Setting up NBA is a Setup workflow: enable the feature, create Recommendation records, build a Strategy in Strategy Builder, drop the Lightning component on a record page, and iterate based on accept/reject feedback.
- Enable Einstein Next Best Action
Setup, Einstein Next Best Action, Enable. The feature requires a Salesforce license that includes NBA (most enterprise editions).
- Create Recommendation records
App Launcher, Recommendations, New. For each possible action, create a Recommendation with Name, Description, Image, and Action (Flow, URL, or record creation). Build 5 to 10 to start.
- Open Strategy Builder
Setup, Strategy Builder. Click New Strategy. The visual canvas opens. Pick the context object (Case, Account, Opportunity).
- Build the strategy logic
Add Load nodes to pull eligible Recommendations. Add Filter nodes to narrow by criteria. Add Sort and Limit nodes to rank and cap. Optionally add Predict nodes to call Einstein Discovery.
- Activate the strategy
Click Activate on the strategy. The strategy becomes available for the Lightning component.
- Add the Einstein NBA component to a Lightning page
Lightning App Builder, edit the record page, drag Einstein Next Best Action from the Standard components. Configure the Strategy reference. Save.
- Train users and monitor accept/reject feedback
Brief the user team on what the recommendations mean. Monitor the AcceptedRecommendation and RejectedRecommendation reports to tune the strategy over time.
The individual action suggestion record with Name, Description, Image, and Action (Flow, URL, record creation).
The branching logic that selects eligible Recommendations for a context. Built in Strategy Builder.
The visual canvas where strategies are designed with Load, Filter, Branch, Sort, Limit nodes.
Optional Predict node that calls an Einstein Discovery model for AI-driven ranking.
The user-facing widget rendered on record pages through Lightning App Builder.
User action telemetry that feeds back into strategy suppression and Einstein Discovery model retraining.
- NBA requires specific licensing. Check the Salesforce edition before scoping the project. Not every edition includes the feature.
- Strategies activated in production cannot be edited; you have to clone and create a new version. Plan the workflow to avoid mid-quarter changes.
- Einstein Discovery predictions require a trained model on the right historical data. Without data, the predictions are not useful and a rules-only strategy is more honest.
- The Einstein NBA component refreshes on record changes. Strategies that perform expensive operations (multiple Apex calls, large SOQL queries) slow record page load.
- The accept/reject feedback loop only improves quality if recommendations are actually relevant to the user. Bad initial recommendations train the model on noise.