AI promises better lead scoring, forecasts, and personalization. But even the smartest model fails when trained on bad data.
Why AI Projects Fail
- Training on noise: Wrong titles/industries skew lead scores.
- Event gaps: Missing activity logs → forecasting overconfidence.
- Identity drift: Duplicates and job changes → wrong recommendations at scale.
- Feedback loops missing: Models never improve without corrections.
It’s no surprise that 70–80% of AI projects fail, often due to poor data quality.
Salesforce AI Readiness Checklist
- Contacts ≥ 90% complete (email, phone, title, company, industry).
- Opportunities ≥ 98% complete (stage, amount, close date, forecast category).
- Key contacts verified ≤ 12 months.
- Duplicate rate <1%.
- Event logging coverage ≥ 85% for active opps.
Three Phases to Trustworthy AI
Phase 1 — Assess (Weeks 1–2): Profile completeness, freshness, dupes, event coverage.
Phase 2 — Remediate (Weeks 3–8): Enrich, merge, validate, standardize.
Phase 3 — Automate (Weeks 9–12): Continuous enrichment, anomaly alerts, feedback loops.
Example: Lead Scoring Without Tears
- Before: Wrong titles → AI down-ranks strong leads.
- After enrichment: Titles standardized → AI prioritizes true ICPs.
By Role: What To Do
- Sales/Marketing Leaders: Gate AI rollout on readiness score.
- RevOps: Own validation rules and feedback loops.
- Data Teams: Train on clean slices; retrain quarterly.
The Payoff
- Higher AI accuracy
- Faster adoption and trust
- ROI on AI initiatives
Salesforce-native enrichment (datatrip.ai) ensures identity and firmographics stay current, so AI acts on reality — not residue.
Call to Action
Score your AI readiness this week. If any domain <80%, fix data before launching models.
