AI Visibility in Telecommunications: Which Sources Shape LLM Recommendations?

When a potential customer asks ChatGPT “Which mobile provider has the best network?” or “Which mobile plan is worth it?”, the brands appearing in the answer can become part of their selection process. 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, which source types are retrieved and how brands are spoken about. Here’s what this standardised snapshot reveals for Telecommunications, with a focus on the source mix and its implications for GEO and Digital PR. 

Contents 

• How We Conducted Our LLM Industry Benchmark 
• AI Visibility in Telecommunications: How Does the Industry Compare? 
• Which Telecommunications Providers Are Most Visible in AI? 
• Which Sources Do LLMs Use for Telecommunications Questions? 
• How Do Telecommunications Providers Become Visible in AI? 
• The Full Picture Is in the LLM Industry Benchmark 
• FAQ: AI Visibility in Telecommunications 

How We Conducted Our LLM Industry Benchmark 

The benchmark follows 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. 

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

AI Visibility in Telecommunications: How Does the Industry Compare? 

In our benchmark, comparison pages lead the Telecommunications source-type lists for recommendations, comparisons and purchasing questions. Articles lead for criteria/guidance, with how-to guides second. Across all 10 industries, how-to guides lead the guidance mix instead.  

Here’s how the leading source types compare: 

The percentages describe the cross-industry retrieval mix, not Telecommunications shares. The industry scorecard recorded 91,439 retrievals. It publishes the order of leading source types, rather than their individual retrieval shares.  

For content planning, the distinction is between choosing an offer and understanding the criteria behind it. Comparisons lead when the question concerns recommendations, alternatives or purchasing. Articles come first when users seek criteria and guidance. A tariff comparison and an explanation of network or contract terms therefore serve different purposes. 

For additional context, Telecommunications records average visibility of 12.6%, compared with 6.94% across all 10 industries. Its reported average SOV is 4.15%, versus 3.72%, and sentiment is 53.8, versus 54.58. The higher mention rate does not come with a higher sentiment score. Sentiment measures answer tone within the dataset, not service quality. 

Its HHI is 1,462, and its Top-3 Share is 63%. HHI measures concentration: higher values mean SOV is distributed less evenly. The three largest SOV contributors account for 63% of the Top-20 total, leaving 37% across the remaining entities. These concentration figures use normalised SOV shares, separate from the reported average SOV above, and are not market shares. 

Which Telecommunications Providers Are Most Visible in AI? 

According to Peak Ace’s benchmark for Germany in August 2026, the top three entities by visibility in the Telecommunications prompt set are: 

• Telekom: 54% visibility 
• Vodafone: 50% visibility 
• o2: 40% visibility 

Their shared advantage is being directly relevant to the buying decision. The white paper explains their prominence through their role as the three major German network operators. Questions about mobile networks, tariffs and providers put these businesses at the centre of the answer: they are the services being evaluated, rather than simply brands publishing content about the topic. 

This helps explain why the same providers appear prominently across the commercial prompt set. A question about network choice and a question about which plan to buy approach the decision from different angles, but both concern the underlying provider and its offer. 

The source mix points to how competing offers can be evaluated. Comparisons lead three intent lists, while category pages rank second for recommendations and purchasing questions. For other providers, the practical task is to make the suitability of an offer clear: who it serves, which network it uses and how its price and contract conditions compare. The useful content question is not just “Are we mentioned?”, but “Is there enough clear information to explain when our offer fits?” 

Which Sources Do LLMs Use for Telecommunications Questions? 

The benchmark analysed which types of content the selected AI systems retrieved when processing Telecommunications prompts. 

Note: retrieval records source access, not necessarily a citation or brand mention in the final answer. The published scorecard does not identify leading telecom domains or link particular sources to individual brands. The patterns below inform content audits; they do not establish the effect of a specific format or placement. 

The source mix varies by intent: 

A few things stand out: 

• Comparison pages lead three lists, including recommendations and purchasing questions. Their role extends beyond explicit provider-versus-provider comparisons. 
• Category pages appear in every list and rank second for recommendations and purchasing questions. Pages grouping tariffs and services belong in the audit alongside individual offers. 
• Product pages feature in three lists, covering recommendations, comparisons and purchasing. Specific tariff information complements broader selection content. 
• Articles lead criteria/guidance, with how-to guides second. Explanatory content has a different role from offer comparison. 

What does the source mix suggest? 

• Make tariff groups useful for selection. Category pages rank ahead of individual product pages for recommendations and purchasing questions. Review whether users can distinguish offers by data allowance, contract length and relevant requirements, rather than seeing only promotional prices. 
• Compare the offer, not just the headline price. Comparison pages lead purchasing questions. Make recurring prices, introductory periods, one-off fees and contract conditions clear wherever applicable, and keep those details aligned across comparison and product content. 
• Explain the criteria before asking for a choice. Articles lead guidance questions. Content about network choice, tariff requirements or contract terms can help users understand which differences matter. Keep those explanations connected to the relevant offers. 

How Do Telecommunications Providers Become Visible in AI? 

The data raises several questions that telecommunications marketers should consider: 

• Are you visible on the comparison, category and product pages retrieved for relevant questions? Review the actual pages and domains in your own monitoring. Check whether they describe your tariffs accurately and explain who each offer suits. 
• Does your content strategy cover all four intent types? Focusing only on “best mobile provider” questions leaves comparisons, criteria and pricing unaddressed. Pair offer-selection content with explanations of network and contract topics. 
• Is your brand represented consistently across different sources? Check network information, tariff names, allowances, prices and contract conditions across your website, comparisons and third-party coverage. Prioritise outdated or conflicting details. 
• Are you connecting SEO, GEO, and Digital PR? Use your monitoring to identify relevant third-party comparison and editorial sources. Build outreach around useful, verifiable information about your services, rather than assuming every telecom publication is equally relevant. Track brand mentions and retrieved sources separately, using a consistent prompt set and reviewing results by intent and system. 

The practical starting point is a tariff audit: choose one offer category, review its comparison, category, product and guidance content, and resolve the information gaps that affect selection. 

The Full Picture Is in the LLM Industry Benchmark 

This blog post covers the Telecommunications highlights. The full LLM Industry Benchmark goes further: 

• How the source mix differs across all 10 industries. 
• How source types shift between recommendations, comparisons, pricing and guidance. 
• Visibility, SOV and sentiment for the reported brands. 
• How concentrated SOV is within each industry’s Top 20. 
• The industry scorecards and complete methodology. 

Download the full LLM Industry Benchmark here. 

Want to understand your own results and improve your telecom brand’s presence in AI answers? Get in touch with our team. 

FAQ: AI Visibility in Telecommunications 

How visible are telecommunications brands in ChatGPT and other AI assistants? 

Average visibility was 12.6% in the German-market snapshot for August 2026, aggregated across Gemini, ChatGPT and Google AI Overviews. This is not a ChatGPT-only figure. 

Which telecommunications brands are most visible in AI search? 

Telekom recorded 54% visibility, Vodafone 50% and o2 40%. The white paper links their prominence to their direct relevance to network, tariff and provider-selection questions. 

What do the Telecommunications AI Visibility Rankings show? 

They show entity mention rates within the fixed commercial prompt set. Separately, the Top-3 Share of 63% describes concentration of normalised SOV within the Top 20, not market share. 

What types of content do LLMs use to answer telecommunications questions? 

In our benchmark, comparisons led recommendations, comparisons and purchasing questions. Articles led criteria/guidance. Category pages appeared among the leading formats for all four intents.  

Is AI visibility for telecom brands relevant? 

It measures presence in answers to commercial questions. Use it alongside traffic, leads and conversions to assess how potential customers encounter and engage with your offers. 

How can telecommunications providers improve their AI visibility? 

Start with one tariff category. Review retrieved comparisons, offer pages and guidance against customer questions, correct outdated or missing information, and monitor changes with the same prompt set.