THE SHORT ANSWER
Before sending, check at least eight items: recipient and account identity, whether referenced content is real and accessible, relevance to the campaign job, brand and product facts, deliverable and budget scope, links and dates, one-to-one tone, and any duplicate contact or stop request. AI produces a draft; batch sending needs human sampling, an error threshold and an immediate pause mechanism.
Verify identity and content references first
Name, pronouns, account, business email and agency relationship must come from traceable sources. Open the referenced content and verify title, theme, date and observation. Do not infer private background or undisclosed traits.
If the content is removed, irrelevant or belongs to a namesake account, stop that message instead of replacing it with generic praise.
Lock facts and mark negotiable scope
Product name, market, availability, specifications, disclosure requirements and prohibited promises should come from approved data. Mark negotiable format, budget range and timing instead of letting the model fill gaps.
Use one current fact sheet per campaign. When facts change, regenerate affected drafts rather than relying on memory to identify outdated messages.
Use small-batch QA before scaling
Start with 10–20 human-reviewed messages and record identity, citation, scope errors and reply quality. Expand only when there are no critical errors and the overall rate is below the team threshold.
Set thresholds by brand risk. Regulated products or high-value partnerships need greater human coverage than ordinary consumer work.
Route replies and stop signals back into the system
Bounce, decline, auto-reply, agency handoff, existing engagement and stop request need different statuses. Any reply should pause later automation and return the decision to an owner.
Keep the final sent version, reviewer, send time and outcome in QuickKOL. Automation should reduce repetitive organization without hiding who approved external communication.
Use batch gates to prevent one error from scaling
Start with full human review of 20 drafts. Classify errors as blocking, major and minor. Wrong identity, invented content citation, incorrect product claim or violation of a stop request is blocking and pauses the batch; wrong scope or date is major; tone issues are minor.
If 20 drafts contain zero blocking, two major and three minor issues, the overall issue rate is 25%, so the batch should not scale to 500. Correct data or prompting and resample until the team threshold is met. NIST AI RMF emphasizes continuous measurement and management; thresholds, samples and pause records turn that principle into a sending control.
AI outreach error levels
One blocking error pauses the affected batch.
| Level | Example | Action |
|---|---|---|
| Blocking | Identity, fabrication, stop request | Pause batch |
| Major | Scope, date, budget error | Correct and rereview |
| Minor | Tone or formatting | Log and improve |
| Pass | Facts and scope verified | Human approval |
CHECKLIST
Pre-send AI outreach QA
Use this concise reference list.
- Name, account, email and agency align
- Content reference opened and verified
- Rationale relates to campaign job
- Product facts use current approved version
- Deliverables, budget and timing clear
- Links, dates and time zone valid
- No duplicate contact or stop request
- Reviewer and batch recorded
Sources and further reading
External pages can change. Recheck platform rules, contracts and local law before launch.