At a glance
- AI reads the manufacturers' own websites. For software or e-commerce questions, it cites large platforms. For industrial questions, it reads standards bodies, associations and company websites. The strongest website in the entire field supplies three percent of the sources. At the same time, the AI keeps a fixed Stammtisch: 171 websites that were present on every one of the 30 days and together account for two fifths of all retrievals. A Stammtisch is the regulars' table in a German pub, reserved for the same group every week. Getting a seat at this table is the goal.
- AI searches most often when your customer needs evidence for internal sign-off. Certificates, inspection documents, deadlines, usual down payments: before signing, your contact has to justify the decision inside their own company. This is exactly when AI looks things up on the web most often, and exactly these pages are missing from most company websites.
- You can steer one of two steps. Your text decides whether AI reads your page at all. The AI system decides whether it then cites your page as a source. Technical packages with FAQ markup and tables made no measurable difference to that second step, so the budget belongs in pages that answer buyer questions.
- Since August 24, Google shows its sources only on click. Before that date, sources were visible in six out of seven answers. Now they are visible in a quarter. Any curve across the whole of August therefore compares two different displays.
When your customer asks ChatGPT, Google or Perplexity a question, the system searches the web and assembles the answer from other people's websites. Until now, nobody had measured which websites these are for industrial companies in Germany. We recorded it for 30 days: 72 real buyer questions, six AI systems, 99,270 retrieved sources. This page shows what that means for your content budget.
About the setting. All 72 questions were asked in German, from a location in Germany, the way a buyer at a German industrial company would ask them. The companies behind these questions belong to the German Mittelstand: the mostly owner-managed small and mid-sized firms that make up the backbone of German manufacturing, from special-purpose machine builders to electronics manufacturers. That is why German and EU sources appear throughout this study, such as europa.eu for EU law, wlw.de (short for “Wer liefert was”, “who supplies what”, the leading German B2B supplier directory), dinmedia.de (the publisher of German DIN standards), vdi.de (the Association of German Engineers) and ihk.de (the German Chambers of Industry and Commerce). The results describe what AI systems show German-speaking buyers in Germany. The language of the question changes the sources, so these numbers apply to German-language questions and cannot be carried over to English ones (see section 9). Austria and Switzerland were outside the panel. If you sell into the German market, this is the picture your buyers see.
All numbers are 30-day averages, and you can recalculate every one of them. Download the study as PDF and the raw data
Who should read which part:
- Leadership: the four key findings right here and the five decisions with effort in section 1, which takes about ten minutes.
- Marketing: sections 2 to 5. They show which pages AI retrieves, which it passes over, and what that means for the editorial plan. The charts are free to use.
- SEO and content specialists: section 6 on Google's redesign, section 7 on how much single queries fluctuate, the methodology, and the raw data with 27,311 pages and 5,970 search queries.
Your next customer is asking AI as well
Picture your next customer: a buyer at an automotive supplier, or a head of development at a medical device manufacturer. Before either of them calls your sales team, they have already asked their question. They ask ChatGPT, Google or Perplexity: “Which certificates does an EMS provider have to show?” “At what volume does a robot cell pay off?” “How long will an embedded module receive security updates?”
The same applies when your customer stays with Google. For industrial search terms, an AI overview now appears for more than half of the search volume (Semrush, September 2026, measured in the US).
The AI answers in seconds, and it builds the answer from websites. Nobody had measured which websites these are for the industrial Mittelstand in Germany. We did, every day from August 10 to September 8, 2026, with 72 real buyer questions taken from first sales conversations and everyday sales work.
1. Five decisions for your content budget, and what they cost
The four key findings are above, and sections 2 to 6 hold the evidence. Five decisions follow from them. Your existing team can carry out all five.
- Ask your sales team for the last five questions before a contract was awarded. These are the questions your customer's purchasing department, quality management or leadership asked. They become your validation pages (section 3). Each page takes one afternoon of expert input and one day of editing.
- Build a guide section two levels deep, with twelve to twenty pages. This has more effect than a single showcase page. The fixed Stammtisch of 171 websites (section 2) consists of websites with an average of 18 retrieved pages each. That makes it a six-month program, and it is the one approach the data supports.
- Put the standard number, the location and the primary source on every compliance page. ISO 9001, ISO 17025, IATF 16949, EN 10204, EU Machinery Regulation 2023/1230. On German pages, write out “Deutschland”. Link the standard on iso.org or EUR-Lex. This costs nothing and matches the search queries ChatGPT actually sends (section 5).
- Put less weight on technical optimization once the basics are in place. This refers to the technical package of FAQ schema, comparison tables and llms.txt that agencies sell as AI optimization. Across 30 days of panel data, it brought no measurable gain in citations (section 4). If it is in the package because “it doesn't hurt”: it costs money that is then missing for points 1 and 2.
- Measure the question on the phone. AI answers produce almost no measurable clicks, and Google's AI overviews have produced even fewer since August 24 (section 6). What arrives is a call or an inquiry that starts with “I read that you …”. A field in the contact form and one question on the phone (“How did you find us?”) are the only attribution that works in the Mittelstand.
What we do not promise: a number of inquiries. This study measures which websites AI reads and cites. Whether your buyer asks the AI, and whether they call afterwards, is what we measure with the clients who implement point 5. That is the next round.
2. The strongest website gets 3 percent, a fixed Stammtisch of 171 websites gets 41 percent
The software market has dominant platforms. A single review platform holds 13.5 percent of all retrievals there (Radyant with OMR Reviews, a German software review platform, June 2026). In German e-commerce, the top fifth of shops receives 72 percent of all AI citations (SISTRIX, September 2026). Anyone missing from the platform there barely exists for the AI.
Industrial questions work differently. The most retrieved website in our panel is europa.eu, at three percent. The ten strongest websites together reach 11.4 percent, the thirty strongest 20.8 percent. Four out of five retrievals go to websites outside the top 30.
| Rank | Website | Share of retrievals | What it is |
|---|---|---|---|
| 1 | europa.eu | 2.9% | EU law, regulations |
| 2 | wlw.de | 1.6% | Supplier directory |
| 3 | iso.org | 1.2% | Standards |
| 4 | youtube.com | 1.1% | Video |
| 5 | tacto.ai | 1.0% | Procurement glossary of a software vendor |
| 6 | elektroniknet.de | 0.8% | Trade publication |
| 7 | vdi.de | 0.7% | Association |
| 8 | ihk.de | 0.7% | Chamber of commerce |
| 9 | wikipedia.org | 0.7% | Reference work |
| 10 | dinmedia.de | 0.7% | Standards |
Fourteen of the thirty strongest websites are institutions: standards bodies, associations, public authorities and research institutes. The top 10 contain no machine builder at all. The sources also change with the buying phase. Supplier directories such as wlw take ten percent of all retrievals in the solution comparison phase and less than two percent in the other three phases. Institutions take a fifth during internal validation and around five percent otherwise. Someone asking “who qualifies?” gets the directory. Someone asking “what applies?” gets the standard. A wlw listing pays off in exactly one phase, where it makes a noticeable difference. Even so, 90 percent of all retrievals from company websites go to actual manufacturers and service providers. The market is open, and it is broad.
This is a German finding. In the same week, Semrush analyzed 458 industrial search terms in the US. There, AI mainly cites distributors, marketplaces and directories such as Grainger, ThomasNet or Vevor, and Semrush recommends partnerships with these sites. The manufacturers themselves barely appear there. Germany has no Grainger. Here, your own website plays the role the marketplace plays in the US, which makes it the most important seat you can take at the AI's Stammtisch.
That is the good news, and it comes with a catch. Because retrievals are spread across many websites, there is also no single switch to flip. You cannot simply “sign up on OMR” and be done. The field is also less open than the top of the list suggests. The AI keeps a fixed Stammtisch: 171 websites, 1.7 percent of 9,898, were present on every one of the 30 days. This Stammtisch receives 41 percent of all retrievals. The 1,011 websites that appear on at least every second day receive 73 percent. The other half of all websites shows up exactly once and shares five percent.
What this means for your budget
You need a seat at your industry's Stammtisch, among the few dozen websites the AI retrieves there every day. Websites at the table are retrieved daily. Websites outside it appear only occasionally, however much they publish.
Numbers as a table
| Group of websites | Share of all retrievals |
|---|---|
| Strongest website (europa.eu) | 2.9% |
| The 10 strongest websites | 11.4% |
| The 30 strongest websites | 20.8% |
| The 100 strongest websites | 35.3% |
| Fixed Stammtisch: 171 websites, present on all 30 days | 41.0% |
| Present once: 4,791 websites, on exactly one day | 5.1% |
3. AI searches the web most when your customer has to justify the purchase internally
Our 72 questions are spread across four phases of an industrial purchase (in German): orientation (“How do I describe my project for a first inquiry?”), solution comparison (“Custom machine or series machine?”), decision (“What down payment is usual?”) and internal validation (“Which documentation does the supplier have to provide so that our quality management approves?”).
The last phase is the one in which your contact has to defend the decision inside their own company: to purchasing, to leadership, to quality management. We had expected AI to look things up least in this phase, because the questions sound more general. The data show the opposite.
| Buying phase | Share of answers for which the AI searched the web |
|---|---|
| Orientation | 68% |
| Solution comparison | 73% |
| Decision | 75% |
| Internal validation | 80% |
The increase holds steadily across all 30 days, before and after Google's redesign, and it passes the statistical test (validation against orientation t = 3.5, with the question as the unit of analysis, corrected for multiple testing). One caveat applies. ChatGPT and Perplexity look things up for practically every question, at 99 and 100 percent. The increase comes from the four systems that have room to vary: Google AI Overview rises from 46 to 61 percent, AI Mode from 66 to 83, Copilot from 88 to 99 and Gemini from 9 to 41.
The reason lies in the type of question. Validation questions are questions of fact: which standard applies, which deadline, which certificate. A language model cannot answer these reliably from memory, so it has to look them up. SISTRIX measured in September that Google's AI overview appears for 70 percent of informational searches and practically never for transactional searches. The validation question comes late in the buying process, and it is still an informational question. AI therefore helps with the checks before the purchase.
The form of the question matters as well as the phase. The five panel questions that begin with “How do I describe”, “How do I justify” or “How do I calculate” trigger a web search in 55 percent of answers on average, the other 67 questions in 76 percent. At the bottom is “How do I describe my automation project so that a special-purpose machine builder can quote it?” at 43 percent. At the top is “How do I comply with the new EU Machinery Regulation?” at 96 percent. A model can give instructions from its own knowledge. Standards and deadlines it has to look up. For your website, this means that a page explaining a standard, a deadline or a piece of documentation gets searched for. A page explaining how to describe a project competes with the model itself.
What this means for your budget
This is the cheapest gap in the whole field. The questions your sales team hears shortly before a contract is awarded appear on almost no website: inspection certificates under EN 10204, reporting obligations under the EU Cyber Resilience Act, usual down payment levels, long-term availability of components, what a quality audit wants to see at a supplier. One such page costs an afternoon with your head of sales and an editor. It faces little competition. And it is read exactly when your customer decides.
This matches what we hear from industrial clients. First contact usually comes through referrals and trade fairs. The website is the check that follows, and that check is the validation phase. This is where AI now looks things up as well.
Whether buyers prepare their trade fair visit list with AI is outside what we measured. We did measure what AI reads when someone asks who qualifies: in the solution comparison phase, mainly supplier directories such as wlw and the company websites themselves (section 2). A company missing there is also missing from a visit list built this way.
More on this phase, with the pages read most often in it: AI searches most often when your customer has to validate internally (in German).
Numbers as a table
| AI system | Orientation | Internal validation |
|---|---|---|
| All six systems | 68% | 80% |
| Gemini | 9% | 41% |
| Google AI Mode | 66% | 83% |
| Google AI Overview | 46% | 61% |
| Copilot | 88% | 99% |
| ChatGPT | 99% | 99% |
| Perplexity | 100% | 98% |
4. Two hurdles: your page decides whether AI reads it, the AI system decides whether it cites it
This finding puts everything else in order. It also explains why many GEO measures achieve less than hoped. GEO, generative engine optimization, is the name for optimizing content for AI answers.
On its way into an AI answer, a website clears two hurdles. First, the AI retrieves it: the page joins the set of candidates the answer is built from. Second, the AI cites it, as evidence in the text, with a link. In this study, “retrieval” means the AI loaded the page for an answer, and “citation” means the page appears in the answer as a source. We examined both hurdles separately, and they follow completely different rules.
Hurdle 1, being considered: your page decides. We compared 198 pages that AI never retrieved in 30 days with 78 retrieved pages from the same 68 company websites. The retrieved pages are visibly built differently. They are marked up as articles (64 against 36 percent), name an author (63 against 39 percent), are longer, more structured and contain more lists. Above all, they cover costs and prices (42 against 10 percent) and end with a conclusion (38 against 10 percent). Guide and knowledge pages make up 51 percent of the retrieved pages. Product pages make up four percent.
Hurdle 2, being cited: your page has practically no influence. We downloaded and measured 101 retrieved manufacturer pages ourselves: 50 that are cited almost every time against 51 that are almost never cited. The two groups are alike in all 21 features we checked, including length, headings, tables, FAQ schema, author and date. The smallest difference had a p-value of 0.107, far from any significance.
Whether a page is cited depends on the AI system that answers:
Numbers as a table
| System | Sources retrieved per answer | Citations per retrieved manufacturer page |
|---|---|---|
| ChatGPT | 19.6 | 0.44 |
| Perplexity | 10.4 | 0.56 |
| AI Mode | 5.5 | 0.84 |
| AI Overview | 4.5 | 1.00 |
| Copilot | 4.1 | 1.92 |
| Gemini | 0.8 | 1.69 |
The more sources a system retrieves, the smaller the share it cites. ChatGPT reads twenty websites and cites four in ten. Copilot reads four and cites each of them almost twice on average. The relationship is almost perfect (rank correlation of minus 0.94). Between the systems, the difference is a factor of 4.4. Between the four buying phases, it is a factor of 1.15.
What this means for your budget
Money spent on “citable structure”, meaning FAQ markup, comparison tables and schema markup, brought no gain on hurdle 2 that we could measure. Every euro that gets one more page over hurdle 1 counts. You can influence whether your page enters the set of candidates. Whether it is cited after that is up to the system.
The measurement in detail: FAQ schema and tables: what they change about AI citations (in German).
5. AI reads guide pages and rarely reads product pages or case studies
Four patterns emerge from 14,580 manufacturer pages in the panel. They apply to hurdle 1, being retrieved.
Guide pages beat product pages by a factor of 3.6.
| Section of the website | Share of retrievals | Citations per retrieval |
|---|---|---|
| Guides, knowledge, glossary | 29.3% | 0.94 |
| Products, services | 8.2% | 0.67 |
| Homepage | 4.8% | 0.58 |
| Company, about us | 2.4% | 0.62 |
| PDF documents | 1.9% | 0.96 |
| References, case studies | 0.6% | 0.49 |
References and case studies, often the most expensive item in a Mittelstand company's content budget, play almost no role in buyer questions to AI: 0.6 percent of retrievals and the lowest citation rate. This says nothing about their quality. They simply answer none of the questions a buyer asks before buying.
Two levels deep is the best place. Pages such as /guide/topic take a third of all retrievals. The homepage takes less than five percent.
The buyer's question is what counts. The titles of the strongest manufacturer pages in the panel read (translated from German): “The most important certifications for industrial suppliers”, “Supplier certificate”, “Finding a special-purpose machine builder”, “Total Cost of Ownership”, “EMS costs explained transparently”, “How high should a down payment be?”, “Custom machine or series machine?”, “Selection criteria for EMS providers”, “Laser cutting tolerances”, “EN 10204”. None of these titles names a product feature. All ten are questions a buyer asks before looking for a product.
A cross-check shows how narrowly this applies. We examined the reference clients of an industrial SEO agency whose landing pages rank at the top of Google for product terms. These pages have question headings and around 900 words, built formally exactly the way hurdle 1 rewards. Six of these seven companies are absent from the panel. Their pages answer the question about the product. AI reads the pages that answer the question that comes before the product.
Many pages have more effect than one showcase page. 65 company websites reach at least 100 retrievals. On average, they collect them across 18 different pages. Only three of the 65 manage it with three pages or fewer. The exception proves the rule: a single 2023 blog post by an embedded systems manufacturer, “How do I find the right embedded systems supplier?”, was retrieved 113 times and cited 108 times, more than entire competitor websites. It answers one buyer question, sits two levels deep, has 1,020 words and has stayed unchanged for three years.
One more finding fits into no guidebook and still belongs here. The most read and most cited manufacturer page in the entire panel is machine-generated text with a grammatically flawed title. It matches the topic, and that is enough for hurdle 1. It is no model to copy. It shows that the systems select by topical fit and that quality plays no part in that selection. What becomes of such pages in six to eighteen months is something we will check in the next round.
What ChatGPT actually searches for. We recorded 5,970 search queries that ChatGPT and Perplexity send to search behind the scenes. ChatGPT never sends the user's question as it was asked. It builds keyword strings of nine words and adds what the question left out: a number in 43 percent of queries, usually a standard number (ISO 9001 alone 652 times), the word “Deutschland” (Germany) in every fourth query, the English word “official” 260 times, and in three percent of cases it restricts the search directly to iso.org or EUR-Lex. It never searches for an adjective and searches for a number almost every second time. It almost never searches for your company name: a manufacturer name appears in 1.8 percent of queries. For your compliance pages, this means writing out the standard number, writing out the location and linking the primary source.
Which page to build for which buying phase, and what it costs: Which page type for which buying phase? (in German).
6. Since August 24, Google shows its sources only on click
We set out to measure one state of affairs. Because Google redesigned its AI answers in the middle of the measurement period, we ended up measuring two.
Up to August 23, Google's “AI Overview” showed its sources directly in 86 percent of answers: small cards with the company name and a link, as you know them. From August 25, this value fell by two and a half percentage points every day, and since September 2 it has been at 24 percent. The overview itself has stayed and has even become longer. What is missing are the cards.
To rule out a measurement error, we cross-checked the value twice. DataForSEO reads Google with a different technique and came to the same result on the same sample. On September 9, we also checked ourselves in Chrome, with the location set to Germany and without being logged in. This showed that Google now delivers the overview in two versions. In the old version, the sources appear as links in the answer. In the new version, which now appears for three out of four questions, a small link icon sits at the end of each paragraph. Only someone who clicks it sees where the paragraph comes from.
What this means for you:
- Google's AI answers still use your website, and show it less often. The sources are still there, one click further away, and very few people click.
- Any visibility curve that runs across August 24 compares apples with oranges. If a service provider shows you a drop in your Google AI visibility in September, ask how they measure it.
- ChatGPT, Copilot and Perplexity changed nothing. Their sources appear as before.
This tells us nothing about whether Google looks things up less often. Whether the model behind the overview keeps reading websites cannot be measured from outside. Only what is displayed can be measured.
Numbers as a table
| Day (2026) | Google AI Overview | Google AI Mode |
|---|---|---|
| Aug 10 | 85% | 90% |
| Aug 11 | 90% | 90% |
| Aug 12 | 90% | 81% |
| Aug 13 | 82% | 85% |
| Aug 14 | 90% | 90% |
| Aug 15 | 83% | 86% |
| Aug 16 | 90% | 89% |
| Aug 17 | 90% | 82% |
| Aug 18 | 91% | 86% |
| Aug 19 | 94% | 79% |
| Aug 20 | 87% | 79% |
| Aug 21 | 89% | 76% |
| Aug 22 | 62% | 78% |
| Aug 23 | 76% | 82% |
| Aug 24 | 75% | 67% |
| Aug 25 | 44% | 67% |
| Aug 26 | 42% | 64% |
| Aug 27 | 46% | 69% |
| Aug 28 | 28% | 61% |
| Aug 29 | 32% | 65% |
| Aug 30 | 27% | 60% |
| Aug 31 | 29% | 62% |
| Sep 1 | 28% | 53% |
| Sep 2 | 23% | 61% |
| Sep 3 | 21% | 67% |
| Sep 4 | 24% | 61% |
| Sep 5 | 26% | 61% |
| Sep 6 | 25% | 57% |
| Sep 7 | 20% | 62% |
| Sep 8 | 28% | 60% |
7. Four expectations the data did not confirm
A study that reports only confirmations is advertising. Four expectations were not confirmed, and we found one limit only while measuring.
Industries differ less than we thought. We had expected machine building sectors with dense directories to get different source types than electronics sectors. The differences within the industry groups are larger than those between them. There are patterns, and they run across our assumption.
Technical vocabulary in the question leaves the sources unchanged. Whether a question uses technical terms or general wording, the source types stay the same. The difference was just significant in one time window and disappeared after correcting for multiple testing.
The two Google surfaces are more similar than we first wrote. An early analysis found that AI Mode and AI Overview share only a fifth of their sources. The complete measurement shows that AI Mode shares just as little with itself on the previous day. We cannot separate the difference between the surfaces from the daily fluctuation. SISTRIX can, with 82,619 questions over 17 weeks, and finds it. We report this as a SISTRIX finding and withdraw our earlier statement.
Structure does not make a page citable. See section 4. This expectation weighs the most, because an entire consulting offer is built on it.
Single queries fluctuate strongly. Every answer is assembled anew, like a throw of the dice at the Stammtisch. If you ask ChatGPT the same question today and tomorrow, the two answers share only a sixth of their sources. Google AI Mode behaves the same way. Between different systems, the overlap is close to zero. Only Perplexity, which passes your question on to search word for word, returns the same sources on quiet days, three quarters on average over 30 days. For all others, a single query is a single throw of the dice (in German). A trend from five queries is therefore mostly chance. Only many throws show on how many sides of the die your name appears.
This also affects our own statistics. The effective sample of this study is 72 questions, while the 13,108 answers are repeated measurements of them. Treating the answers as independent produces confidence intervals that are three times too narrow. All intervals here are calculated at the level of the question.
8. What morefire, Radyant and Semrush measured, and what this study adds
Three studies mapped the field before us, each covering a different slice.
morefire (summer 2026) measured the brand side: is a company's own website cited when someone asks about its category? The study covered 62 categories in German industrial B2B, 12,250 answers and five runs. Its finding: almost nobody owns their category, a company's own website is cited in around five percent of cases, and 46 percent of brands are missing from at least one run.
Radyant (June 2026) published two studies: a persona study with 17,929 answers and sound statistics, and, with OMR Reviews, a source study for software with 20,160 answers. Its finding: in the software field, review platforms dominate, and one of them holds 13.5 percent of retrievals.
Semrush (September 8, 2026) analyzed 458 industrial search terms in the US. Google AI overviews now appear for 57 percent of search volume (January: 38), and only 0.48 percent of visits to industrial websites come from AI systems. Both match our picture: AI answers appear more and more often, and they lead to hardly any clicks to the website. Buyers who do get in touch show up in your analytics as “direct” traffic or simply call.
What this study adds: the source side for industry, which so far existed only for software and here shows the reverse result; Google AI Overview and AI Mode, including the redesign of August 24; the decision and validation phases, which no study had measured so far; the separation of retrieval and citation, which explains why structural measures move citations so little; the threshold above which a difference is more than daily fluctuation; and downloadable raw data at page level.
Where morefire reaches further: 620 questions in 62 categories against our 72 questions in six industries. If you are looking for your own niche, you are more likely to find it there. We, on the other hand, measured daily for 30 days, while morefire ran five rounds. That is why we see Google's redesign, the drift and the daily fluctuations, which the earlier studies could not capture.
A note on comparing: morefire's five percent and Radyant's 13.5 percent are different metrics. The first is a company website's share of the citations in its own category, the second a platform's share of all retrievals across all questions. We report both separately.
9. Methodology in brief
Panel: 72 questions from six industries (special-purpose machine building, automation and robotics, manufacturing technology, measurement technology and sensors, embedded hardware, electronics manufacturing), twelve questions each, spread across four buying phases. The questions are brand-neutral and come from real first sales conversations and sales questions.
Systems: ChatGPT, Google AI Overview, Google AI Mode, Gemini, Microsoft Copilot, Perplexity. All were measured through the user interface, without an API. Location Germany, language German.
Period: August 10 to September 8, 2026, daily, 30 days. 13,108 answers, 99,270 retrieved sources, 9,898 websites. All numbers in the text are 30-day averages, with time windows only where Google's redesign requires them.
Measurement: Peec AI as the panel platform. DataForSEO as a second instrument for the Google surfaces. A cross-check in the browser on September 9. Our own downloads and measurements of 101 retrieved pages and 198 pages that were never retrieved.
Statistics: The question is the unit of analysis. Confidence intervals are calculated at that level. Correction for multiple testing (Benjamini-Hochberg), with corrected and uncorrected values reported. We calculated citation rates ourselves, independently of the platform's own metric.
Limits: Six industrial sectors; regulated industries and software are outside the panel. 72 questions. One country, one language. That is more than a footnote: according to Żatuchin (Rankfor.AI, June 2026, 35,640 answers in twelve European languages), asking in the local language lifts local providers far more than large corporations. Our numbers apply to German questions, and they cannot be transferred to English questions. The page types were classified by the platform; a recalculation with our own classes will follow in the next round. The language of the cited pages was outside the measurement. Google's redesign was measured over time with one instrument, and the level after it with two instruments plus the browser. Claude was outside the panel.
Conflict of interest: The B2B Answer advises industrial companies on visibility in AI systems. The study measures what we sell. That is why we set the hypotheses before measuring, publish the raw data, include a section on what did not happen, and recommend putting less budget into a widespread consulting package.
Data: The question set, all retrieved websites and pages, daily series per system and the search queries of ChatGPT and Perplexity as CSV, free to use with attribution. Go to the download.
Next round: The panel will be repeated identically in the first quarter of 2027. Every number in this text then becomes a time series.
Raw data and download
You can recalculate every number in this study from the raw data. The data package contains the study as a PDF, six CSV files with semicolons and decimal commas, all four charts as PNG and SVG, and a description of the fields. The PDF, the column names and the field description are in German.
- fragenset.csv: the 72 questions with industry, buying phase and question type
- websites.csv: all 9,898 retrieved websites with retrievals, citations and days in the panel
- seiten.csv: all 27,311 retrieved pages with title, retrievals and citations
- tagesreihe-quellenanzeige.csv: share of answers with a web search per day and system
- suchanfragen-chatgpt-perplexity.csv: the 5,970 search queries behind the answers
- systeme.csv: the key figures per system
Download the study and the raw data
Free to use with attribution. Both files are in German. For AI systems and agents, the study is also available as a Markdown version (in German).
How to cite this study: Bock, Oliver (2026): Which websites does ChatGPT read when your customer looks for a supplier? What 13,108 AI answers show. Panel study on AI sources, August 10 to September 8, 2026. The B2B Answer, Munich. English version of “Welche Websites liest ChatGPT, wenn Ihr Kunde einen Anbieter sucht?”. https://theb2banswer.de/en/studies/ai-sources-german-industry-2026/
Short form for posts and slides: The B2B Answer, panel study on AI sources in German industry and Mittelstand 2026, theb2banswer.de/en/studies/ai-sources-german-industry-2026/
What leadership and marketing teams ask about the study.
Does this apply to my industry?
The panel covers six industrial sectors: special-purpose machine building, automation and robotics, manufacturing technology, measurement technology and sensors, embedded hardware, and electronics manufacturing. The differences within these groups are larger than those between them, so the findings apply to industry and the Mittelstand as a whole. Software and e-commerce follow other rules, with dominant platforms. All questions were asked in German from Germany, so the findings describe what German-speaking buyers in Germany see.
Google still has 90 percent of search. Why should AI search matter to me?
Because Google itself has become an AI search engine. For industrial search terms, an AI overview now appears for more than half of the search volume, up from 38 percent in January (Semrush, September 2026, 458 terms, US). For informational queries, it appears in 70 percent of cases according to SISTRIX. This overview is an AI answer built from websites, and since August 24 Google shows its sources only on click (section 6). The real question is which websites supply the answer your customer reads, on Google and on ChatGPT alike.
How robust are 72 questions?
The 72 questions are the sample, and the 13,108 answers are the measurements of them. Every question ran daily for 30 days in six systems, so the averages are stable. All confidence intervals are calculated at the level of the question, and section 7 lists the expectations the data did not confirm.
Why is Claude missing, and does it matter?
Yes, it matters, and it is a gap in this study. Our measurement platform did not cover Claude during the measurement period. Claude is growing fast: according to Similarweb, Claude had 9.6 percent of visits among the major AI chatbots in August 2026, up from about 2 percent a year earlier, while ChatGPT's share fell from 78 to 57 percent. Anthropic explicitly positions Claude for work inside companies, and industrial clients already tell us about inquiries that came through Claude. There is also a technical difference: according to Anthropic's own list of service providers, Claude runs its web search through Brave Search. The sources in this study therefore cannot be transferred one to one to Claude. Claude will be in the panel in the next round. Until then, a quick test is worthwhile: search for your most important guide pages on search.brave.com. Whatever is missing there is much harder for Claude to find.
May I use the data and charts?
Yes, freely, with attribution. The data package contains all CSV files, the four charts as PNG and SVG, and the study as a PDF, all in German. The English charts on this page can be downloaded below each chart. The citation suggestion is just above the download.
What is the first step for an industrial company?
Ask your sales team for the last five questions customers asked shortly before awarding a contract. Each of these questions becomes a page with the standard number, the location and a link to the primary source. Each page costs an afternoon of expert input and one day of editing.
Will the study be repeated?
Yes, in the first quarter of 2027, with the same 72 questions and systems. Every number then becomes a time series, and we will see whether Google's change of August 24 lasts.
Sources and contextclick to expand
- morefire with ucited: GEO study on the AI visibility of B2B brands, July 14, 2026. 62 categories in German industrial B2B, ten brand-neutral questions each, four AI systems, five runs, 12,250 answers. Measures the brand side: which manufacturers are cited. View study (in German)
- Radyant and OMR Reviews: Software Prompt Study, June 2026. 20,160 answers to buyer questions about software in eight categories. OMR Reviews receives 13.5 percent of all retrievals. View study
- Radyant: Persona Study, June 2026. 17,929 answers, eight roles, five AI systems, with correction for multiple testing. View study
- SISTRIX: AI SEO in e-commerce, September 1, 2026. The top fifth of shops receives around 72 percent of all AI citations. AI Overviews appear for 69.7 percent of informational and 0.2 percent of transactional searches. View article (in German)
- SISTRIX: AI Citation Drift, April 28, 2026, updated August 21, 2026. 82,619 prompts over 17 weeks in six countries. Google AI Overviews and AI Mode share 17 percent of their domains for the same prompt. View article (in German)
- Żatuchin, Dmitrij (Rankfor.AI, EUAS Tallinn): The Language Blind Spot, arXiv 2606.23165, June 22, 2026. 35,640 answers, twelve European languages, 66 brands, three AI systems. Asking in the local language lifts the recommendation share of local market leaders by 0.80 and that of large corporations by 0.15. Data and code openly available on Zenodo. The author runs his own provider business. View paper
- Semrush: AI Search and Manufacturing SEO, September 8, 2026. 458 industrial search terms in the US. AI overviews appear for 57 percent of search volume, and 0.48 percent of visits come from AI systems. View article
- Similarweb Investor Solutions: Anthropic IPO analysis, September 24, 2026. Share of visits among the major AI chatbots worldwide in August 2026: ChatGPT 57 percent (78 a year earlier), Claude 9.6 percent (about 2 a year earlier). Figures for Germany or B2B are unavailable. View analysis
- Usage shares of AI searches, worldwide, cited second-hand: First Page Sage with Similarweb and Semrush, April 2026 (ChatGPT 60.7 percent, Gemini 21.5 percent, Copilot 13.2 percent, Perplexity 5.8 percent, Claude 4.1 percent of AI searches). View report (in German)
Our own measurement: panel on Peec AI, 72 questions, six AI systems, daily from August 10 to September 8, 2026, location Germany, language German. Google surfaces additionally checked with DataForSEO and in the browser on September 9. We read the third-party studies at their primary source and cite them only where they measure a different question from this study.