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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

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...