Definition
Machine Learning (ML) is a branch of artificial intelligence in which algorithms learn patterns from data to make predictions, classifications, or decisions without being explicitly programmed for each scenario. In Salesforce, machine learning powers many Einstein features, including Einstein Lead Scoring (predicting which leads are most likely to convert), Einstein Opportunity Scoring (predicting deal close likelihood), Einstein Prediction Builder (custom predictions on any object), Einstein Discovery (automated pattern detection and recommendations), and Einstein Next Best Action (recommending optimal steps). Salesforce also supports custom ML models through Einstein Model Builder and external model integrations.
Real-World Example
At their company, an AI specialist at Nexus Innovations leverages Machine Learning to bring intelligent automation to a process that previously required significant manual effort. Machine Learning analyzes patterns in the data and surfaces insights that would take a human analyst hours to uncover, enabling the team to act proactively rather than reactively.
Why Machine Learning Matters
Machine Learning (ML) is a branch of artificial intelligence where algorithms learn patterns from data to make predictions, classifications, or decisions without being explicitly programmed for each scenario. In Salesforce, machine learning powers many Einstein features, including Einstein Lead Scoring (predicting which leads are most likely to convert), Einstein Opportunity Scoring (predicting deal close likelihood), Einstein Prediction Builder (custom predictions on any object), Einstein Discovery (automated pattern detection and recommendations), and Einstein Next Best Action (recommending optimal steps).
Salesforce also supports custom ML models through Einstein Model Builder and external model integrations, letting organizations bring their own trained models or use Salesforce's tools to build models on their data. The combination of pre-built ML features and custom model support means most Salesforce organizations can benefit from ML without needing data science expertise on their team. The most impactful uses of ML are typically in scoring (predicting outcomes) and recommendation (suggesting next steps), where the model's predictions drive concrete actions.
How Organizations Use Machine Learning
- •Cobalt Ventures — Uses Einstein Lead Scoring to prioritize which leads reps work first, with the ML model learning from historical conversions.
- •NovaScale — Built custom predictions with Einstein Prediction Builder for churn risk, feeding the predictions into proactive retention workflows.
- •TrueNorth Software — Combines multiple ML features (Lead Scoring, Opportunity Scoring, Deal Insights) for a complete AI-driven sales view.