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Beyond the AIOps hype: Part 2


In my first blog post in this series, I talked about the definition of AIOps and the five key processes for enabling AIOps in your organization: observe, contextualize, think, act, and learn. Many organizations are already working towards enabling these AIOps processes.

However, ensuring the success of these AIOps initiatives requires a pragmatic approach. A recent  Capgemini Research Institute report, The AI-powered enterprise: Unlocking the potential of AI at scale suggests that wide-scale deployment of AI is a challenge, with just 13% of organizations having scaled AI across multiple teams. One of the key findings is that organizations that deployed AI at scale are the ones that realized quantifiable benefits from their AI deployments.

Hence, it is especially important to pick the right AIOps use cases that not only provide the required scale but also deliver those benefits. So, what are the AIOps use cases and how do they help your organization’s automation journey?

A view of the AIOps use cases mapped to your AIOps maturity model is provided below. We recommend organizations look at this maturity model and enable the right AIOps use cases.

In my view the seven fundamental use cases for AIOps are:

  1. Intelligent alerting – Intelligent alerting helps find abnormal behavior or anomalies based on past data using machine learning.
    This reduces the number of false alerts and not only enables prioritization and proactive alerting through incidents but can also recommend actions for self-remediation.
  2. Intelligent co-relation – Finds connections between alerts from multiple systems using multiple criteria (e.g., event/alert times, frequency, topological proximity, contextual similarity, etc.) using machine learning.
    This enables automated correlation of alerts into actionable scenarios and recommends actions for self-remediation.
  3. Single visualization – Of the business service or application and allows IT operation teams to see a single source of truth in order to visualize the system as a whole and enable self-remediation workflows.
    This reduces the mean time to detect and resolve incidents.
  4. Causal analysis and self-remediation – Causal analysis allows the examination of all underlying components in order to identify root causes based on machine learning in which an automation-assigned confidence score drives self-remediation actions based on pre-defined self-heal solutions.
  5. Predict outages and failures – Early warnings/alerts and prediction of outages based on learnings from past data using machine learning allow us to take early actions to prevent them.
    Reduction in critical outages saves millions of dollars in lost revenue or productivity loss. This also improves SLA compliance and reduces the number of incidents.
  6. Capacity planning and resource management – Based on current use and forecasted trends, using AIOps enables proactive capacity upgrades, thus reducing outages and increasing overall resource utilization.
  7. Change and impact analysis – Modelling system behavior using AIOps allows us to do impact analysis by simulating conditions and deploy the changes.

Picking the right use cases as per your AIOps maturity model enables you to scale your AIOps investments and realize quantifiable benefits. As organizations move up the maturity model, end-to-end automation can be realized through operational, predictive, and prescriptive use cases, thus enabling organizations to realize value through their AIOps investments.

To find out more about how our AIOps maturity model and intelligent automation platform can provide a unique blend of technology agnostic, agile approach to create enterprise wide AIOps (AI enabled IT operations) at scale, visit the Capgemini Intelligent Automation Platform (CIAP) web page.

To read the first of this series of blogs, where the author explores the definition of AIOps and why is it important for your organization, click here.