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When Automating Forever Becomes Faster Than Doing It Once

Jake Stauch
Serval

I spent five years building products for IT teams at Verkada, and I kept hearing the same thing: "Get me out of the help desk."

People want to be doing more engaging work, yet their day often gets overrun by addressing urgent IT tickets.

But thanks to advances in AI "vibe coding," where a user describes what they want in plain English and the AI turns it into working code, IT teams can automate ticketing workflows and offload much of that work. Password resets that used to take 5 minutes per request now get resolved automatically, forever, by building a workflow prompted with "{tool} password reset". As a result ticket queues that stretched to hundreds shrink to dozens, and IT professionals spend their time building systems instead of resetting passwords.

Why IT Teams Don't Automate More

The problem isn't knowing what to automate. IT teams can list exactly which tasks eat their time: password resets, access requests, onboarding workflows. The real problem is uncertainty.

Traditional automation platforms ask you to invest an unknown amount of time building something for an unknown return. You don't know if it'll take two hours or two months to build. You don't know if you're accidentally granting overly broad access that violates least-privilege principles. You don't know how long it'll work before some API change breaks it. And because manual processes don't create audit trails, you don't even know if you can prove what happened when security or compliance asks questions later. As a result, teams decide to spend extra time doing the task manually because they're comfortable with what they know.

Natural language workflow builders eliminate that uncertainty. An IT admin types " Google Workspace password reset," and gets production-ready code in seconds. For something more sensitive, manager or team approvals are one-click. The system writes the workflow, and even handles edge cases — like what happens when the manager is out of office — enforces security controls, and creates audit trails.

When building the automation takes less time than doing the task once, you stop debating whether it's worth it, and you just automate.

What Changes When IT Admins Can Build

The obvious result is coverage. Teams that used to automate only their highest-volume requests now automate 50-80% of tickets. Password resets happen instantly. Access requests that sat in queues for days get resolved in seconds. Onboarding workflows that needed manual coordination across multiple systems now run themselves.

But here's the bigger shift: IT admins who've spent their careers managing infrastructure can now ship production workflows that used to require developers. An admin can create an offboarding workflow — lock the device, revoke access across applications, notify the manager, archive the data, remove from Slack and email groups — from a single prompt.

This changes how IT capacity works. You used to scale by adding headcount, but now teams absorb more volume by automating faster than requests arrive. The question shifts from "can we build this?" to "what rules should govern this?"

The workflows don't just run faster, they're more secure by default. Because the automation enforces the rules you describe ("manager approval required," "access expires in 30 days," "notify security team"), you stop relying on admins to remember policies under pressure. Every action gets logged automatically, and least-privilege access becomes the path of least resistance instead of something you have to manually verify.

The Future of IT

The old model assumed IT's job was infrastructure management: keeping systems running and handling tickets when they break. But when your entire team can ship automation faster than requests arrive, IT's role transforms entirely. Tedious ticket queues disappear, replaced by strategic work building systems that scale across the organization. The role of IT professionals doesn't go away, it gets better.

The IT teams who escape the help desk don't just get their time back, they become the function that makes every other team's infrastructure problems disappear before they're felt. Onboarding that used to take three days happens in seconds, security policies that relied on human memory get enforced by code, and the systems that used to require careful maintenance start maintaining themselves.

This is what IT looks like when it's not a bottleneck and it's building the organization's operating system instead of just keeping it from crashing.

Jake Stauch is the Co-Founder and CEO of Serval

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When Automating Forever Becomes Faster Than Doing It Once

Jake Stauch
Serval

I spent five years building products for IT teams at Verkada, and I kept hearing the same thing: "Get me out of the help desk."

People want to be doing more engaging work, yet their day often gets overrun by addressing urgent IT tickets.

But thanks to advances in AI "vibe coding," where a user describes what they want in plain English and the AI turns it into working code, IT teams can automate ticketing workflows and offload much of that work. Password resets that used to take 5 minutes per request now get resolved automatically, forever, by building a workflow prompted with "{tool} password reset". As a result ticket queues that stretched to hundreds shrink to dozens, and IT professionals spend their time building systems instead of resetting passwords.

Why IT Teams Don't Automate More

The problem isn't knowing what to automate. IT teams can list exactly which tasks eat their time: password resets, access requests, onboarding workflows. The real problem is uncertainty.

Traditional automation platforms ask you to invest an unknown amount of time building something for an unknown return. You don't know if it'll take two hours or two months to build. You don't know if you're accidentally granting overly broad access that violates least-privilege principles. You don't know how long it'll work before some API change breaks it. And because manual processes don't create audit trails, you don't even know if you can prove what happened when security or compliance asks questions later. As a result, teams decide to spend extra time doing the task manually because they're comfortable with what they know.

Natural language workflow builders eliminate that uncertainty. An IT admin types " Google Workspace password reset," and gets production-ready code in seconds. For something more sensitive, manager or team approvals are one-click. The system writes the workflow, and even handles edge cases — like what happens when the manager is out of office — enforces security controls, and creates audit trails.

When building the automation takes less time than doing the task once, you stop debating whether it's worth it, and you just automate.

What Changes When IT Admins Can Build

The obvious result is coverage. Teams that used to automate only their highest-volume requests now automate 50-80% of tickets. Password resets happen instantly. Access requests that sat in queues for days get resolved in seconds. Onboarding workflows that needed manual coordination across multiple systems now run themselves.

But here's the bigger shift: IT admins who've spent their careers managing infrastructure can now ship production workflows that used to require developers. An admin can create an offboarding workflow — lock the device, revoke access across applications, notify the manager, archive the data, remove from Slack and email groups — from a single prompt.

This changes how IT capacity works. You used to scale by adding headcount, but now teams absorb more volume by automating faster than requests arrive. The question shifts from "can we build this?" to "what rules should govern this?"

The workflows don't just run faster, they're more secure by default. Because the automation enforces the rules you describe ("manager approval required," "access expires in 30 days," "notify security team"), you stop relying on admins to remember policies under pressure. Every action gets logged automatically, and least-privilege access becomes the path of least resistance instead of something you have to manually verify.

The Future of IT

The old model assumed IT's job was infrastructure management: keeping systems running and handling tickets when they break. But when your entire team can ship automation faster than requests arrive, IT's role transforms entirely. Tedious ticket queues disappear, replaced by strategic work building systems that scale across the organization. The role of IT professionals doesn't go away, it gets better.

The IT teams who escape the help desk don't just get their time back, they become the function that makes every other team's infrastructure problems disappear before they're felt. Onboarding that used to take three days happens in seconds, security policies that relied on human memory get enforced by code, and the systems that used to require careful maintenance start maintaining themselves.

This is what IT looks like when it's not a bottleneck and it's building the organization's operating system instead of just keeping it from crashing.

Jake Stauch is the Co-Founder and CEO of Serval

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...