Record categorization

  • Release version: Zurich
  • Updated July 31, 2025
  • 4 minutes to read
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    This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.

    Summary of Record Categorization

    The Record Categorization feature in Task Intelligence for Customer Service leverages machine learning models to analyze text and attachments in cases, interactions, and emails. It predicts and automatically populates relevant fields to categorize records efficiently. This capability supports multiple languages and multiple input channels such as email, web, and chat, enabling consistent and automated case classification and routing.

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    Key Features

    • Predicted and Recommended Field Values: Fields with AI-predicted values are marked with an AI icon and labels. Agents receive top three recommended values in dropdown lists, improving accuracy and ease of selection.
    • Multi-Channel and Multi-Language Support: The feature supports English, French, German, Spanish natively, with additional languages available on demand to accommodate global operations.
    • Attachment-Based Categorization: Machine learning models analyze email and record attachments alongside text fields to enhance prediction accuracy.
    • Auto-Routing: Categorization results can automatically route cases to appropriate service desks, reducing manual triage, inbox management, and reliance on RPA bots.
    • AI Prediction Banner: A banner alerts agents when predictions are available or require review, enhancing visibility and facilitating timely validation.
    • Prediction Feedback and Monitoring: Feedback on prediction accuracy is stored in the Predictor Result table, accessible to admins for monitoring and continuous improvement.
    • Configurable Filtering: Inactive field values can be excluded from predictions via a system property to maintain relevance.

    Practical Benefits for ServiceNow Customers

    • Streamlines case and interaction categorization to reduce manual effort and increase accuracy.
    • Enables faster and more precise auto-routing to specialized service desks, improving customer response times.
    • Supports multilingual customer bases with a single ML model trained on multiple languages.
    • Incorporates rich data from attachments, enhancing categorization quality.
    • Provides visibility and control through AI icons, banners, and prediction feedback, enabling agents and administrators to trust and optimize the process.

    The record categorization feature included with Task Intelligence for Customer Service uses machine learning models to evaluate text, predict field values, and automatically populate fields on case and interaction records.

    Record categorization supports multiple languages and can scan attachments in addition to evaluating text from emails and records. Use this feature to categorize cases, case types, and interactions from multiple channels including email, web, and chat.

    You can use the results of the categorization to automatically route records to the right service desk, which avoid the need for multiple email inboxes and RPA bots. Auto- routing also frees up your employees to work on other tasks.

    Figure 1. Case form with predicted and recommended field values
    Case form view highlighting the predicted values for the Account and Category sections.

    Predicted field values

    On case and interaction records, the fields that contain predicted values are identified with an AI icon and label (generative AI sparkle icon and label). These fields also include an information icon that displays a message with additional context about the predicted values.
    Note:
    in CSM Configurable Workspace, the AI icon is persistent over the life-cycle of the record. In Core UI, the AI icon is removed when the agent changes the field value.

    In CSM Configurable Workspace and Core UI, the fields on the record that contain predicted values are identified with the Predicted or Recommended messages.

    Recommended field values

    Fields that are predicted by AI include recommended values. When an agent selects a predicted field, the system displays a drop-down list that includes the top three recommendations at the top of the list followed by all other values. This feature is available for the following types of fields:
    • Choice lists
    • Single lookup
    • Multi lookup
    • Single and multi text fields

    If the top three recommendations aren’t available, the system displays a message in the Top Recommendations section of the dropdown list that no predictions are available. The other values follow this message.

    The agent can select any value from the dropdown list, either one of the top three recommended values or one of the other values. The agent can also type in the field and filter the results in the dropdown list, except the top three recommendations which are always displayed.
    Note:
    The top three recommendations remain in the dropdown list through the case life cycle regardless of the selected field value.

    Filtering inactive field values from predictions

    Enable the sn_csm_ml_task.case.categorization.enable_inactive_filter to remove inactive field values from predictions. The default setting for this property is false.

    AI prediction banner

    When a case record includes a categorization prediction, the system displays a banner at the top of the record. The banner is displayed when there is atleast one field with auto-fill predictions in categorization models. The banner is displayed for records in the Case table and extensions of the Case table.
    Note:
    The banner is displayed in CSM Configurable Workspace and in the Core UI.
    The system displays the banner when there is at least one field with an auto-fill prediction in categorization models or when one of the extracted fields hasn’t been reviewed by the agent. The banner is displayed for records in the Case table.
    Note:
    If the agent changes the predicted value, the banner is removed. If all predicted fields are recommended, the banner isn’t displayed.

    The banner can be enabled or disabled by the sn_csm_ml_task.ui.banner.enabled system property.

    Prediction feedback

    The system stores feedback on prediction results in the Predictor Result [ml_predictor_results] table. ml_admin role can access the table and view the results.
    • Autofill: A value is considered to be predicted correctly (set to true) if the predicted value and the final value are the same.
    • Recommendation: A value is considered to be predicted correctly if any one of the predicted values matches the final value.

    The Predictor Result table also stores information about skipped and failed predictions. For more information about this table, see Components installed with Task Intelligence for Customer Service.

    Multi-lingual record categorization

    Use a machine learning model that understands the following languages to evaluate text in emails and records created in different languages and to predict and automatically populate fields on cases, case types, and interactions.
    • English
    • French
    • German
    • Spanish
    Multi-lingual record categorization uses one machine learning model that is trained to understand multiple languages. This model can:
    • Understand the text in emails and records.
    • Evaluate the text and predict field values.
    • Add the predicted values to fields on cases, case types, and interactions.
    Multi-lingual record categorization can support the following additional languages on demand:
    • Arabic
    • Chinese (PRC)
    • Chinese (Taiwan)
    • Dutch
    • Italian
    • Japanese
    • Korean
    • Polish
    • Portuguese
    • Russian
    • Thai
    • Turkish
    Note:
    If you must evaluate emails or cases in these additional languages, reach out to the product team for support.

    Attachment-based record categorization

    Attachments can include valuable signals that help support desks to categorize and route records automatically. To take advantage of attachment information, you can use a machine learning model to parse email and record text and attachments and automatically populate fields on cases, case types, and interactions based on signals contained in the text.

    The model can predict information about a case based on:
    • Text in the subject line and body of a customer email.
    • Text in the short description and description of a case or interaction.
    • Text in email and record attachments.
    Note:
    The model can be configured to use different fields for prediction.

    Attachment-based categorization uses all of this information to predict field values. As a result, you can automatically route records to the appropriate service desk based on these values.