The Top 5 AIOps Use Cases
AIOps helps manage IT operations effectively and reduce the overall IT budget by leveraging AI technologies. Here are the top 5 AIOps use cases.
The main purpose of AIOps is to optimize IT operations. By providing visibility and automation, AIOps can drive important business and IT innovations. The following article shares the top 5 AIOps use cases.
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AIOps Use Case #1
The first AIOps use case is anomaly or threat detection. AIOps tools are valuable contributions to the making of a strong security management posture. Established processes and algorithms sift through traffic data to identify any botnets, scripts, or other threats that can take down a network. This can be incredibly helpful since many threats are complex, multi-vector, and unique. AIOps leverages machine learning to expose patterns that can undermine business service availability.
AIOps Use Case #2
The next AIOps use case is event correlation. Infrastructure teams are faced with numerous alerts when only a handful really matter. AIOps identifies the important alerts, groups them together using inference models, and identifies the core root causes of the problem. This means your infrastructure teams will no longer have overloaded inboxes filled with alert emails and get the one or two notifications that really matter instead.
AIOps Use Case #3
The third AIOps use case is intelligent alerts and escalation. After issues are identified by root cause alerts, ITOps teams leverage artificial intelligence to automatically notify subject matter experts or incident response teams to quickly resolve the problem. Artificial intelligence starts the remediation process prior to anyone even getting involved. Many AIOps tools continuously monitor hardware using machine learning to predict errors based on previous and real-time data prior to its occurrence. A ticket with all the necessary details on how to resolve the issue is automatically sent to inform you of the issue.
AIOps Use Case #4
The fourth AIOps use case is incident auto-remediation. AIOps is used as an end-to-end bridge between IT service management and IT operation management tools. IT service management teams traditionally sift through infrastructure data to identify and resolve root cause issues. AIOps understands the root cause through inference from infrastructure alerts and sends them to the IT service management team or tool through API integration pathways.
AIOps Use Case #5
The last AIOps use case is capacity optimization. This includes predictive capacity planning and references statistical analysis or AI-based analytics to optimize application availability and workloads across infrastructure. Capacity optimization continuously monitors raw utilization, bandwidth, CPU, memory, and others to increase overall application uptime.
AIOps helps manage your IT operations effectively and reduce the overall IT budget by leveraging AI technologies to bring efficiencies to ITOps. Problems are resolved automatically within complex modern IT environments.
AIOps is important because it uses machine learning and data science to provide modern ITOps teams with a real-time understanding of any type of issue. Traditional IT management solutions typically can’t keep up with the sheer volume of issues while at the same time providing real-time insights or predictive analysis. According to Gartner, 4 out of 10 organizations are expected to strategically implement an AIOps platform to enhance performance monitoring by 2022.
How AIOps is Transforming IT Operations
AIOps eliminates IT operational noise by reducing low priority and unnecessary alerts that distract from real service affecting issues. This produces better decision-making by making companies more agile, more productive, more reactive to customers, and ultimately, more profitable. The goal of AIOps is to discover and act on meaningful insights into the IT operations as well as increase operational efficiency, provide better decision making, along with business continuity.
- End-to-end visibility into company applications and infrastructure
- Improved performance monitoring
- Noise reduction
- Increased company-wide collaboration
- Breakdown of data silos
- Simplified root cause analysis
- Seamless customer experience
- Reduction of IT service ticket volumes
- Predictive and proactive IT self-healing
ignio AIOps combines artificial intelligence and machine learning through automation. ignio first mines different data sources within an enterprise to learn cross-layer technology dependencies and component behaviors. ignio then leverages contextual awareness to mimic human behavior in handling situations. It isolates the root cause for an observed IT fault, prescribes the best fix, and applies it autonomously for full recovery.
ignio proactively checks the health of business-critical technology components, identifies potential hotspots, and recommends options to prevent any business disruption with the appropriate action. The outcome is highly resilient, agile, and efficient IT operations that allow enterprises to cash in on business opportunities and run their operations optimally.
Are you ready to reimagine your enterprise IT operations? Get an ignio AIOps demo.