AIOps Use Cases: Key Benefits and Applications
In This Article
As organizations undergo rapid digital transformation to stay relevant and profitable in today's competitive business environment, IT operations have taken on a more strategic role. However, the exponential growth in the use of digital technology also creates substantial challenges for IT leaders.
For example, as highly specialized tools are used to perform discrete tasks (such as security or performance management), it's becoming more challenging to gain a holistic view of the entire IT operation. Meanwhile, manually managing a growing network is time-consuming and labor-intensive, which often keeps IT teams stuck in a reactive mode instead of proactively driving strategic initiatives.
The rise of AIOps and expanding AIOps use cases are helping IT leaders take control of their operations. Equipped with this powerful approach, they can take on a proactive and strategic role in their organizations.
To understand more about this fascinating and growing movement, we will explore some of the key benefits and applications of AIOps.
AIOps is shorthand for Artificial Intelligence Operations. It refers to the application of AI-driven technologies to IT operations. An AIOps platform provides IT leaders a holistic view of their operations. It aggregates and analyzes data from multiple sources (e.g., security systems, performance management, DevOps, etc.) to derive actionable insights promptly to support accurate decision-making. It also includes a predictive component to help you understand trends and self-healing capabilities to preempt issues and lower mean time to remediation (MTTR).
AIOps use cases are built on four core functional components: (1) Enterprise monitoring, (2) network performance management, (3) application performance monitoring, and (4) security monitoring and management.
Along with all this, AIOps can support a wide range of IT operations to increase cost-efficiency while minimizing errors and delays. The following are just some of the most common and powerful use cases for AIOps.
AIOps platforms can ingest and filter data from IT environments to identify incidents and consolidate alerts. For example, if a failure in system A impacts system B--which affects system C and so on--you could receive an onslaught of notifications that create confusion and slow down response time. AIOps gathers and analyzes all the information, then sends out a single alert so that IT teams can prioritize response while reducing alert fatigue that could lead to missed opportunities.
Real-time anomaly detection
When you receive a threshold alert, it could be too late to prevent downtime or damages. AIOps tools can detect contextual anomalies early on using data collected across your networks. This allows your IT team to uncover patterns, stay on top of hidden trends and minimize the impact of these anomalies on the business.
Cross-domain situational analysis
When an incident is caused by issues across multiple domains, it's challenging to manually collate all the data and understand causality. An AIOps platform can analyze large amounts of data from multiple systems and networks--a task that's impossible to perform manually--to give your IT team a bird's-eye view of the situation, understand what is at stake, and prioritize response based on business objectives.
Identification of root causes
AIOps tools can help identify probable root causes of incidents to reduce the time-consuming and often frustrating troubleshooting process. Your IT team can get to the heart of the problem right away to reduce MTTR and minimize downtime. Moreover, IT experts can provide feedback to the software to help the AI engine learn from the experience and increase the accuracy of future diagnostics.
Insights on application blind spots
Legacy tools can only detect a fraction of application outages, leaving you with many blind spots. AIOps software gives you visibility into the relationships across app components, your IT infrastructure, and the context. You can effectively map your applications to the infrastructure to gain a holistic view, so you can identify and eliminate these blind spots.
AI technologies make analyzing vast amounts of data feasible. For example, an AIOps platform can gather information from highly distributed architectures and analyze a large number of instances simultaneously. The process can help you find outliers in configurations, identify meaningful patterns and trends, and deploy the right application versions promptly.
Configuration management database (CMDB)
Your CMDB is critical to the upkeep of configuration item inventory and relationships. But it's only as good as how well you can maintain the accuracy and timeliness of the information. In today's ever-changing IT ecosystem, it's virtually impossible to keep a CMDB current through manual processes. Instead, AIOps software updates your CMDB in real-time so it can serve as a foundation for effective and accurate system automation.
AIOps tools can automate closed-loop remediation of known issues by identifying problems with historical data. The software can then recommend the best course of action for the fastest remediation. As a result, you no longer have to wait for an IT team member to discover, identify, and fix an issue. This can improve response time, minimize downtime, improve user experience and free up resources to focus on strategic initiatives.
Discovery of hidden opportunities
Thanks to its augmented analytics capabilities, AIOps software can help you derive insights to identify hidden opportunities. For example, you can evaluate if you're allocating resources to the right place at the right time to optimize efficiency. The near real-time insights can help you make decisions to capitalize on opportunities that you may otherwise overlook.
Automated incidence management
An AIOps platform allows you to set alerts and automate system responses to deliver a seamless user experience. It can identify similar incidents and predict incident assignment groups to speed up resolution. It also shows correlated time-series metrics or symptoms, which helps your team quickly infer and identify root causes to minimize the time spent on troubleshooting.
Capacity planning and management
AI-driven data analytics can help you monitor system and application performance (e.g., CPU types, memory, bandwidth, etc.) so that you can proactively balance workload across your IT infrastructure. Using AI-generated recommendations, IT teams can accurately forecast demand and map workload to the right combination of servers. This can help you improve capacity, prevent outages and reduce operating costs.
While cloud migration is key to supporting digital transformation, many organizations fail to optimize the efficiency of their cloud infrastructure. For example, they may be using too many public cloud platforms or having ungoverned Kubernetes environments. AIOps allows you to gain complete visibility into your organization's cloud consumption so you can avoid cloud sprawl, which is not only costly but may also cause inefficiencies and impact productivity.
AIOps can isolate high-value decisions that require human attention while using AI-driven technologies, such as machine learning, to handle repetitive or less consequential operational decisions. As a result, enterprises can keep up with today's fast-paced business environment and customer expectations through automation while allowing leadership to focus on strategic initiatives that have long-term impacts on business success.
AIOps can be applied widely to augment IT operations, including monitoring, automation, and service desk functions. It also enables the concurrent use of multiple data sources, data collection methods, and real-time analytics to deliver a high level of visibility and transparency.
Thanks to big data, machine learning, predictive analytics and other new developments, AIOps' impact will expand rapidly. But, as illustrated by the many use cases in place today, it is already having a major impact and making its presence felt. And this is just a prelude to things to come.