Digital visibility in Switzerland in the age of AI Part 1: AEO vs GEO

In short:

In the first part of our series, we explain that classic SEO remains important, but is no longer sufficient on its own. Today, visibility is also created where AI systems provide direct answers - which is why companies also need Answer Engine Optimization (AEO) for clearly extractable answers and Generative Engine Optimization (GEO) so that AI models cite content as trustworthy sources. Especially in Switzerland, the consideration of regional language and target groups plays a major role.

What has happened in the digital visibility field in recent years can only be described as a fundamental change. Traditional search engine optimization (SEO) as a disciplined practice consisting of keyword research, technical structuring and backlink building is not an obsolete model – it will remain the ground on which every modern information strategy grows in 2026. But this ground has changed its surface: Search queries no longer happen exclusively via Google rankings, but in a multimodal landscape of traditional search engines, voice interfaces, AI response systems and generative assistance services. If you want to reach your target groups in Switzerland, you need to understand how these systems retrieve, interpret and ultimately reproduce information.

In essence, two new optimization approaches are coming to the fore: Answer Engine Optimization (AEO) aims to prepare content in such a way that it appears directly as an answer in AI-supported answering services and voice assistants – clear, short, structured answers that can be easily extracted by systems such as Google AI Overviews, Siri or Alexa. Generative Engine Optimization (GEO) goes one step further by focusing not just on answer quality, but on the likelihood that AI-based systems will cite or reference content and thereby build authority. GEO is therefore less tied to rankings and more to visibility in generative answers themselves.

From an academic perspective, the evolution towards generative systems has posed new questions for research. Recent studies show that the mechanisms used by large language models such as ChatGPT or Google Gemini work in a fundamentally different way to traditional index and ranking algorithms. While search engines rely on link structures and semantic indices, generative models analyze large amounts of text, weight earned media (i.e. third-party, industry-specific and trustworthy sources) significantly higher and ideally select content that is considered evidence-based. These empirical findings suggest that while SEO measures create the basis, additional strategies are necessary for long-term visibility in generative AI responses.

It would be wrong to say that these new disciplines have emerged out of nowhere. The difference to earlier SEO trends lies less in whether, but in how and why. Even classic SEO principles – clear structure, good user guidance, semantically rich content – are also relevant for AEO and GEO. But where SEO used to aim to place a page high up in the organic search rankings, today it is about appearing in the answers that users see in the first place before they scroll or click. This means that content needs to be formulated in a way that is easy to extract (typical AEO), on the one hand, and argued deeply enough to function as a trustworthy source (typical GEO), on the other.

In the Swiss language and market landscape in particular, this means that regional characteristics, linguistic nuances and local search intentions must be taken into account. While generative AI tools work globally, they also interpret local terms, regional spellings and cultural contexts – a point that many international articles often only mention in passing. Swiss companies that write content explicitly for Swiss-German, Romand or Ticino audiences create additional relevance signals that both traditional search engines and AI models give greater weight to. Multilingualism thus becomes not only a language issue, but also a visibility strategy.

Integrating AEO and GEO into an existing SEO strategy also brings challenges that need to be talked about openly. Many companies tend to treat these disciplines as separate “campaigns” – a significant misconception. Instead, it’s about an integrative understanding: AEO techniques such as concise direct answers, FAQs or structured data are only effective if the content also has the depth and relevance that generative systems reward. Similarly, a focus on generative visibility without a solid technical SEO foundation can lead to content that is often cited but never actually visited, which has a long-term negative impact on brand awareness and conversion.

Feature	AEO (Answer Engine Optimization)	GEO (Generative Engine Optimization)
Core objective	Provision of the direct, isolated answer.	Inclusion as a reference source in AI syntheses.
User intention	Quick information retrieval (e.g. “When is the driveway open?”).	Comprehensive research & decision-making.
Content form	FAQs, definitions, tables, bullet points.	In-depth analyses, case studies, white papers.
Technology focus	Schema.org markup, structured data.	Authority (E-E-A-T), uniqueness of the data.
Reference to Switzerland	Local facts (zip codes, cantonal rules).	Expert status in the overall Swiss market.
Performance measurement	Appearance in featured snippets / voice search.	Quotes and links in AI responses (e.g. ChatGPT).

Typical stumbling blocks on this path become particularly apparent when teams try to create “content for AI” without clear target group analyses or data-driven optimization. Many AI models prefer clear, logical structures, short intro answers and further, reliable source references. If content is formulated too generally, too technically or too “marketing-heavy”, it often performs worse when selected by generative systems. At the same time, there is still a risk that by optimizing exclusively for AEO snippets, either the depth of the content suffers or the reader no longer finds enough substance to take the next step. It is important to find a balance here.

Another stumbling block is measurability. Traditional SEO KPIs such as rankings, clicks or impressions are clearly defined. Visibility in AEO or GEO contexts requires new metrics – such as the number of mentions in generative responses, the volume of AI references or the ratio between snippet excerpts and actual website clicks. These metrics are often not standardized, vary between platforms and can quickly become confusing. Setting up consistent monitoring is therefore not just a technical gimmick, but a strategic imperative.

In conclusion, it is clear that SEO, AEO and GEO are not competing disciplines, but elements of an evolving whole. While classic SEO continues to create traffic and visibility, AEO measures ensure that answers appear where users expect them, and GEO strategies ensure that content is also cited as trustworthy sources in AI answers. This transformation is neither a fad nor short-term hype – it reflects the fundamental shift in information search in a world where AI models are more than search engines, while redefining the interfaces between humans, questions and answers.