When prospective buyers ask an AI assistant which family SUV to choose or where to book a service, the answer is often formed before they visit a single website. The question for automotive brands is which sources that answer draws on. Peak Ace’s LLM Industry Benchmark analysed 1,000 standardised, brand-neutral prompts across ten industries. This article looks at the automotive sources mix and what it may mean for content and Digital PR.
Note: This article is an excerpt from the LLM Industry Benchmark. The complete analyses, including the source mix for all ten industries, are available in the benchmark itself.
In short:
- In our benchmark, comparison formats are the most frequently retrieved source type across industries, and they lead in three out of four search intents.
- For automotive, listicles lead recommendation questions, while articles lead criteria questions.
- The automotive source mix differs from the cross-industry picture. This underlines that industry is a lens for interpreting results, not a ranking.
- All findings refer to this prompt set, this time period (August 2026), and this setup.
Methodology and data corpus
The benchmark is based on a clearly defined data corpus. The following sections describe its structure and the key figures derived from it.
Prompt set and structure: The prompt set comprises 1,000 standardised, brand-neutral bottom-of-funnel prompts. There are 100 commercially relevant prompts per industry, distributed across ten topic clusters. The prompts reflect questions people ask shortly before a purchase or booking.
Search intent: Each industry covers four search intents, with 25 prompts per intent:
- Best/Top/Test Winner: Recommendation questions, such as “Which family SUV is best for…?”
- Comparisons: Contrasts, such as “Which is better, A or B?”
- Prices/Where to buy: Questions about costs and sources, such as “Where can I book a test drive?”
- Criteria/Advice: Questions about selection criteria, such as “What should I consider when choosing an electric car?”
Market, language and time period: The prompts are in German and geared towards the German market. Answers were collected from 1 to 31 August 2026.
Systems and repetitions: ChatGPT, Gemini, and Google AI Overviews were observed. Each prompt was executed 15 times, five times per system. Individual responses were treated as samples, which allows fluctuations to be classified more reliably.
Database: The data comes from Peec AI exports. Fifty brands per industry were monitored, and the report focuses on the top 20 by visibility.
Key figures
- Visibility: Shows how frequently an entity appears in responses to the prompt set.
- Share of Voice (SOV): Describes the proportion of an entity among all mentions in the observed dataset.
- Sentiment: Rates the tone of responses on a scale of 0 to 100.
Search intents and source patterns
Search intent appears to influence which types of sources shape a response, and this pattern differs by industry.
Automotive in focus Within the automotive prompt set, the leading source types per intent are as follows:
- Best/Top/Test Winner: Listicles, followed by comparison pages, articles, and category pages.
- Comparisons: Comparison pages, followed by articles, listicles, and product pages.
- Criteria/Advice: Articles, followed by comparison pages, how-to guides, and listicles.
- Prices/Where to buy: Comparison pages, followed by category pages, articles, and how-to guides.
Across all ten industries The cross-industry picture shows a different mix:
| Intent | Leading source types and share of retrieval (across industries, descending) |
| Best/Top/Test Winner | Comparison 28.31%; Listicle 14.99%; Product page 12.75%; Item 9.47% |
| Comparisons | Comparison 32.65%; Item 16.06%; Product page 10.46%; Other 9.45% |
| Criteria/Advice | How-to guide 22.33%; Article 19.82%; Other 12.20%; Comparison 12.03% |
| Prices/Where to buy | Comparison 22.64%; Product page 14.57%; Category page 12.35%; How-to guide 10.96% |
These figures are aggregated and do not refer to a single industry. “Other” is a residual category and not a specific format.
Four patterns stand out in the data:
- Comparisons play a leading role in three out of four intents. They appear not only in explicit “A versus B” questions, but also in recommendation and reference questions.
- Advice changes the source mix. For criteria and advice questions, how-to guides and articles are more prominent. This may indicate that AI systems draw on explanatory content when users seek to understand a decision.
- Product and category information appear alongside purchase-related questions. Product pages take second place and category pages third.
- The pattern is not universal. Source mixes vary by industry, so the same intent can draw on different formats depending on the sector.
Retrieval means that a source has been accessed. This does not automatically imply that it will be cited or that a brand will appear in the final answer. The URL types describe formats, not ownership or authority.
AI Visibility snapshot
An average value does not show whether mentions are spread across many brands or concentrated on a few. Within the top 20 of the automotive set, two concentration metrics are therefore considered, both based on SOV shares normalised to 100 per cent:
- HHI: Describes the concentration of SOV components. Higher values indicate a less even distribution.
- Top 3 share: The combined share of the three largest SOV contributions.
For automotive, the benchmark reports an HHI of 976 and a top 3 share of 48 per cent. Nearly half of the normalised SOV within the top 20 falls on three entities. This describes how mentions are distributed in this dataset, not market share.
In the snapshot, the automotive averages are 9 per cent visibility, 4.10 per cent SOV, and a sentiment score of 57.4. Across all ten industries, the averages are 6.94 per cent visibility, 3.72 per cent SOV, and a sentiment score of 54.58.
Benchmark snapshot, not a market ranking: In the observed dataset, ADAC (29 per cent visibility), VW (28 per cent), and Škoda (19 per cent) recorded the highest visibility. ADAC is a mobility club and roadside assistance provider rather than a manufacturer. This may indicate that advisory and comparison sources are directly relevant to purchase-near vehicle questions. It does not show a general preference for advisory providers.
Concentration scores help to understand how mentions are distributed. However, they do not explain why a particular brand appears. For that, it is worth examining the questions and the pages accessed behind your own results.
What this means for content and Digital PR
The data reveals a clear trend: depending on intent and industry, AI responses draw on different types of sources. Practical implication: the goal is not to be visible everywhere, but to understand which formats matter for which questions. Our results suggest several starting points:
- Comparison and listicle formats: Check whether your models and offers are described correctly in relevant comparison and listicle sources. Confirm that key specifications, such as engine type, pricing tiers, equipment lines, delivery timelines, and financing options, are consistent across sources.
- Product and category pages: Ensure that pricing, configuration options, availability, delivery timelines, and financing terms are clear and up to date. Contradictions between product pages, overview pages, and third-party sources increase the risk of outdated statements in generated answers.
- Guidance and criteria: Explain selection criteria clearly, for example fuel type versus electric, total cost of ownership, insurance classes, safety ratings, and use-case fit (city, family, or long-distance). Link these explanations to suitable models so that criteria and product information stay aligned.
- Digital PR: Identify the most important third-party sources per intent and check what information they carry about your brand. Not every publication is equally relevant, so use the source mix as a starting point for a prioritised list.
- Monitoring: Track brand mentions and accessed sources with a consistent prompt set, and analyse results by category, intent, and system. This complements traditional search and conversion metrics.
Limits of expressive power
The results are valid only for this specific prompt set, time period, and setup. They do not represent a complete measurement of an industry’s or brand’s visibility in AI systems. In particular, please note the following:
- The prompt set shapes the outcome. Different questions or wording can lead to different answers.
- Individual responses are samples. Small differences should not be overinterpreted.
- Three systems are not one system. Gemini, ChatGPT, and Google AI Overviews cannot simply be equated.
- Retrieval is not citation. The scorecard does not link individual sources to specific brands, so no causal mechanisms can be derived from the source mix.
- Sentiment is not a quality assessment. It describes the tone of responses in the dataset, not real customer satisfaction or brand reputation.
Read the full benchmark
This article provides an excerpt. The complete LLM Industry Benchmark includes, among other things:
- The source mix for each of the ten industries and all four intent types
- Visibility, SOV, and sentiment within the benchmark
- HHI and top 3 share within the top 20 of each industry
- The leading entities observed per industry
- The cross-industry shares of the source types
- The industry scorecards and the complete methodology
Download the full LLM Industry Benchmark here.
Had a look at the benchmark and want to strengthen your telecoms brand’s presence in AI answers? Get in touch with Peak Ace’s LLMO and GEO team.
FAQ: AI Visibility in the Automotive Industry
Which types of content do AI systems draw on for car questions?
In our benchmark, listicles lead for recommendation questions, comparison pages for comparison and price questions, and articles for criteria questions. These are retrieved source types rather than necessarily cited sources, and they apply only to this prompt set and period.
How visible are automotive brands in AI answers?
A single cross-system visibility figure cannot currently be quantified reliably, because there is no unified, complete measurement methodology across all models, markets, prompts, and points in time. Our benchmark offers a standardised snapshot based on a fixed commercial prompt set.
Which automotive brands are most visible in AI search?
The benchmark can only state which brands were mentioned most often in this dataset during the observation period. It does not indicate overall brand strength or market leadership.
Does AI visibility depend only on your own website?
No. Comparisons, listicles, articles, and guides all play a significant role. It is therefore worth checking the third-party URLs that appear for your key questions alongside your own content.
How can we test whether measures are effective?
In three steps: audit your sources and content, check the timeliness and consistency of your information, and then re-measure with the same test questions under the same conditions.
