AI is increasingly changing how people research cars, compare models and choose their next vehicle. When a potential buyer asks ChatGPT, “What’s the best family SUV under €50,000?”, the brands appearing in that answer can enter the consideration set. But what sources form the information base behind those answers?
Peak Ace’s LLM Visibility Benchmark analysed 1,000 commercially relevant prompts across 10 industries to measure which brands appear in AI-generated answers, which sources are retrieved and how brands are represented. Based on a fixed German-language prompt set tested across ChatGPT, Gemini and Google AI Overview during August 2026, the benchmark provides a standardized snapshot of the automotive source mix.
Here’s what the data reveals for automotive, and what it may imply for content and Digital PR.
Contents
- How we conducted our LLM Visibility Benchmark
- Which sources do AI systems use for automotive questions in our benchmark?
- How does the automotive source mix compare with other industries?
- Which automotive entities are most visible in the benchmark snapshot?
- How can automotive brands approach AI visibility?
- The full picture is in the LLM Visibility Benchmark
- FAQ: AI Visibility in the Automotive Industry
How we conducted our LLM Visibility Benchmark
The benchmark is built on a clear, standardized methodology:
- 1,000 prompts across 10 industries, each with 100 commercially relevant, bottom-of-funnel prompts.
- Prompts were split across 10 topic clusters per industry to capture different decision-making situations.
- Four intent types were tested: best/top/test winner, comparisons, pricing/where to buy, and criteria/guidance.
- All prompts were brand-neutral. No brand names were included in the questions.
- The analysis focused on the German market and German-language answers from 1–31 August 2026.
- Each prompt ran 15 times in total: five times each across ChatGPT, Gemini and Google AI Overview.
- Data was sourced from Peec AI exports. Fifty brands per industry were monitored; reporting focuses on the Top 20 by visibility within the tracked set.
The three core KPIs are visibility, Share of Voice (SOV) and sentiment. Visibility measures how often an entity appears in answers for the prompt set. SOV measures its share of total visibility within the Top 20. Sentiment describes the tone of answers mentioning an entity on a scale from 0–100, where higher means more positive. These are indicators within the benchmark, not market share or absolute quality ratings.
Important distinction: the source analysis measures retrieval (the pages accessed as an information base) not necessarily explicit citation in the final answer.
For the full methodology, definitions and cross-industry breakdowns, the complete AI benchmark is available for download.
Which sources do AI systems use for automotive questions in our benchmark?
The benchmark analysed which types of content the tested AI systems retrieve when processing prompts centred around the automotive sector. The source mix varies significantly by intent.
| Intent | Benchmark source types, ranked |
| Best/Top/Test winner | Listicle, Comparison, Article, Category Page |
| Compare vs. | Comparison, Article, Listicle, Product Page |
| Criteria/Guidance | Article, Comparison, How-To Guide, Listicle |
| Pricing/Where to buy | Comparison, Category Page, Article, How-To Guide |
A few things stand out:
- Comparison pages appear prominently across the automotive intent mix. They lead comparison and pricing questions and are also among the leading formats for recommendations and guidance.
- Listicles play a particularly strong role for “best of” and test-winner questions. They lead the recommendation mix and remain among the leading formats for comparison and guidance.
- Articles appear across all four intents. In this benchmark snapshot, the editorial layer is present throughout the automotive decision journey.
- How-to guides gain importance for criteria and pricing questions. These formats can help users understand decision frameworks and next steps.
- Category and product pages appear where users need orientation around options or specific offers. The scorecard places category pages among the leading pricing sources and product pages among the leading comparison sources.
Across all industries, comparison pages, product pages and listicles form important parts of the retrieval mix. For automotive specifically, the scorecard points to a combination of editorial, advisory and commercial formats. This may reflect the high-information nature of vehicle decisions, but it does not establish that one format causes a recommendation.
How does the automotive source mix compare with other industries?
Across the full benchmark, comparison pages are the largest individual source format by retrieval volume. However, the automotive scorecard shows why industry should be used as a lens rather than a scoreboard.
For best/top/test-winner questions, comparison pages account for 28.31% of the cross-industry retrieval mix, followed by listicles at 14.99%. In the automotive scorecard, listicles appear first, followed by comparisons, articles and category pages.
For criteria and guidance, how-to guides lead the cross-industry mix at 22.33%, followed by articles at 19.82%. In automotive, articles appear first, followed by comparisons, how-to guides and listicles.
For pricing and “where to buy”, comparison pages lead the cross-industry mix at 22.64%, followed by product pages at 14.57% and category pages at 12.35%. The automotive scorecard places comparisons first, followed by category pages, articles and how-to guides.
The practical implication is not that automotive performs better or worse than another industry. It is that automotive marketers should examine the specific source ecosystem behind vehicle questions instead of applying one generic AI-content formula.
Which automotive brands are most visible in the benchmark snapshot?
As additional context, the three tracked entities with the highest visibility in the automotive scorecard are:
- ADAC – 29% visibility
- VW – 28% visibility
- Škoda – 19% visibility
ADAC’s position is particularly notable. It is not a car manufacturer but a mobility club and roadside assistance provider. This may reflect the fact that automotive prompts include advisory and comparison contexts, where an entity can be relevant without being a vehicle manufacturer. It could also be influenced by the benchmark’s brand-mapping methodology and the specific prompt set. No universal preference for advisory entities can be inferred.
The automotive scorecard reports average visibility of 9%, average SOV of 4.1% and sentiment of 57.4. Its Top 3 Share is 48%, and total retrievals amount to 79,859. These figures describe the benchmark snapshot and tracked set, not automotive market share, sales performance or product quality.
The brand findings add context. The more actionable question remains which sources and formats make relevant automotive information available for different intents.
How can automotive brands approach AI visibility?
The data raises several questions that automotive marketers should consider:
- Are you represented on relevant comparison and listicle pages? A strong brand website is only one part of the source ecosystem. Investigate which independent sources are retrieved for your own vehicle topics and whether they represent your brand accurately. A retrieved source is not necessarily cited in the final answer.
- Does your content strategy cover all four intent types? Brands that optimize only for “best car” queries address one quarter of the benchmark’s prompt set. Comparisons, pricing and guidance together account for the other 75% – a defined study structure, not an estimate of overall consumer demand.
- Do your pages explain the criteria behind a vehicle decision? Articles and guides appear prominently in the automotive scorecard. Review whether your content helps users understand trade-offs, requirements and use cases, rather than only presenting specifications.
- Are your category, model and offer pages easy to interpret? Category and product pages appear among the leading formats for selected intents. This makes them relevant audit priorities, but the benchmark does not prove that a particular page type causes visibility.
- Have you considered an automotive GEO strategy? Generative Engine Optimisation (GEO) combines SEO, content and Digital PR into a measurable approach to improving how a brand is represented in AI-generated answers. Use the benchmark to formulate hypotheses and test them with your own prompts.
Expert tip: Earned media is a core pillar of automotive GEO
It’s not about producing more content on your own site. It’s about implementing a holistic publishing strategy – building comparison and list formats where they make sense, placing your brand in relevant rankings and listicles, and above all strengthening citable authority on independent third-party sources through consistent digital PR.
The full picture is in the LLM Visibility Benchmark
This blog post covers the automotive highlights. The full LLM Visibility Benchmark goes considerably deeper:
- How the source mix differs across all 10 industries within the standardized snapshot.
- How source formats change depending on whether users ask for recommendations, comparisons, pricing or guidance.
- The industry scorecards, including visibility, SOV, sentiment and concentration measures.
- The methodology and interpretation limits behind the findings.
- Cross-industry implications for content, Digital PR and further AI visibility research.
Download the full LLM Visibility Benchmark here.
Had a look at our benchmark and looking to develop your automotive AI visibility strategy? Get in touch with our LLMO and GEO team.
FAQ: AI Visibility in the Automotive Industry
What types of content do AI systems use to answer automotive questions?
In our fixed German-market prompt set, listicles lead best/top/test-winner questions, comparison pages lead comparison and pricing questions, and articles lead criteria/guidance. These are retrieved source types, not necessarily explicit citations.
How visible are automotive entities in ChatGPT and other AI systems?
The automotive scorecard reports average visibility of 9% for the tracked set and study period. This is a benchmark-snapshot measure, not a general visibility rate for every automotive brand or a measure of market share.
Which automotive entities are most visible in the benchmark?
ADAC appears at 29% visibility, VW at 28% and Škoda at 19%. This ordering applies only to the tracked entities, fixed prompts and August 2026 study period.
Does ADAC’s visibility show that AI systems prefer advisory sources?
No. ADAC’s position may reflect the automotive prompt set, the entity mapping and the relevance of advisory content to certain questions. A general preference cannot be inferred from this result alone.
Does AI visibility matter for automotive brands?
Appearing in an AI-generated answer can place a brand or entity in a user’s consideration set. The benchmark helps identify source patterns to investigate, but it does not measure sales uplift, conversion impact or a guaranteed advantage.
What is a GEO strategy for automotive brands?
A GEO strategy combines SEO, useful on-site content and relevant third-party coverage to improve how a brand can be found and represented in AI-generated answers. For automotive, the observed mix supports examining comparisons, listicles, articles, guides, category pages and product information together, then testing those priorities over time.
