What is AIOps. In this episode, we discuss artificial intelligence for it operations (AIOps), examining how machine learning and ai automation are reshaping the landscape. Discover the impact of these technologies on performance testing and ai devops for a more efficient IT environment.
Air Date: 08/23/2022
Guest: Heath Newburn
https://www.linkedin.com/in/heathnewburn
What Exactly Is AIOps?
Coined by Gartner (originally as “Algorithmic IT Operations” before evolving to Artificial Intelligence), AIOps leverages machine learning, big data analytics, and AI to automate and enhance IT operations. It ingests vast amounts of data from monitoring tools, logs, metrics, and events to detect anomalies, correlate issues, and enable proactive responses.
Scott Moore sat down with Heath Newburn, a seasoned expert from PagerDuty with decades of experience in monitoring and IT service management. Newburn, who has worked with companies like IBM, Dynatrace, and now PagerDuty, shared practical insights into what AIOps really means, especially for performance engineers dealing with complex, microservices-based applications.
Newburn explains that AIOps is fundamentally about converging diverse signals—metrics, logs, traces, changes, and events—into a unified platform where AI helps humans make better decisions. Vendors often overhype it, leading to confusion, but at its core, it’s not about fully autonomous systems replacing humans. Instead, it provides situational awareness in overwhelmingly complex environments.Gartner defines AIOps as combining big data and machine learning to automate processes like event correlation, anomaly detection, and causality determination.
In 2025, as IT environments grow more distributed (multi-cloud, containers, microservices), AIOps has become essential for handling the explosion of data. PagerDuty reports a 70% year-over-year increase in operational noise, projecting triple the volume in just a few years.
Why Performance Engineers Should Care
For performance engineering professionals, AIOps is a foundational layer beneath tools like Application Performance Monitoring (APM) and observability platforms. It excels at:
- Anomaly Detection in Testing: During load tests, AIOps can spotlight subtle inefficiencies—even if end-user timings look fine—by correlating data across microservices.
- Root Cause Analysis: In production, it sifts through massive observability data (metrics, traces, logs) to find the proverbial needle in the haystack faster than manual review.
- Predictive Capabilities: While basic regression predictions are common, domain-specific AI better accounts for seasonality and real-world patterns.
Newburn emphasizes that “faster isn’t always better.” AIOps helps distinguish true improvements from shortcuts (like disabled checks making things artificially speedy).
The Reality of Automation and Trust
Does it fix everything automatically? Newburn calls this a cultural challenge more than technical. In high-stakes environments (e.g., financial systems processing billions daily), humans remain in the loop for critical decisions.We’ve shifted from treating servers as “pets” (nursing them individually) to “cattle” (quickly replacing failed instances). AIOps supports this by identifying repeatable problem signatures for safe auto-remediation.
However, full autonomy—like the 1990s vision of “autonomic computing”—remains elusive due to complexity. Incidents often stem from chains of events, not single root causes.Automation matures gradually: Start with augmented diagnostics (pre-running common commands), then build toward proactive fixes. Newburn notes some clients achieve 60-90 second response times via automated remediation.
Is AIOps a Must-Have?
Absolutely, for high-performing organizations. Environments aren’t simplifying; instrumentation is increasing, generating more data than teams can handle. AIOps delivers the context and actionable intelligence needed to scale. Without it, teams rely on tribal knowledge, hindering growth. As we approach 2026, trends include deeper integration with observability, agentic AI for autonomous actions, and a focus on ethical, proactive operations. Gartner has even rebranded parts of the space to “Event Intelligence Solutions” to clarify focus.
Getting Started
Newburn advises aligning AIOps with business outcomes: What metric will prove value to leadership? Resources include Gartner’s Market Guide, GigaOm reports, and active communities on X (formerly Twitter) and LinkedIn.
The conversation wrapped on a lighter note—both Moore and Newburn being Austin barbecue enthusiasts—promising more BBQ content alongside tech deep dives.
AIOps isn’t hype; it’s a necessity for surviving modern IT complexity. It empowers performance engineers to focus on innovation rather than firefighting. If you’re in DevOps, SRE, or performance testing, exploring AIOps could be your edge.
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