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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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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

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

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.