If an AI generates content that does not follow the brand's visual guidelines, that content must be corrected or rejected before publication, exactly like the work of an external freelancer. That is the role of human approval: catching the deviation in colour, typography, framing or tone before it reaches the company's social media, then reporting the error to improve the next pieces of content.
What happens if AI generates off brand content?
Off brand content is not published: it is either regenerated with an instruction describing the deviation, or edited directly before approval. In a well organised workflow, the error stays internal and has no consequence for the brand; it only becomes a problem when the tool publishes without review.
Why can AI produce off brand content, even when well calibrated?
Generative AI produces statistically variable results: even calibrated on a brand's DNA, it can suggest a nearby colour, an unusual framing or an off tone phrasing, especially for new kinds of requests. The phenomenon is measured: according to GetApp's GenAI for Social Content survey, conducted in May 2024 among 1,680 professionals including 135 in France, 47% of respondents struggle to align AI generated content with their brand's voice and values, 36% mention absurd answers and 30% factual errors.
The table below summarises the most frequent deviations and the appropriate correction.
| Type of deviation | Example | Correction |
|---|---|---|
| Colour | Shade close to but different from the palette | Direct edit or regeneration with the colour code |
| Typography | Font not included in the guidelines | Direct edit in the editor |
| Framing and staging | Product off centre, set outside the brand universe | Regeneration with a composition instruction |
| Copy tone | Caption too casual or too salesy | Rewrite and add examples of approved tone |
| Fact or mention | Wrong price, opening hours or mandatory mention | Systematic manual correction |
How do you stop off brand content from going live?
The only reliable guarantee is systematic human approval before any publication, whatever the level of trust in the tool. Here is the method I recommend to an SME:
- Write the guidelines in a form AI can apply: colour codes, fonts, framing rules, examples of approved and rejected content.
- Appoint one approver per piece of content, with a set response time.
- Check facts first (prices, dates, mandatory mentions), then form.
- Schedule approved content only.
- Record every deviation detected to enrich the brand guidelines.
What should you do in practice when a deviation is detected?
Two options exist: request a new generation describing the deviation precisely ("use only the palette blue", "centre the product"), or correct the element directly before approval. In both cases, recording the error and adding it to the guideline examples reduces its recurrence. Guidelines containing ten examples of approved content and five examples of rejected content guide an AI tool far better than an abstract description of the brand's style. These tools are becoming widespread: according to the France Num 2026 Barometer, published on 28 September 2026, 40% of French micro, small and medium businesses use an AI solution, and 34% use it to generate text, voice or images.
Who should approve content in a small team?
The approver of a piece of content must be someone who knows both the brand and the sensitive information: prices, current offers, mandatory mentions. In an SME, it is often the manager or the person in charge of marketing; in a shop, the store manager. What matters is having a single approver per piece of content, with a set response time (for example 24 hours), to avoid back and forth between several people that slows publication down.
When several people need to be involved, for example a partner for commercial offers, it is better to set up a simple flow in the tool: a first review of form, then a final approval. A configurable workflow lets you adapt this flow to each type of content without multiplying messages.
How does Isalis limit this risk upstream?
Isalis, the AI assistant we are building at Sundatalab to help SMEs produce more quality content without increasing their budget, relies on the brand DNA imported by the company (logo, colours, fonts, tone, reference brands) as its generation framework, and submits every piece of content to a configurable approval workflow: nothing is published without approval. What I often see is that deviations drop significantly once the guidelines include concrete examples of accepted and rejected content. The process is detailed here: https://www.isalis.ai/en/plateforme.
Read also: FAQ: How long does it take to set up an AI content validation workflow in an SME?; Glossary: what is an AI brand charter (or brand system prompt), and why should a company have one in writing?.
In short, 3 questions
Is off brand AI content a failure of the tool? No, it is an expected case, which human approval catches before publication.
Should you edit or regenerate? Edit for a detail (colour, font), regenerate for a composition or tone problem.
How do you reduce deviations over time? By enriching the guidelines with examples of approved and rejected content.
And you, which kind of deviation from your guidelines do you encounter most often with AI tools?
Sources
- GetApp, generative AI and social media, GenAI for Social Content survey (May 2024): https://www.getapp.fr/blog/6520/ia-generative-reseaux-sociaux
- French Ministry of the Economy, France Num 2026 Barometer, summary of 28/09/2026: https://www.economie.gouv.fr/node/3242215