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# Explore Predictive Intelligence

# Explore Predictive Intelligence {#ariaid-title1}

* Versão de lançamento: Australia
* 
* Atualizado 12 de mar. de 2026
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 min. de leitura

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://servicenow-prod.fluidtopics.net/bgO5JleyQZH5OcvCMIB8hA "Train and use Predictive Intelligence solutions to accomplish various tasks and that integrate with other ServiceNow products, such as Document Intelligence and Task Intelligence.").

## 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.  
Nota:  
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://servicenow-prod.fluidtopics.net/zYkbg3dysjN5uMnq1Lq8bA)**   
  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://servicenow-prod.fluidtopics.net/P90YfPqa1eN85we231VNdQ)**   
  Learn about ServiceNow applications and features that leverage Predictive Intelligence. Solutions that you can adapt are available for various business units and industries.