---
sourceDocument: Zurich Enable AI
sourceDocumentLink: https://www.servicenow.com/docs/r/zurich/intelligent-experiences

 Release :

    - zurich

ft:locale :

    - en-US

ft:publication_title :

    - Zurich Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Using Predictive Intelligence

# Using Predictive Intelligence {#ariaid-title1}

* Release version: Zurich
* 
* Updated July 23, 2026
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read

Train and use Predictive Intelligence solutions to accomplish various tasks.

## Overview of Predictive Intelligence

Predictive Intelligence is the interface by which you can train models on the ServiceNow AI Platform. These models enable you to predict, estimate, and identify patterns that can be used to route work, populate form fields, and more.

* Show suggestions for relevant articles.
* Assign, categorize, and prioritize tasks.
* Detect major incidents.
* Recommend case resolutions.
* Prevent duplicate articles and ideas.
* Detect phishing attempts.

For more information about the different types of solutions available, see [Explore Predictive Intelligence](https://www.servicenow.com/docs/IEyfyAgKArOq4i6mSS8sgg "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.").

## Training your ML solutions {#using-predictive-intelligence__section_xtt_hdh_j1b}

Predictive Intelligence enables you to train predictive models and machine-learning solutions that you can apply using data on your instances. The solutions you create use the frameworks to predict, recommend, and organize
data. To get started, see [Creating and training solutions](https://www.servicenow.com/docs/Hx4BVS0S0B8brnWvixyHzQ "Use one of the Predictive Intelligence (PI) frameworks to create and train machine-learning solutions. Each framework delivers a different solution type for training the system to predict, recommend, and organize data outcomes.").

You can also extend Predictive Intelligence to other processes and applications. For more information, see [ServiceNow apps and features that use Predictive Intelligence](https://www.servicenow.com/docs/JfX8I_NAmAgr0LBxGHY~LA "Learn about ServiceNow applications and features that leverage Predictive Intelligence. Solutions that you can adapt are available for various business units and industries.").

## Testing and monitoring predictions {#using-predictive-intelligence__section_fvc_q32_ntb}

After creating and training your solutions, call the Predictive Intelligence API to make a solution prediction. Use the results to gauge the performance of the solution and iterate as needed.  
You can track the coverage and precision of deployed predictive models using the Solution Statistics dashboard, which provides reporting on these prediction areas by default.{#using-predictive-intelligence__table_gc5_4wj_ntb__entry__2}

| Report | Description |
|-|-|
| Average Prediction Coverage (last 30 days) | The percentage of predictions that yielded an outcome out of the total number of predictions attempted. Select the coverage score to see a breakdown by class. |
| Daily Prediction Coverage | The percentage of records created on a given day in which the solution was able to predict an outcome. |
| Average Prediction Precision (last 30 days) | The percentage of predictions in which the predicted value was the same as the final value of the field when the record closed. Select the precision score to see a breakdown by class. |
| Daily Prediction Precision | The percentage of records closed on a given day in which the predicted field value was the same as the final value. |
[ ]

{#using-predictive-intelligence__table_gc5_4wj_ntb}

For more information, see [Testing and monitoring predictions](https://www.servicenow.com/docs/jFFxh7IrikURoIontWw7RQ "Evaluate the coverage and precision of your machine-learning (ML) solutions by testing them. Once deployed, track their performance over time. Improve predictions by using performance information to refine your solutions.").

## Preparing your instance

Preparation is the key to getting the most out of Predictive Intelligence. You don't need to write code or do calculations, but deciding what you hope to do with the solution definitions will
make implementation easier.

* Identify the problems that you want to solve with Predictive Intelligence.
* Have 30,000--300,000 high-quality records from which Predictive Intelligence can learn.
* Set your expectations.

Note:  
Inconsistencies or gaps in training data can cause incorrect or unreliable predictions.

## Implementation Process

Predictive Intelligence takes approximately 14 days to implement on a production instance.

* Day 1: Clone production instance over to a non-production instance.
* Days 2--10: Create a solution definition, train it on historical records, and validate that the solution works as desired on the non-production instance.
* Days 11--13: Create import and update sets to move the solution to production, train and validate on the new instance, and set the retraining frequency.
* Day 14 and on: Monitor the solution.

In general, non-production environments are where workflows can be tested and formatted before being moved over to the production instance to further train models and test predictions.

For more information about getting started with Predictive Intelligence, see [our guide on how to get started with Predictive Intelligence](https://www.servicenow.com/docs/GKBS6a31gTfLBUHCHHzqRA#install-predictive-intelligence "Activate Predictive Intelligence on your instance and get started with basic configuration.").