How to Create a Consistent Brand Identity With AI
Consistency used to be enforced by scarcity: few people could make assets, so few people could break the rules. Generation removes that constraint, so consistency now has to be designed deliberately.
Here is the five-part system we install at the end of an identity project.
1. Design tokens, not descriptions
Write colour, spacing, radius and type as named values that can be pasted into a design tool, a codebase or a prompt. 'Warm charcoal' is not a token. A specific colour value with a documented contrast pairing is.
2. A locked reference set
Choose a small set of approved images that define lighting, palette, composition and styling. Every generated batch is judged against these, not against the previous prompt attempt. When the reference set changes, that is a versioned decision, not a Tuesday afternoon.
3. A versioned prompt library
Store prompts per asset type with the reference images attached, plus a list of known failure modes to reject. Version them like code. When output quality shifts because a model updated, you want to know which prompt version produced last month's approved work.
4. Templates for everything recurring
Any format you produce more than twice a month should be a template with locked type styles and image slots. This is what allows a non-designer to publish without the result looking like it came from somewhere else.
5. A batch review with a named owner
One person reviews each batch against the reference set before it ships, checking brand fit and factual accuracy. Fifteen minutes here prevents the slow drift that nobody notices until the catalogue is inconsistent.
A simple weekly rhythm
Generate on Monday, review Tuesday, publish Wednesday, log the rejects. The reject log is the most valuable artefact of the whole system — it is how the prompt library gets better instead of just longer.