The way people search for, discover, and compare brands is undergoing a profound change. More and more users are no longer relying solely on Google; they are directly asking questions to ChatGPT, Gemini, or Perplexity, which synthesize the information for them. In this new paradigm, visibility no longer depends solely on ranking in search results, but on a brand’s ability to be understood, recognized, and cited by AI models.
If you ask ChatGPT a simple question, such as: what is Girlfriend GPT? When you are gptgirlfriend.online, you don’t want ChatGPT to explain what a virtual girlfriend is; you certainly don’t want it to give you examples by mentioning your competitors’ brands while omitting your own. You want questions and search terms related to your brand to lead the AI to mention it and only it.
For businesses, the stakes are high. It’s no longer just about optimizing a website for traditional SEO, but about ensuring that the entire brand ecosystem—website, images, videos, social media content, and external mentions—is clear, consistent, and structured enough to be correctly interpreted by generative models. In other words, it’s now necessary to optimize not only for search engines, but also for answer engines.
This evolution transforms the very notion of digital reputation. A brand is no longer visible only when it appears in search results: it is visible when an AI selects it as a reference source in a generated response. And that changes everything.
In this context, optimizing your brand for generative AI is becoming less of an option and more of a natural extension of modern SEO. But with expanded rules, new channels of influence, and above all a new strategic question: how to ensure that AI systems correctly understand your brand, contextualize it, and recommend it in the right situations?
What concrete actions can be taken to optimize your brand for AI
John Muller, at Google, implies that traditional SEO is the same as optimization for AI Search. It’s true that good search engine optimization (SEO) is essentially good optimization for AI tools. However, some elements that were already important in SEO are even more crucial with the advent of AI. Other factors must also be considered due to multimodality, which more closely resembles human-like behavior in machines.
This transformation significantly expands the scope of SEO. Every element becomes a potential entry point for being understood, indexed, and recommended. For SEO specialists, this represents increased complexity, but above all, a strategic opportunity: brands capable of optimizing all of their content—and not just their text—gain a significant advantage over those that remain limited to a traditional approach.
Here are some concrete examples.
Optimizing based on multimodality: everything is connected
Let’s take, for example, a beauty product: The Ordinary’s 7% glycolic acid scrub.
A person can take a picture of the product and ask the generative AI: what brand is this? Optical character recognition (OCR) and image quality determine whether or not it recognizes the product. Have you filled in the image’s alt text to describe the product for machines? Is generative AI able to read the text on your packaging? It’s important to understand that what pleases the human eye isn’t necessarily what machines need; they require a minimum level of contrast and characters of at least 30 pixels.
Using the same photo, someone might ask generative AI: is this product tested on animals? Generative AI must not only recognize the brand from a photo, but also be able to find out what is being said about animal testing. If this information isn’t available on your website, it could retrieve it from a YouTube video where a user has researched the topic.
This same person could ask: Is this product effective? Generative AI could then draw on Instagram posts from dermatologists who recommend this product, or not.
A person unfamiliar with the brand but seeking a solution to a problem might ask the generative AI: how do I get rid of my dandruff? Again, if your website doesn’t explicitly state that this product addresses this issue, among others, the AI could simply look at TikTok videos of people using the product on their scalp and suggest the brand as a solution to the dandruff problem.
One last example, still using the same product. A person could take a photo of the hyperpigmentation on their face and send it to the generative AI, saying: “I don’t know what this is, can you help me identify it and find a solution?” If your website includes before-and-after photos of hyperpigmentation reduced by the product, the generative AI will understand that it is hyperpigmentation and that your product is a solution to this problem.
It is up to brands to ensure they communicate about their brand in every way possible, and to take control of its narrative.
Writing for AI
Writing for generative AI and for search engines is not exactly the same thing.
While SEO specialists once observed that content exceeding 2,000 words was more likely to rank in the top 10 Google search results, generative AI tools are now showing a trend toward citing content of 1,900 words or less more frequently. Why? Large Languages Models (LLM) are looking for clear, concise, and easy-to-understand information. According to a Dejan’s study based on the analysis of over 7,000 queries, Gemini’s information retrieval budget is estimated at approximately 380 words per page. Therefore, we must be precise and concise in our communication.
Semantic SEO aims to connect different entities related to a brand with keywords, data, facts, and so on. LLMs can isolate each sentence from an article and combine it with sentences from other articles to generate a tailored response from multiple sources. To optimize our brand for AI, we will therefore prioritize complete sentences that represent a self-contained and verifiable statement and that connect the different entities.The concept of entity was already important in SEO, it becomes even more so with AI.
LLMs love statements at the beginning or end of articles. If your article answers a question, for example, “What is an AI model?”, start your article with a short, comprehensive answer: “An AI model (artificial intelligence model) is a computer program trained to recognize patterns in data in order to make predictions, generate content, or make decisions.” Skip the long, drawn-out introductions and opt for concise and impactful content.
Technical Optimization: the importance of structured data
The technical optimization of your site remains the foundation of SEO and GEO. Semantic referencing, widely used by crawlers and LLMs, emphasizes the use of entities such as people, places, objects, and concepts. By clearly identifying these entities, the use of structured data becomes truly meaningful.
A business can use Organization markup schemas for its homepage, making sure to include its location (in the case of a local business), its main entity, social media links, etc. Product pages will be clearly identified thanks to the schema markups related to Product, and each product page may also include 3 to 5 questions/answers related to the product while using the schema markups for Q&A, and so on.
We know that Gemini relies heavily on relationships between entities to generate AI-generated insights. Google hasn’t confirmed that structured data helps appear in its search engine’s AI insights, but Google spokesperson John Muller encouraged the use of structured data during a live event in April 2025.
Final word: Performance tracking
Optimizing a brand for AI is possible, but it requires integrating all possible sources where the brand can appear, not just the website. The brand must be viewed holistically, including text, videos, social media, images, and its overall web presence. AI optimization goes far beyond SEO, and performance metrics are lacking.
Vanity metrics like SEMrush’s AI Visibility Score exist, but they don’t tell us how many people saw our brand mentioned and then visited our site from another traffic source, nor which images they submitted to the AI to generate a mention. Generative AI isn’t part of any attribution model in GA4. For now, we’ll have to treat AI visibility the same way we treat a TV ad and be content with that: measuring its performance by the number of people who were exposed to the brand.
Article written by: Veronique Bettez
Veronique Bettez is a technical branding and SEO specialist who helps companies increase the visibility and notoriety of their digital brand across search engines and AI tools. She combines technical SEO, public relations, and multimodal strategies to drive organic traffic, direct traffic, and AI visibility for clients.