LogicMonitor’s acquisition of Catchpoint comes with bold claims: “The era of reactive IT is over.” AI-driven platforms that “predict issues, prevent downtime, and make systems as smart as the people who run them.” Sounds great, right? Except—it’s not reality.
The promise of “predictive” monitoring has been promised every 2-3 years with each subsequent tech merger, new product release, or net-new company coming into the market. Those who use IT tools daily know that the only way to predict the future is to wait for it.
The Myth of Prediction
“Predictive monitoring” sounds futuristic: algorithms crunching data, spotting anomalies before they happen, and fixing problems without human intervention. But here’s the truth: prediction in complex systems is probabilistic, not deterministic. AI can forecast trends based on historical patterns, but it can’t account for everything—especially the human factor. Not to misquote Dr. Ian Malcolm, but “[IT problems] will find a way.” See? Chaos.
Systems fail because of:
- Human-driven changes (deployments, config tweaks, rushed fixes)
- Contextual factors (business priorities, compliance, risk tolerance)
- Emergent complexity (unexpected interactions between services and networks)
These aren’t just technical problems—they’re socio-technical. No algorithm can fully anticipate them. If you want to dig into some of the ways that human oversight is still required and how AI and LLMs could help in this regard, then A Guide to the Limits of Predictive Analytics is an excellent read.
The “Spaceballs” Lesson
Any monitoring or observability solution works by collecting data on an endpoint. It doesn’t matter if that endpoint is a server, a switch, an application, a network port, or an API. Some of these endpoints may offer historical information, but—most likely—you’ll get now data. Those metrics, logs, and traces are the cornerstone of modern monitoring/observability solutions. By the time you (the human or the monitoring system) read it, it’s no longer now.
I’m taken back to the scene in Spaceballs where Dark Helmet and Colonel Sanderz talk about instant video cassettes.
Colonel Sanderz: We’re looking at now now.
[…]
Dark Helmet: When will then be now?
Colonel Sanderz: Soon.
The future remains unwritten and your systems will never know what’s coming, nor will your human IT teams. It doesn’t matter if you have the best model in the world, the problem is— and always will be—humans. Or more accurately “humanity.”
Why Humans Still Matter
Even the smartest models rely on assumptions. Those assumptions break when:
- Data is incomplete or biased
- External conditions shift (traffic spikes, geopolitical events)
- Business or ethical trade-offs override “best practices”
Humans bring context, ethics, and adaptability—things machines don’t have. Predictive systems can suggest, but they can’t decide responsibly.
Real-World Examples
- Netflix Chaos Engineering
Netflix injects failures on purpose. Automation handles some recovery, but engineers analyze results and adjust strategies based on business priorities. No algorithm can do that. [See Chaos Monkey] - Financial Services
Market volatility triggers traffic spikes. Predictive models fail. Humans step in to reprioritize resources and ensure compliance. [Remember the Reddit/GameStop financial shakeup?] - Healthcare IT
Pandemic surge overwhelms systems. Predictive models underestimate demand. Humans override automation to keep patient care running. [COVID-19 wasn’t so long ago.]
The Bottom Line
“Predictive observability” is marketing, not reality. At best, these tools offer predictive assistance, not prediction. They make humans faster and smarter—but they don’t replace human expertise. As systems grow more complex, the human factor becomes more critical—not less.
The future isn’t about removing humans from the loop. It’s about amplifying human intelligence with better tools.