This post is about how AIOps will change the way IT Operations personnel (IT Ops) work and the new skill sets they have to adopt in an AIOps world. For a definition of AIOps, refer to the blog post: "What is AIOps?" Gartner explains that an AIOps platform (figure 1) uses machine learning and big data to aggregate observational data (from monitoring systems output, job logs, syslogs, etc.) and engagement data (from ticketing, incident, and event recording system data) to produce a virtuous circle of continuous insights yielding continuous improvements and fixes. Automation is both an input and output of AIOps. The results or statuses of automated workloads and jobs can be used like operational data and engagement data for analytic purposes.

The world is highly competitive and is high paced. There is increased tension to reduce operational costs, rework and time wastage. Artificial Intelligence (AI) is the right technology for these requirements. Every industry uses AI to analyze the data from sensors, historical data for understanding the industry output and input patterns thereby improving the productivity. System failure or machine failure is unavoidable in any industry.

The world got its first robot citizen'Sofia', an artificial intelligence based robot. Facebook recently purchased the production of VR Oculus Rift headset. What more happened that ruled the digital world in 2017? As per the McKinsey Global Institute Report 2017 on AI, globally tech giants spent $20 billion to $30 billion on AI in 2017, with 90 percent of this spent on R&D and deployment, and 10 percent on AI acquisitions. Intelligent apps that can understand and predict future behaviour of users are transforming workspaces from prioritizing emails to infusing AI capabilities in marketing systems to understand customer behaviour.