LLM Industry Benchmark 2026: What Makes Brands Visible in AI Search?

When a potential customer asks ChatGPT or Gemini for a product recommendation, the brands in the answer can become part of their consideration set. Which sources sit behind those answers? 

Peak Ace’s LLM Industry Benchmark analysed a fixed set of 1,000 commercially relevant prompts across 10 industries in Germany during August 2026. Across Gemini, ChatGPT and Google AI Overviews, we measured which brands show up, how visibility is distributed, which source types are retrieved and how brands are spoken about. Here’s what this standardised snapshot reveals about the source mix behind AI answers, and what it implies for GEO and Digital PR.  

Contents 

• How we conducted our LLM Industry Benchmark 
• AI visibility across industries: How visible are brands in AI search? 
• How concentrated is AI visibility among German brands? 
• Which sources do LLMs retrieve for commercial questions? 
• How can a GEO strategy improve AI visibility? 
• The full picture is in the LLM Industry Benchmark 
• FAQ: AI Visibility, AI Benchmarks and GEO Strategy 

How we conducted our LLM Industry Benchmark 

The benchmark is built on a defined methodology: 

• 1,000 standardised bottom-of-funnel prompts across 10 industries, with 100 commercially relevant prompts per industry. 
• Prompts were divided into 10 topic clusters per industry. 
• Four intent types were tested: best/top/test winner, comparisons, pricing/where to buy, and criteria/guidance, with 25 prompts per intent in each industry. 
• The analysis focused on the German market and German-language prompts during August 2026. 
• Gemini, ChatGPT and Google AI Overviews were monitored. Each prompt ran 15 times, five per system. 
• Data was sourced from Peec AI exports; 50 brands per industry were monitored, with reporting focused on the Top 20 by visibility. 
• The three core brand KPIs are visibility, Share of Voice/SOV and sentiment. Visibility measures how often an entity appears in answers for the prompt set; SOV records its share of voice in the monitored dataset; sentiment measures the tone of answers mentioning an entity on a 0–100 scale. For concentration analysis, SOV shares are separately normalised to 100% within the Top 20.  

The source analysis describes retrieval counts and URL types by industry and intent. All results apply to the selected prompts, monitored entities, systems and study period. The tracked entity can be a product, brand or programme, rather than its parent company. 

For the full methodology, definitions and cross-industry breakdowns, the complete benchmark is available for download. 

AI visibility across industries: How visible are brands in AI search? 

The clearest content finding is that the same type of question can draw on a different source mix depending on the industry. In our benchmark:  

• Health & Wellness: articles lead recommendations, comparisons and guidance; category pages lead purchasing questions. 
• E-Commerce & Retail: listicles lead recommendations, while category pages lead purchasing questions. 
• Marketing & Advertising: product pages lead recommendations and purchasing questions. 
• SaaS & Technology and Travel & Hospitality: comparisons lead recommendations, comparisons and purchasing questions; how-to guides lead guidance. 

The practical distinction is not which industry performs best. It is which formats appear behind the questions relevant to your offer. An article-led health-product question and a product-page-led advertising-platform question call for different content audits. 

The brand metrics provide additional context. Across all 10 industries, the unweighted averages are 6.94% visibility, 3.72% SOV and 54.58 sentiment. Each industry contributes equally to those averages.  

Here’s the breakdown, listed alphabetically rather than as an industry ranking: 

Visibility ranges from 3.55% to 12.60% in this dataset. Sentiment ranges from 51.6 to 57.4 and should be read as a relative measure of answer tone, not brand or product quality. 

How concentrated is AI visibility among German brands? 

An industry average does not show whether mentions are spread across many brands or concentrated among a smaller group. The benchmark uses two measures to describe that distribution:  

• HHI: summarises how concentrated SOV is within the Top 20. Higher values mean a less even distribution. 
• Top-3 Share: records the combined share of the three largest SOV contributors within that group. 

Both measures use SOV shares normalised to 100% within the Top 20. They are separate from the reported average SOV in the table above and are not market shares. 

These four industries illustrate the distinction: 

In Telecommunications, 63% of the normalised Top-20 SOV sits with three entities. In SaaS & Technology, the equivalent figure is 24%. That describes two different distributions of brand presence, not two different levels of market opportunity. 

The composition of the prompt set helps explain some prominent brands. The telecom scorecard connects Telekom, Vodafone and o2 to questions about networks and tariffs. The SaaS scorecard connects Salesforce, HubSpot and Power BI to CRM and BI selection. These are products and providers directly relevant to the buying decisions tested. 

For marketers, use concentration to understand the distribution of mentions, then inspect the questions and retrieved pages behind your own results. A concentrated snapshot alone does not explain why a particular competitor appears. 

Which sources do LLMs retrieve for commercial questions? 

The source analysis shows how the retrieved formats change with the information users seek. 

Note: retrieval records source access, not necessarily a citation or brand mention in the final answer. URL types describe formats, not ownership or authority. The published industry rankings do not provide domain-level or brand-to-source breakdowns. These patterns inform content audits; they do not establish the effect of a specific format or placement. 

Across all 10 industries, the four most frequently retrieved source types for each intent are: 

These are pooled cross-industry shares, not percentages for any single sector. “Other” is a residual category, not a specific format. 

A few things stand out. 

• Comparisons lead three intents. Their role extends beyond explicit “A versus B” questions into recommendations and purchasing. 
• Guidance changes the mix. How-to guides and articles lead criteria/guidance. How-to guides account for 22.33% of guidance retrievals, compared with 5.81% for recommendations. 
• Product and category information features in purchasing questions. Product pages rank second and category pages third, alongside comparisons and guides. 
• The aggregate pattern is not every industry’s pattern. In Media & Publishing, comparisons lead all four intent lists. In Health & Wellness, articles lead three. Industry is the lens for deciding which formats to investigate.  

What does the source mix suggest? 

• Separate selection content from practical guidance. A comparison should explain alternatives and trade-offs; a guide should help users define requirements or complete a task. Do not expect one generic article to do both well. 
• Treat pricing as part of evaluation. Comparisons lead purchasing questions. Keep product facts and commercial conditions consistent between offer pages and relevant comparison content. 
• Prioritise formats within your own category. The cross-industry percentages provide context. Use the industry scorecard, then actual retrieval URLs from your monitoring, to identify pages relevant to your buyers’ questions. 

How can a GEO strategy improve AI visibility? 

The benchmark gives marketers a starting point for reviewing the information behind AI answers. A GEO strategy, or Generative Engine Optimisation strategy, brings that work together across SEO, content, Digital PR and monitoring. 

For marketers, this means focusing on a few key areas: 

• Cover different user intents. Map recommendations, comparisons, pricing and guidance questions for one product category or buyer profile. Review gaps in the content answering those questions. 
• Be represented across relevant sources. Inspect the comparison pages, listicles, articles, guides, product pages and category pages actually retrieved. Identify inaccurate descriptions, missing information and unexplained differences between alternatives. 
• Build appropriate third-party coverage. Use the retrieved domains and pages in your own monitoring to prioritise Digital PR. Give publishers useful, verifiable information rather than treating all coverage as equally relevant. 
• Keep information consistent and findable. Check product names, capabilities, prices and conditions across your website and relevant external sources. Resolve outdated or conflicting details. 
• Maintain strong SEO foundations. Make key content accessible, clearly structured and easy to navigate. Treat this as a foundation for information access, not a promise of AI recommendations. 
• Measure AI visibility. Track brand mentions and retrieved sources separately with a consistent prompt set. Review results by category, intent and system alongside traditional search and conversion metrics. 

The full picture is in the LLM Industry Benchmark 

This blog post covers the cross-industry highlights. The full LLM Industry Benchmark goes further: 

• The source mix for each of the 10 industries and four intent types. 
• Brand visibility, SOV and sentiment within the defined benchmark. 
• HHI and Top-3 Share within each industry’s Top 20. 
• The leading monitored entities in each industry. 
• Cross-industry source-type shares. 
• The industry scorecards and complete methodology. 

Download the full LLM Industry Benchmark here. 

Have you looked at our benchmark and want to improve your brand’s presence in AI answers? Get in touch with our team. 

FAQ: AI Visibility, AI Benchmarks and GEO Strategy 

What is an AI benchmark? 

An AI benchmark evaluates defined outputs against a fixed test set. Peak Ace’s benchmark examines brand mentions, answer tone and retrieved source types for commercial questions across 10 industries. 

What does an LLM benchmark measure? 

Ours measures visibility, SOV, sentiment, concentration and retrieval patterns for a defined prompt set, market and period, aggregated across Gemini, ChatGPT and Google AI Overviews. 

What is an AI benchmark ranking? 

It orders the entities or industries measured within a benchmark. It describes the tested prompts and study period, not market share or overall brand performance. 

What is AI visibility? 

AI visibility measures how often a monitored brand or entity appears in answers to a defined prompt set. It is separate from whether a source is retrieved or cited. 

What is a GEO strategy? 

Generative Engine Optimisation combines SEO, content, Digital PR and monitoring to work on presence in AI answers. Priorities should follow audience questions and the sources retrieved for them. 

Does AI visibility depend only on a brand’s website? 

An own-site-only audit is too narrow. Our benchmark retrieves comparisons, articles, guides and other formats. Inspect actual URLs to distinguish your own content from relevant third-party sources. 

What does the LLM benchmark tell us about AI visibility? 

The source mix changes by intent and industry. Comparisons lead three cross-industry intent lists; guides lead guidance. Brand mentions are also distributed differently across the monitored sectors.