How to measure and build Your Brand’s position when customers ask AI instead of Google

27.01.2026 | Tomasz Jaroń | 11 min

The key success metric in modern marketing is no longer your ranking position in a search engine, but your Share of Voice in the generative answers of systems like ChatGPT, Gemini, and Google AI Overviews. Because language models are taking over the conversation about your brand, the traditional click count matters less than being recommended inside the AI’s narrative. The material below explains the mechanics of this shift, how to measure it, and how to adapt.

Picture a board meeting. The CEO asks: „Are we number one on Google for ‘CRM system’?”. You answer: „Yes, but sales are falling”. Why? Because your prospective customer never typed the phrase into Google. They asked ChatGPT: „Compare three CRM systems for small-scale logistics and point me to the easiest to use”. The AI recommended a competitor, because that competitor „explained” its offer to the algorithms better. Traditional SEO is the fight for the best billboard along the highway. Optimizing for AI (GEO) is making the hotel concierge (the AI) recommend your restaurant to the guest.

Imagine changing your customer’s advisor

Why is Share of Voice in AI becoming a strategic KPI?

Presence in the generative answers of LLM (Large Language Models) systems is replacing traditional positions in search results, becoming the new standard of visibility. This is a direct consequence of changing user behavior and of how digital platforms present data.

Here are the key market figures confirming this trend:

Growth in AI traffic: In 2025, traffic coming from AI recommendations grew by 527%.
Shifting generational preferences: About one-third of Gen Z (33%) and of millennials (26%) prefer to use ChatGPT, Perplexity, or Gemini for product research rather than typing phrases into Google.
A satisfaction edge: AI Search has pulled ahead of the classic search engine in e-commerce, B2B, and YMYL services (Your Money Your Life – finance, health).

If a brand’s share of AI answers stays low, the company loses contact with the customer at the very first point of information. AI Share of Voice objectively shows whether a brand is truly present in the new channels, not just in historical rankings.

What are the consequences of “zero-click search”?

The dominant trend is a drastic drop in clicks on source links, because users get a complete answer directly in the search engine or chatbot interface.

Key statistics of the phenomenon:

No clicks: Reports indicate that 93% of queries in Google AI Mode end without a click on any link.
CTR decline: The classic organic click-through rate (CTR) for queries that generate AI Overviews dropped by 61% year over year.
Changing habits: After getting a summary, users rarely search further – they stay within the ready-made answer.

For a business, this means a brand absent from the generative narrative ceases to exist for the user – even if it holds a high position in traditional SEO.

How do SEO, AIO, and GEO strategies differ?

The core difference lies in the goal of optimization: classic SEO fights for a link ranking, while AIO and GEO aim for the brand’s presence “in the conversation” and being cited by the model as an authority.

Definitions and goals of each approach:

SEO (Search Engine Optimization):

Goal: Optimizing for a link ranking in Google.
Success metric: Position in the SERP (Search Engine Results Page).

AIO (AI Optimization):

Goal: Optimizing content and technical structure so it can be easily cited and summarized by an LLM.
Success metric: Presence in AI answers, number of citations, Share of Voice.

GEO (Generative Engine Optimization):

Goal: A strategy of building broad brand authority (“algorithmic trust”) so that algorithms treat it as a credible source for recommendations and roundups.
Success metric: Maximizing the share of global generative answers.

How do AI algorithms choose which sources to cite?

AI models such as Gemini, ChatGPT, and Perplexity generate answers by synthesizing information from a limited number of sources (usually 5-15) that they judge to be the most relevant and credible.

How each system works:

Google AI Overviews: Selects sources based on E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness) and data structure. It cites brands that have well-organized content (schema) and are recommended beyond their own site.
Gemini: Decides on citation based on freshness, domain authority, and the consistency of brand signals across the Google ecosystem.
Perplexity: Favors community sources (Reddit, Quora), high data freshness, expert content, and a “fresh” reputation.

Think of an AI model as a journalist writing a „Top 5 solutions for business” piece due yesterday. There is no time for deep research. It looks for information served on a plate: clear data, a comparison table, concrete numbers. If your site is a chaotic jumble of loose thoughts („we are innovative”), the journalist skips it. If it is a well-organized dossier full of specifics („we cut time by 20%”), it takes your quote into the article. GEO is the art of preparing that dossier so the journalist always reaches for it first.

How do you prepare your site technically for AI crawlers?

The foundation of AI visibility is a technical readiness audit (AI-readiness) that ensures crawlers can read and interpret your site’s content without obstacles.

Key elements of the technical audit:

Crawler accessibility:

The site must be accessible to bots such as GPTBot, PerplexityBot, GeminiBot.
You need to remove blocks in the robots.txt file and make sure key content isn’t hidden in JavaScript or in interactive blocks invisible to the models.

Structured data (Schema.org):

Implement formats such as FAQPage, Article, HowTo, and data in the JSON-LD format.
This lets an LLM automatically recognize the brand and its offering.

Core Web Vitals and Crawl Budget:

Fast indexability and strong performance metrics make it easier for AI to use your content.
Optimizing the crawl budget through clear sitemaps.

How do you write content so it appears in AI answers?

Content optimized for AI must have a logical hierarchy, precise language, and a structure that makes machine extraction of facts easy.

Principles for creating AI-friendly content:

Logical structure: AI picks fragments with a clear construction (H1-H3). The most frequently cited answers are those that state something concrete (a number, a definition, a recommendation) in the very first sentence.
Formatting: Models prefer step lists, checklists, comparison tables, and Q&A sections with short answers (up to 3 sentences).
Avoiding “fluff”: Avoid evaluative adjectives with nothing behind them. Instead of “an innovative solution”, write: “the system cuts implementation time from 6 to 2 weeks in 70% of cases”.
Credibility: Every fact and figure must be backed by a source or a citation, to avoid model hallucination.

Why is external reputation crucial for GEO?

Building brand authority in the external environment (off-site) is essential, because algorithms verify the credibility of information through so-called “algorithmic trust”, checking for the brand’s presence in independent sources.

Elements of an off-site strategy:

E-E-A-T: Experience, expertise, and authority built through expert publications and industry sources increase the number of citations.
An ecosystem of mentions: Brands cited by AI usually have a broad presence on platforms such as Reddit, Quora, YouTube, Wikipedia, and in user reviews.
Actions: The strategy includes building company profiles, guest posts, community activity, and systematically acquiring citations.

What mathematical formulas measure a brand’s share in AI?

To measure a brand’s position precisely, you use the Share of Voice metric and its more advanced, weighted version, which factors in the quality of the mention. A reliable analysis requires repeatable prompt tests and data aggregation.

1. The basic formula – AI Share of Voice (SOV):

$$AI\ SOV = \left( \frac{\text{number of brand mentions in AI answers}}{\text{total mentions of all brands in that category}} \right) \times 100\%$$

This metric shows how often the brand appears in the AI narrative compared with the competition.

2. The advanced formula – Weighted AI SOV:

This metric factors in the position within the recommendation and whether the mention is accompanied by a link.

$$Weighted\ AI\ SOV = \frac{\sum(\text{number of mentions} \times \text{position weight} \times \text{citation multiplier})}{\sum \text{all weighted mentions in the category}} \times 100\%$$

Weight parameters:

Position weight:

1.0 – for the primary recommendation
0.7 – for the second position
0.4 – for subsequent positions
Citation multiplier:
1.5 – if a direct link leads to the mention (attribution)

Which tools should you use to monitor GEO?

The market offers both Polish and global solutions for analyzing phrases in AI Overviews, the number of citations, and the sentiment of statements. These tools also let you measure GEO effectiveness and AI visibility, which is crucial for assessing a brand’s presence in AI-generated answers.

An overview of the tools:

Polish solutions: Senuto, Semstorm, SEOstation (share-of-voice analysis, citations).
Global solutions: Profound, Semrush Enterprise AI, ZipTie, Otterly.AI, Peec.AI, AthenaHQ, Ahrefs Brand Radar.

Selection criteria: The tool should analyze multiple platforms (ChatGPT, Gemini, Perplexity), support Polish, and examine not just the fact of a citation but also its position in the answer and its sentiment. These tools should be integrated into daily monitoring and dashboards.

What does the GEO implementation process look like step by step?

Effective AI optimization requires a systematic, 4-week cycle of actions that combines technical aspects with content marketing.

Implementation plan (weekly schedule):

Week 1 (Analysis): AI-readiness audit, verifying visibility in GEO tools, competitor analysis, manual prompt tests.
Week 2 (On-site optimization): Expanding content (FAQ blocks, tables, comparisons), implementing schema.org, improving E-E-A-T parameters.
Week 3 (Building off-site authority): Acquiring mentions on industry sites, activity on Reddit/Quora, updating expert data.
Week 4 (Verification): Monitoring results, repeating tests, and reporting changes in AI Share of Voice.

Building a position in AI is like buying land in a new, up-and-coming city district. Traditional SEO is the crowded downtown – fighting for every square meter is expensive and exhausting. AI Search is the new district right next door. Today you can grab the best plots there (build semantic authority) for a fraction of the price. In two years, once all the customer traffic moves there, prices will be prohibitive. Those who build there now gain an advantage that can’t be caught up.

What determines a long-term advantage in generative search?

Keeping a high Share of Voice requires continuous adaptation to changes in LLM algorithms and treating GEO as an integral part of the digital strategy.

Key factors for long-term success:

Traffic quality: Traffic from AI recommendations converts on average 4.4x better than classic organic traffic, because the user is at a more advanced stage of the buying journey.
First-mover advantage: LLMs prefer proven, credible brands when generating subsequent answers. Investing in SOV early yields a lasting advantage.
Monitoring and adaptation: You should regularly update prompts, review the competition every month, and adapt content formats to model preferences (e.g., changes in source weights in Gemini or ChatGPT).

A hybrid strategy combining SEO (the foundation of visibility) and GEO (reinforcement in citations) is the only way for a brand to exist for a customer looking for a ready-made recommendation.

FAQ

Share of voice in AI is a brand’s percentage share of the answers generated by AI tools (such as ChatGPT, Gemini, or AI Overviews) compared with competitors in a given category. It is calculated as the ratio of brand mentions to all mentions in the category. In 2025, traffic from AI recommendations grew by 527%, and most queries end without a click on a link, which is why presence in AI answers is starting to replace classic positions in search results.

About 33% of Gen Z and 26% of millennials use AI tools more often than the traditional Google search engine when researching products and services. This trend is especially visible in e-commerce, B2B, services, and sensitive categories such as finance and health.

Google AI Overviews usually draws on 5-15 of the most trustworthy and relevant sources. Taken into account are eamong others: experience, expertise, authority, trustworthiness, the quality of the content structure, and the brand’s reputation beyond its own website.

SEO focuses on the fight for positions in classic search results. GEO focuses on getting the brand cited and recommended directly in AI-generated answers. SEO builds the foundation of visibility, while GEO strengthens the brand’s presence in new, generative search channels.

Year over year, traffic from AI recommendations grew by 527%. Importantly, this traffic converts more than four times better than classic organic traffic, even though the number of clicks is lower.

CTR for queries where AI Overviews appear dropped by about 61% year over year. Users very often get a full answer without needing to visit the site.

Weighted share of voice accounts not only for the number of mentions but also for their „weight”. Top scores go to primary recommendations and citations with a link. This gives a more accurate picture of a brand’s real visibility in AI answers.

E-E-A-T – experience, expertise, authoritativeness, and trustworthiness – are the key factors deciding whether a given brand gets cited by AI. This matters especially in YMYL industries such as health and finance.

The first noticeable effects usually appear after 4-8 weeks. Full results, however, require systematic effort and are best seen over a horizon of several months.

Both Polish and foreign analytics tools are used for monitoring. The key is that they support various AI platforms, analyze the brand’s position within the answer, and account for sentiment and the presence of links.

The most common mistakes are overly general content without concrete data, failing to update content, and an incomplete or chaotic site structure that makes it hard for AI to cite the material.

Zero-click search is a situation where the user gets an answer directly in the AI and doesn’t click any link. This phenomenon reduces traffic but increases the importance of the brand’s mere presence in the answer.

What works best is short, concrete answers, a logical heading structure, bulleted lists, comparison tables, and FAQ sections containing figures rather than generalities.

Citations with a link carry more weight, but the number of citations itself is more important than the number of links. The goal is to be the source of the answer, even if the user doesn’t visit the site.

Brands that build their presence in AI answers early gain an advantage that’s hard to overcome. AI models prefer proven sources, so consistency and long-term effort are key.