to AI-powered NetDevOps to improve efficiency, reduce toil, and increase reliability.”

2. AI-Driven Pre- and Post-Change Validation

One of the biggest challenges in network automation is ensuring changes achieve the intended outcome. AI enhances validation processes by:

• Pre-change verification: AI simulates and tests network changes before deployment.
• Post-change validation: AI assesses network health and compliance after changes.
• Self-healing automation: If an issue is detected, AI automatically reverts changes or recommends fixes.

According to Gartner, AI-enhanced tools will provide “dynamic virtual representations of a network to safely apply changes and predict their impacts.”

3. AI-Integrated CI/CD Pipelines for Networking

NetDevOps is built on the principles of CI/CD (Continuous Integration/Continuous Deployment), but traditional pipelines require manual intervention and testing. AI removes these bottlenecks by:

• Automating CI/CD workflows, ensuring that every network update is validated before deployment.
• Enhancing GitOps practices with AI-powered anomaly detection and rollback mechanisms.
• Accelerating network upgrades through AI-driven testing in staging environments.

This approach aligns with Gartner’s prediction that AI-driven networking assistants will be embedded in nearly all network management suites by 2027.

4. Predictive Analytics & Self-Healing Networks

AI doesn’t just react—it predicts and prevents issues before they impact operations. With:

• AI-driven anomaly detection, network teams can proactively identify performance degradation, configuration oversights, and security threats.
• Digital twins, engineers can simulate network change revisions in a risk-free environment.
• Autonomous remediation, AI-powered automation can self-correct misconfigurations and network faults.

Gartner highlights that AI-powered digital experience management (DEM) tools will be key in ensuring “consistent application performance for all end users.”

How to Get Started with AI-Powered NetDevOps

Adopting AI-driven NetDevOps requires a strategic approach. Here’s how enterprises can start:

1. Implement a Network Source of Truth (NSoT) for automation consistency.
2. Leverage AI copilots for automation development and workflow optimization.
3. Integrate AI-driven automation into the CI/CD pipeline for seamless deployments.
4. Use AI-enhanced validation to run pre-/post-checks and ensure change success.
5. Pilot AI-driven automation in digital twin environments before production rollouts.

By following these steps, enterprises accelerate automation maturity while reducing operational risks.