Why IT support can’t afford to be reactive anymore

Why IT support can’t afford to be reactive anymore

For years, IT support has been measured by how quickly teams respond when something breaks.

The phone rings. A ticket arrives. The team jumps into action.

That model worked when most issues were tied to failing hardware, overloaded servers, or capacity shortages. Today, the stakes are higher. An unpatched operating system, an outdated application, or a forgotten virtual machine can expose an organization to security vulnerabilities long before anyone notices a performance problem.

Waiting for users to report issues is no longer enough. Support organizations must become proactive.

The risk has changed

Years ago, aging infrastructure was mostly an availability problem. Old servers failed. Storage is full. Applications slowed down.

Today, unsupported technology creates a different kind of risk. When operating systems or applications stop receiving security updates, every day they remain in production increases the attack surface. Those systems may continue running perfectly well from a user’s perspective, but behind the scenes, they’re becoming increasingly vulnerable.

That’s why support teams can no longer measure success by uptime alone. They also must understand security risk.

Stop looking for what’s broken

One of the biggest shifts we’ve made within NetApp IT is changing the way we think about monitoring. Traditional monitoring waits for something to fail. Observability looks for signs that something is about to fail.

Instead of reacting only to alerts, we’re analyzing trends across performance, utilization, latency, and application behavior. AI helps correlate thousands of signals that no human could realistically analyze in real time.

The goal isn’t just to identify broken systems. It’s to identify systems that are breaking.

If we can intervene before employees notice a slowdown, or before an application experiences an outage, we protect productivity across the business.

AI makes better operators

There’s no shortage of excitement around AI right now, but AI isn’t magic. It’s another tool in the toolbox.

Used correctly, AI helps us process enormous amounts of operational data, identify patterns, correlate events, and reduce the time it takes to troubleshoot incidents. What once required engineers to sift through thousands of alerts can now be narrowed to likely root causes in seconds.

That doesn’t eliminate the need for experienced operations teams. It allows them to spend less time searching for problems and more time solving them.

At the same time, AI introduces new responsibilities. Large language models require access to large amounts of information. Organizations must understand what data they’re exposing, where it’s being processed, and what safeguards exist to protect sensitive information.

Security and governance have to evolve alongside AI adoption.

A modern operations center looks different

Network Operations Centers have existed for decades, and many people still picture giant rooms filled with screens showing endless dashboards.

Today’s operations centers are becoming smarter.

Within NetApp IT, we’ve been evolving toward what we call a next-generation Network Operations Center. Instead of forcing engineers to monitor dozens of disconnected tools, observability platforms bring operational data together into unified dashboards that provide actionable insights rather than raw alerts.

When an issue appears, AI can correlate related events, identify likely causes, recommend possible resolutions, and provide engineering teams with the information they need to eliminate recurring problems.

Troubleshooting that once consumed hours can often be reduced to seconds. More importantly, the operational intelligence we collect helps prevent future incidents altogether.

Culture is the hardest part

Technology is only part of becoming proactive. Culture matters even more.

Most support professionals have spent their careers being rewarded for fixing problems quickly. We’re asking them to think differently. We want engineers looking for tomorrow’s problems instead of waiting for today’s tickets. That requires curiosity, continuous learning, and leadership.

One of the biggest mistakes organizations make is waiting until everything is perfect before making progress. I’ve seen teams spend months planning the ideal solution instead of building something useful.

My advice is simple: fail fast, learn quickly, and iterate continuously. Perfection is rarely achievable on the first attempt. Waiting for perfection usually means never starting.

As leaders, we have to create environments where experimentation is encouraged, lessons are shared, and teams aren’t punished for responsibly trying new approaches.

Three pieces of advice

If I were talking to another IT operations leader who’s constantly living in firefighting mode, I’d offer three recommendations.

First, invest in observability. You can’t prevent problems you can’t see.

Second, build a culture that looks for risks before they become incidents.

Finally, stay current. Technology, and especially AI, is evolving too quickly to rely on last year’s knowledge. Read constantly. Learn continuously. Challenge assumptions.

At the end of the day, the goal of IT support isn’t simply to respond faster when something breaks. It’s to keep the business productive by preventing problems from happening in the first place.

Explore the full podcast series: https://letssolveitnetapp.podbean.com/

Listen to the podcast: https://www.podbean.com/ew/pb-xvd9e-1b6759b