When would you recommend Einstein AI features versus building a custom ML solution?

Use Einstein features when:
- The use case aligns with pre-built Einstein capabilities (Lead Scoring, Opportunity Scoring, Activity Capture, Einstein Bots, Recommendation).
- The business wants minimal implementation time and maintenance overhead.
- Data volume meets Einstein's training requirements (typically 1,000+ labelled records).
- Transparency of model decisions is acceptable (Einstein provides limited explainability).
- The org has the required license (Sales Cloud Einstein, Service Cloud Einstein, etc.).

Use custom ML when:
- The use case is highly specific to the business (custom churn prediction, proprietary pricing model).
- Full control over features, model architecture, and explainability is needed.
- Data resides in external systems where Salesforce cannot train effectively.
- The organisation has data science expertise.

Hybrid approach: Build models externally (AWS SageMaker, Vertex AI) and expose them via a REST API called from Salesforce. Einstein Model Builder (part of Data Cloud) allows BYOM (Bring Your Own Model) and exposes external models as first-class Einstein features.