Predictive Intelligence frameworks

  • Release version: Australia
  • Updated March 12, 2026
  • 2 minutes to read
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    Summary of Predictive Intelligence frameworks

    The Predictive Intelligence frameworks in the Australia release provide three specialized models: classification, similarity, and clustering. Each framework is designed to enhance the prediction capabilities of ServiceNow, enabling better categorization, resolution recommendations, and data pattern identification for improved operational efficiency.

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    Key Features

    • Classification Framework: Utilizes machine-learning algorithms to automatically set categorical field values during record creation, such as categorizing incidents based on their short descriptions. This automation reduces task resolution times and error rates in categorization and assignment.
    • Similarity Framework: Identifies existing records similar to new ones, facilitating faster resolution recommendations by leveraging past incident data. This framework understands context and synonyms, enhancing its applicability across different industries.
    • Clustering Framework: Groups data to identify patterns and gaps, allowing for collective addressing of similar records, such as identifying major outages from new incidents.

    Key Outcomes

    By implementing these frameworks, ServiceNow customers can automate and optimize their workflow processes, reduce operational costs, and improve task resolution efficiency. Note that the regression framework was deprecated in the Washington DC release, preventing the creation of new regression solutions.

    Predictive Intelligence provides three different model frameworks in the Australia release: classification, similarity, and clustering. Each framework specializes in different types of predictions.

    Predictive Intelligence classification framework

    The Predictive Intelligence classification framework enables you to use machine-learning algorithms to set categorical field values during record creation. For example, you can use the model to set the incident category based on the short description. You can train predictive models so they act as an agent to categorize and route work automatically based on your past record-handling experience.

    Enable Predictive Intelligence to handle volumes of incoming requests at lower costs. Automate the categorization and assignment of requests to reduce:
    • Task resolution times.
    • The number of interactions required to resolve tasks.
    • The error rates of categorizing and assigning work.

    For more information, see Create and train a classification solution.

    Predictive Intelligence similarity framework

    The Predictive Intelligence similarity framework identifies existing records that have similar values to a new record. For example, you can train a subset of your incident records to recommend a resolution based on the information of a similar incident record. By borrowing from similar closed incidents that have a proven resolution, you can help agents and fulfillers quickly provide the best resolution for an incoming incident.

    The similarity framework doesn't need an exact match of keywords for its text comparisons because its algorithms identify similar words and synonyms based on similar contexts. For example, the phrases printer not working and printer broken are both recognized as similar. The framework also collects, learns, and applies your industry-specific context. For example, the phrase unable to join network has a different context in a computer networking company than it does in a healthcare insurance company.

    The similarity framework uses a workflow similarity solution. For more information, see Create and train a similarity solution.

    Predictive Intelligence clustering framework

    Clustering divides data into groups that can then be used to identify patterns. You can then address records collectively or find gaps in existing data. For example, you can group similar new incidents to identify a major outage.

    The clustering framework uses a workflow clustering solution. For more information, see Create and train a clustering solution.

    Deprecated in the Washington DC release: Predictive Intelligence regression framework

    Important:
    Support for creating new regression solutions was removed in the Washington DC release. You can train and edit existing solutions, but you can't create new ones. This information is provided for legacy context.
    Regression is a machine-learning framework that uses historic data to predict numeric outputs, such as a temperature or a stock price.

    For more information, see Create and train a regression solution.