Predictive Intelligence frameworks

  • Release version: Australia
  • Updated July 16, 2026
  • 2 minutes to read
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    Summary of Predictive Intelligence frameworks

    Predictive Intelligence in the Australia release offers three specialized machine-learning model frameworks—classification, similarity, and clustering—to enhance automation and decision-making in ServiceNow workflows. These frameworks enable customers to automate categorization, recommend solutions, and identify patterns within their records, improving efficiency and accuracy in service management.

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    Classification Framework

    This framework uses machine learning to automatically set categorical field values during record creation, such as assigning incident categories based on short descriptions. It helps automate work categorization and routing, reducing task resolution times, minimizing required interactions, and lowering error rates in categorization and assignment. This enables handling high volumes of incoming requests more cost-effectively.

    Similarity Framework

    The similarity framework identifies records with similar content to a new record, assisting agents by recommending resolutions based on comparable past incidents. It recognizes synonyms and contextually similar phrases, adapting to industry-specific terminology. This helps speed up resolution by leveraging proven solutions from past similar cases.

    Clustering Framework

    Clustering groups data into meaningful clusters to identify patterns or gaps. For example, grouping similar incidents can help detect major outages. This framework supports collective handling of related records and enhances insight into data trends.

    Additional Information

    The regression framework, which predicted numeric outputs, is deprecated as of the Washington DC release; existing regression models can still be trained and edited, but no new regression models can be created.

    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

    Note:
    Support for creating new regression solutions was removed in the Washington DC release. You can train and edit any existing solutions, but you can't initiate new ones. The following 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.