Since June 2026, the rules of digital sales have changed. For the first time, Google reports AI Overviews and AI Mode separately from classic organic search results in Search Console. For B2B shops, this means: anyone who still measures visibility by rankings alone is falling behind. For search queries with an AI Overview, the click-through rate on classic results nearly halves. The good news: with the right measures, AI visibility can be built up and measured systematically. This article shows in five concrete steps how mid-sized manufacturers and wholesalers can secure their presence in AI search.
Why AI Overviews are changing classic search engine optimization
The introduction of a separate performance report for generative AI search in Google Search Console marks a turning point. Until May 2026, AI Overviews and organic results were reported together. Since June 2026, Google shows both areas separately. The problem: users who already get their answer in the AI Overview click on further links less often. Studies show that the click-through rate for queries with an AI Overview drops by up to 50 percent.
For B2B companies with their own online shop, this means a direct decline in qualified visitors. At the same time, a new opportunity opens up: whoever is cited in AI Overviews gains visibility and authority. The challenge is to prepare content in a way that AI systems recognize as quotable. This requires a different approach than classic SEO measures – the article B2B SEO 2026: 5 levers that still bring traffic alongside ChatGPT & Co. shows which levers continue to drive traffic beyond AI search.
The new Search Console report distinguishes between AI Overviews (short answers above the search results) and AI Mode (interactive, multi-step conversations). Both formats influence user behavior differently. B2B companies need to understand in which context their content appears and how to respond.
Step 1: Analyze the new Search Console report
The first step in measuring AI visibility leads directly to Google Search Console. Under "Performance", a new report for generative AI search has been available since June 2026. It shows impressions, clicks and average position for AI Overviews and AI Mode separately.
The most important metrics at a glance:
AI impressions: How often does content appear in AI Overviews or AI Mode?
Click-through rate (CTR): What share of users clicks through to the website despite the AI Overview?
Average position: Where is the content mentioned within the AI answer?
Comparison with organic impressions: How is the ratio between classic and AI-powered search results developing?
B2B shops should first establish a benchmark: which pages and topics already generate AI impressions? Are there product categories or FAQ pages that appear more frequently in AI Overviews? These insights form the basis for targeted optimization.
A common mistake: many companies focus exclusively on the click-through rate. But in AI search, brand mentions also count. Being cited as a source in an AI Overview builds trust, even if the direct click does not happen. Brand mentions should therefore be tracked as a separate metric. The analysis should be carried out at least once a month to spot trends early.
Step 2: Build quotable content instead of chasing rankings
Classic SEO aims at rankings. AI visibility requires quotable content. The difference: while search engines rate pages by relevance and authority, AI systems look for precise, structured answers. Content must be prepared so that it works as a source.
Quotable content is characterized by the following features:
Concrete figures and facts: Provide measurable data instead of general statements, for example "The average delivery time is 48 hours" instead of "We deliver fast".
Clear structure: Headings, bullet points and short paragraphs make it easier for AI systems to process the content.
FAQ sections: Frequently asked questions with concise answers are cited preferentially.
Source references: References to studies, norms or industry standards increase credibility.
B2B shops should systematically revise existing content. Product descriptions consisting only of marketing phrases offer no value to AI systems. Instead, technical data, application examples and comparison tables should be integrated. FAQ sections are particularly valuable: they answer typical questions directly and in a structured way. Ideally, the questions are based on real search queries from Search Console.
Another approach is glossaries and definition pages. AI systems like to draw on pages that explain technical terms clearly. B2B companies with technical products benefit from building a glossary of industry-specific terms.
Step 3: Maintain product data and structured data
Structured data is the key to AI visibility. It enables search engines and AI systems to interpret content precisely. Schema.org markup provides standardized formats. The following schema types are particularly relevant for B2B shops:
Product: Product name, description, price, availability, manufacturer
FAQPage: Structured questions and answers
HowTo: Step-by-step instructions, such as assembly guides
Organization: Company information, contact details, logo
Review: Customer ratings and reviews
Maintaining structured data is not a one-off task. Product data changes, prices are adjusted, new FAQ entries are added. A common problem is inconsistent data: if the price in the structured markup deviates from the displayed information, the page loses trust. Structured data should therefore be generated directly from the ERP or PIM system.
Shopware shops have had an additional lever since version 6.7.12: the platform natively supports the new llms.txt and agents.md standards. The article AI Discovery Files in Shopware: 5 steps for B2B shops describes how B2B shops can use these files.
A practical tool for verification is Google's Rich Results Test. It shows whether structured data is implemented correctly and which errors need to be fixed.
Step 4: Test visibility in ChatGPT, Perplexity and other AI platforms
Google is not the only platform where AI-powered search takes place. ChatGPT, Perplexity, Claude and other AI assistants are increasingly used for research. B2B decision-makers use these tools to quickly obtain information about products, suppliers or technical solutions. Those who are not present here miss out on potential customers.
Visibility on these platforms cannot be measured directly in Search Console. Instead, spot-check testing is recommended:
Define relevant search queries: What questions do potential customers ask?
Run the queries in different AI tools: ChatGPT, Perplexity, Claude – each platform uses different data sources.
Document brand mentions: Is your company mentioned? As the main recommendation or as one of many options?
Analyze sources: Which pages are cited as references – your own website or those of competitors?
Perplexity is particularly interesting because the platform explicitly cites sources. Whoever is cited here benefits from direct traffic and increased credibility. The platform prefers up-to-date, well-structured content with clear source references. The article AEO for manufacturers: getting cited in ChatGPT, Claude & Perplexity goes deeper into how manufacturers get cited in AI answers.
Step 5: Adapt KPIs – measure citations, not just clicks
The classic metrics of search engine optimization – rankings, clicks, CTR – are no longer sufficient in the AI era. B2B shops need to expand their success measurement. Two new metrics come to the fore: citations and brand mentions.
Citations refer to how often a website is named as a source in AI Overviews or on AI platforms. They can partly be tracked via Search Console, but also require manual spot checks. Brand mentions go one step further: even without a direct link to the website, the mention of the company name in an AI answer strengthens brand awareness.
Other relevant metrics:
Share of voice in AI Overviews: How often is your company mentioned compared to competitors?
Topic coverage: For how many relevant search queries does the website generate AI impressions?
Conversion rate from AI traffic: How high is the quality of visitors coming via AI Overviews?
Adapting KPIs also requires a change in internal communication. A decline in organic clicks can be offset by an increase in brand mentions. A monthly dashboard comparing classic SEO metrics with AI metrics creates transparency. In the long term, the impact on revenue should also be measured – for example via dedicated segments for AI traffic in web analytics.
Common mistakes when building AI visibility
Despite the clear benefits, many B2B shops make avoidable mistakes. The most common: they treat AI visibility as a purely technical topic. Structured data is implemented, but the content remains unchanged. The result: the data is read correctly, but the information is too general or too promotional to serve as a source.
Another mistake is a lack of consistency. FAQ sections are created but not regularly expanded. AI systems prefer up-to-date, well-maintained content – outdated information leads to declining visibility. Neglecting mobile users is also problematic: AI Overviews appear particularly often on mobile devices. Finally, speed matters: a loading time of more than three seconds reduces the likelihood of being cited as a source.
Conclusion: lay the foundations now
The separation of AI Overviews and organic search results in Search Console is only the beginning. The fragmentation of the search landscape is increasing, and the demands on content are rising. AI systems are getting better at distinguishing quality from quantity – expertise, timeliness and usefulness are what counts.
For B2B shops, this means: take stock in the new Search Console report, build quotable content, deliver structured data from the ERP or PIM system, regularly test visibility on AI platforms, and expand success measurement to include citations and brand mentions. AI visibility is not a sprint but a marathon: those who continuously work on the quality of their data and content build a sustainable competitive advantage.
