Inside the bustling Security Operations Centers (SOCs) across the globe, IT experts grapple with a deluge of information that would make even the most hardened Intel analyst blanch. Alerts stream in by the thousands, an incessant cacophony of potential threats and system glitches. Meanwhile, help-desk tickets pile up on digital dashboards like Tetris blocks, each one a cry for attention. This is what IT’s version of ‘too much information’ looks like.

But there’s a revolution happening, quiet but profoundly disruptive, in these digital war rooms. A Gartner report from May 2022 declared, rather dramatically: “The future of IT operations is inextricable from AIOps.” With a predicted market size of $2.1 billion by 2025 and a growth rate clocking in at 19%, AIOps—or Artificial Intelligence for IT Operations—is more than just another acronym in an industry brimming with them.

How does this machine oracle function? Gartner defines it succinctly: AIOps platforms utilize AI to sift through telemetry and event data, drawing out the needles of insight from haystacks of noise. Imagine Big Data as your all-seeing reconnaissance, scanning through everything from cloud ecosystems to network traffic. The intelligence thus gathered is then deployed to resolve problems ranging from app crashes to sudden spam attacks. Companies like Elastic and Splunk offer the analytics toolbox, while DataDog and LogicMonitor infuse AI into their monitoring systems.

IBM lays out the modus operandi in three steps: First, the AI ops platform ingests raw data to set a ‘normal’ baseline for applications. It then transforms this data into actionable insights, often forwarding them to IT professionals through collaborative platforms like Slack. The coup de grâce? Automated scripts that spring into action to fix the issues.

Anand Rao of the PwC US advisory practice detailed a real-world case. Faced with a flood of time-sensitive help-desk tickets, mostly revolving around stock transactions, Rao helped a firm deploy an AIOps model. The AI system was trained to identify ‘low-hanging fruit’—tickets that could be quickly resolved. “The resource sequencing achieved was far superior to human efforts. It was a game-changer,” Rao said.

What’s driving this quiet revolution? An explosion of new data, for one. Gartner’s report articulated that manual scrutiny of data, now accumulating at gigabytes per minute, is neither feasible nor efficient. Especially in industries like finance, media, and retail, where real-time data is the lifeblood, manual analysis is going the way of the Irish elk.

And the story doesn’t end here. Rao hinted at the next frontier—advancements in generative AI that can not only spot issues but also create test data and run code to verify its functionality. “The software is on the verge of becoming self-adaptive,” he said.

In the control rooms of IT operations, AIOps is slowly taking the pilot’s seat, guiding the system through complex terrains with a machine’s unerring precision. Gone are the days of solely rule-based responses; the future lies in an evolving, learning algorithm capable of adapting itself to whatever digital conundrum is thrown its way.

The future, indeed, is written in AI.