AI and Site Reliability Engineers: Lessons from the Field.

Root cause analysis has traditionally been a reactive discipline—often only activated after something breaks. While that will always be its core, emerging AI technologies are giving reliability professionals more information before a failure occurs—data that can make post-event RCAs faster and more informed.

At the recent Mainstream Reliability Conference, a standout session titled “Empowered Reliability Engineers to Predict Failures Using an AI Self-Service Tool” featured how frontline reliability engineers—working with physical assets like industrial compressors—are beginning to apply AI in proactive, site-level decision-making. These aren’t abstract tech demos. They’re grounded use cases that show what’s possible when reliability professionals are given tools that prioritize speed, insight, and ease-of-use.

Highlights from the Session

The presentation focused on a field-tested, self-service AI tool that enabled reliability engineers to draw insights directly from equipment behavior. One example featured an industrial compressor and how AI helped anticipate potential failures before they disrupted operations.

The tool wasn’t positioned as a replacement for root cause analysis—but as a way to supercharge it. It gave engineers the ability to:

• Spot failure precursors faster
• Surface patterns too subtle for manual analysis
• Make informed interventions earlier in the lifecycle of a failure

Clarifying the Role of AI in RCA

At Reliability.com, we focus on tools that help teams perform effective post-incident RCAs—because no matter how advanced your detection systems become, the real learning happens after an event is clearly defined. Our AI in EasyRCA is purpose-built for this moment.

Rather than trying to guess what might fail, EasyRCA’s AI accelerates the investigation once a failure or significant incident occurs. It helps your team:

• Structure an RCA faster by instantly building causal factor charts and logic trees
• Surface patterns and connections that may be hidden in your incident data
• Suggest potential contributing factors for review—without replacing human judgment
• Keep investigations consistent by guiding users through proven methodologies

This is a critical distinction. AI-assisted RCA isn’t about replacing the human investigator—it’s about giving them a head start and better evidence to work with. In the same way early-warning AI tools can give engineers more lead time to respond, EasyRCA’s AI ensures that once the event happens, you can move from “something went wrong” to “here’s exactly why it happened” as quickly and thoroughly as possible.

For most organizations, a mature RCA process is the necessary foundation before predictive tools can ever deliver on their promise. Without it, early detection data risks being ignored, misinterpreted, or acted on incorrectly. EasyRCA ensures your RCA program is ready to make the most of both today’s tools and tomorrow’s advancements.

Root Cause Culture, With or Without AI

Whether your team is exploring early-warning AI tools or wanting to improve your RCA process, the foundation is the same: a strong root cause culture. Technology can assist, but it can’t replace the habits and discipline that drive lasting reliability improvements.

That means cultivating teams who:

• Stay curious—constantly asking why until they reach the underlying cause
• Investigate rigorously—following structured, repeatable methods instead of relying on guesswork
• Learn continuously—capturing insights so future teams don’t repeat past mistakes

AI can accelerate certain steps, but the real power comes from embedding root cause thinking into daily operations and giving your team the right tools to act on it. EasyRCA helps you do exactly that—ensuring that when a failure happens, your people have both the process and the platform to turn it into a lasting fix.

Want more examples of how RCA is being reimagined across industries? Explore more at Reliability.com/resources.