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AI use case · Generative AI

Automated Incident Summarisation & Runbook Generation

During incidents, operations teams spend time manually correlating signals, writing incident summaries and searching for relevant runbooks — time that should be spent on resolution.

Generative AISummarisationRAG
MediumBusiness priority
Generative AIDomain
5Main data sources

The problem

During incidents, operations teams spend time manually correlating signals, writing incident summaries and searching for relevant runbooks — time that should be spent on resolution.

The AI approach

Generative AI synthesises incident signals (alerts, logs, metrics, previous similar incidents) into a concise natural-language summary. Automatically identifies the most relevant runbook and pre-fills it with observed values for the current incident context.

How it works

  1. 1
    Gather

    Collect alerts, logs, tickets and chat from the incident.

  2. 2
    Summarise

    Write a clear timeline and current status.

  3. 3
    Suggest

    Propose the matching runbook steps.

  4. 4
    Learn

    Draft a post-incident review and new runbooks.

Data it uses

Monitoring PlatformAlert HistoryIncident DatabaseRunbook RepositoryLog Aggregation

How to measure value

Practical tips

Standards & references

Data governanceUse governed, consented data only
Responsible AIExplainable, monitored, human-in-the-loop

Related pages

This page describes generic industry practice and public standards. It is not based on, and does not describe, any particular vendor's product or operator's systems.