AI Marketing Copy Workflow
Generate copy from a factual brief, then evaluate it against a specific message and audience.
Introduction
AI can turn out copy variants fast. Without a tight brief, those variants fall back on stock marketing phrasing and make claims the product cannot back up.
Use the model to draft and revise. Keep positioning, sign-off on facts, and experiment design with the people who know the product and the audience.
Understanding the Workflow
A copy brief sets out the product truth, the audience, the message, the voice, and what you want the reader to do.
Definition
Copy Brief
A written set of facts and constraints that defines the audience, product claim, message, voice, and desired reader action.
Product truth is what the copy is allowed to claim: behavior you have seen, results you have measured, use cases you support, and limits you have agreed to state.
Definition
Product Truth
The verified product behavior and evidence that marketing copy may state or imply.
Good copy and copy that works are two different things. A clear sentence can still answer the wrong worry, or leave the reader doubting the claim. To know it works you need a stated outcome and real reader behavior to measure.
Applying It in Practice
Prepare a brief with:
- one audience and the problem they recognize
- one supported product claim
- evidence for that claim
- the page's place in the reader journey
- the action the reader should take
- voice examples and banned language
- legal or compliance constraints
Ask for variants that test different ideas about the message. Five rewrites of one claim teach you less than two drafts built around different reader worries.
Evaluate each draft against the brief:
| Check | Question |
|---|---|
| Truth | Does every explicit and implied claim have evidence? |
| Audience | Does the copy address a concern this reader has? |
| Message | Is the main point clear after one reading? |
| Voice | Does it match approved examples without copying them? |
| Action | Does the next step fit the page and reader stage? |
Revise with concrete constraints. "Keep the measured deployment result, remove the unsupported cost claim, and shorten the heading to eight words" is testable feedback.
On a page that already works, leave the structure alone unless the structure is what you are testing. Change the message, the layout, and the call to action at once and you will not know which one moved the number.
Test variants through an agreed experiment or a user-research method. No model can read prose and tell you how it will convert.
Engineering Considerations
Do not ask the model to invent positioning from a feature list. Positioning needs customer evidence, a read on competitors, and a decision about who you are serving.
Check implied claims as carefully as explicit ones. Words such as "automatic," "secure," and "instant" create expectations even when the copy gives no number.
Keep customer data and unreleased product details out of prompts. Use approved tools, and clean up research material before you hand it to a model.
A person has to sign off on pricing, legal claims, regulated products, testimonials, and any comparison. Match the depth of that review to the risk, as in human-in-the-loop development.
Scaling and Operations
Keep briefs in version control like any other source. Update them when the product, the evidence, the positioning, or the voice moves. Do not reuse a campaign brief for a different audience or a different ask.
Limit generation to the team's evaluation and testing capacity. Unreviewed variants create inventory, not learning. Review capacity caps generated copy the same way it caps generated code.
For each experiment, record the guess you tested, the approved copy, the audience, the dates, and what happened. Feed those results into later briefs, but do not turn one win into a house rule.
Keep a register of the numbers and comparisons you publish. Tie each claim to its evidence, its owner, and the date it was last checked.
Next Steps
- AI Documentation Writing Workflow: produce factual technical content from source material
- AI Feature Development Workflow: implement the product behavior behind the copy
- Human-in-the-Loop Review Workflow: require human approval for generated content
- What is Human-in-the-Loop Development?: match oversight to risk