How the Pareto Principle reveals the hidden truth about network automation

When I first heard about network automation tools promising to eliminate 80% of manual work, I was excited. Finally, we could focus on strategic projects instead of repetitive configuration tasks. But after implementing several automation initiatives across enterprise networks, I’ve discovered something counterintuitive: even when you successfully automate 80% of your tasks, you still end up spending 80% of your time on manual work.

This isn’t a failure of automation—it’s the Pareto Principle in action, revealing a fundamental truth about complex systems that the automation vendors don’t talk about in their glossy presentations.

The Easy 80% vs. The Impossible 20%

Network automation excels at the straightforward, repeatable tasks that follow predictable patterns. VLAN provisioning, standard router configurations, firewall rule deployments—these tasks automate beautifully because they have clear inputs, well-defined processes, and predictable outcomes.

But then there’s the other 20%. The edge cases. The snowflake systems. The “this should work but doesn’t” scenarios that make you question your life choices at 2 AM.

What the 80% looks like:

• Deploying identical configurations across multiple sites
• Provisioning new users with standard network access
• Generating routine compliance reports
• Rolling out security patches to standardized equipment

What the 20% looks like:

• Troubleshooting why the automation failed on that one site with the weird legacy switch
• Handling the executive who needs special network access that doesn’t fit any existing template
• Investigating intermittent connectivity issues that only happen during specific conditions
• Integrating with that critical business application that has unique network requirements nobody documented

The cruel irony? That “easy” 80% might represent only 20% of your actual time investment, while the “impossible” 20% consumes 80% of your working hours.

Why the 20% Fights Back

The tasks that resist automation do so for fundamental reasons that go beyond technical limitations:

Context Dependency: Automation excels when decisions can be made based on clear, quantifiable inputs. But network troubleshooting often requires understanding business context, historical patterns, and organizational politics that can’t be easily codified.

Exception Handling: Every enterprise network is a unique snowflake, accumulated over years of mergers, technology refreshes, and “temporary” solutions that became permanent. These exceptions don’t fit neat automation templates.

Human Judgment: When a critical application goes down, you need someone who can weigh the risks of different recovery approaches, communicate with stakeholders, and make judgment calls under pressure. Scripts can’t read the room.

Unknown Unknowns: The most time-consuming problems are often the ones you never anticipated. Your automation handles the scenarios you planned for, but networks have a way of failing in creative new ways.

The False Promise of “Lights-Out” Operations

The automation industry loves to paint a picture of “lights-out” network operations—fully automated environments that require minimal human intervention. But this vision fundamentally misunderstands how complex systems behave in the real world.

I’ve seen organizations implement sophisticated automation platforms that handle thousands of routine tasks flawlessly, only to have engineers spend more time than ever dealing with the edge cases that slip through the cracks. The automation doesn’t eliminate work; it changes the nature of the work.

Before automation: Engineers spend time on routine tasks and troubleshooting. After automation: Engineers spend time troubleshooting why the automation didn’t work, handling exceptions, and managing increasingly complex toolchains.

The promise of “set it and forget it” ignores a fundamental truth: the more automated your environment becomes, the more critical it is to have skilled engineers who understand how everything fits together when things go wrong.

The Strategic Shift: From Doing to Managing

This isn’t an argument against network automation—quite the opposite. Automation is incredibly valuable, but we need to set realistic expectations about what it can and cannot do.

The real value of automation isn’t eliminating human work; it’s elevating the type of work humans do. Instead of spending time on routine configuration tasks, skilled engineers can focus on:

• Architecture and Design: Building more resilient, scalable network foundations
• Complex Problem Solving: Tackling the sophisticated issues that require human insight
• Business Alignment: Understanding how network decisions impact business outcomes
• Tool Development: Creating better automation for the next wave of challenges

Building Better Automation Strategy

Understanding the automation paradox leads to better strategic decisions:

Design for Failure: Build automation that fails gracefully and provides excellent diagnostics. When your script encounters an edge case, it should give the human engineer enough context to understand what went wrong and why.

Invest in Skills: As automation handles more routine tasks, the remaining human work becomes more sophisticated. Invest in training engineers to handle complex scenarios rather than assuming you can reduce headcount.

Embrace the 80/20 Split: Stop trying to automate everything and focus on making the 20% of manual work as efficient as possible. Better documentation, improved troubleshooting tools, and streamlined escalation processes often provide more value than trying to automate the unautomatable.

Measure Correctly: Don’t measure automation success by the percentage of tasks automated. Measure it by the reduction in time-to-resolution for the tasks that still require human intervention.

The Bottom Line

The Pareto Principle teaches us that 80% of results come from 20% of causes. In network automation, this translates to: 80% of your time savings will come from automating 20% of the right tasks, while 80% of tasks might be automatable but only represent 20% of your actual time investment.

The most successful automation initiatives I’ve seen recognize this reality upfront. They focus on eliminating the routine work that truly consumes significant time, while building better tools and processes to handle the complex edge cases more efficiently.

Network automation isn’t about replacing network engineers—it’s about freeing them to work on the problems that actually matter. And those problems, the ones that require human judgment, creativity, and deep technical expertise, will always be there.

Because in complex systems, the exceptions aren’t bugs—they’re features. And managing those exceptions effectively is what separates good network engineering from great network engineering.

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What’s your experience with the automation paradox? Have you found ways to make that stubborn 20% more manageable? I’d love to hear your stories in the comments.