Einstein features are AI-driven; testing differs.
Pre-conditions:
- Einstein enabled in org.
- Sufficient data for AI to learn (typically 1000+ records).
- Time for model training.
Test scenarios:
1. Configuration.
- Einstein feature enabled correctly.
- Permission to access feature.
- Right users see right insights.
2. Functionality.
- Score / insight populates on records.
- Updated periodically.
- Visible in expected UI components.
3. Data quality.
- Score range plausible.
- Insights make sense (sanity check).
- Doesn't update for trivial changes.
4. Performance.
- Score computation doesn't slow page load.
- Bulk operations not affected.
Limits to expect:
- Non-deterministic — same data may produce slightly different scores.
- Lag — model updates periodically, not real-time.
- Trust Layer intervenes for sensitive data.
Common pitfalls:
- Testing too early — before model trained.
- Insufficient data — model can't produce meaningful scores.
- Testing as deterministic — failures from variation.
Senior insight: Einstein testing requires patience and statistical thinking. Not 100% deterministic.