ValueMomentum Logo
AI & Emerging Technologies
View Only

Incident Summarization

This PoC demonstrates the summarization of IT incidents exported from from ServiceNow using a combination of traditional ML and Generative AI.

Target Users

  • IT support
  • IT managers
  • IT analysts

Industry Verticals

  • Enterprise IT

Business Benefits

  • Enhanced operational efficiency
  • Reduced processing time
  • Improved accuracy and consistency
  • Better resource allocation

Operational Impact

  • Reduced time spent on incident management
  • Improved incident resolution
  • Increased customer satisfaction

Customer Impact

  • Faster resolution of incidents
  • Improved customer support

Technical Highlights

Traditional ML for feature extraction and clustering
Generative AI for classification and summarization
Pattern recognition and anomaly detection

Incident Summarization

V1
V2

Upload a CSV Document (Recommended size: less than 50k rows)

Drag and drop your file here, or click to select

CSV files only

Business Context

Insurance companies deal with a high volume of IT incidents across multiple systems that support critical business functions like Claims, Policy Management, and Billing. Currently, incident patterns and trends are analyzed manually, which is time-consuming and may miss important insights. This leads to:

  • Delayed identification of systemic issues
  • Inefficient resource allocation
  • Missed opportunities for proactive problem management
  • Extended Mean Time to Resolution (MTTR)

Problem Statement

The IT Operations team needs an automated way to analyze incident patterns and identify trends across critical insurance applications. Without automated analysis, recurring issues go unnoticed, leading to increased downtime and operational costs. The team specifically needs to:

  • Identify common incident patterns
  • Group similar incidents across applications
  • Generate actionable insights for problem management
  • Highlight opportunities for automation and self-service

Impact and Importance

Successfully implementing automated incident analysis will deliver:

  • Cost Reduction:
    • 20-30% reduction in MTTR through faster pattern recognition
    • Decreased operational costs from automated analysis vs manual review
  • Operational Efficiency:
    • Early detection of emerging issues before they become critical
    • More effective resource allocation based on incident patterns
    • Improved problem management through data-driven insights
  • Service Quality:
    • Reduced system downtime through proactive issue resolution
    • Enhanced user experience through faster incident resolution
    • Better alignment of IT operations with business priorities

This PoC demonstrates how AI-powered analysis can transform raw incident data into actionable insights, enabling a more proactive approach to IT service management.

Developer Setup

To set up and run this PoC locally, follow these steps:

  1. Ensure you have Python 3.9+ and Flask installed on your system.
  2. Clone the repository containing the PoC code. Navigate to the folder and install the dependencies:
    pip install -r requirements.txt
  3. Ensure the Flask service is running on https://www.valuemomentum.studio:5000 by executing:
    flask run
  4. Upload documents via the PoC front end for summarization.

How to Use This PoC

Follow these steps to use the PoC:

  1. Click the "Choose File" button and select a document (.csv).
  2. Use toggle to switch versions if needed.
  3. Click "Upload and Summarize" to send the file to the Flask service.
  4. Wait for the response, which will return a summary of the document.
  5. The summarized text will be displayed in Markdown format on the page.