Truth and Proof: Building Trust in Machines Through AIOps Posted on : Apr 21 - 2022

How can humans learn to trust self-healing machines? See how teams can build trust in machines through “truth and proof,” evidence-driven AIOps tools.

IT systems are only getting more complex, with greater pressures to solve issues faster and demonstrate value consistently. Issues within systems, which dev teams could once handle all on their own, sprout up too fast and too often for direct human intervention. Artificial intelligence for IT Operations (AIOps) tools exist today to deliver automated monitoring and solution development, “no humans required” — significantly easing dev teams’ many burdens.

Adopting AIOps should be simple enough, then. But one of the tougher sticking points has been trust. Can humans trust a machine to identify root causes of issues and create accurate and effective solutions? The stakes are high — if a machine gets it wrong, the burden on human teams compounds quickly.

The sheer volume of issues and the data they generate necessitates automated solutions. But building trust is a slow process requiring an incremental approach. Once achieved, human teams alleviate themselves of significant toil while system issues are resolved more efficiently. Where can teams start to learn more about leveraging AIOps tools and begin building trust?

Understand Your AIOps Tool

A powerful benefit of an AIOps tool is its ability to be proactive and self-heal issues. Properly integrated tools can seek out and solve problems automatically. It goes beyond surface-level problems, too — AIOps can eliminate the root causes and, by extension, the manual toil associated with issues caused by those root causes. Is this something where we’re going to see full humanless automation? Probably not. Experience tells us that sometimes the “fix” can be worse than the fault. The goal here should be to attempt rapid root cause analysis and offer the best probable fixes, automated if practical, and let the user authorize the action. View More