Operations | Monitoring | ITSM | DevOps | Cloud

Real problems need real solutions. We’ve all heard the same lofty claims about AI in IT operations: “Reduce alert noise” and “Detect anomalies.” While these sound great on paper, they often fall flat when critical systems fail during peak buying seasons or a major security threat goes undetected.

On February 7, 2025, SolarWinds announced that they will be acquired by Turn/River for $4.4 billion and go private as soon as Q2 2025. This development has left customers questioning what’s next. Acquisitions often promise innovation, but Turn/River’s track record with similar purchases, like Paessler PRTG, has raised concerns.

Attend our webinar on April 3 to see our latest innovations live. Register IT Operations are more complex than ever, with modern data centers spanning on-premises, containers, multi-cloud environments, and AI-powered infrastructure. The rapid expansion of data sources has created an overwhelming volume of information, making manual monitoring across multiple tools impractical. Visibility gaps slow down troubleshooting and delay critical decisions, impacting business performance.

I know you’ve been there: a critical system goes down, and suddenly, you’re in a war room, staring at a blizzard of alerts, conflicting logs, and a dozen theories pointing in different directions. Time slips by as you sift through fragmented data, chasing symptoms instead of solutions. Hours of digging later, all you have are more questions and a cup of lukewarm coffee. This isn’t just frustrating—it’s draining.

Your tech stack is growing, and with it, the endless stream of log data from every device, application, and system you manage. It’s a flood—one growing 50 times faster than traditional business data—and hidden within it are the patterns and anomalies that hold the key to the performance of your applications and infrastructure. But here’s the challenge you know well: with every log, the noise grows louder, and manually sifting through it is no longer sustainable.

Most IT leaders know they need AIOps. Few have a strategy for making it work. The problem isn’t a lack of AI-powered tools; it’s the absence of a clear, outcome-driven plan. Especially given the rapid adoption of ChatGPT and LLMs in general, organizations are spending billions on AI. But without a defined strategy, AIOps quickly turns into a patchwork of disconnected tools, rising costs, and disappointing ROI.

For years, the term “AIOps” has been tossed around, but for IT teams, it hasn’t really brought the change it promised. Gartner coined the term, promising that machine learning and AI would forever change how we manage IT operations. Yet, the reality has been underwhelming. For most teams, traditional AIOps has amounted to little more than event management with a shiny new label.

Imagine discovering that 40% of your cloud infrastructure went unmonitored for a week because monitoring agents failed to deploy during an auto-scaling event. This scenario isn’t just hypothetical—it’s a growing reality for organizations relying on traditional agent-based monitoring in dynamic cloud environments.

Managing cloud infrastructure is challenging enough without adding the burden of deploying and maintaining monitoring agents. What if there was a simpler, more efficient way to monitor your virtual machines (VMs)? In the first part of this series, we looked at the (link) and presented a better solution: agentless monitoring. Agentless monitoring is an efficient approach to observability that eliminates the need to install and manage software agents on each monitored device.

Every new layer of infrastructure piles onto an already fragile web of interconnected challenges, making it painfully clear: traditional monitoring can’t keep up. You’re drowning in alerts, buried in data, and yet somehow still flying blind when real issues arise. More notifications don’t mean more insight, and more data doesn’t guarantee better decisions.