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 effective, solution.
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 and devices.
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.