I don't have to tell you that modern IT infrastructures are typically very complex with a combination of physical, virtual and cloud components. A lot of IT organizations are using DevOps, Continuous Delivery and Agile approaches to enable continuous improvement, which likewise increase the complexity.
On top of that, enterprises invest in a variety of application monitoring tools (e.g. Dynatrace, AppDynamics and New Relic), data lakes (e.g. Splunk and Elastic) and infrastructure monitoring tools (e.g. Nagios and Zabbix) to manage and monitor their services across on-premise and cloud environments.
Despite all these investments, enterprises are still struggling finding the root cause of problems/failures when downtime or performance degradation occur. This traditional approach of manually correlating data sources from different tools falls short. Not only is this process slow and time consuming, but it also requires domain experts from different departments to be involved in troubleshooting. That's not efficient and requires a different and new approach.
This new approach is explained in our guide "Guide to Artificial Intelligence for IT Operations". The guide gives an introduction to AIOps, how it works, the benefits and what you'll need to look for in your tool set to help IT Operations deliver efficient and high quality services.