AI Ops has garnered a ton of hype, but are organizations actually prepared to implement it and get value?

Question

Answer

AI Ops is really just collecting everything from your incident command center and all these other tools.  It’s the aggregator for all the tools we have in our arsenal. But my organization is not a development shop (we're not the Uber, the Netflix or the Yahoos) so our needs are very different.  I buy commercial products more than I develop products.  It's a very different model in the health care provider space, so AI Ops is a very different use case for me.

Answer

Overall, IT teams are not equipped with data scientists that understand how to take data in, build proper models, continuously train them and apply AI techniques, potentially leveraging natural language processing to power chat-ops. That's a massive chasm and I feel like this ‘hashtag AI-ops’ is so much more aspirational than Dev –Sec-Ops, or anything else we've seen in the past few years. I'd love to hear from practitioners confirm if that’s true, or am I just bashing on the hype cycle?

Answer

AI Ops takes in all of these data sources, but it doesn't really think about the context of the data sources. Just because something can be measured or monitored doesn't mean it should be. In my review of the top 15 vendors (at least), none of them take that into account. They don't ask the leaders, lieutenants, or directors, “Does this data source really matter to the business? Will it understand context from this, and be able to move the needle in improving something?”

Answer

For the 4% of the organizations that have successfully implemented AIOps, it is not hype.For the other 96% though……