Artificial intelligence for IT Operations (AIOps) applies AI and its subset of technologies, including machine learning and natural language processing (NLP), to traditional IT operations tasks and activities.
AIOps is a critical tool for IT Ops, DevOps, and SRE (site reliability engineer) teams, enabling them to identify cyber issues early and resolve them more efficiently. With AIOps, an organization's digital transformation can be smoother, allowing it to operate at the pace that modern business requires.
The monitoring aspect of AIOps IT operations enables organizations to identify the most relevant information from systems, discover patterns in that data, diagnose root cause and recurring issues, and notify stakeholders of these results. An AIOps platform allows for organizing IT monitoring tools and integrating them.
Observability refers to the insights gleaned from applications' data (e.g., logs, metrics, traces). It involves using telemetry software tools and practices that ingest, aggregate, and analyze performance data from various sources in near real-time.
The third pillar describes the ability to automate tactical activities that are often manual. Some things you can automate include anomaly detection, event correlation, and causality determination. Beyond detection and identification, AIOps also enables automation of remediation of issues. As a result, you can enact solutions for these more precisely and faster. Automation doesn’t replace human knowledge and oversight but augments human capabilities.
At the top of its advantages is the ability to reduce complexity. AIOps also provides a path to transform operations and drive cohesion between teams. The advantages support the entire enterprise, driving business innovation because you can adapt quickly to changing market conditions. This approach also ensures a proactive stance in mitigating and preventing downtime. All IT stakeholders will be more efficient with the visibility of every system state and a single source of truth for analytics.
Improve availability and uptime
Accelerate digital transformation
Enhance employee productivity and user experiences
As a result of this rapidly changing ecosystem, you have more pressure to secure it and ensure its accessibility for IT operations. By adopting AIOps, you can manage these at three levels:
Rein in dense systems, which can be distributed, dynamic, modular, and with ephemeral components.
Systems create data related to internal operations, including logs, metrics, traces, event records, and more. The data is multifaceted due to its breadth, specificity, variety, and redundancy.
The final layer involves the tools you use to monitor and manage data and systems. The list continues to grow with narrow functionality, which can create operational or data silos if not interoperable.
With this approach, you can find both recurring and new issues. AI uses specialized algorithms with a focus on specific activities. Each algorithm can call out alerts even in a very noisy environment. It augments human work by automating tactical tasks as well as strategic oversight.
AI and workers work together by extending human capabilities. It’s been a key component in IT operations scaling for remote and hybrid frameworks, which increase an organization’s digital footprint.
Real-world AIOps platforms ingest heterogeneous data from many sources, aggregating all components of your cyber environment — networks, applications, infrastructure, clouds, storage, and more.
While the value of AIOps is great, some businesses remain non-adopters. The reasons why are often misconceptions about the technology.