--- title: Explore Predictive Intelligence description: ServiceNow Predictive Intelligence is a platform function that provides a layer of artificial intelligence that empowers features and capabilities across ServiceNow applications to provide better work experiences. locale: en-US canonical_url: https://www.servicenow.com/docs/r/australia/intelligent-experiences/predictive-intelligence/predictive-intelligence.html release: australia product: Predictive Intelligence classification: predictive-intelligence topic_type: concept last_updated: "2026-03-12" reading_time_minutes: 3 breadcrumb: [Predictive Intelligence, Enable AI experiences] --- # Explore Predictive Intelligence ServiceNow® Predictive Intelligence is a platform function that provides a layer of artificial intelligence that empowers features and capabilities across ServiceNow® applications to provide better work experiences. ## Overview of Predictive Intelligence Predictive Intelligence is a powerful set of tools applying artificial intelligence and machine learning to make predictions. You can create and train models in three different frameworks: classification, clustering, and similarity. A trained solution can be invoked by any ServiceNow application through an API.\[Omitted image "predictive-intelligence-resolves-faster.png"\] Alt text: The benefits of using Predictive Intelligence. To learn more about ways to use existing models, see [Using Predictive Intelligence](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/intelligent-experiences/predictive-intelligence/using-predictive-intelligence.md). ## Predictive Intelligence for on-premise customers Predictive Intelligence is also available for on-premise customers. If you're interested in deploying this product on-premise, contact your account manager. For on-premise installation and configuration instructions, see the complete instructions for [Machine Learning Engine installation and configuration for self-hosted customers \[KB0782052\]](https://support.servicenow.com/kb?id=kb_article_view&sysparm_article=KB0782052) in the Now Support Self-Hosted Knowledge Base. **Note:** Only on-premise accounts can access the Now Support Self-Hosted Knowledge Base. ## Terminology - **Artificial intelligence** Systems designed to do work that needs a level of human intelligence to accomplish. - **Machine learning** Ability for models to improve over time with more experience. - **Models** Collections of algorithms, math, and statistics that make predictions and decisions based on input-output data. - **Training** Adding or changing data that the model is based on to affect future predictions. - **Supervised Training** Providing input-out pairs so that the model can generate rules that connect the two. - **Unsupervised Training** Providing raw data so that the model can identify structures in the data set. - **Training frequency** How often models are retrained to incorporate new data into an existing model. - **Word corpus** Vocabulary that a model can use to look for textual similarity. ## Predictive model components A predictive model includes these components, some of which you must provide. - **Solution definition** A data record you create and configure that specifies these values for training a predictive model. - The records used to train the model. For example, only train on incidents that are resolved or closed within the last six months. - The input fields that the model uses to make predictions. For example, use the incident short description to make a prediction. - The output field whose value the model predicts. For example, set the incident category based on the short description. - The frequency to retrain the model. For example, retrain the model every 30 days. - **Solution** The solution is the result of a solution definition that you've trained in a ServiceNow datacenter. Predictive Intelligence uses the solution to predict a target field value given one or more input field values. All solutions specify these values. - The solution precision is the aggregate percentage of correct predictions. For example, a precision of 50 means that out of 100 predictions, half of them should have the correct value. - The solution coverage is the aggregate percentage of records that receive a prediction. For example, a coverage of 50 means half of all eligible records actually receive a prediction. - The solution classes are the output field values for which the model can make predictions. Each class is an output field value with a list of possible precision, coverage, and distribution metrics to choose from. For example, the Incident Categorization solution has a class for each category such as software, inquiry, and database. - The class distribution is the percentage of records from the entire table that have this particular output field value. For example, a distribution of 50 for the inquiry class means that half of incidents have the inquiry category. - **[Predictive Intelligence frameworks](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/intelligent-experiences/predictive-intelligence/predictive-intelligence-frameworks.md)** Predictive Intelligence provides three different model frameworks in the Australia release: classification, similarity, and clustering. Each framework specializes in different types of predictions. - **[ServiceNow apps and features that use Predictive Intelligence](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/intelligent-experiences/predictive-intelligence/servicenow-apps-features-use-predictive-intelligence.md)** Learn about ServiceNow applications and features that leverage Predictive Intelligence. Solutions that you can adapt are available for various business units and industries. **Parent Topic:**[Predictive Intelligence](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/intelligent-experiences/predictive-intelligence/predictive-intelligence-landing.md)