The term “AIOps” stands for “artificial intelligence for IT operations.” Originally coined by Gartner in 2017, it refers to the way data and information from an application environment are managed by an IT team -- in this case, using AI. Here’s Gartner’s definition:
“AIOps platforms utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.
Since AIOps is an emergent space, the definition is highly fluid. But the core elements consist of:
The Elements of AIOps
Automated root cause analysis
AIOps Use Cases
AIOps has the potential to help IT professionals in three major areas:
AIOps platforms provide faster resolutions to outages and other problems, and in the process, decrease MTTR and costs associated with performance challenges.
Proactive performance monitoring
Build a more proactive approach to performance monitoring. By taking in the totality of application environment data, AIOps platforms connect performance insights to business outcomes.
Drive faster and better decision-making
AIOps platforms can help surface insights to IT professionals to drive better and faster decision-making.
Want to Learn More About AIOps?
The Rise of AIOps: How Data, Machine Learning, and AI Will Transform Performance Monitoring
AppDynamics surveyed 6,000 global IT leaders about application performance monitoring and AIOps. Read on to discover the trends shaping the space.