QA for AI marketing content means checking every asset against six things before it ships: facts, numbers traceable to a source, brand voice, claims and legal, localisation and accessibility. Treat each post, ad or email like a release candidate: run the same checklist every time, record what failed, and give a named person the final sign-off, because the tool never carries the responsibility.
Key takeaways
- AI drafts fail in predictable places: invented details, numbers with no source, drifting voice, risky claims, literal translation and missing alt text.
- Every number in a published asset should trace to a post, study, report or ID that a reviewer can open in under a minute.
- Advertising rules apply however an ad is made, and the UK's ASA says marketers stay responsible for their ads.
- Localisation is a quality check, not a translation step: CSA Research found 76% of online shoppers prefer to buy with product information in their own language.
- Automate the mechanical checks and keep a person on meaning, claims and final sign-off.
Why does AI-generated content need its own QA?
Because AI copy fails differently from human copy: it reads fluently while being wrong. A review that asks "does this read well?" passes exactly the errors that matter.
SOMIN's own data shows teams feeling this. In our audience-research workspace, a 30-day whitespace study of 220 posts (September 2026) found its largest pain-point theme, 91 posts, centred on the AI marketing maze. Its summary includes AI-generated content that needs substantial human revision and reliance on AI tools without adequate oversight, which leads to misallocated budgets.
A concept-whitespace study covering January to April 2026 (166 posts) carries a related theme of 64 posts about inaccurate consumer insights, including unverified sources in AI models. And in the creative-content workspace, a theme drawn from 57 posts about marketing teams in the Philippines describes the pressure to deliver quality content at scale without losing authenticity. Speed is not the problem; unchecked speed is.
What should a QA checklist for AI marketing content cover?
Eight checks, each with a clear pass standard and an owner. The first six are the core; disclosure and links catch the rest.
| Check | What to verify | Passes when | Owner |
|---|---|---|---|
| Facts | Product details, names, dates, prices, availability | Each one matches an approved source, and nothing appears that the brief did not contain | Writer |
| Numbers traceable to source | Every statistic, percentage and count | Each carries a source link or ID with a date; no "studies show" without the study | Writer and reviewer |
| Brand voice | Tone, banned words, house spelling, sentence length | Reads like approved examples; no filler or a competitor's line | Brand lead |
| Claims and legal | Superlatives, comparisons, health or finance claims, endorsements, AI imagery | Every claim is substantiated, no endorsement is implied, and images do not overstate results | Legal or compliance |
| Localisation | Meaning, idiom, currency, dates, cultural references, local rules | A native reviewer in the market approves it against local audience data | Market lead |
| Accessibility | Alt text, captions, colour contrast, link text, hashtag casing | Every image has a text alternative serving the same purpose; video is captioned | Producer |
| Disclosure | Whether AI use would change how the audience reads the asset | AI use is disclosed wherever the audience could otherwise be misled | Brand lead |
| Links and tracking | URLs, UTM tags, landing pages | Every link resolves to the right page with the right campaign tag | Producer |
How do you check facts and numbers in AI copy?
Make traceability a rule: no number ships unless someone can open its source in under a minute. Then look specifically for anything the model added that was not in the brief.
- Diff against the brief. List every name, number and claim in the draft and tick each one off against the brief or a source. Anything new is a defect until proven otherwise.
- Ask for sources in the output. Have the model attach a source link or ID to each figure, and reject drafts that cannot.
- Check rounding and dates. "Nearly half" from a 2020 survey is not this year's number.
Grounding helps but does not remove the check. The grounding model behind SOMIN's Social Media Manager scored 0.83 nDCG@10 for retrieval accuracy in a peer-reviewed evaluation, against 0.75 and 0.77 for ChatGPT and Gemini on the same test. In the free Marketing Mondays plugin, every number in an output traces back to a SOMIN post, study or ID. That makes the reviewer's job faster, not optional.
What do advertising rules say about AI-generated content?
The rules do not change because a model wrote the ad. The advertiser carries the same responsibility for claims, endorsements and harm.
The UK's Advertising Standards Authority put it plainly in May 2025: existing rules apply "regardless of how content is generated, edited, or targeted". It adds that disclosure alone is very unlikely to fix a fundamentally misleading message, and that an AI image of a cosmetic effect that does not reflect real-world results is likely to be materially misleading. A follow-up piece of ASA advice for marketers using AI warns against implied endorsements that do not exist and ends with a line every QA lead should pin up: "marketers are ultimately responsible for their ads."
Codes differ by market, so check the local rules wherever an asset runs. For web content, Google's helpful-content guidance asks a similar question from the reader's side: whether the use of automation, including AI generation, is self-evident to visitors through disclosures or in other ways.
How do you QA localisation and accessibility?
For localisation, a native reviewer in each market checks meaning against local audience data, not just words. For accessibility, check text alternatives, captions and contrast before anything publishes.
The stakes for localisation are commercial. CSA Research surveyed 8,709 consumers in 29 countries and found that 76% of online shoppers prefer to buy products with information in their native language, and 40% will never buy from websites in other languages.
Localisation also means fitting the audience, not only the language. In the Keeta HK case study, AI-generated ad lines in English and Cantonese were checked against GWI data for Hong Kong food orderers. That audience under-indexed on budget-driven behaviour, so lines such as "kind to your wallet" were rewritten around curation and quality. A literal translation would have passed a language check and failed the audience.
For accessibility, the W3C's WCAG success criterion 1.1.1 requires that non-text content has a text alternative serving the equivalent purpose. Image generators do not write that alternative for you, and an auto-caption describing "a person smiling" rarely serves the purpose of a product image.
Which checks can you automate, and which need a person?
Automate the mechanical checks; keep a person on meaning. A second model that reviews the first shares many of its blind spots, so it can flag problems but should not approve.
- Automate: link and UTM validation, banned-word and spelling-variant scans, character limits, alt-text presence, and a check that every figure carries a source tag.
- Keep human: facts in context, claims and legal review, cultural fit, disclosure decisions and the final sign-off.
- Log every failure. Tag each defect by checklist row. After a month, the log tells you which prompt, data source or template to fix, the way a regression suite points to the failing module.
SOMIN's AI agents follow the same split: they can run autonomously or pause at a sign-off checkpoint you control. Better inputs also mean fewer defects, which is why we brief models with the persona, tension, angle and story framework.
FAQ
What is QA for AI marketing content?
It is a repeatable review that checks AI-generated posts, ads and emails for factual accuracy, traceable numbers, brand voice, legal risk, localisation and accessibility before they are published, with a named person signing off.
Do I have to disclose AI-generated content in ads?
In the UK there is no blanket legal requirement, but the ASA says you should disclose AI use where the audience would otherwise be misled. Disclosure does not fix an ad that is misleading in itself.
How do I check statistics written by AI?
Require a source link or ID for every figure, open each source, and confirm the number, the date and the population it describes. Delete any figure you cannot trace.
Can AI check its own marketing content?
It can run mechanical checks and flag likely problems, but a model reviewing a model shares many of the same blind spots. Keep a person on claims, cultural fit and the final approval.
Put this checklist into your workflow tool this week as eight required fields, and run it on the next ten AI-assisted assets your team ships. Log every failure by row, then fix the most common cause at its source: the brief, the data or the prompt.
