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    Case study-Optimizing Incident Management-1920-520

    Optimizing Incident Management: 30% Faster Response, 25% Reduced Handling Time

    Highlights

    Quality and consistency

    AI-driven text and image-based classification improved categorization quality and consistency.

    Real-time data

    Real-time category recommendations automated routing, reducing manual overhead.

    Customer Satisfaction

    Enhanced customer satisfaction (CSAT) with faster, more accurate incident handling.

    Overview

    An organization in the government IT services sector faced significant challenges with inefficient incident categorization for citizen inquiries and IT Service Desk submissions. Manual errors and delays were causing slow routing and resolution times. We implemented an AI-driven categorization model on Azure to help the organization automate classification, improve accuracy, and accelerate service delivery across its Oracle Service Cloud (OSvC) platform.

    Client

    A major North American municipal IT services provider

    Geography

    CA, USA

    Industry

    IT Services / Government

    Offering

    Azure Data Factory, Azure Blob Storage, Azure Machine Learning, Azure App Service

    Tags: AI and Analytics

    The Challenges

    • Inefficient and incorrect incident categorization led to frequent manual interventions.

    • Delayed routing and longer resolution times impacted service levels.

    • Inconsistent categorization reduced first contact resolution (FCR).

    • Manual processing increased operational costs and lowered customer satisfaction.
    Challenges-image 1 (1)