EmailQAGuides

Email Copy QA Checklist: 12 Signals That Mean 'Don’t Send' When Using AI

UUnknown

2026-02-08

10 min read

12 operational signals to stop bad AI-generated emails—fast checks for factuality, tone, brand safety, spam triggers, and deliverability.

Stop the Slop: 12 Signals That Mean 'Don’t Send' When Using AI for Email Copy

Hook: Your team is saving hours with AI—until one send tanks deliverability, confuses customers, or triggers a PR headache. In 2026, with inbox AI (like Gmail’s Gemini 3 features) summarizing and flagging messages for billions of users, a single “AI slop” email can cost conversions, trust, and inbox placement. This checklist gives you 12 fast, operational signals to catch before you send—plus immediate fixes and automation tactics to scale review without slowing down the team.

The short version (inverted pyramid)

If any one of these 12 signals is true for a draft, pause the send. Run a focused remediation action (these are included). Implement automated pre-send tests to trap common failure modes so human reviewers can focus on judgment calls, not hunting for typos.

Why this matters in 2026

Late 2025 and early 2026 brought two trends that change how email QA must work: (1) inbox AI features—Google’s Gemini 3-powered Overviews and smart labels—now surface semantics and perceived AI style to recipients, and (2) deliverability models increasingly penalize generic, repetitive, or factually incorrect content. Merriam-Webster’s 2025 Word of the Year was “slop,” and marketers are feeling the reputational impact.

Practical result: AI-leaning language can reduce engagement and increase spam-folder placement. Your team needs a compact, repeatable QA ritual to stop bad output at scale.

How to use this checklist

Run automated checks first (linguistic linting, spam-filter heuristics, fact-lookup calls).
Only escalate to human reviewers for high-severity signals (brand safety, legal claims, privacy risk).
Keep a short log of “why paused” to train prompts and reduce repeated errors.

12 Signals That Mean 'Don’t Send' (and what to do immediately)

1. Vague or unverifiable claims

Why it matters: Factual errors and vague superlatives ("best", "number one") damage credibility and can trigger legal or compliance review.

How to detect: Look for numeric claims, comparative adjectives, dates, or “as seen on” style lines. Use an automated entity extractor + fact-check API to flag items without a source.

Immediate fix: Replace with verifiable language (e.g., "X customers", "according to Y report") or remove the claim.

Automation tip: Run a lookup against your internal knowledge base or public datasets; fail sends if a claim can't be validated within 30 seconds.
2. Misstated pricing, features, or policy

Why it matters: Incorrect pricing or policy text causes customer service overhead and refunds.

How to detect: Compare email copy to canonical product and pricing data via API. Flag mismatches or obsolete terminology (example: "legacy plan" when you moved to tiered plans).

Immediate fix: Pull canonical product and pricing data from the product repo and insert as a single-sourced paragraph.

Automation tip: Integrate content generation with a “source of truth microservice” so templates always insert live data.
3. Tone-of-voice drift

Why it matters: Inconsistent tone confuses recipients and dilutes brand equity. Inbox AI also surfaces how “salesy” or “robotic” a message sounds.

How to detect: Use a tone classifier trained on your approved templates (friendly, professional, concise). Flag drafts that score outside tolerated bands.

Immediate fix: Re-run the message through a prompt tuned to your brand voice and compare before/after token-level diffs.

Automation tip: Add a lint rule that rejects copy with >20% passive structures, or that uses words in a “do not use” list.
4. Overuse of generic AI phrasing (“as an AI”, “I can help”, clichés)

Why it matters: Generic phrasing screams machine-generation and lowers engagement. Industry data in 2025–26 shows audiences react poorly to AI-sounding text.

How to detect: Simple regex and phrase lists detect common AI giveaways. Classifiers can catch “template-itis.”

Immediate fix: Personalize the message with specific customer context, remove boilerplate, or rewrite the opening line.

Automation tip: Maintain a dynamic banned-phrases list that updates based on deliverability analytics.
5. Pronoun confusion and reference errors

Why it matters: Incorrect pronouns or ambiguous antecedents cause misunderstanding and reduce trust.

How to detect: Run a lightweight NLP coreference check. Flag ambiguous references ("they/it/we") where the antecedent isn't the subscriber or brand.

Immediate fix: Make references explicit ("Your account", "Acme Pro plan").

Automation tip: Insert an automated rewrite that replaces values with specific tokens ({{customer_name}}, {{plan_name}}) and ensure the template engine fills them correctly.
6. Privacy or data exposure risk

Why it matters: Accidentally including PII, internal notes, or credentials can be catastrophic.

How to detect: Run a PII detector on the draft. Block if national ID numbers, payment data, or internal ticket links are found.

Immediate fix: Remove sensitive text and double-check the personalization tokens used.

Automation tip: Add a rule that fails any send with raw tokens that look like internal IDs (e.g., "SR-12345") unless masked.
7. Poor subject line: spammy language or mismatched to content

Why it matters: Subject lines determine open rates and are heavily weighted by spam classifiers.

How to detect: Run a spam-trigger scanner (excessive punctuation, all caps, money-words like "free" or "guaranteed"). Check subject-to-body semantic alignment using an embedding similarity test.

Immediate fix: Replace spammy tokens and tighten subject-body match. Use benefit-focused, specific subject lines.

Automation tip: Reject subjects with more than one exclamation or >40% uppercase. Auto-suggest 3 alternatives that score higher on deliverability heuristics.
8. Overly long or dense body copy

Why it matters: Long, unscannable emails have lower read-through and higher unsubscribe rates—especially on mobile.

How to detect: Compute reading time and paragraph length. Flag copy with >200 words before the first clear CTA.

Immediate fix: Move the CTA up, break into bullets, or use a condensed summary plus link to details.

Automation tip: Enforce a “lead + bullets + CTA” template for promotional sends and refuse drafts that deviate by structural score.
9. Conflicting calls-to-action (multiple primary CTAs)

Why it matters: Multiple primaries reduce conversion; inbox AI may summarize the wrong action to recipients.

How to detect: Scan for multiple CTA patterns (buttons, links with imperative verbs). Flag if >1 CTA of equal prominence exists.

Immediate fix: Pick a single primary CTA and secondary them ("learn more" vs "start trial").

Automation tip: Template-driven CTA slots ensure only one primary can be set per campaign type.
10. Brand-safety or legal red flags

Why it matters: Language that could trigger complaints, political/medical claims, or defamation must be handled by legal.

How to detect: Use a content-safety model to flag topics in regulated categories (health, finance, legal, politics) and escalate to compliance reviewers.

Immediate fix: Replace with neutral language or add required legal disclaimers verified by counsel.

Automation tip: Insert mandatory review gates for any email tagged with a regulated topic and route it through a manual legal gate.
11. Links to low-quality or private resources

Why it matters: Broken links, redirects to login-only pages, or URLs that leak internal paths harm conversions and brand trust.

How to detect: Automated link checker visits each URL and evaluates HTTP status, response time, and whether the page requires authentication.

Immediate fix: Swap in canonical public links or attach instructions for authenticated flows.

Automation tip: Fail sends if any CTA link returns 4xx/5xx or contains a localhost/internal IP pattern.
12. Low personalization or incorrect segmentation

Why it matters: Irrelevant messages increase unsubscribes. AI can produce perfectly written copy that’s wrong for the audience.

How to detect: Check segment tags (behavioral, product usage, lifecycle) against the draft. Flag if copy references features or promotions not relevant to the segment.

Immediate fix: Use conditional blocks in templates or swap to the appropriate audience-specific variant.

Automation tip: Require a segment-to-template binding in the campaign payload; reject sends when bindings mismatch. Tie segmentation checks back to your personalization playbook and canonical feeds.

Quick remediation playbook (what reviewers should do next)

Mark the highest-severity signal(s) in the draft metadata.
Run the specific remediation: fact-check claims, swap in canonical content, or escalate to legal.
Record the root cause (prompt issue, stale template, wrong data feed) in a QA log.
Rerun automated tests. Don’t send unless all checks pass.

Automation architecture: lightweight pipeline you can build in a week

Design goal: trap four categories automatically—linguistic lint, factuality, deliverability heuristics, and safety—so humans focus on judgment calls.

Input: Campaign draft + meta (segment, send time, canonical data pointers).
Step 1: Linting layer — grammar, pronoun resolution, tone classifier, banned phrases.
Step 2: Fact and canonical-data verifier — run claims through knowledge endpoints and internal APIs.
Step 3: Deliverability heuristics — subject spam-scan, link checker, CTA structure.
Step 4: Safety scoring — PII detector, regulated-topic classifier, brand-safety model.
Output: Pass / Soft-fail (editable fixes auto-suggested) / Hard-fail (requires human sign-off).

Implementation note: You don’t need a single monolith. Orchestrate checks with a lightweight workflow engine and webhooks from your ESP. Many teams run this as a pre-send step that must return a green status for the campaign manager to proceed.

Real-world example

Case: A mid-size SaaS marketing team adopted this checklist in Q4 2025 after a major promotional send caused a spike in spam complaints. After instituting automated subject and link checks plus a legal gate for claims, they reported a 35% reduction in post-send support tickets and restored open rates within two months.

"We saved time and avoided customer confusion—AI helped produce drafts, but this checklist stopped the worst outputs from ever seeing a mailbox." — Head of Growth, anonymized

Advanced strategies and future-proofing (2026+)

Continuous learning loop: Log why each send was paused. Retrain prompts and the banned-phrases list monthly.
Human-in-the-loop for high-risk sends: Require a 2-person review for legal or brand-safety flags.
Analytics-driven thresholds: Tie QA thresholds to KPI impact (e.g., allow a bit more promotional language for re-engagement lists where past data shows tolerance).
Embed explainability: When an inbox AI (like Gemini-powered summaries) surfaces a bad summary, analyze which sentence triggered it and iterate copy.

Common objections — and short answers

"This will slow us down." Automation handles the low-hanging checks in seconds; humans only review escalations.
"AI can fix itself." Models aren’t connected to your canonical data or legal playbooks. Use AI for drafting, not final approval.
"We don’t have engineering bandwidth." Start with simple tools: regex checks, off-the-shelf PII scanners, and a manual legal gate—you can add automation incrementally.

Checklist summary (one-page cheat sheet)

Factuality: Are claims verifiable? (Yes/No)
Pricing/Policy: Matches canonical data? (Yes/No)
Tone: Within approved band? (Yes/No)
AI Phrases: Any banned phrases? (Yes/No)
Pronouns: Clear references? (Yes/No)
PII: Any sensitive data? (Yes/No)
Subject: Spam tokens? (Yes/No)
Length: CTA within first 200 words? (Yes/No)
CTAs: Single primary? (Yes/No)
Brand/legal: Regulated content flagged? (Yes/No)
Links: All healthy and public? (Yes/No)
Segmentation: Template matches audience? (Yes/No)

Final notes: Make this part of your sprint

Turn the checklist into a lightweight story in your next sprint. Start with the top 4 signals that historically caused the most problems for your team. Monitor inbox AI features and deliverability metrics—what changed in late 2025 and early 2026 should make you skeptical of “one-size-fits-all” AI output.

Call to action: Don’t let AI speed become a liability. Download the editable QA template, integrate one automated check this week (subject spam-scan recommended), and schedule your first 2-person legal/brand review for high-risk sends. If you want a hands-on audit of your email QA pipeline, contact our team for a focused 90-minute audit and implementation plan.

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Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.