--- sourceDocument: Australia Enable AI sourceDocumentLink: https://www.servicenow.com/docs/r/australia/intelligent-experiences Release : - australia ft:locale : - en-US ft:publication_title : - Australia Enable AI ft:clusterId : - platai bundleId : - platai workflow : - Platform --- # Explore Predictive Intelligence # Explore Predictive Intelligence {#ariaid-title1} Release version: Australia Updated March 12, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI 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 Explore Predictive Intelligence ServiceNow® Predictive Intelligence integrates artificial intelligence and machine learning into ServiceNow applications, enhancing work experiences by enabling predictive capabilities. Customers can create and train models using three frameworks: classification, clustering, and similarity. These trained models are accessible through an API across ServiceNow applications. Show full answer Show less ## Key Features * **On-Premise Availability:** Predictive Intelligence can be deployed on-premise. Interested customers should contact their account manager for details. * **Model Training:** Customers can engage in supervised or unsupervised training to improve model predictions by providing appropriate data sets. * **Predictive Model Components:** Includes solution definitions that specify input and output fields, training frequency, and precision metrics. * **Frameworks:** Offers three frameworks for modeling: classification for categorization, similarity for finding related items, and clustering for grouping similar records. ## Key Outcomes Implementing Predictive Intelligence enables ServiceNow customers to enhance their operational efficiency by automating decision-making processes through accurate predictions. Solutions offer measurable precision and coverage, ensuring that a significant percentage of records receive predictions, thereby improving service delivery and responsiveness across various business units and industries. 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. To learn more about ways to use existing models, see [Using Predictive Intelligence](https://www.servicenow.com/docs/X1bdbqo~7_kJBjpdPYxHMg "Train and use Predictive Intelligence solutions to accomplish various tasks."). ## Predictive Intelligence for on-premise customers {#predictive-intelligence__section_l3s_gm3_lcc} 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 {#predictive-intelligence__section_hmr_1y4_n3b} 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. {#predictive-intelligence__ul_jnl_jm1_22b} 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__ul_wct_5m1_22b} * **[Predictive Intelligence frameworks](https://www.servicenow.com/docs/A9XSxf61_A~rvFacteTGSA)** 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://www.servicenow.com/docs/UuhQaJ1oAUp8RmO8LkhITg)** Learn about ServiceNow applications and features that leverage Predictive Intelligence. Solutions that you can adapt are available for various business units and industries.