Jed Ayres Instead of an "AI arms race," this post posits that trustworthy AI in IT operations focuses on augmenting expert decision-making and existing patterns to improve efficiency and pre-empt issues.
AI should act as a junior analyst, detecting anomalies, surfacing recommendations, and handling low-risk fixes based on real-world operational data and proven team workflows.
This approach elevates human experts by shifting their attention from repetitive, pattern-based tasks to proactive problem-solving, identifying critical issues before they become major incidents.
The ControlUp ONE Platform, utilizing its Pulse AI engine, exemplifies this by integrating operational data for predictive analytics and autonomous remediation to support self-managing IT infrastructures.
You don’t get to trustworthy AI by skipping the fundamentals. You get there by elevating what already works.
I talk to a lot of IT leaders who feel like they are being pushed into some kind of AI arms race. Every conference keynote has a big slide about “autonomous operations” and “self-driving IT.” And you can almost see the stress levels rise when people start imagining what that means for their teams.
The truth is much simpler: real progress usually starts with something small that actually works.
A good example came from a workflow one of our healthcare customers created. It was nothing fancy. Basically, they asked ControlUp to drop a note into their Google Chat room if any user logon crossed four minutes. Not a high-stakes rule. Just a quiet early warning system.
For weeks, the alerts came in once in a while, making them easy to ignore. Then one morning, around 7:30, the alerts hit rapid-fire. Everyone logging in had suddenly slowed down. I was on site that day. The team saw the pattern almost immediately, pulled up the data, spotted the culprit, and fixed it. Their NOC didn’t call until 8:35 to announce it had become a major incident. By then, the problem was solved.
That moment has shaped how I think about AI in IT. It is not about replacing experts. It is about giving experts more time to notice what matters.
A Realistic Way to Bring AI into the Mix
I like to think of AI as a promising junior analyst. Eager. Helpful. Needs guidance. And definitely not something you want making big decisions alone.
We already have a decade of patterns, scripts, automations, and lived experience. The platform knows how to spot a driver that keeps causing blue screens, like the case of the employee whose machine rebooted constantly. She spent months thinking no one believed her. ControlUp saw every crash and gave the help desk the evidence they needed, providing the kind of material AI should learn from. It’s not a theoretical model, but real experience from real environments instead.
Where AI is Already Delivering Value
AI is already detecting anomalies, surfacing recommendations, and handling low-risk fixes inside real IT environments. It flags unusual logon patterns, identifies recurring crash signatures, and highlights performance issues before they turn into major incidents. It suggests next steps based on patterns your team has already proven.
What changes is not the role of the expert. It is how attention is spent.
Repetitive, pattern-based work moves into the background. Important patterns stand out earlier. Teams spend less time proving there is a problem and more time solving the right one.
This is what sustainable AI in IT operations looks like. Not replacing people, but reinforcing what already works.
We are following that path, one proven step at a time.
Ready to Elevate Your IT Operations?
If you’re ready to move beyond the AI hype and start implementing trustworthy, effective AI that enhances your team’s expertise, then it’s time for you to see the ControlUp Pulse AI engine in action.
Discover how ControlUp ONE Platform integrates real-world operational data with Pulse AI to deliver predictive analytics and autonomous remediation, leading to self-managing IT infrastructures. Request a free trial today!
