If you've ever gotten a letter from a bank that explained how different financial issues influenced a credit application, you've seen explainable AI at work -- a computer used math and a set of complex formulas to calculate a score and determine whether to approve or deny your application. In making that decision, some data points were either more or less important. Maybe your long history of on-time payments or your low amount of debt contributed to your application's approval. Similarly, explainable AI shows humans how it arrived at a decision by evaluating different inputs in its calculations. While that might sound obscure or only relevant to the most hardcore data people, explainable AI brings significant business advantages that anyone interested in applying AI should consider.

Artificial intelligence is an intuitive principle that integrates machine learning in creating the shift from the traditional ways to a new paradigm.

AIOps platforms leverage big data, machine learning, and analytics to enhance IT operations via monitoring, automation, and service desk functions …

"AIOps," which stands for "AI for IT operations," refers to the way data and information from a dev environment is managed by an IT team -- in this case, using AI. AIOps platforms leverage big data, machine learning, and analytics to enhance IT operations via monitoring, automation, and service desk functions with proactive and personal insights, enabling the use of multiple data sources and data collection methods. In theory, AIOps can provide faster resolutions to outages and other performance problems, in the process decreasing the costs associated with IT challenges. The benefits of AIOps are driving enterprise adoption. Eighty-seven percent of respondents to a recent OpsRamp survey agree that AIOps tools are improving their data-driven collaboration, and Gartner predicts that AIOps service usage will rise from 5% in 2018 to 30% in 2023.