Within the IT operations and monitoring space, AIOps is most suitable for application performance monitoring, information technology infrastructure management), network performance monitoring and diagnostics and information technology event correlation and analysis, say analysts at Boston Consulting Group.
The common denominator for AIOps is that the systems help automate routine manual operations activities.
Three use cases hold the greatest short-term potential, according to BCG:
Anomalies and unusual behavior—such as a sudden spike in application use—is a clear use case, reducing the amount of manual observation and hard-coded rules that require teams to define anomalies up front. BCG says only about 11 percent of CIOs currently use anomaly detection AIOps tools, but that figure should grow to 42 percent by 2021.
Machine learning algorithms built into AIOps platforms could prioritize alerts on the basis of their business impact and filter out false positives, freeing IT operations teams to spend their time addressing critical alerts instead of managing static filters, writing rules, and adjusting thresholds to reduce alert noise, BCG argues.
About nine percent of CIOs now use noise reduction tools in AIOps, but our survey data shows that the percentage could rise to 42 percent by 2021.
AIOps also could automatically associate alerts that cut across various IT services into a single incident to speed up triage. That could help teams determine whether different alerts are related, and then cluster the results into a single, unified incident.
For example, a monitoring tool might create multiple memory and page-fault alerts from hosts of the same SQL cluster. The ML algorithm in the AIOps tool, when properly trained through supervised learning, could correlate alerts into a single incident, allowing the IT operations team to distinguish between alerts belonging to that incident and similar but unrelated alerts.
About 10 percent of the CIOs surveyed by BCG say they already use some sort of AIOps-enabled triaging solution today, and 40 percent say that they’re open to using this type of solution within the next three years.
The market for core AIOps is projected to grow from $9.4 billion in 2017 to $13.8 billion in 2021, a compound annual growth rate of 10 percent. AIOps orchestrators—platforms built to orchestrate insight and actions on the basis of log data from various monitoring solutions—are expected to grow by 26 percent over the same period, BCG predicts.
BCG believes AIOps will help transform IT operations in three critical ways, providing end-to-end visibility, providing evidence-backed insights and recommendations and executing recommendations automatically.
Eventually, IT teams will have to decide how much they allow the machine learning algorithms to make independent decisions about configuration of services and environments. Few will be comfortable, early on. Later on, with experience, attitudes could change.
