We sent 25 real buying questions to ChatGPT and Google AI Overviews – the questions households actually ask. Then we counted which providers get named, and how often. The result is a ranking built from real AI answers, not opinion.
Gerolsteiner (11.4%) and Volvic (6.3%) dominate the answers – the remaining 93 named providers split what's left. If you're not here, you simply don't exist to AI users. That's exactly the gap BuzzView makes visible.
Share of all brand mentions across 25 prompts (Share of Voice). The longer the bar, the more often AI names the provider – across every question tested.
Behind the ranking — what the numbers actually mean for brands in this space.
Across 25 real prompts, AI systems collectively named 93 distinct mineral water brands — a number that initially suggests a wide-open playing field. Yet when you look beneath that surface, the distribution is anything but democratic. The top six providers — Gerolsteiner, Volvic, Saskia, Adelholzener, Black Forest, and Fürst Bismarck — collectively captured 114 of the 317 total brand mentions, accounting for 36.0% of all voice. The remaining 87 brands compete for the other 64%, with most appearing only once or twice across the entire prompt set.
This power-law dynamic is a hallmark of mature consumer packaged goods categories when they enter the AI recommendation era. Mineral water is not a category where AI hedges or presents a genuinely balanced market overview. Instead, AI models draw on the same sources that have historically driven brand salience — consumer press, health journalism, comparison tests, and retailer prominence — and compress a market of hundreds of producers into a handful of names that appear again and again. Gerolsteiner alone, with 36 mentions and an 11.4% share of voice, appears in more than one in three of every meaningful brand reference.
What makes this particularly relevant for mineral water brands is the structural divide between national champions and regional specialists. Brands like Gerolsteiner and Volvic benefit from decades of above-the-line advertising, extensive editorial coverage in health and lifestyle media, and consistent presence in major retail chains. Regional producers such as Lichtenauer, RhönSprudel, or Ensinger have built loyal regional customer bases but have rarely translated that loyalty into the kind of cross-platform digital content that AI systems index and learn from.
The 81 providers that collectively hold 48.6% of mentions but remain unnamed in any individual AI response represent a long tail that is structurally invisible to AI-driven discovery. For these brands, the challenge is not product quality — it is content scarcity. Without sustained editorial presence, third-party reviews, and health-claim documentation that AI can reference, even excellent regional waters remain permanently off the AI radar regardless of how they perform in taste tests or sustainability rankings.
In mineral water, AI has already picked its favourites — and they mirror traditional brand awareness rankings. If your brand is not consistently surfaced across editorial, health, and retail content online, AI will not find you, regardless of how good your water tastes.
The visibility scores across the 16 tracked mineral water brands reveal a gap of 3.5x between the category leader and the bottom tier. Gerolsteiner achieves a visibility score of 56.0, meaning it surfaces in more than half of all the prompts we tested. Aquintus and Saxonia Quelle sit at 16.0, appearing in roughly one in six prompts — and only in specific, narrow contexts where their particular product attributes happen to match the question. This is not a marginal difference; it represents a fundamentally different level of AI discoverability.
Understanding what drives visibility in the mineral water category requires looking at the types of content AI systems rely on. Mineral water is a category where health claims matter enormously: magnesium content for athletes, low sodium levels for hypertension patients, high bicarbonate content for digestive health, and calcium concentrations for bone density. Brands that have invested in publishing and distributing scientifically grounded health content — through their own websites, through partnerships with health portals, and through presence in product comparison databases — consistently outperform those that rely on traditional advertising alone.
Adelholzener, with a visibility score of 40.0, is a telling example of how a regional brand can punch above its market-share weight in AI contexts. The brand has long been associated with sustainability credentials and health-oriented positioning, and this has translated into substantial coverage in German consumer media and health journalism. Black Forest, at 36.0 visibility, similarly benefits from a distinctive product story — its source in the Schwarzwald region and its mineral profile have generated consistent editorial interest that AI can draw upon.
For brands in the 16–20 visibility band — including Vilsa, Rosbacher, Ensinger, Staatl. Fachingen, and Evian — the path to improved AI visibility is not through advertising spend but through content depth. Each of these brands has a distinctive mineral profile, a source story, or a health positioning angle that is currently underrepresented in the content formats that AI systems prioritise: detailed product pages, third-party health comparisons, and structured data that makes mineral composition machine-readable. Closing the visibility gap starts there.
A 3.5x visibility gap separates the category leader from the bottom tier. The difference is not product quality — it is the volume, depth, and accessibility of health-oriented and comparison-ready content that AI systems can reference and recommend.
Best-of prompts — such as "What is the best mineral water in Germany?" or "Which mineral water do you recommend?" — consistently surface the same core cluster: Gerolsteiner, Volvic, Adelholzener, and Saskia. These brands have the broadest content footprint and appear across the widest range of editorial sources, so general best-of queries reliably land on them. For brands outside this cluster, appearing in response to a best-of prompt is extremely difficult without a significant and sustained increase in third-party editorial presence.
Comparison prompts are where the leaderboard becomes more interesting. Queries like "Gerolsteiner vs Volvic — which is better?" or "Evian vs Black Forest comparison" force AI systems to be more specific, and this specificity opens space for brands that might otherwise be overlooked. Black Forest and Fürst Bismarck both appear more prominently in comparative contexts, because their distinctive mineral profiles and source stories give AI systems concrete differentiating content to work with. Brands that invest in publishing clear, factual product comparison content — including mineral analyses and independent taste test results — benefit disproportionately from this prompt type.
Alternative-seeking prompts — "What's a good alternative to Gerolsteiner?" or "Which sparkling mineral water can I use instead of Evian?" — open a second tier of visibility for regional brands like Lichtenauer, RhönSprudel, and Rosbacher. These brands appear in contexts where AI is explicitly being asked to broaden its scope beyond the category incumbents. This means that for a Lichtenauer or a Vilsa, the most accessible path to meaningful AI visibility is positioning their content explicitly in relation to the category leaders — not by avoiding comparisons but by leaning into them.
Use-case and vertical prompts reveal the most niche-specific patterns. Questions like "Best mineral water for baby food," "Which mineral water has low sodium for high blood pressure?" or "Best sparkling water for athletes" create entirely different winner lists. Staatl. Fachingen, for example, is specifically associated with therapeutic mineral water applications. Adelholzener appears frequently in health-specific use cases. Saskia surfaces in budget-conscious and value-oriented prompts. Brands that map their content strategy to specific use cases rather than seeking generic visibility will win these long-tail queries — and long-tail queries in health categories drive highly motivated, conversion-ready audiences.
No single content strategy wins all prompt types. Brands like Gerolsteiner and Volvic dominate broad best-of queries, while regional specialists like Lichtenauer and Staatl. Fachingen find their best AI visibility opportunity through use-case and alternative-seeking prompts — where specificity beats volume.
Frequency of mention is only half the picture. The sentiment attached to those mentions determines whether AI is building or eroding a brand's reputation with every query it answers. Looking at the sentiment breakdown across the leaderboard, two brands stand out for exceptionally high rates of positive framing: Saskia, with 13 positive mentions out of 18 total (72%), and Black Forest, with 10 positive mentions out of 14 total (71%). Both brands are mentioned in contexts where AI is explicitly recommending them for a specific purpose — value, sustainability, or a distinctive mineral profile — rather than merely listing them as market participants.
Gerolsteiner, despite its dominant 11.4% share of voice, shows a more neutral sentiment profile: 13 positive mentions against 23 neutral ones (64% neutral). This reflects its role as the category default — AI references it in almost any mineral water context, but often simply as the established market leader rather than as the best choice for a specific need. Being the most-mentioned brand does not automatically mean being the most-recommended brand in a useful, actionable way. Neutral mentions carry less persuasive weight with users who are actively seeking guidance.
Evian is the most concerning case in the tracked set: 2 positive, 6 neutral, and 2 negative mentions out of 10 total. Negative framing in AI responses typically arises from one of three sources: criticism in consumer media, environmental controversy, or unfavourable comparison in taste or health tests. For Evian, the likely driver is the combination of a premium price point and recurring environmental criticism around plastic packaging — topics that are extensively covered in German consumer media and that AI systems have clearly absorbed. A brand with 20% negative framing in AI responses faces a fundamentally different challenge than one with zero negative mentions.
What drives positive sentiment in the mineral water category is largely content that ties product attributes to specific, verifiable health or taste outcomes. Brands like Adelholzener (8 positive out of 16, 50%) and Black Forest (10 positive out of 14, 71%) have invested in positioning around health and origin stories in ways that give AI concrete, positive claims to repeat. Apollinaris and Fürst Bismarck, both at roughly 25–30% positive framing, have more work to do. The lever for improving sentiment is not advertising — it is the quality and positivity of the third-party content that exists about the brand: reviews, health assessments, editorial recommendations, and comparative tests.
High mention frequency without positive framing is a missed opportunity. Saskia and Black Forest outperform their raw mention counts by earning genuinely positive AI framing — a result of specific, credible health and value positioning that AI systems can confidently recommend rather than merely acknowledge.
The complete data picture for mineral water tells us that this category is neither a finished AI-visibility story nor a blank slate. With 93 distinct brands named across just 25 prompts, fragmentation is extreme — yet the top tier is already consolidating rapidly, with Gerolsteiner, Volvic, and Saskia pulling away from the rest in both mention frequency and visibility scores. This is a pattern typical of categories that are mid-way through AI maturation: the training data exists in volume, AI systems have enough signal to form preferences, but the lower ranks are still fluid and contestable.
The comparison with other FMCG categories is instructive. In fully AI-mature categories — think laptop brands or streaming services — the top three players hold 60–70% of all AI mentions with very little movement. In mineral water, the top three hold roughly 23% of mentions combined, and a dozen regional brands each hold between 2–6% of voice. This means the leaderboard is still genuinely moveable. A Rosbacher or an Ensinger investing meaningfully in content and digital PR today can realistically expect to see their AI visibility score improve from the current 20.0 band into the 28–36 band within 12 to 18 months.
The high fragmentation in the long tail — 81 unnamed providers sharing 48.6% of mentions collectively — signals that the category's content ecosystem is still incomplete. AI systems are clearly hungry for more source material: they acknowledge a broad market exists but lack the specific, citable content to name most of its participants individually. This is a window of opportunity that is typically open for three to five years before the training data stabilises and the ranking becomes much harder to shift. Brands that move first to fill this content vacuum will claim positions that will compound in value over time.
For brands in this category, the strategic implications are clear: AI visibility in mineral water is still a game that can be won through deliberate action, but the window is narrowing. The priorities are structured health-claim content tied to specific mineral analyses, use-case landing pages that address the specific prompts consumers and AI systems ask (low-sodium, baby food, sports recovery, sparkling), comparison content that positions the brand explicitly against the top three, and third-party validation from consumer tests, health portals, and nutrition experts. Brands that execute on these four pillars consistently will find themselves climbing the AI leaderboard — not by gaming a system, but by giving AI the credible content it needs to recommend them confidently.
Mineral water is in the active maturation phase of AI-search development — concentrated at the top but highly contestable in the middle tier. Brands that invest in health-specific, use-case-driven, and comparison-ready content now will lock in leaderboard positions before the ranking hardens in the next two to three years.
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Visibility score = share of prompts where the provider appears in the AI answer at all. 100% means: present for every relevant question.
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