The Network Operations Paradox: Why More Automation Often Leads to Less Understanding

Author:  Nils Werner​, Director of Customer Success

Here’s an uncomfortable truth: As our networks become more automated, many teams are losing their fundamental grasp of how they actually work and, in turn, their ability to diagnose underlying problems. This isn’t just someone who’s been working in network observability for 10 years, lamenting the “good old days.” It’s a growing blind spot that’s making our networks simultaneously more efficient and more fragile.

SD-WAN deployment will “manage itself perfectly”—until it doesn’t. When applications suffer sporadic performance issues, teams can spend weeks chasing their tails because they’ve lost touch with the underlying network behavior patterns and observability capabilities that would have made the root cause obvious.

This isn’t an argument against automation. But it is a warning about what we’re unwittingly trading away if we aren’t strategic in how we adopt automation. 

Consider three uncomfortable realities:

1. Automation doesn’t just abstract away complexity—it obscures context. When your SD-WAN makes an automated routing decision, do you understand what factors drove that choice? More importantly, do you understand what it didn’t consider?
2. We’re creating a generation of network operators who can read dashboards but can’t read networks. The ability to interpret raw network behavior—the packets, the protocols, the patterns—is becoming a lost art.
3. Most troubling, many organizations are automating their way into a dangerous dependency. They can operate their networks but no longer truly understand them.

The core issue isn’t automation: it’s our relationship with it. Instead of using automation as a tool to make networks more efficient, we’re now delegating our understanding to it.

This lack of root cause understanding would be unacceptable in modern SOCs. Why is it acceptable in network operations?

But there’s a way forward that doesn’t require abandoning automation’s benefits. The key is maintaining visibility into the why behind automated decisions while still leveraging what they provide.

This shift has real consequences. When automated systems make decisions, they often optimize for a narrow set of metrics while missing crucial context. Your SD-WAN might see an optimal path based on latency and jitter but miss application-specific behaviors that make that path problematic for certain workloads.

The Technology Debt We’re Not Talking About

Most discussions of technical debt focus on aging infrastructure or unpatched systems. However, we’re accruing another form of debt: “understanding” debt. Every time we abstract away complexity without maintaining visibility into how that abstraction works, we borrow against our future ability to troubleshoot, optimize, and innovate.

Think about it this way: When was the last time your team traced a packet’s actual journey through your network rather than just accepting what the automation told you about its path? If the answer is “not recently” or “we can’t,” you’ve accrued understanding debt.

Breaking the Paradox

The good news is we can have automation efficiency and deep network understanding. But it requires a deliberate approach:

1. Maintain visibility into automated decisions. Your automation shouldn’t be a black box. Every automated action should be logged with context about why that decision was made.
2. Validate automated behavior regularly. Plan regular “reality checks” where you compare what your automation reports with what’s actually happening at the traffic level.
3. Keep your fundamentals sharp. Include flow analysis, packet inspection, and protocol behavior review in your team’s regular skillset maintenance.

We’re approaching a critical inflection point in network operations. The teams that maintain deep understanding while leveraging automation will thrive. Those who fully delegate their understanding to automated systems will eventually be unable to innovate or effectively troubleshoot.

The choice isn’t between automation and understanding. It’s between active participation in your network’s operation and passive consumption of automated decisions. The next time your automation makes a decision, ask yourself: Do you understand why it made that choice? More importantly, do you understand what other choices it could have made?

Your network’s future might depend on the answer.