How can AI help with brand consistency audits? Practice examples from well-known brands

The point isn’t that AI will replace strategists, analysts, brand managers, designers, or consumer research. Rather, it’s that AI can very quickly take the first operational step. It can analyze hundreds of brand touchpoints simultaneously, pinpoint recurring elements, identify dominant signals, and show where the system is consistent and where it’s starting to diverge.

This is especially important because many smaller companies have elements in their portfolio that they’ve never formally named. A distinctive color they’ve used for years. The packaging layout. The way a product is photographed. The format of headlines. The shape of labels. The style of icons. These elements often act as brand signals, even if they haven’t been recorded in the brandbook as key assets.

AI can help uncover them.

Source: own work based on ChatGPT

When analyzing McDonald’s materials, AI quickly picks up on several dominant elements: the golden arches, the color yellow, the simple language, the packaging, the menu boards, the restaurant architecture, and the names based on the “Mc” prefix.

The most important signal is, of course, the logo. The distinctive “M” stands alone and doesn’t need much additional explanation. It appears on cups, fries, apps, pylons, restaurant facades, and advertising. This is an example of an asset with a very high degree of autonomy.

AI can help here not only confirm the obvious power of the logo but also demonstrate how widely the brand uses it. The audit shows that McDonald’s doesn’t rely solely on the logo. Equally important are the color yellow, distinctive product photography, simple communication language, promotional layouts, and the brand’s physical presence in space.

The theme of color is also interesting. Historically, the brand has been strongly associated with yellow and red. In newer applications, dark green is increasingly appearing, especially in the context of restaurants. AI can identify such a shift as a significant change in the system: the brand retains its recognition, but changes its tone slightly – from more impulsive to calmer and more contemporary.

Recommendations: the material collected by AI can be a basis for illustrating brand-specific signals, but it does not replace the analysis of individual materials.

Source: own work based on ChatGPT

In the case of Allegro, AI immediately detects the color orange as the primary brand signal. It appears in the logo, buttons, banners, ads, app, social media, and outdoor advertising. This is a good example of a brand where color not only influences image but also functionality. The brand logo with its distinctive “a” is also a crucial element.

Allegro is a digital brand, so branding consistency must be assessed differently than in the case of restaurants or FMCG. The transition between advertising and the interface is crucial here. Users see a promotional message, click, navigate to the app or website, and should still feel they are in the same brand environment.

AI can help verify whether this continuity actually exists. Analyzing Allegro’s materials, it’s easy to see a recurring set of elements: orange, the logo, simple typography, icons, product compositions, and short sales messages.

Allegro doesn’t need to build a complex emotional narrative in every execution. Its branding is primarily intended to structure a very broad shopping experience.

Source: own work based on ChatGPT

In FMCG, a brand audit should begin with the packaging. It’s on the shelf where consumers most often encounter a brand and make purchasing decisions. In the case of the Tarczyński brand, AI identifies two dominant pillars: the logo and a color scheme based on navy blue and orange.

The logo—a white name on a navy blue background, often with an orange border—acts as the manufacturer’s trademark and a guarantee of recognition. Orange serves as an energetic accent: it increases visibility, adds impulsiveness, and helps the brand stand out in displays.

AI can perform very practical work here: comparing different product lines and verifying whether the brand is still easily recognizable despite the different variants. This is especially important when the portfolio is broad: kabanosy sausages, protein snacks, natural products, premium lines, and impulse formats.

Recommendations: The packaging data collected by AI can serve as a map illustrating the initial situation (e.g., before portfolio changes) and after the packaging rebranding process. However, it will not be perfect for precise work with a specific package.

Kärcher is a prime example of a brand where AI can detect both visual cues and a consistent product narrative. The most obvious elements are the black logo, the yellow beam, and the yellow-and-black color scheme of the devices.

But the audit shouldn’t stop at color. Kärcher primarily communicates the effect of its actions. Brand materials often feature the device in use, water, movement, the cleaned surface, and the visible difference before and after. The slogan “Bring back the WOW” summarizes this logic well: the brand sells not just the equipment, but the result.

AI can help detect this pattern. It can indicate that the best materials are consistent with the brand when they show the cleaning effect, not the product itself in isolation. This is an important distinction. For Kärcher, the brand signal is not just the color yellow but also the visual promise: it was dirty, it is clean.

Source: own work based on ChatGPT

In practice, an audit can begin very simply. We write a more or less sophisticated prompt and ask the AI ​​to analyze our data, either online or provided: ads, website screenshots, apps, packaging, social media, outdoor, in-store displays, key visuals, and sales materials. Everything visible in the space and relevant to our brand. As a result, we can obtain a brand identity board, as in the examples presented. It’s crucial to pay attention to the AI’s tendency to hallucinate and add to the mix – therefore, it’s particularly important to properly write the prompt and specify what it shouldn’t do – to avoid executions that were never implemented in the market.

With a ready-made brand identity board, the best approach is to analyze the consistency and draw conclusions independently. AI facilitates and accelerates the work of a brand strategist, but it doesn’t replace the ability to draw conclusions and carefully observe.

The greatest benefit of using AI in a branding audit is its speed and scale. The model can quickly analyze far more materials than we typically encounter on a daily basis.

This doesn’t mean that AI knows what consumers truly remember. Research is still necessary for that. But AI can pinpoint what a brand consistently sends out into the world. This is the first step in determining whether these signals have the potential to build brand recognition.

In each of these cases, AI can help us see not only “what a brand looks like,” but more importantly, “what the brand is made of.”

And this is the essence of a good audit. Because brand consistency isn’t about everything looking identical. It’s about different elements—advertising, product, packaging, app, store, social media—contributing to the same brand memory. AI can help us refine this memory.

Conclusions, recommendations, and next steps are still the domain of humans and our decisions.