Breaking the Urgency Cycle: Why IT Professionals Stay Reactive—and How to Start Recovering
From the Systems Check episode: Kishan Kunver — Breaking the Urgency Cycle
By Naina
In enterprise IT, urgency is the default operating mode. Alerts fire constantly. Tickets pile up. Every request competes for the same finite block of your attention. And if you've spent any time in a high-accountability technical environment, you know exactly what it feels like when the pressure never fully releases.
On episode 3 of our podcast Systems Check, I sat down with Kishan Kunver — an endpoint tech analyst with experience in cybersecurity, database support, and digital workplace services — to talk about what it actually takes to perform sustainably in IT without burning your system out.
What emerged wasn't a list of wellness tips. It was a diagnostic framework, built from years of working inside the machine.
When every alert feels like a P1
Kishan started his career treating nearly every request as urgent. In enterprise environments, that instinct makes sense on the surface — alerts are constant, stakeholders expect responsiveness, and early-career professionals often feel the pressure to prove themselves through immediate availability.
But constant reactivity has a compounding cost.
"Constantly reacting was actually hurting my performance. I was switching between tasks all day and it made it hard to focus on work that actually mattered." — Kishan Kunver, Endpoint Tech Analyst
The shift happened when Kishan stopped triaging by urgency and started triaging by impact. That reframe — simple in concept, genuinely hard to execute under pressure — reduced his cognitive load, improved his output quality, and lowered his stress levels significantly.
For tech professionals, this is a systems thinking problem. If everything is labeled critical, the triage system itself has failed. The fix isn't to work faster. It's to rebuild the classification logic.
The physical cost of high alert mode
Burnout in IT is often framed as emotional exhaustion or disengagement. But Kishan's experience points to something more concrete: the body keeps the score, even in a desk job.
He describes a period during a technology leadership development program when he was managing requests from two managers simultaneously, fielding personal obligations, and trying to hit a short-turnaround deadline — all at the end of a meeting-heavy Friday. The cognitive overload didn't stay cognitive.
"I physically got very nauseous. I had an ocular migraine — it kind of feels like someone's stabbing the side of your head. I ended up going to the ER. Long story short: dehydration and major stress levels." — Kishan Kunver
This is a pattern I see regularly in my work with tech professionals seeking therapy in Connecticut and beyond. High-functioning, high-achieving individuals often don't register their own distress signals until the system throws an error they can't ignore. Chronic dehydration (compounded by heavy coffee consumption as a stress coping mechanism), poor sleep, physical tension, and disrupted digestion are common presentations — and they're often hiding in plain sight.
3+: Cups of coffee daily — a common pattern in high-stress IT roles.
2x: Managers Kishan reported to during peak burnout period.
0: Times "no" was necessary — smart prioritization conversations did the work.
Practical system architecture for sustainable performance
Kishan's approach to managing workload is less about self-care rituals and more about structural design — which resonates with how engineers actually think.
Reframe how you communicate capacity Instead of saying no to an incoming request, Kishan learned to redirect the conversation toward prioritization: "Sure — but how does this stack against everything else I'm currently running? Where do you want it in the queue?" This approach respects leadership's authority while surfacing the real tradeoff. Often, the requester recalibrates on their own.
Batch similar work to reduce context switching Context switching is one of the most underestimated performance drains in technical work. Kishan blocks his calendar in Outlook to group similar tasks — ticket reviews in the morning, project work in the afternoon — so his cognitive state stays aligned with the type of work being done. It's the equivalent of co-locating similar processes to reduce I/O overhead.
Build in buffer by design A small but high-leverage tactic: Kishan schedules 25-minute meetings instead of 30-minute ones. That five-minute margin isn't wasted — it's used to transition, hydrate, stretch, or simply reset before the next context load.
Leverage your team as a load balancer Solo heroics are a liability, not an asset, in enterprise IT. Kishan's current team distributes specializations while maintaining cross-functional coverage — each person leads a domain, but everyone can absorb adjacent responsibilities. This structure provides redundancy, reduces single points of failure, and makes PTO actually feel like PTO.
Key takeaway #1 — validate prioritization, not just effort: Ask where new requests fit in your existing stack. You don't have to say no — you have to surface the tradeoff so leadership can make an informed call.
Key takeaway #2 — find a creative outlet, not just a coping mechanism: Kishan uses Excel VBA automation and piano as outlets. Creative problem-solving and non-work skill-building provide the kind of recovery that passive rest alone doesn't.
Key takeaway #3 — quality of circle matters more than size: "Four quarters over 100 pennies." A small group of trusted people — at work and outside it — provides more resilience than a wide network of surface-level connections.
Key takeaway #4 — stay present amid industry-wide anxiety: Especially with AI reshaping roles in IT, it's easy to spiral into what might happen. Kishan's advice: do your work, stay curious, and let recovery be part of your performance strategy.
On AI, cognitive load, and the co-pilot distinction
Kishan offered one of the clearest framings I've heard on AI in the workplace: Microsoft calls it Co-Pilot for a reason. It's not Pilot. You're still the one with your hands on the controls.
AI tools like Co-Pilot can genuinely reduce cognitive burden — automating meeting transcripts, surfacing action items, accelerating repetitive tasks. But over-reliance introduces its own risks: hallucinations, security gaps, and the quiet erosion of the foundational skills that make you effective in the first place.
For IT professionals anxious about job displacement, Kishan's perspective is grounding: the engineers who will thrive are the ones who use AI as a force multiplier while maintaining the judgment and domain knowledge to catch what it gets wrong.
As a therapist, I'd add: the urgency to "keep up with AI" is itself a form of urgency worth examining. Fear-driven upskilling looks different from curiosity-driven growth — and one of them burns people out.
What this means if you're a tech professional struggling with burnout
If you're an IT professional reading this and recognizing yourself in Kishan's story — the constant context switching, the difficulty disconnecting on PTO, the physical symptoms you're attributing to "just stress" — that recognition matters. It's data.
Burnout in technical roles is often invisible until it isn't. The systems you build at work deserve optimization. So does the one running underneath all of it.
Listen to the full episode
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Systems Check: The Human Operating System for Ambition & Burnout with Naina — Episode 3: "Breaking the urgency cycle" with Kishan Kunwar. Available wherever you get your podcasts.