Monitoring and Analytics for Task Intelligence for ITSM

  • Release version: Australia
  • Updated March 12, 2026
  • 2 minutes to read
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    Summary of Monitoring and Analytics for Task Intelligence for ITSM

    The Task Intelligence Analytics dashboard provides insights into the performance of trained incident prediction models, allowing you to monitor model performance, track business value, and analyze agent usage of predictions. Access the dashboard via All > Task Intelligence for ITSM > Monitoring.

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    Key Features

    • Performance Overview: Visuals represent model performance, including the number of predictions made and the average time to resolve incidents (MTTR).
    • Predictions Tracking: Widgets display the number of accepted, replaced, and skipped predictions to help identify areas for retraining the model.
    • Usage Analysis: A bar chart illustrates individual field prediction usage over time, allowing for a detailed comparison of accepted, replaced, and skipped predictions.
    • Percentage View: Switch to a percentage display to evaluate model performance against baseline data, providing deeper insights into prediction accuracy.

    Key Outcomes

    By utilizing the analytics dashboard, ServiceNow customers can effectively monitor and enhance the performance of their incident prediction models. This enables more accurate case management, reduces incident resolution times, and improves overall service delivery. If trends indicate a decline in prediction acceptance or an increase in replacements, customers can take proactive steps to retrain their models for better outcomes.

    You can view the impacts of your trained incident prediction models. Monitor model performance overtime, track business value, and view what predictions your agents did and didn't use.

    To access the Task Intelligence Analytics dashboard, navigate to All > Task Intelligence for ITSM > Monitoring.

    Use the Model drop-down list to select a model. Select Apply to open the model's configuration.

    The Analytics dashboard contains the following sections:
    • Get an overview
    • See how your trained model is doing

    Get an overview

    The Analytics dashboard uses visuals to represent the performance overview of the model.
    Number of predictions
    The line graph shows the number of fields that the Incident categorization and Similar Incidents models predicted over time. As the model continues to learn, it can increase the number of predictions.
    Incident Mean time to resolve (MTTR)
    The line graph shows the average amount of time that it takes to resolve incidents over time. As the model makes more predictions, the MTTR must decrease as the predictions help agents.

    See how your trained model is doing

    The Analytics dashboard uses visuals to track how the model used predictions over time.
    Predictions agents accepted
    The widget shows the correct predictions that your agents used during case management over time. If this number is trending downward, you can look to retrain your model. For more information on editing a trained model, see Edit an incident prediction model in Task Intelligence for ITSM.
    Predictions agents replaced
    The widget shows the incorrect predictions that your agents removed during case management over time. If this number is trending upward, you can look to retrain your model. For more information on editing a trained model, see Edit an incident prediction model in Task Intelligence for ITSM.
    Predictions the model skipped
    The widget shows the number of predictions that were skipped by the model based on the model, output field, and date range selection. For more information on editing a trained model, see Edit an incident prediction model in Task Intelligence for ITSM.
    Performance overview
    The performance overview table shows the mean percentage values for correct, incorrect, and skipped data for each combination of model and output field.
    Track usage of individual field predictions over time
    The bar chart tracks the usage of the individual field predictions over time. Each bar in the chart shows three components, which are the predictions accepted, the predictions replaced, and the predictions that were skipped by the model. A red outline around each bar represents the total number of records for each day. To compare specific components, navigate to the legends and deselect the ones that you don't want to include so that you can have a more customized and focused comparison based on user preferences. By default, the view displays the number of predictions. However, you have the option to switch to the percentage view by toggling the Show Percentage option. In the percentage view, you can also access the information about the baseline that was replaced and accepted, which is derived from the training data. This option enables you to gain insights into the performance of the model with the baseline.