AI-based scoring inconsistency is one of the most common complaints. Users report getting random results even when specifying clear yes/no/uncertain criteria. Here's how to get reliable results:
1. Use structured scoring, not open-ended AI prompts.
Instead of asking an AI column "does this company fit my ICP?", build a weighted scoring formula using concrete data points:
- +10 if target industry
- +5 if hiring sales roles (signal they're investing in growth)
- +20 if valid email found
- +15 if headcount in range
- +10 if target tech stack
Community best practices recommend combining firmographics, technographics, intent signals, and enrichment quality into a weighted composite score.
2. Use native enrichments before AI columns.
Start with existing enrichment providers before turning to AI - native enrichments are typically faster, more reliable, and more cost-effective than custom AI research. Use AI for judgment calls only after structured data is populated.
3. Validate against closed-won data.
The most reliable scoring models aren't built on described criteria - they're built on actual deal outcomes. Export your closed-won and closed-lost deals, identify which attributes differentiate winners from losers, then encode those patterns into your scoring weights. This grounds your model in reality rather than assumptions.
4. Test at small scale first.
AI accuracy degrades significantly in large batches (400+ companies) compared to small batches (1-10). Run 50 leads through your scoring model, manually review every result, adjust weights, then scale.