Many people associate automation with large transformation programmes or complex deployment pipelines. In reality, it often starts with solving a practical operational problem.
One common starting point is collecting LLDP neighbour information and cross-referencing it with a server inventory to understand exactly which services are running on which ports. In environments with more than 20,000 physical servers, that isn't a nice-to-have anymore; it's a necessity.
From there, automation naturally evolves into reducing operational risk. Servers can automatically be signalled when a switch requires an update, allowing Anycast services to move out before maintenance begins. No deployment pipeline and no production risk, just seeing things more clearly.
The same applies to configuration management. Git is often introduced simply to version-control device configurations, but it quickly becomes much more than a changelog. A well-maintained configuration repository becomes a searchable archive of an entire infrastructure's history. When did that BGP policy change? What did this device look like six months ago? The answers are already there, committed and timestamped.
That's not just automation. That's institutional memory, made queryable.
Across many organizations, one observation keeps returning. Network engineers are rarely intimidated by the technology itself; they can handle that. The hesitation comes from somewhere else.
"I'm not a developer. That's not what I do."
When you've built your identity around a discipline for a decade, learning adjacent skills can feel like an admission that what you already know is no longer enough.
At the same time, organizations often encounter a different obstacle. Before automation can succeed, there has to be a reliable source of truth. Spreadsheets that are months out of date or CMDBs that nobody trusts quickly become a bigger challenge than the automation tooling itself.
The first step isn't writing a script. It's knowing what you actually have under management.
For many organizations, this is where automation initiatives succeed or fail. Without a reliable inventory and trusted operational data, even the best automation tools have little to build on.
The assumption that automation replaces network engineers misses the point entirely.
Automation arrived because networks became too large, too dynamic and too interconnected to manage manually at scale. The demand didn't disappear; it grew. What changed is how that demand gets met.
That also changes how organizations should think about their network teams. Scaling infrastructure is no longer just about adding more engineers; it's about enabling experienced engineers to work differently, supported by automation that allows them to focus on the decisions that matter most.
Infrastructure-as-code illustrates that shift particularly well. Building highly available environments with Terraform isn't simply about learning another tool. It introduces a different way of thinking about infrastructure: the declarative model, dependency management and the distinction between what should exist versus what actually exists.
Sometimes those lessons come the hard way. A misconfigured dependency graph in AWS has a remarkable blast radius. But understanding why it failed often teaches more than a clean deployment ever could. That mental model transfers directly back to network infrastructure.
Automation can generate configurations, collect operational data and execute repetitive tasks. It cannot determine whether a BGP policy is appropriate for a specific topology, redundancy model or SLA.
That judgement still belongs to the engineer.