AIOps at $18B: The Year IT Operations Changed (Whether You’re Ready or Not)
The budget math is clear. The operational stakes are higher. The winners will know why—and how—to act on the new AIOps imperative.
“Are We Flying Blind?” (Why This Matters Right Now)
I’m going to start with a familiar feeling. You’re in the office, or maybe on a Teams call, when yet another “major incident” alert pings your phone. The ops team is already scrambling. Slack is blowing up. Leadership wants answers—what failed, why, how soon until it’s fixed, and, most importantly, could we have seen this coming? Sound familiar?
It doesn’t matter whether your business makes software, runs hospitals, or flies airplanes—the fundamentals of IT service management are the same: everyone’s looking for certainty in an uncertain world. But as we hurtle toward 2025, that uncertainty has a new name: complexity. Systems multiply. Data explodes. The stakes—both reputational and financial—grow ever higher.
That’s why the recent headline from openPR caught my attention:
“AIOps Platform Market Grows to $18.1B in 2025; $40.7B Forecast by 2029.”
If you’re leading an IT function, this isn’t just industry noise. It’s a signal—a market-wide recognition that the old ways of managing IT no longer suffice. The world is, in real time, putting real money behind the need for AI-powered help.
But what does that mean for you, right now? Let’s take a step back, breathe, and have the conversation every IT executive needs: not just how AI and AIOps are evolving, but what that means for the very architecture—technical and organizational—of your team.
From Chaos to Clarity—Why AIOps Isn’t Optional Anymore
Let me be blunt.. Most organizations I’ve worked with over the past 20 plus years, from banking giants to airlines, to global retailers, start every year believing they have “incident response” under control. They have dashboards, playbooks, even automated runbooks. But every year, they’re surprised when the biggest, costliest outages come not from hackers or earthquakes, but from mundane, cumulative failures—a missed alert here, a subtle configuration drift there, an escalation that languished in a queue because it “didn’t look urgent enough.”
The reality is that human brains, no matter how skilled or diligent, cannot outpace the volume, velocity, and variety of signals modern IT produces. By the time a pattern emerges, it’s often too late. This is the precise moment—right now—when AIOps becomes not a luxury, but a necessity.
When I say the market is putting $18B behind AIOps, that’s not just vendor sales—it’s a reflection of collective industry pain and a desperate search for leverage. What are we buying?
A chance to move from reactive firefighting to proactive prevention
The ability to see context, not just noise
Tools that augment, rather than overwhelm, the people at the heart of operations
But here’s the thing that too many executives are missing: AIOps is not a “tool” you plug in. It’s a paradigm shift—one that changes how you staff, govern, and even think about IT operations.
And as with any true shift, the winners and losers will be defined not by how much they spend, but how thoughtfully they adapt.
The Numbers Behind the Narrative—Why the $18B Growth Is a Signal, Not a Solution
Let’s pause and look at that $18.1B figure. The growth is staggering—over 22% CAGR, with projections barreling toward $40B. On paper, that sounds like vendor marketing, but in my experience, it’s something much deeper: it’s an acknowledgment that the complexity of modern IT—multi-cloud, hybrid, always-on, often-glued-together by a patchwork of legacy and SaaS—is simply unmanageable by human effort alone.
Let me give you a flavor of the real conversations happening in boardrooms and budget meetings:
“We have 30+ monitoring tools, and yet our average incident MTTR is up this year.”
“We automated alerting, but our teams are overwhelmed—nobody knows what’s actually important.”
“We bought an AIOps platform, but it’s a black box; leadership doesn’t trust it, and engineers bypass it.”
Sound familiar? That’s because buying AI-powered tools is easy. Real transformation? That’s the hard part. This is why the next wave of AIOps spending is being driven not just by a desire for more technology, but for better explainability, integration, and business alignment.
The AI-Driven Operational Imperative—Moving Beyond Magic
Here’s where the story shifts from spending to strategy. The days when “AI in IT” meant a few predictive alerts or clever anomaly detection are gone. CIOs and their teams are learning the same lessons, over and over:
You can’t automate what you can’t explain.
You can’t prioritize what you can’t measure in business terms.
And you can’t win with a mess of siloed tools, each with their own AI, none sharing context.
Let’s get personal:
A few years ago, I was working with a Fortune 500 financial firm. They’d spent millions on a patchwork of best-of-breed monitoring, alerting, and ITSM platforms. But at 2AM, when a critical payment system failed, what mattered was not the clever dashboards, but whether the right team got the right signal at the right time.
They didn’t. The incident escalated, revenue was lost, and the post-mortem was a finger-pointing mess: “Was it a network issue? A bad deployment? Cloud latency?”
What untangled it?
It wasn’t just better tech. It was a rethink—moving from isolated tools to an AIOps platform that could ingest, correlate, and explain why it flagged the event in the first place. More importantly, it piped context straight into ServiceNow, so the humans in the loop didn’t waste hours triaging false alarms.
The lesson: AI is only as valuable as the clarity and trust it brings.
It’s not the automation—it’s the explainability, the business context, and the ability to close the loop across ITSM and ITOM.
Case Study—How Predictive Triage Transformed a Major Retailer
Let me share another story that illustrates the real value of the new AIOps paradigm.
The Situation:
A global retailer, famous for both online and brick-and-mortar, was facing a persistent problem: every Friday afternoon, as traffic spiked, critical order systems would slow or crash. IT teams were on “heightened alert,” but the incidents kept happening, costing millions in lost sales and customer trust.
What We Did:
Instead of adding more dashboards, we deployed a predictive triage solution—a blend of machine learning models, built on years of their own incident data, and integrated directly with their ITSM workflows. Crucially, the solution didn’t just flag anomalies; it prioritized them based on business impact (“this alert will impact 12,000 carts in the next 30 minutes if not resolved”).
The Shift:
Engineers didn’t waste cycles chasing ghosts.
Business teams could see, in plain language, why the AI prioritized what it did.
And, for the first time, the CIO could walk into the boardroom and explain not just what happened, but how they were preventing future outages.
The Result:
40% reduction in “critical” incident volume
50% improvement in mean time to resolution
A real, measurable uptick in NPS and customer retention
But here’s what stood out to me most: The culture changed. Instead of blaming “bad luck” or “bad code,” teams trusted the system and—just as importantly—trusted each other.
We went from fighting fires to actually seeing the smoke—before it became a blaze.
Why Explainable AI Is Now Non-Negotiable
Let’s talk about explainability. You’ll hear vendors toss the term around, but here’s why it’s now a non-negotiable at the executive table.
When you’re accountable for regulatory compliance (think SOX, HIPAA, PCI), it’s not enough to say “AI fixed it.” You need to show auditors, regulators, and sometimes courts why a decision was made. But it’s not just about risk. Your best engineers will not blindly trust a black box, especially when their jobs and reputations are on the line.
True AIOps maturity means you can look a stakeholder in the eye and say,
Here’s why the system flagged this event. Here’s what the AI saw, what it correlated, and why it suggested this action. Here’s the audit trail. Here’s the value.
I’ve seen this in action: at a global bank, introducing explainable AI cut incident resolution time in half—not because the models were “smarter,” but because the people finally trusted and acted on the insights.
ITSM and Toolchain Integration—The Glue That Makes AIOps Work
Here’s a hard truth: I’ve never seen AIOps deliver its promise in a silo. The organizations that win are those that make integration—across monitoring, ITSM, ITOM, and even DevOps—a foundational design principle.
What does this look like in practice?
Incident context moves with the ticket—from detection, through triage, to resolution, without being lost in translation.
Change management is informed by real data—AI helps flag risky changes before they’re deployed, instead of after the outage.
Problem management isn’t a monthly meeting—it’s a live, data-driven process, constantly tuned by both human insight and machine learning.
Here’s a direct quote from a head of IT operations I advised a recently:
The biggest value wasn’t in how fast we resolved incidents, but in how quickly we learned from them. The AI surfaced patterns we never saw, but it was the integration with our ITSM platform that made those insights actionable.
The Human Side—How Culture, Trust, and Upskilling Change Everything
If you take nothing else from this, let it be this:
The best technology in the world cannot fix a broken process or a mistrustful culture.
Every time I’ve led an AIOps-driven transformation, the inflection point is always the same—it’s not the code, it’s the people.
In one memorable engagement, a senior engineer pulled me aside and said,
I’m not afraid of the AI. I’m afraid I’ll become irrelevant.
It was an honest fear—and a real one.
The solution wasn’t to ignore the concern or pretend it would disappear. We built a change enablement program from the ground up:
Upskilling, so the team understood not just what the AI did, but how it worked
Workshops where AI explainability was front-and-center
Clear communication that AIOps would be an augmentation, not a replacement, for their expertise
It worked. Not overnight, but over time, the team shifted from skepticism to advocacy. They became champions—because they could see the tangible value in their own work lives.
The Road Ahead—Strategic Recommendations for IT Leaders
Let’s land the plane.
If you’re responsible for IT operations, digital transformation, or technology spend, what should you do—right now?
Reassess Your Budget Priorities
Don’t chase shiny dashboards. Instead, invest in platforms and programs that emphasize explainability, predictive triage, and above all, seamless integration with your existing ITSM and ITOM stacks.Audit Your Toolchain for Redundancy and Gaps
Where is context lost? Where are incidents slipping through the cracks? You may find that less is more—fewer, better-integrated platforms out-deliver a sprawl of disconnected tools.Make Explainability a Board-Level Requirement
Push your vendors. Demand that every “AI decision” can be traced, explained, and audited. If they can’t do it, don’t buy it.Pilot Before You Scale
Pick one business-critical process—major incident triage, change risk scoring, or proactive problem management. Run a visible, measurable pilot. Document results. Learn fast, adapt faster.Invest in People and Process
Train your teams. Bring them along for the journey. Show, don’t just tell, how AIOps will make their work better—not just faster.
Technology is the lever. But culture is the fulcrum. Without both, AIOps is just another expensive shelfware.
Case in Point—My Own Consulting Experience
I’ll close with a lesson that’s stuck with me through years of IT transformation work.
I once sat in a war room with a major airline’s ops team as a system outage delayed flights across the country. The root cause wasn’t some exotic cyberattack. It was a minor hardware fault, overlooked because nobody could see the forest for the trees.
The irony? They had every modern tool—AIOps included. But what changed the game was when we integrated those tools with the incident response process, put explainability at the center, and gave their engineers the confidence to trust the insights. The next outage? They saw it coming—and prevented it—before passengers even noticed.
That’s not just technology at work. That’s leadership.
Final Takeaways: The Playbook for 2025 and Beyond
AIOps isn’t an add-on. It’s an operational transformation.
Explainability is now the price of admission.
Integration, not proliferation, is the key.
People matter more than platforms.
Ready to Move from Reactive to Strategic?
If you’re wrestling with alert fatigue, tool sprawl, or just want to see measurable ROI from your AIOps investments, my Strategic ITSM Value Program is designed for you. no pitch, just real answers.
Let’s audit your current operations, align technology with business value, and build a roadmap that works for both your teams and your board.
References:
Gartner, “Market Guide for AIOps Platforms,” 2024
Forrester, “The Forrester Wave™: Artificial Intelligence for IT Operations, Q3 2024”
Direct consulting experiences, 2024–2025
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𝓦𝓪𝓼𝓮𝓮𝓶