How to measure the visibility of AI in search

Contents

AI Search Optimization: why visibility in AI search is becoming critical for e-commerce

AI Search Optimization is not just another term hastily added to the SEO vocabulary. It's the practical response to a very specific behavioral change: users are no longer just searching with traditional keywords on Google, but are asking for comprehensive answers from ChatGPT, Google AI Overviews, Perplexity, Copilot and other answer engines. For an e-commerce owner, this means that the brand can influence the purchase decision without the user having even visited the online store. If the AI answer suggests a competitor, mentions the wrong product features or doesn't include your brand at all, then some of the demand is lost before it even registers as a session in analytics.

Semrush's article on how AI visibility is measured sets an important context: visibility in AI search is not measured in the same way we measure rankings in a traditional SERP. It's not enough to know if a page is in the top position. We need to understand whether the brand is mentioned in responses, whether it's used as a source, what sentiment it's presented with, what prompts it appears in, and which competitors occupy a larger AI share of voice. Simply put, AI Search Optimization moves SEO from “where I rank” to “how I am perceived and when I am recommended by AI models”.

This change is not theoretical. According to McKinsey, the percentage of organizations regularly using generative AI increased from 33% in 2023 to 65% in 2024. This means that AI is not just for early adopters or technology teams; it has entered the marketing, sales, customer service and market research function. As shown in the graph below, the adoption of generative AI has accelerated in a matter of months, creating a new environment for brand and product discovery.

What AI visibility means and how it differs from traditional SEO

AI visibility is the extent to which a brand, product, category or domain is displayed, referenced or used as a resource within responses generated by AI systems. In traditional SEO, an e-commerce manager tracks rankings, impressions, clicks, CTR, organic conversions and revenue. In the AI Search Optimization environment, the key questions change: is our brand mentioned when a user asks for “best running shoes for beginners”; is our store recommended when someone compares “premium espresso coffee makers for small office”; does AI derive information from category pages or independent reviews; do we appear as a trusted choice or just another commercial reference?;

Semrush proposes to treat AI visibility as a multidimensional measurement. A brand may have a high Google ranking, but low presence in AI-generated responses. It can also appear frequently, but with neutral or negative sentiment, which for e-commerce is just as problematic as absence. The concept of generative engine optimization, often known as GEO, is based on exactly this logic: we optimize content, entities, structured data, credibility sources and overall digital presence so that AI systems better understand who we are, what we sell, who we appeal to and why we are trustworthy.

The big difference is that AI systems synthesize responses from many signals. They can leverage content from websites, knowledge from indexes, structured data, reviews, third-party mentions, forums, news sources, comparative articles and information linked to the brand entity. This makes semantic SEO and entity SEO more important than ever. We don't just optimize a page for a keyword; we build a clear footprint around product categories, expertise, features, audience, service areas, return policies, warranties, reviews and after-sales experience.

The key metrics that an e-commerce brand should monitor

To make AI Search Optimization practical, you need a measurement system. The first metric is AI brand mentions, meaning how often the brand appears in responses to prompts related to your market. For example, an online home goods store might check prompts like “what are the best online furniture stores in the country”, “what to look for when buying a mattress online” or “compare options for minimal living room lighting”. Just looking good, however, is not enough. The location of the reference within the response, whether it is accompanied by a positive justification, whether there is a link or citation, and whether the brand compares favorably with competitors must be recorded.

The second metric is the share of voice AI. If in 100 relevant prompts the brand appears in 18 responses, while the main competitor appears in 42, then the problem is not just SEO; it's a problem of perceived authority in the AI search ecosystem. The third metric is citation rate, which is how often AI platforms use your domain as a source. This is especially important for Google AI Overviews and Perplexity, where sources can drive referral traffic, but also build user trust. The fourth metric is sentiment: does the brand appear as reliable, premium, affordable, fast in shipments, or are there reports of delays, low availability and problematic support?;

The fifth metric is prompt coverage. Many brands make the mistake of only testing branded prompts, such as “is store X good?” The real value lies in non-branded and commercial investigation prompts, where the user hasn't yet decided where to buy. This is where keywords like AI Overviews SEO, ChatGPT SEO, answer engine optimization and AI search rankings come in. If your brand appears in pre-purchase responses, then you're impacting the consideration stage. If it only appears when the user already knows you, then you're missing out on opportunities for new demand.

The need for this type of measurement becomes even more pronounced when we consider Gartner's prediction that traditional search volume will decline by 25% by 2026 as users move to AI chatbots and other virtual agents. The chart below illustrates the prediction as an indicator, with 2024 serving as a base 100.

Step-by-Step guide to measure AI Search Optimization in practice

The biggest mistake an e-commerce brand can make is to treat AI visibility as an occasional audit. A manual search on ChatGPT or Perplexity can give a clue, but it doesn't create a reliable benchmark. It takes an iterative process, consistent prompts, clear categorization and comparison to competitors. Here is a practical framework that can be implemented by marketing teams, SEO agencies and in-house e-commerce teams.

Next, select the platforms that are relevant to your market. For many categories, Google AI Overviews is critical because it's tied to search. For more research-based markets, Perplexity may have value because of citations. For B2B or premium markets, ChatGPT SEO becomes interesting because users use it for summarization, comparison, and shortlisting vendors. Don't forget that results can vary by country, language, timing and prompt wording. That's why consistency in measurement is essential.

Step 2: Determine competitors and entities. Don't limit yourself to the competitors you know from Google Ads or organic rankings. AI answers may show marketplaces, media sites, affiliate publishers, review platforms, forums or international brands that don't directly compete at checkout but influence the decision. Create a list of 5 to 10 key competitors and record when they appear, with what description and next to which suggestions. This process reveals the true AI share of voice in your category.

Step 3: Count mentions, citations and sentiment. For each prompt, record whether the brand is featured, whether there is a link to the domain, whether the response mentions specific products or categories, whether the mention is positive, neutral or negative, and whether there are any inaccuracies. Inaccuracies are especially dangerous for e-commerce: incorrect prices, non-existent shipping policies, old products, or incorrect descriptions can affect buyer confidence. At this point, structured data implementation in product, review, organization, FAQ and breadcrumb schema helps engines understand content more accurately.

Step 4: Link the findings to content actions. If AI responses cite competitors for having better buying guides, create or update buying guides with real-world experience, comparison charts, clear selection criteria, and responses to objections. If citations to your domain are lacking, boost pages that can serve as resources: detailed category pages, evergreen guides, glossaries, FAQ hubs and expert-signed editorial content. If sentiment is negative due to reviews, the problem can't be solved with SEO alone; it requires improving expertise, logistics, support and reputation management.

Step 5: Create a monthly AI visibility report. A useful report should show change in brand mentions, AI share of voice, citation rate, sentiment, prompts with losses, prompts with opportunities, and competitors gaining presence. The value of the report is not to impress with lots of numbers, but to drive decisions: which category page needs strengthening, which buying guide needs to be written, which reviews need to be answered, which products need better documentation, and which external sources are worth pursuing.

Content that helps AI systems understand you

An effective AI Search Optimization strategy starts with content, but it doesn't end there. AI systems prefer clear, well-structured and reliable information. For an e-commerce site, this means that category pages shouldn't just be product grids with two lines of text at the bottom. They should explain which products belong in the category, what needs they are suitable for, what selection criteria are important, what differences exist between subcategories and what the buyer should look for before deciding. This approach enhances both semantic SEO and the likelihood that the content will be used in AI responses.

Equally important is E-E-A-T: experience, expertise, authoritativeness and trustworthiness. Whether you sell nutritional supplements, cosmetics, electronics, children's products or safety equipment, AI platforms and users need clear trustworthiness signals. Add authors or reviewers with real attributes, document claims, update old content, display return policies, company details, certifications, warranties, instructions for use and real reviews. Content authority isn't built with over-promises, but with consistency and accuracy.

At the same time, the market is rapidly investing in enterprise generative AI. Menlo Ventures recorded an increase in spending from $2.3 billion in 2023 to $13.8 billion in 2024. For e-commerce brands, this suggests that AI tools will be increasingly integrated into search, merchandising, customer support, CRM, paid media and analytics workflows. The chart below shows the scale of this change.

At the technical implementation level, prioritize structured data on products, prices, availability, ratings, breadcrumbs, organization and FAQs where they are really useful. Make sure that important information is not hidden exclusively behind JavaScript that is not rendered correctly, that pages load quickly, that internal linking helps understand thematic associations, and that canonical versions are correct. AI visibility doesn't replace the technical foundations of SEO; it makes them more challenging.

How to turn measurement into a competitive advantage

The more mature next step is to treat AI visibility as part of the marketing strategy rather than as an experiment for the SEO department. If a prompt consistently shows competitors, analyze why. Do they have more reviews? Better leads? A stronger presence in third-party sources? Clearer positioning? More comparison pages? The answer will indicate whether you should invest in content, digital PR, product data, customer experience or brand positioning.

A practical prioritisation model is to score each prompt based on three criteria: commercial value, current visibility and difficulty of improvement. Prompts with high commercial value and low AI visibility should be put first on the roadmap. For example, if you sell premium home appliances and don't show up in “best washing machine for a family with kids” queries, there is an immediate opportunity for buying guide, product comparison, FAQ content and category page enrichment. Conversely, an informational prompt with low commercial intent can wait.

AI Search Optimization requires collaboration. The SEO team brings research and structure, the content team creates reliable answers, the e-commerce team improves product data and availability, customer support highlights real customer questions, and management decides where to invest budget. The faster you build this process, the harder it will be for competitors to overtake you when AI search becomes even more mainstream.

For TWO DOTS, the right approach for e-commerce owners is clear: first measure, then optimize, then iterate. It's not enough to write a few articles for the AI to “find us”. You need a system that integrates SEO, content, technical implementation, brand authority and conversion thinking. AI Search Optimization is the evolution of SEO in an environment where the answer comes before the click. Brands that learn to measure AI visibility from today will be in a better position when the user asks the AI for not just information, but a purchase recommendation.

Semrush: How to Measure AI Visibility

McKinsey: The State of AI in Early 2024

Gartner: Search Engine Volume Will Drop 25% by 2026

Menlo Ventures: The State of Generative AI in the Enterprise 2024

Frequently Asked Questions

Google Search Central: Introduction to Structured Data