By Ernest Lefner, Chief Product Officer, Gluware
There is an old orchard technique where a young branch is gently bent downward and held in place, training it to grow outward rather than straight up. The idea is simple: make the fruit accessible to everyone, without needing a ladder or any climbing. Network automation needs the same thing right now, and AI is the tool that can finally bend the branch low enough for every network engineer to reach it.
The Plateau Problem
Network automation has a coverage problem. According to Gartner’s Market Guide for Network Automation Platforms, organizations can realistically achieve somewhere between 10% and 25% automation coverage by leaning heavily on vendor-specific tools and open source projects. For many teams, that feels like progress. It is. But it is also, in practice, a plateau.
Vendor CLIs, Terraform configurations, and Python scripts can carry a team a meaningful distance. They automate the predictable, the repetitive, the well-understood. But the gap between 25% and something approaching comprehensive automation coverage does not close on its own. It requires a different kind of platform, and a different approach.
NAPs Can Help, But Adoption Is Still the Gate
Network Automation Platforms (NAPs) exist to push past this ceiling. Per the Gartner definition, they bring intent-based network data management, cross-domain workflows and orchestration, and operational consistency that collections of point tools can replicate.
But NAPs carry their own adoption challenge. Deploying a platform is not the same as operationalizing one. And operationalizing one requires that the people responsible for day-to-day network operations can actually use it fluently, build with it confidently, and extend it without friction.
Here is where we need to have an honest conversation about a significant barrier to adoption: not every network team is automation-native.
The Automation-Native Gap
What does it mean to be automation-native? It means being comfortable writing Python scripts to interact with APIs. It means crafting regular expressions from memory to parse show command output. It means thinking in variables, loops, and conditionals before thinking in interfaces or topology.
Many highly skilled, deeply experienced network engineers are not automation-native. They are routing experts, they are troubleshooting experts, they know their device platforms intimately. But ask them to write a regex to extract an IP address from a block of text, and you have just introduced a non-trivial friction point. The automation that could save them hours each week is sitting on a branch just out of practical reach.
It’s not because they’re not smart enough. Or motivated enough. But the reality is that they have day jobs in the current, unautomated reality. Expecting them to just become seasoned automation veterans is unrealistic.
So when that hapens, the default is to punt to the developer team, which creates a queue, a dependency, a bottleneck. Automation stops rolling and starts collecting moss. Pretty soon, network growth has stalled out too.
AI Bends the Branch
This is exactly where AI is tailor-made to change the equation. Not in some abstract, futuristic sense, but in a specific, practical one. AI can serve as the translator between intent and implementation, and that translation is what automation-native engineers have always done naturally, but non-native engineers have had to struggle through or outsource.
Ask an AI assistant to generate the regular expression for a specific output pattern. Ask it to draft the Python snippet that pulls interface state from a REST API. Ask it to explain what a failing workflow step is doing and suggest a fix. These are not trivial capabilities. They are the exact capabilities that lower the barrier from “I need to call a developer” to “I can build this myself.”
The downstream effect becomes compounding. Network engineers who can build automation without needing to be fluent in its underlying craft become autonomous. Automation picks up speed. Coverage grows, and the team breaks through previous plateaus.
The Real Opportunity
The goal of AI in network automation is not to replace the network engineer. It is also not to turn all network engineers into developers. One of the most important goals of AI is to make every network engineer an automation builder. When AI bends the branch lower, the fruit is no longer reserved for the most technical members of the team. It becomes accessible to a whole industry.
That is how organizations move from 25% automation coverage to something that dramatically changes how networks are operated.
How Gluware, DIAL, and Titan AI Put This Into Practice
We built branch-bending into every layer of the Gluware full-stack network automation platform.
DIAL (Device Interaction and Automation Layer) is the foundation, and its most important role is one that is easy to overlook. DIAL is the semantic translation layer between the messy reality of brownfield networks and a clean, functional intent model. Most enterprise networks were not built for automation. They are a patchwork of vendors, OS versions, CLI dialects, and legacy configurations that accumulated over years, each with its own syntax, its own quirks, its own way of expressing state. Getting automation to work consistently across that environment has historically required engineers to write and maintain custom scripts for each combination, a task that is both technically demanding and never truly finished.
DIAL abstracts all of that. It adapts and binds 56 operating systems from 22 vendors to the Gluware platform, translating device-level complexity into a unified, intent-based data model that the rest of the platform can reason about and act on. Engineers stop having to think in CLI syntax and start thinking in network intent. The brownfield estate becomes, in effect, a coherent, automatable surface, without requiring anyone to first clean it up by hand.
If AI is meant to bend the branch, DIAL is what allows you to plant a thriving automation orchard in a brownfield network, and expect it to grow.
Gluware’s platform brings that device coverage together with cross-domain workflow orchestration and a real-time source of truth for network state. The intent-based model means that automation logic is expressed in terms that network engineers already understand, not in lines of code that require a developer mindset to construct or maintain. A host of applications allow network engineers to harvest value, by detecting and dealing with drift, ensuring compliance, patching CVEs, building workflows, etc.
Gluware Titan is where AI completes the picture and bends the branch even further. Titan introduces Gluware Co-Pilot, an agentic AI assistant with a conversational chat interface that lets network operators and NetDevOps teams interact with the platform in plain language. Need to build a network state assessment using vendor show commands? Co-Pilot generates it. Need a LiquidJS template to format workflow output? Co-Pilot writes it. Need to integrate a third-party API in hours rather than days? Co-Pilot gets you there. Every action is validated through Titan’s three-stage validation architecture, grounded in DIAL’s comprehensive, validated semantic translation capabilities that reads and writes accurately to the live network. This means the speed AI enables does not come at the cost of control or compliance.
Together, DIAL, Gluware, and Titan create a continuous path from a vendor-specific and ad-hoc 25%-coverage plateau to a state where any engineer on the team can build, extend, and run automation confidently, regardless of whether they ever wrote a line of Python in their life. It’s why our customers get to comprehensive network coverage, and full team participation in building and growing automation.
Ready to bend the branch? Request a personalized Gluware demo and discover how your team can accelerate automation coverage without requiring every engineer to become a developer first.
