Brand guidelines for AI are a topic many brands underestimate. Most brand books were written for people: for agencies, designers and teams who can read between the lines. A generative tool cannot do that. It takes literally what is written and fills every gap with the average of what it has learned.
In my current work I use analytics, consumer insight and AI-driven marketing every day. Earlier, I built up a creative team and set quality standards for it. Together, both experiences showed me that a rulebook that is enough for people is far from enough for machines.
- Brand guidelines for AI need concrete, testable rules instead of pleasant adjectives.
- Good examples and clear counter-examples teach more than long descriptions.
- Every output that customers see needs human approval.
- Brand rules for AI are a living document with one accountable person.
Table of contents
Why classic brand books are not enough
A typical brand book describes a brand with words like bold, warm or premium. For an experienced team these are useful hints, because it fills in the rest from experience. A model understands warm differently from the brand. The result then looks correct and is still interchangeable.
On top of that, many rules only exist implicitly. What a brand never shows, which words it avoids, which framings it rejects: long-standing colleagues know this, but it is written down nowhere. It is exactly this quiet knowledge that a tool lacks.
Another point is often overlooked: many brand books have grown over years and contradict themselves in places. People resolve such contradictions by asking or weighing things up. A tool picks a side at random. Before writing rules for machines, it is therefore worth checking your own brand book for contradictions and deciding them.
Brand guidelines for AI: what has to become concrete
The first step is translation. Adjectives become rules that can be checked: which sentence length, which form of address, which colour values, which light, which motifs are forbidden. The more concrete the rule, the better the result. What I have written about visual language as an attitude applies especially here, in the article on the visual language of a brand.
McKinsey describes how companies can adapt models with their own material, such as brand guidelines and past campaigns, to generate on-brand content, and recommends human review for anything that goes directly to customers (see here: McKinsey, How generative AI can boost consumer marketing). For me this means: brand guidelines for AI only become a good match when the rules are training material and checklist at the same time.
The same applies to language. A brand that says it speaks personally should decide whether it uses a formal or informal address, which technical terms are allowed and which phrases it would never use. Such decisions seem petty, but they decide whether a generated text sounds like the brand or like any company.
Examples are stronger than descriptions
The most effective addition to a rulebook is examples. Ten good images, ten good texts, and next to each an example that just misses, with a short explanation. This side-by-side comparison teaches people and tools alike where the line runs, and it is the core of good brand guidelines for AI.
When I built up a creative team, I worked exactly like this: not with long rules, but by looking at concrete work together. For machines this principle matters even more. I describe how to turn image examples into reliable production in the article on AI in image production.
Approval and accountability for brand guidelines for AI
No rulebook replaces judgement. That is why brand guidelines for AI always come with a clear approval step: whatever customers see is checked by a person who knows the brand. Tools speed up the work, but the responsibility stays with the team.
Just as important is one person who maintains the rulebook. Every output that had to be corrected points to a missing or unclear rule. Whoever collects these signals and works them in makes the guidelines better every month. The skills a team needs for this are described in the article on AI literacy in the team.
What I would do differently
I would think of the brand book in two versions from the start: one that inspires and one that checks. The first explains what the brand stands for. The second states precisely what is allowed and what is not. For a long time many brands only had the first. With generative tools the second becomes indispensable.
What remains open for me is how much room such a rulebook should leave. Rules that are too tight create uniformity, rules that are too loose create arbitrariness. I believe the answer lies in a clear middle, and it shifts as the tools get better.