Last Updated: January 2026
Look, I’ve been doing DevOps for over seven years now, and I’m tired of reading yet another “AI will revolutionize everything!” article that sounds like it was written by someone’s marketing team. So here’s what’s actually happening with AI in DevOps, based on what I’m seeing day-to-day in 2026.
The Reality Check Nobody Wants to Hear
AI isn’t magic. There, I said it.
Last month, our VP excitedly showed me this new AIOps platform that promised to “eliminate 80% of incidents.” I nodded politely while thinking about the three weeks I’d probably spend just getting our messy logs into a format it could actually parse. Spoiler: I was optimistic. It took five weeks.
But here’s the thing—once we got it working? Yeah, it’s actually pretty useful. Just not in the way the sales deck promised.
What’s Actually Working (and What Isn’t)
Observability Tools That Don’t Completely Suck
We switched to an AI-powered observability platform in September. The first two weeks were hell. It flagged literally everything as an anomaly—a 2% CPU spike at 3am? ALERT. Someone running a manual backup? CRITICAL INCIDENT.
I almost gave up.
But our SRE lead (shoutout to Priya) convinced me to stick with it. After about a month of training and tuning thresholds, something clicked. Now it actually catches weird patterns before they blow up. Two weeks ago it flagged a gradual memory leak in our payment service that our old monitoring would’ve missed until customers started complaining.
The catch? You can’t just install it and walk away. We still review its suggestions every morning during standup.
CI/CD Assistance (When It Feels Like It)
Our Jenkins pipelines are a nightmare—legacy stuff from 2019 mixed with new microservices. When something breaks, it used to take anywhere from 20 minutes to 2 hours to figure out why.
Now we’re using this AI assistant that analyzes build failures. Sometimes it’s brilliant—”Hey, this exact error happened in the checkout-service pipeline three days ago, here’s the fix.” Sometimes it’s hilariously wrong—it once suggested we were missing a Python dependency in a pure Go project.
Still beats manually grepping through 5000 lines of build logs, though.
Test Automation (The One Thing That Actually Delivered)
Okay, I’ll admit it self-healing tests sounded like complete BS when I first heard about it. Our QA engineer Miguel was skeptical too.
We tried it anyway on our e-commerce frontend. Every time the designers changed a button class or moved a form field, we used to spend hours fixing broken Selenium tests. Now? The AI tool adapts most of them automatically. Not all of them—maybe 70%—but that’s enough to make a real difference.
Miguel’s still employed, by the way. He just spends more time designing better test scenarios instead of maintaining brittle selectors.
Security Scanning (Better, Still Annoying)
Our security team was drowning in vulnerability alerts. Like, thousands of them. Most were in dependencies we didn’t even use or affected internal tools nobody could access anyway.
The new AI-powered scanner we’re testing prioritizes based on actual risk. Public-facing API with a critical SQL injection vulnerability? Top of the list. Low-severity issue in a dev-only logging library? Bottom.
It’s not perfect—last week it missed a serious auth bypass in a staging environment—but it’s way better than the old “everything is critical” approach.
The Stuff That Actually Surprised Me
Early Warning Systems
This is where AI in DevOps has genuinely impressed me.
Three weeks ago, our monitoring AI started flagging increased latency on our authentication service. Nothing major—response times went from 45ms to 68ms. Our old alerts wouldn’t have triggered because we set the threshold at 200ms.
We investigated anyway. Turns out one of our database queries was getting slower as a particular table grew. We optimized it, added an index, problem solved. If we’d ignored it, that would’ve become a customer-facing issue within a week.
That kind of early detection? That’s valuable.
Auto-Remediation (With Training Wheels)
We’ve enabled some basic self-healing on our Kubernetes clusters. Failed pods get restarted, unhealthy nodes get drained and replaced, that kind of thing.
If you are new to Kubernetes, understanding its architecture and how it works is a ver good start, and to get a basic understanding you can refer my blogs on Kubernetes b following this link -> Kubernetes
But we learned the hard way to set boundaries. In December, we let the system auto-scale too aggressively and ended up with a $12K AWS bill for a single day. Now we have spending limits and require approval for anything beyond basic operations.
Self-healing is real, but you need guardrails. Lots of them.
What This Means for Engineers (Spoiler: You’re Still Needed)
I’ve had junior engineers ask me if they should even bother learning DevOps since “AI will do it all soon.”
Here’s what I tell them: AI changes the work, it doesn’t eliminate it.
I spend way less time on repetitive troubleshooting now. That 2am page about a disk running out of space? The system handles it. But designing our disaster recovery strategy, planning our migration to a new region, mentoring the team, understanding why our architecture works the way it does—that still requires a human who knows what they’re doing.
If anything, the foundational knowledge matters more now. When the AI suggests something, you need to understand whether it makes sense or not.
The Frustrating Parts
Can we talk about what doesn’t work?
The data problem is real. Our logs were a mess—inconsistent formats, missing timestamps, random encoding issues. We spent two months cleaning them up before the AI tools could even be useful. Nobody talks about this.
Black boxes make me nervous. When a system tells me to change a configuration but won’t explain why, I’m not doing it. Period. I’ve seen too many outages caused by blindly following automation.
The hype cycle is exhausting. Every vendor claims their AI is revolutionary. Most of it is just regex with extra steps and a fancy UI.
Where This Is All Going
Honestly? I think AI in DevOPs still in the early stages. The tools will get better, more integrated, more reliable.
But the teams that’ll succeed aren’t the ones chasing every new AI feature. They’re the ones who understand their systems deeply, implement AI thoughtfully, and know when to trust automation and when to trust their gut.
I’ve been wrong about technology trends before (I thought Docker was overhyped in 2014—yeah, about that). But I’m pretty confident about this: AI in DevOps is a tool, not a replacement. Use it well, stay skeptical, and keep learning.
That’s what’s actually happening in 2026, at least from where I’m sitting.
FAQ
Is AI replacing DevOps engineers?
No. AI reduces repetitive tasks, but system design, decision-making, and architecture still require human expertise.
What is AI in DevOps used for?
AI in DevOps is used for anomaly detection, alert reduction, CI/CD insights, cost optimization, and limited self-healing.
Is AIOps worth using in production?
Yes, when implemented carefully with clean data, tuning, and human oversight. It is not a plug-and-play solution.
Additional Resources
Following is list of some of the popular AI agents which you can check to see the possible potential of AI in our day to day technical work.
Kedar Salunkhe
DevOps Engineer | Seven years of fixing things that break at 2am
Kubernetes • OpenShift • AWS • Coffee