As hardware and software becomes more powerful, it also becomes more
intricate, creating increased demand on the IT departments who are
responsible for managing it. And with every new advancement and
capability, tool complexity increases. Until recently, IT operations
teams have had few options when it comes to tackling the expanding
complexity of vital technologies—hiring new IT data science talent and
increasing department staff being the most obvious, if not the most cost
However, some advances actually do help take certain pressures off of
IT Operations (ITOps). Consider the emerging technologies of Artificial
Intelligence for Operations (AIOps).
AIOps is a combination of the terms artificial intelligence (AI) and
operations (Ops). More specifically, it represents the merging of AI and
ITOps, referring to multi-layer tech platforms that apply machine
learning, analytics, and data science to automatically identify and
resolve IT operational issues.
The term AIOps was first coined by Gartner in 2016, and grew out of
the digital-transformation shift from centralized IT to anywhere
operations with workloads in the cloud and on-premises across the globe.
As the pace of innovation increased, so did the complexities of the
technologies. This placed significant strain on IT operations, who would
now be responsible for managing and servicing a range of new systems
AIOps introduced a new model for managing IT operations. Machine
learning has revolutionized modern business. In fact, according to The
Global CIO Point of View, nearly nine out of ten CIOs are either already
employing this technology, or are planning to adopt it soon.