How long should an SDR spend on AI personalization QA per day?

30 to 60 minutes per rep per day, blocking review time for the day's AI batch with focus on Tier 1 manual edit and Tier 2 review-queue clearance. The compounded reply-rate protection is worth more than the time cost.

What is the single most important QA check?

The signal match check, because it catches both hallucinated facts and signals applied to the wrong account. Without it, the model can produce confidently wrong openers that pass every other check.

When does an opener cross into creepy?

When it references information beyond the prospect's public professional surface (company, role, public posts, hiring announcements). Anything that signals personal-life investigation crosses the line and collapses reply rate.

Can QA be fully automated?

Tier 3 spot-check QA can be automated with rules-based filters. Tier 1 and Tier 2 still need human review because tone, persona match, and creepiness require judgment that automation does not yet handle reliably.

What are the 9 points on the QA checklist?

Identity check, signal match, specificity, source spot-check (Tier 1), tone, creepy line, spam-trigger scan, length, and cross-touch consistency.

AI Personalization QA: A Checklist to Catch Bad Assumptions

AI personalization QA is a pre-send checklist on every batch of AI-generated openers, scanning for bad data, hallucinated facts, missed signals, off-brand tone, and creepy framing. Personalization based on wrong data is worse than generic.

9-Point Checklist

Identity check, signal match check, specificity check, source spot-check (Tier 1), tone check, creepy line check, spam-trigger scan, length check, cross-touch check.

Tier-Based Routing

Tier 1 (10-25/week): manual + AI-assisted + full edit, all 9 checks. Tier 2 (50-100/week): AI-drafted + review queue, 6 checks. Tier 3 (200+/week): automated + 10% spot-check, 4 checks.

Operating Cadence

Daily: 30-60 min SDR QA. Weekly: 30-min team calibration. Monthly: data audit. Per-quarter: rubric refresh.

Common Failure Modes

Wrong job title (stale CRM), hallucinated milestone (AI fluent fallback), vague generic opener (thin signal), off-brand tone, personal-life reference (over-zealous scraping), spam-trigger phrasing, length drift, cross-touch contradiction.