In IT support, success has traditionally been measured by how fast you can respond when something breaks.
But as AI moves deeper into operations, the job is shifting from reactive troubleshooting to proactive problem prevention. Predictive maintenance—using AI to monitor systems, analyze telemetry data, and forecast potential failures—is turning IT support into a data-driven discipline that can deliver measurable business outcomes.
This transformation is not just changing workflows—it’s rewriting career trajectories. The next generation of IT support professionals will be valued less for firefighting and more for foresight.
Those who can harness AI-driven insights, interpret data, and communicate results in business terms will find themselves well-positioned for advancement into reliability, DevOps, or leadership roles.
“AI-driven predictive maintenance is reshaping IT support from a reactive function into a proactive, strategic one,” says Derek Ashmore, AI enablement principal at Asperitas.
Instead of spending their days firefighting outages and responding to tickets, support professionals now monitor streams of telemetry data enriched by AI models that can spot anomalies before they escalate.
The implications go far beyond operational efficiency. Predictive maintenance tools can detect problems hours—or even days—before users feel the impact. They can forecast performance bottlenecks, identify configuration drift, and even suggest automated fixes.
That means fewer outages, better service-level agreements (SLAs), and more reliable systems—all measurable metrics that IT professionals can point to when negotiating raises or taking on leadership roles.
As Vivek Mishra, IEEE senior member, puts it, the shift represents a complete rethinking of what support work means. Instead of chasing alerts, teams are now tasked with interpreting AI-generated insights and turning them into strategic actions.
“Support staff will need to master data literacy to understand AI insights and become automation builders who can design and manage automated fix workflows,” Mishra says.
Summary
How is AI changing daily work of IT support?
For decades, IT support has been dominated by reactive tasks: responding to user complaints, restarting servers, patching vulnerabilities, and closing tickets. AI has changed that rhythm. Predictive maintenance tools embedded into monitoring systems can now detect unusual patterns across thousands of logs and performance indicators in real time.
Ashmore says that this evolution means the day now feels less like break/fix troubleshooting and more like data analysis and decision-making.
Instead of responding after an incident, IT pros are reading dashboards, prioritizing anomalies, and coordinating with DevOps teams to address issues before they cause downtime. This proactive approach also elevates IT support’s visibility within the business.
“Professionals who embrace these tools are no longer judged solely on responsiveness; they are seen as partners in maintaining resilience and enabling the business to move more quickly,” Ashmore explains.
What skills should IT pros be learning?
To stay relevant, IT professionals must blend technical expertise with analytical and business skills--data literacy comes first.
“You need to understand what the AI is showing you—what metrics mean, how anomalies are detected, and how to interpret that data in context,” Ashmore says.
He recommends certifications that combine cloud and AI awareness, such as Microsoft’s Azure AI Engineer Associate, AWS Machine Learning Specialty, and Google’s Professional Cloud DevOps Engineer.
“Cloud platforms and AI capabilities are now inseparable in most enterprise environments,” he says.
Mishra adds that familiarity with predictive maintenance tools—like AI-enhanced monitoring, observability platforms, and automation frameworks—is crucial.
“To stay competitive, IT support pros should learn how their tools directly apply AI to monitor IT performance and predict failures in real time,” he says, pointing out communication and business fluency are the next frontier.
“It’s not enough to know what’s broken; you need to explain how fixing it—or preventing it—adds value to the organization,” he says.
How does predictive maintenance advance IT careers?
AI isn’t just improving infrastructure—it’s opening new career paths. Predictive maintenance allows IT pros to demonstrate tangible value and transition from reactive support to strategic reliability roles.
“AI is accelerating the evolution of IT support from a tactical, ticket-driven role into a stepping-stone for leadership,” Ashmore says.
Professionals who can interpret predictive insights, coordinate across teams, and communicate outcomes in business language are already being promoted into site reliability, platform, or operations strategy roles.
Mishra says he agrees, noting IT pros who learn predictive maintenance can move into roles helping companies plan better and stay ahead.
“They can also become team leads or even IT strategy leaders by showing they can fix problems before they happen,” he says.
How can IT pros measure and communicate value?
Brandon Baguley, director of DevOps at Pluralsight, explains Every business has commitments and KPIs that define success.
“Predictive maintenance directly supports one of the most common and critical metrics, uptime, while enabling IT professionals to focus on differentiated business value,” he says.
Mastering predictive maintenance also builds a deeper understanding of the interdependencies within complex systems, a key factor distinguishing entry-level roles from advanced ones.
“With predictive insights, you gain the tools, metrics, and fixes to demonstrate measurable business value, which positions you strongly for promotions or raises,” Baguley says.
In fact, one of the biggest advantages of predictive maintenance is that it generates quantifiable results. AI tools track key metrics like mean time to resolution (MTTR), uptime, and performance consistency—numbers that can translate directly into ROI.
“Mastering predictive maintenance directly improves SLAs because it allows IT support staff to prevent or resolve issues before they affect end users,” Ashmore says. “Those gains show up in SLA metrics like mean time to resolution (MTTR) and system availability.”
Mishra emphasizes the importance of translating technical metrics into business impact
“Show how AI reduced downtime, improved recovery times, or prevented revenue loss,” he says. “That’s how you turn technical success into a story leadership understands.”
By quantifying uptime improvements and linking them to productivity or cost savings, IT professionals can demonstrate their strategic value—and position themselves for higher pay or management roles.
What pitfalls should be avoided?
While predictive maintenance offers clear benefits, implementation can be complex. Ashmore cautions against assuming that AI tools are “plug-and-play.”
“These tools are only as effective as the data you feed them,” he explains. “If your telemetry data is incomplete or mislabeled, you’ll get false positives that waste time and reduce confidence.”
Integration is another challenge. If predictive insights aren’t connected to existing ticketing or automation systems, they often go unused.
Mishra suggests a staged rollout to avoid disruption, which start by monitoring AI output in parallel with existing systems, validating its accuracy, and then automating small, low-risk tasks before scaling up.
Baguley says common pitfalls of early and unguardrailed AI monitoring tools include data exfiltration.
“If the terms and conditions of your use don’t explicitly state that they won’t be used to enhance the model or tool, then you can assume that your data is now a public entity,” he says.
He adds it is in any IT professional’s best interest to have a strong alignment with your company strategy around data protection. Secondarily, most AI tools are introducing a pattern of metered billing.
“Because of this, you should be aware of the implications of your budgets when using these tools and ensure that you either have strong alerting mechanisms or that you have a hard capped budget to understand the usage profile of AI monitoring tools,” Baguley says.