*Thisarticle is based on a YouTube video by Sophie Hundertmark, an expert in the use of artificial intelligence with a focus on chatbots and strategic AI applications in companies and public institutions. Sophie is a researcher and lecturer at the Lucerne University of Applied Sciences and Arts and is doing her doctorate in Conversational AI at the University of Fribourg. The blog text was created using a custom GPT model that was trained on Sophie’s video content, language style and expertise. The result is well-founded, up-to-date articles based on Sophie Hundertmark‘s own expertise.
You can find the link to the video at the end of this article.
Product discovery is currently undergoing a noticeable shift: More and more people are no longer looking for information first via classic Google search results or directly on brand websites, but in AI interfaces such as ChatGPT, Gemini, Perplexity or Google AI Mode. That sounds convenient for users – but it’s a real wake-up call for brands and retailers.
Because if your products don’t appear in these AI conversations, a new risk arises: you lose visibility before anyone even visits your website. And, in case of doubt, also customers.
In this podcast episode of “Sophie’s Next AI Talk”, I talk to Tonio Meier, co-founder and CEO of Guuru, about precisely this development – and about why community content (i.e. content from real users) has such a strong impact in AI searches.
What does “AI visibility” actually mean?
AI visibility means: If someone researches a product, a brand or a specific problem in an AI tool, your brand should be mentioned, cited or even recommended there.
It’s not just about “I want to buy a winter jacket”, but also about questions such as:
- Which jacket is really warm?
- How does model X turn out?
- How do I change the battery in product Y?
- What is the alternative to brand Z?
The more frequently users ask such questions in AI tools, the more crucial the question is: What sources does the model use for its answers?
Why community content so often wins in AI responses
Tonio describes a core mechanism very clearly: language models prefer content that sounds human, is concrete and contains real experiences – in other words, content as we know it from communities (Reddit is the classic example here).
This type of content works particularly well because it:
- is formulated in natural language
- specific instead of promotional
- depicts real usage situations
- contains trust signals (experience, opinions, context)
And that is a difference to classic “brand content”, which is often slick, generic or marketing-heavy.
What Guuru does: Users help users – and the output becomes AI-relevant
Guuru historically comes from the “customers helping customers” approach: People ask questions, other users answer, a real exchange takes place.
The next step is exciting: the platform extracts specific “community opinions” on individual products from these conversations. These are prepared in a structured manner and displayed directly on the product detail page.
Something important is happening:
- You have real user content on your own domain
- The content is close to the product (not buried somewhere in the forum)
- They work like social proof – and are also suitable for AI feeding
The aha moment: “Grounding” and why your content is suddenly being quoted
Tonio introduces a concept that many people experience but rarely name: Grounding.
If an AI model cannot provide an answer with certainty from its corpus of knowledge, it starts a web search, pulls snippets from various sources and builds the answer from them. It is precisely in this step that it is decided whether your brand will appear – or not.
And this is where Guuru content comes into play: according to Tonio, these community opinions are often used in precisely these grounding processes.
What the figures show: 50-60 % more citations
Particularly interesting: Guuru has observed and measured the effect across several customers. Among others, the following were mentioned:
- Jack Wolfskin
- Bicycle parts (dealer)
- Polo motorcycle
The result: On product pages that display these community opinions, the number of citations in AI searches increased by around 50-60%.
It is important to note that this is not static content. The content is constantly updated because new contributions, questions and experiences are constantly being added.
Freshness and authorship: two signals that AI models love
Two signals came out particularly strongly in the conversation – and you can use them directly as a checklist for your content:
- Freshness (topicality)
AI models often prefer content that is recognizably new or regularly updated. Community content brings this dynamic with it by nature. - Authorship (who wrote it?)
If it is clear who has made a statement (and ideally with a brief context to the expertise), trustworthiness increases. Tonio describes that Guuru plays out exactly that in a structured way: Name/profile + brief context as to why this person can say something about it.
This is a clear distinction from “AI-generated product text”, which many are currently testing as a quick solution.
Why “simply generating content with ChatGPT” is not a sustainable strategy
An important point from the conversation: If companies simply let ChatGPT generate their product content, this can work in the short term – but is risky in the long term.
The reason is logical: AI providers have no incentive to cite AI-generated content. They can produce it themselves. This is why they invest in mechanisms to recognize and prioritize human, authentic content.
This does not mean that AI has no place in the content process. But “AI-only” as a content strategy is unlikely to be the way to permanently appear in AI responses.
What happens to traffic and conversion when AI search grows?
An exciting reality check: If more users research via AI interfaces, traditional click rates will tend to fall.
Tonio’s assessment is pragmatic:
- There are fewer website clicks overall
- But the clicks that come from ChatGPT & Co. are often more valuable because the users are already pre-qualified
In other words: If someone lands on your product page after a longer AI conversation, the purchase intention is often significantly higher than with a “winter jacket” Google click that is still completely open.
At the same time, attribution becomes more difficult: users can inform themselves in ChatGPT and then buy offline or search directly in the store later – without you being able to measure it properly.
AI visibility is not only relevant for e-commerce
Even though Guuru comes from the e-commerce sector, it was clear in the end that the topic affects all industries.
Tonio refers to analyses from the banking context in which it became clear: External mentions by users have a strong impact on AI visibility – especially for smaller providers that are not already known as a “well-known institution”.
And that fits a pattern that we see time and again: Those who occupy a clear niche and deliver credible signals for it can end up very high up in AI responses – even compared to much larger players.
What you can do now: Mini checklist for AI visibility
If you want to tackle the topic, these questions will help you get started:
Do you display relevant user content on your own website?
Does your brand currently appear in AI searches at all?
Do you have content that sounds like real human experience (reviews, Q&A, community)?
Is your content up-to-date and dynamic?
Is it recognizable who said something – and why the person is credible?
Any further questions?
Do you have any questions? I am happy to support you, act as a sparring partner and answer your questions. I am always happy to receive your messages, preferably by WhatsApp message or e-mail.
