Evidence-led AI marketing content
Turn an AI-assisted draft into a useful, reviewable asset by tracing factual claims to evidence, separating fact from interpretation, and assigning a human approval owner.
When to use it
Use for articles, landing pages, product explainers, comparison content, and campaign copy containing factual or performance claims. For regulated claims, route to the qualified reviewer for that market.
What you need
- Audience, market, channel, and the decision the content should support
- Draft and a list of every factual, comparative, quantified, or outcome claim
- First-party records and current primary sources with dates and scope
Workflow
- 01
Define the promise
State the audience problem and the narrow promise the asset can honestly support.
- 02
Extract claims
Mark each factual statement; split sentences that mix a fact, interpretation, and recommendation.
- 03
Trace evidence
Record source, date, geography, population, method, and what it actually supports. Prefer first-party records and primary sources.
- 04
Test the wording
Check that the draft does not broaden the evidence, imply causation from correlation, omit material limits, or invent proof.
- 05
Localize and review
Adapt language to the market; send regulated or high-risk claims to the appropriate qualified reviewer.
- 06
Approve and maintain
Name the human approver, keep the evidence log, and set a review date for claims that can change.
Output
A claim-to-evidence register, a publishable draft with qualified wording, unresolved questions, and a named reviewer.
Boundaries
A language model is not a source of truth or a legal reviewer. Do not publish a claim because several generated answers repeat it.
How it fits existing work
Distinct from the MENA Advertising Pre-Flight Review: this framework verifies editorial claims and evidence chains; use the playbook for campaign-specific review questions.