Building trust in AIOps may take time, but not because enterprise users are uninterested in artificial intelligence and machine learning (AI/ML) solutions.
Why do enterprises need AIOps? Because complexity is exploding. Scale is massive. A single interaction on your smartphone, for instance, may touch dozens of computers and services—and millions of lines of code—across the internet. Anecdotally, I’ve also found this to be true. As a CTO, I’ve met with many customers who request the same thing: An ecosystem of connected tools that enables them not only to monitor application traffic going to/from the public or private cloud, but also to third-party systems. The message I heard was that enterprises want to ingest data from a variety of tools— not just the application—to make sure all the components in their ecosystem are working well together.
The First Steps to Establishing Trust in Automation
But to tell the truth, when it comes to identifying and remediating problems, many customers aren’t ready to fully automate the process. Rather, they often say: “Tell me what the problem is, in non-technical terms, so that I know what to do. But don’t fix the problem right away—just tell me what to do.”
Why the hesitation? It seems that while customers are onboard with AIOps, many aren’t quite ready to let automaton fly by itself. Of course, to fully automate the loop, trust is essential. Trust takes time, though, particularly when remediation is required. I believe this wariness—not uncommon with groundbreaking technologies—will be short-lived, particularly as the benefits of AIOps become evident. It’s conceivable, for instance, that after 25 or 50 times of receiving the right recommendation, a hesitant customer will allow an automated system to repair a problem without human intervention. But to reach this level of trust, the user will need to receive the right recommendation again and again, without fail.
This article originally appeared on Forbes.com. To read the full article, click here.
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