In my travels, I speak to a lot of people about artificial intelligence for operations, or AIOps. When I do, I feel I often need to dispel some common misperceptions. Most often, people will immediately latch onto the “A” of the equation, the artificial, and how it can evoke sinister images of automation run amok, the large-scale replacement of internal staff and so on.
Or, folks can focus on the “I,” but in terms of intelligence, that’s isolated. One may think of a data scientist as someone off in some remote lab, crunching numbers and algorithms. Any intelligence gathered gets thrown back to business leadership, who may or may not act on the information.
When I see what leading enterprises are doing in this area, these perceptions completely miss the mark, however. The reality is that significant value is being realized today, and the surface is just starting to be scratched in terms of what’s possible.
People, Process And Technology: Before And After AIOps
To fully appreciate the power of this concept of AIOps, it’s important to start by looking at the history of intelligence within traditional IT organizations. The reality is that IT and operations data sets were siloed as were people, processes, and technologies. Here’s a high-level picture of each:
• People: Historically, people gathered in disparate, isolated groups, with teams focused on networks, applications, storage and so on.
• Processes: Processes were also siloed in nature. When issues arose, for example, processes in place revolved around troubleshooting and remediating the specific technologies in a given administrator’s purview. Across silos, the process, if you could call it that, was to have massive, all-hands-on-deck calls in which different teams shared what they were seeing. From a development standpoint, waterfall-based approaches ruled, where one disparate team, say development, handed a product off to a QA team for testing.
This article originally appeared on forbes.com. To read the full article, click here.
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