Artificial intelligence AIOps machine learning concept

What is AIOps?

AIOps is using Artificial Intelligence for IT operations management.


Defining AIOps

AIOps stands for Artificial Intelligence for IT Operations. It is advanced analytics including machine learning and AI to monitor and manage the performance and reliability of applications and hardware systems, detect anomalous problems, adapt to changes in requirements, handle failures, and proactively or rapidly adjust with no or minimal disruption of services. Other names for AIOps are IT operations analytics (ITOA), advanced operational analytics, AI for ITOM, or simply IT data analytics.

The promise of AIOps is:

  • Reduce mean time to detect problems (MTTD) and mean time to repair (MTTR) those problems in essential IT systems
  • An end to alert fatigue by greatly reducing false positive incident alerts.
  • A rapid process for narrowing down the causes of failures during incidents and solving them before they impact users.
  • A way to detect, predict, and proactively solve problems that might have become failures before they even occur.
  • General improvement in quality of service due to optimization of networks, going beyond solving problems to actual improvements that make IT systems better.

All of which will improve the quality of service and customer satisfaction and reduce churn, while saving costs significantly over more manual methods of IT operations management.

Three Levels of AIOps

Identify incidents when they happen or have happened.
Identify potential incidents before they happen.
Automatically fix incidents, or provide information to humans to make it simpler for them to fix the problem.


Identify incidents when they happen or have happened.

Use Cases

  • Historical analysis
  • Anomaly detection
  • Performance analysis
  • Find bottlenecks
  • Show which networks or hardware are overloaded
  • Find service faults
  • Correlate and contextualize various logs and metrics

Use cases for AIOps

Tasks include performance monitoring, early fault and failure detection, and predictive maintenance to provide continuous fixes and improvements to IT systems such as networks, compute hardware, telecom towers, and supply chain systems. AIOps helps with a wide variety of use cases including resource capacity and usage forecasting, root cause analysis, energy usage optimization, performance diagnostics and remediation, predictive maintenance, and telco data analytics.

Telecommunications Network Analysis

Telecom data analytics requires integrating many different types of network interface data and contextual information. AIOps can fully optimize operator networks, improving the quality of service for higher customer satisfaction and reduced customer churn. AIOps can also help with capacity planning, and when combined with geospatial analytics is highly useful for tasks as diverse as automated call re-routing and new cell tower location planning.

See how Sysmech combined as many as 75 network interfaces to provide a self-analyzing network with 80 – 90% reduction in false alarms.


Smart Hardware / Network Optimization

AIOps helps compute hardware, network, and data center providers add a cutting-edge extra bit of optimal power to their products. By collecting sensor data from arrays, netflow data, traces, logs, etc., hardware providers can provide AI-driven predictive intelligence that makes sure hardware is always-on and always-fast. AIOps doesn’t just reduce MTTD (Mean Time to Detect) issues and MTTR (Mean Time to Repair), it can proactively detect potential problems. Armed with this information many simple problems can be automatically fixed before the customer ever knows there’s a problem. More complex issues are quickly resolved by technicians with the essential root cause analysis at their fingertips.

HPE Infosight, for example, has used AIOps to provide 6 9’s of uptime on their hardware and predict and solve over 85% of issues automatically.

Energy Usage Optimization

Modern electric vehicles and smart buildings all seek to use energy as efficiently as possible. Whether your company makes the skyscrapers smarter, or you’re trying to make sure the electric car you manufacture will stay on the road a few more miles before needing a charge, you know that analysis of every sensor and every single data point can put you far ahead of the competition. AIOps can analyze energy usage, and optimize regeneration of power to improve designs and eke out every ounce of power.

Jaguar TCS Racing, for example, analyzes billions of data points every race, with as little as 0.5% of the battery power left at the end of each race that makes the difference between a trophy and a stalled car.

Predictive Maintenance

Predictive maintenance is one of the most popular applications of AIOps. Finding problems before they affect the robots that manufacture multi-million dollar chip sets, or the MRI and CT scanners that hold human lives in their hands, or the engine parts of a passenger airplane can mean life or death for a company, a person, or hundreds of people. AIOps helps companies approach zero downtime for all these essential systems.

Optimal+, now owned by National Instruments, manufactures the chips that make self-driving cars possible. AIOps monitors each robot in the manufacturing line with edge analytics designed to shut down the machine in milliseconds if a potential problem is detected, saving millions each time.

“AIOps has become a must-have technology to deliver great customer experiences and differentiation for network and telecommunications organizations.”

Explore how AIOps technology offers the speed and scale to stay ahead of service reliability problems that impact the customer experience, and ultimately revenues.

More on AIOps

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