EinsteinAISettings in SFDX & Metadata API

Architectural Overview#

The EinsteinAISettings metadata type controls foundational org-level preferences for Salesforce Einstein AI and Machine Learning services (Setup > Einstein > Einstein AI / Einstein Prediction Builder).

Before the advent of generative Large Language Models, Salesforce pioneered predictive machine learning through native algorithms for classification, regression, propensity scoring, and automated insight discovery. Predictive AI features—including Einstein Prediction Builder, Einstein Opportunity Scoring, Einstein Lead Scoring, and Case Classification—rely on platform-level infrastructure to ingest historical CRM data, train machine learning models, and write predictive scores directly onto standard and custom records.

EinsteinAISettings serves as the global switch that turns on this machine learning framework. Without activating EinsteinAISettings, attempts to deploy custom prediction configurations, AI-driven field definitions, or scoring automations fail during metadata validation.

Directory and File Suffix Reference#

In SFDX source format, EinsteinAISettings is stored as a singleton XML file under the settings directory.

Key Metadata Fields#

The EinsteinAISettings metadata type extends Metadata and exposes the following platform switches:

Declarative XML, Scratch Org, and Package.xml Examples#

1. Settings Metadata File (EinsteinAI.settings-meta.xml)#

<?xml version="1.0" encoding="UTF-8"?>
<EinsteinAISettings xmlns="http://soap.sforce.com/2006/04/metadata">
    <enableDataCollection>true</enableDataCollection>
    <enableEinsteinAI>true</enableEinsteinAI>
    <enablePredictionBuilder>true</enablePredictionBuilder>
</EinsteinAISettings>

2. Scratch Org Definition (config/project-scratch-def.json)#

To configure automated scratch orgs with Einstein AI capabilities for CI/CD test runs:

{
  "orgName": "Einstein Predictive AI Test Org",
  "edition": "Enterprise",
  "features": ["SalesUser", "ServiceUser"],
  "settings": {
    "einsteinAISettings": {
      "enableEinsteinAI": true,
      "enablePredictionBuilder": true,
      "enableDataCollection": true
    }
  }
}

3. Package Manifest (manifest/package.xml)#

<?xml version="1.0" encoding="UTF-8"?>
<Package xmlns="http://soap.sforce.com/2006/04/metadata">
    <types>
        <members>EinsteinAI</members>
        <name>Settings</name>
    </types>
    <version>68.0</version>
</Package>

Retrieval and Deployment Commands#

Manage EinsteinAISettings using the modern Salesforce CLI (sf):

# Retrieve EinsteinAI settings from the target org
sf project retrieve start --metadata Settings:EinsteinAI

# Deploy settings to staging sandbox
sf project deploy start --metadata Settings:EinsteinAI --target-org staging-sandbox

# Check deployment result
sf project deploy report

Senior Architect Deployment Best Practices and Gotchas#

• Separation from Generative AI: Architects must distinguish between predictive AI (EinsteinAISettings) and generative AI (EinsteinGptSettings). Predictive AI models operate on structured tabular data to calculate probabilities, while Generative AI models operate on unstructured prompts to generate natural language. Both settings are often required in comprehensive AI implementations.
• Model Training and Minimum Data Thresholds: Deploying EinsteinAISettings enables the platform feature, but individual prediction models will not begin training until target objects satisfy minimum record thresholds (typically 400 historical records with both positive and negative outcomes). Automated data seeding scripts should be run in scratch orgs to support model validation.
• Data Governance and Global Model Training: Setting enableDataCollection: true allows Salesforce to process de-identified data for platform model enhancements. Regulated clients in defense, finance, or public healthcare may require this flag to remain false to comply with strict jurisdictional data sovereignty mandates.
• Post-Deployment Verification: When deploying EinsteinAI.settings-meta.xml across sandboxes, verify in Setup that Einstein status changes to Active before executing automated smoke tests that depend on prediction field values.

Frequently asked questions

What is the purpose of EinsteinAISettings in Salesforce?

EinsteinAISettings controls foundational org-level toggles for Salesforce Einstein machine learning, including Prediction Builder and automated data scoring.

How is EinsteinAISettings deployed using SFDX?

It is deployed under the Settings metadata type using member EinsteinAI, which corresponds to the force-app/main/default/settings/EinsteinAI.settings-meta.xml file.

Why is EinsteinAISettings important for CI/CD scratch orgs?

Enabling EinsteinAISettings in scratch org definitions ensures machine learning test suites and prediction field scoring logic run without missing-license exceptions.

Does EinsteinAISettings enable Generative AI or Predictive AI?

EinsteinAISettings primarily governs Predictive AI and Prediction Builder, whereas Generative AI LLM features are governed by EinsteinGptSettings.