In the evolving landscape of technology, where both hardware and software become increasingly complex, the demand on IT departments has intensified. Traditional approaches, such as expanding IT staff, are not always the most cost-effective solutions. This is where the concept of Artificial Intelligence for Operations, or AIOps, comes into play, offering a groundbreaking approach to IT operations.

The Genesis of AIOps

AIOps, blending artificial intelligence (AI) with IT operations (Ops), was first coined by Gartner in 2016. It emerged from the digital-transformation trend that shifted from centralized IT to more distributed operations, encompassing cloud and on-premises workloads globally. As technologies advanced, so did their complexities, burdening IT operations with a wider range of systems and devices to manage.

The Core of AIOps

AIOps stands on several pillars:

  1. Comprehensive IT Data: It aims to break down data silos by aggregating information from various IT management spheres, speeding up root cause identification and enabling automation.
  2. Aggregated Big Data: AIOps platforms utilize big data, employing advanced analytics on both historical and real-time data.
  3. Machine Learning: Given the volume of data, AIOps relies on machine learning to automate analytics, scaling up both speed and accuracy.
  4. Observation: AIOps platforms are designed to observe data behavior across various IT environments.
  5. Engagement: These platforms manage and coordinate IT systems, enhancing reliability and enabling automation.
  6. Action: The ultimate goal is to fully automate IT operations, though this is still an evolving aspect of AIOps.

The Workings of AIOps

AIOps operates much like human cognitive functions, employing algorithms for data selection, pattern discovery, inference, collaboration, and automation. Its goal is to centralize system engagement by analyzing data from all IT monitoring sources.

Driving Forces Behind AIOps

Key trends driving AIOps include:

  • Expanding IT environments
  • Increasing ITOps data
  • Need for faster infrastructure-problem resolution
  • Decentralization of networks
  • Growing developer influence without corresponding accountability

The Three-Phased AIOps Approach

An effective AIOps strategy involves:

  1. Predicting issues before occurrence.
  2. Preventing impact on end users.
  3. Automating remediation and resolution.

AIOps Tools and Their Functionality

AIOps platforms come with tools focusing on machine learning analysis and insights, aiding in data understanding and operational insight.

The Impact of AIOps

AIOps is vital in various scenarios like incident event correlation, anomaly detection, predictive analytics, root cause analysis, streamlining support, automated incident response, digital transformation, cloud adoption/migration, and DevOps adoption.

Implementing AIOps in Organizations

Launching AIOps requires understanding common barriers, creating a business case, selecting an AIOps stack, developing a rollout plan, and engaging employees.

AIOps Powered by ServiceNow

ServiceNow’s IT Operations Management (ITOM) integrates AIOps capabilities, transforming ITOps into intelligent, proactive processes, establishing automation, breaking down data silos, and more.

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For more information and for best practices related to ServiceNow AIOps, reach out to speak with an Infocenter expert today!