There is a lot of interest around AIOps, with multiple studies finding that organizations are adopting AI at an increasing rate. In a recent Forrester report 68% of companies surveyed have plans to invest in AIOps-enabled monitoring solutions over the next 12 months. Gartner forecasts that by 2022, 40% of all large enterprises will combine big data and machine learning (ML) functionality to support and partially replace monitoring. AIOps has become a major focus of many IT Operations Management (ITOM) organizations in the short and long term.
When adopting AIOps into a mature ITOM environment, there are numerous challenges that IT organizations must address. One that frequently emerges is providing context to the ML output. In laymen’s terms, what does the machine learning output mean to the business?
In IT surveillance organizations, context is what allows an agent to take action against an alert that is surfaced. Without context, an agent cannot take a remediation action, instead relegating themselves to a lengthy diagnostic and triage process. Context is an important issue because many of the events presented to agents today are symptoms or false positives. A 2018 study by Digital Enterprise Journal found that more than 70% of the operational data collected by IT organizations is not considered actionable. Without context, the agent has a difficult time determining when and when not to take action.
