Artificial intelligence is no longer just a technological novelty. Currently, over 53% of American consumers declare that they intend to use AI when shopping online. Importantly – 41% indicate "Shopping" as the main reason for using AI-based search engines, ahead of uses such as news tracking (19%) or searching for health information (12%) (Search Engine Land).

So this is not a cosmetic change, but a fundamental transformation in the way users interact with the internet. User expectations are clear: answer relevance, the ability to get search results instantly, and content personalization, of course taking the right context into account. Language models such as ChatGPT, Gemini, or Perplexity respond exactly to these needs.

Unlike classic Google algorithms, modern conversational models do not search the internet in the traditional way. Being on the first page of search results no longer guarantees visibility. Something more matters: semantic clarity, technical order, timeliness, and above all – the ability to be cited by AI.

This forces a new approach to online visibility, now called GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization).

From a data perspective, it is clear that AI is not only gaining popularity, but is becoming the preferred shopping tool:

• 25% of consumers have tested AI search engines, and among them 37% rated their relevance as higher than classic search engines.
• 56% of respondents declare a preference for AI-generated results compared to traditional link lists.
• Among Millennials and Generation Z, awareness of AI tools reaches 58% – an important group for the future of eCommerce.
• 92% of customers say that personalized recommendations influence their purchasing decisions.

With the dynamic development of artificial intelligence, the way users search for information on the internet is evolving. And as a result, the rules of the game in the battle for brand visibility are also changing. The well-known SEO is now being expanded with new approaches: GEO and AEO.

GEO is the process of optimizing a website for generative AI engines. Unlike classic search engines, language models do not present a list of links, but generate a direct answer – based, among other things, on content they consider most valuable.

Optimization in the spirit of GEO means comprehensive website preparation: from structure and technical data, through semantic context, to content quality and formatting. Key elements include: question-form headers and FAQ sections, structured data (schema.org), as well as domain authority (EEAT) and expert, up-to-date, and clear content.

It's not just about keywords, but about making the site “understandable” and willingly cited by AI models.

AEO focuses on one goal: ensuring that your site appears directly in answers generated by AI-based chatbots. What matters is not only the content, but also how it answers specific user questions.

In this sense, AEO is a modern form of content marketing – content must be specific, useful, precise, and close to the intent of the questioner.

Example question:
“Which store has the fastest shipping for sports shoes?”

If your site contains clearly formulated information (e.g. delivery time comparisons, customer reviews, a list of certificates, or a Q&A section), you really increase your chances of being included in an answer generated by a language model.

In short: GEO and AEO are a response to the growing importance of conversational search. In the AI era, these practices can determine whether your brand will be visible.

A new channel for reaching users is emerging. Alongside the competition for position in Google search results, presence in conversations with generative AI is becoming increasingly important.

For eCommerce owners who want to maintain a competitive edge, this means acting on several levels:

• New source of traffic – Chatbots are becoming an alternative gateway to the internet's resources. Users are increasingly directing questions directly to AI instead of typing phrases into a search engine. Properly optimized content can redirect this traffic to your site.
• Visibility in the context of recommendations – AI models select only the most valuable sources. A site that meets GEO and AEO criteria can be cited, recommended, and presented as an authority on a given topic.
• User experience matters – Clear structure, intuitive navigation, and properly implemented technical data (e.g. schema) increase the chance that AI will qualify the site as a reliable source of information. Deficiencies in these areas may result in exclusion from answer results.
• A new level of competition – The competition is no longer just about Google ranking. The stakes are whether the AI model considers your site worth mentioning – and this requires not only content optimization, but also building trust, authority, and technical consistency.

Language models are based on huge collections of texts publicly available on the internet. Although it may seem that they analyze everything without exception, in reality the selection is very strict. Algorithms operate according to clearly defined technical and logical rules, filtering data for quality, timeliness, and usefulness.

Not every website has an equal chance of becoming a source cited by a language model. High content quality is a necessary condition, but not the only one. There are several important criteria that affect a site's visibility in AI answers:

• Credibility and authority - AI models use selected sources (including rankings and opinion-forming services such as Trustpilot or Clutch) to assess whether a site deserves a mention. From the algorithm's perspective, credibility and positive user reviews are most important. If your site is often cited there and has high ratings, it is more likely to be included in answers generated by the model.
• Information timeliness - Both in SEO and in the context of language models, outdated content loses significance. AI prefers sources that are regularly updated.
• Structure and semantics - Logical content division, properly marked headers (H1-H3), bullet lists, and clear information hierarchy make it easier for AI to understand the context.
• Technical compliance - Structured data (e.g. schema.org) works like a “roadmap” for AI – helping to identify key information such as prices, reviews, availability, or content type.

To increase the chance of being cited by a language model, content should meet a few basic requirements:

• Directly answer the user's question – specifically and without digressions.
• Contain strong context – semantic keywords.
• Be useful – e.g. prices, availability, contact details, FAQ.
• Combine simplicity with expert quality.

Search engines and chatbots cite content from websites as sources for answers. However, for your site to be cited, it must be prepared for “conversation with AI.” What does this actually mean?

The site should include schema.org structured data – for example, for products (Product), articles (Article), or FAQ sections (FAQPage). It is also worth ensuring the presence of visible and unambiguous information, such as: “Shipping in 24h”, last update date, number of reviews, or star ratings.

A clear question and answer section is also important. Even a few well-formulated questions with specific answers can increase the chance that your site will be cited. Published content should bring new value and contain up-to-date information that AI can use as a reference point.

Let's discuss this with a practical example:

“Which store in Poland offers Garmin smartwatches available off the shelf with 24h shipping?”

If you want AI to cite your site, check if:

• the schema.org/Product tag is used,
• delivery time is clearly specified (e.g. “Delivery in 24h”),
• the last content update date is visible (e.g. “As of: April 2025”),
• the site contains user reviews and a high rating (e.g. 4.8/5),
• the product page includes a Q&A section or an accessible product description.

The rule is simple: the clearer and more understandable your site is for AI, the more likely it is to be cited.

Before an online store is noticed by search engine algorithms and AI chatbots, it must meet specific technical requirements. Language models analyze the structure of a site in a way similar to Google indexing robots – clarity, logical content layout, loading speed, and compliance with recognized SEO practices all matter.

This is why on-site optimization is the starting point for any strategy – whether we are talking about SEO, GEO, or AEO.

At the beginning, it is worth focusing on page load time. Neither users nor algorithms will waste time loading inefficient sites. Many factors affect speed – from hosting quality, to the size of multimedia files and their compression, to cache management and the structure of HTML, CSS, and JavaScript code.

The next important element is header structure. The H1-H6 hierarchy should reflect the logical layout of the content. This way, both users and AI can more easily find the most important sections of the site. A clearly defined H1 as the main title and clear subheadings help chatbots better understand the context of the site, and thus more accurately summarize and cite its content in answers.

In this context, it is worth pointing out a few technical elements that should be present on every website:

• Friendly URLs containing keywords and reflecting the content structure,
• Structured data (schema.org) used for products, articles, FAQ sections, or reviews,
• SSL certificate (HTTPS) ensuring secure data transmission and increasing user trust,
• Properly configured robots.txt file allowing indexing of important resources and restricting access to those that should not be crawled.

The above elements not only support site visibility in search results. They are also the foundation of communication with AI algorithms.

Of course, the aspect of responsiveness cannot be overlooked. For several years, Google has used the mobile-first indexing model, which means that the mobile version of a site determines its position in search results. Since chatbots use the same data, optimization for mobile devices directly affects not only SEO, but also GEO and AEO.

Even the most valuable content can remain invisible to AI if it is placed on a poorly optimized site. That is why on-site optimization is not an addition to content activities, but their foundation.

Structured data determines whether the content on a site will be properly interpreted and used by AI chatbots. Thanks to their use, language models can quickly recognize the type of content, classify it, and use it as a source in generated answers.

The structured data discussed here is a way of describing content on a website in a format that is understandable to search engines and language models such as ChatGPT. Its implementation involves adding special tags (so-called markup) to the HTML code, which specify exactly what is on the page – product name, product price, current stock availability, user review, or event date.

The standard in this area is Schema.org. It is a universal set of tags that allows you to describe elements such as: products, services, reviews and ratings, events, FAQ entries, articles, videos, location, and many others.

The use of schema.org tags allows chatbots to precisely recognize key product data, such as:

• name,
• price,
• product code (sku),
• availability,
• image.

Thanks to this, when a user asks about a specific product, the chatbot can quote exact information from your site.

Importantly, the implementation of structured data also enables the automatic reading of customer reviews and ratings, which increases the chance that your site will be cited as a reliable source of opinions.

Additionally, if the site contains an FAQ section marked with the appropriate markup, chatbots can directly use the questions and answers found there, e.g. in the form: “According to the information on the [YourCompany] website...”

Structured data is the digital business card of your site in the eyes of AI. The better you prepare it, the more likely it is that a chatbot will not only notice it, but also use the content when formulating answers to user questions.

Although the presence of structured data is not a necessary condition for a site to be noticed by AI, it definitely increases the chances. In practice, it is one of the most important elements that can really affect content visibility, so it is worth ensuring its implementation.

In the era of conversational search and generative AI, what matters is not only what is on the site, but also how clearly it is communicated to algorithms.

Core Web Vitals are a set of three metrics developed by Google that measure the quality of user experience on a website. Their importance in classic SEO is well known, but their role in the context of visibility in AI-generated answers is equally important.

Here are the three key metrics:

• LCP (Largest Contentful Paint) – determines the time needed to load the largest content element visible on the screen (e.g. a large image or text block). The optimal result is below 2.5 seconds.
• INP (Interaction to Next Paint) – a relatively new metric included in CWV. It measures the page's response time to user interactions (such as clicks, screen touches, or key presses).
• CLS (Cumulative Layout Shift) – shows how stable the page layout is during loading, i.e. whether elements “jump.” A value below 0.1 indicates good stability.

High Core Web Vitals scores mean not only a better user experience. They are also a signal to algorithms that the site is optimized and worthy of being cited by AI.

In the context of GEO and AEO optimization, it is not only what you publish that matters, but also how you present it. For chatbots, content quality is not limited only to substantive value – technical accessibility of the site is just as important.

Language models learn from resources – fast, stable, and easy to process. Sites that load too slowly or have an unstable layout may be skipped by algorithms.

The faster a chatbot can read and interpret the content of a site, the more likely it is to include it in its answer. In this context, Core Web Vitals become not only a UX indicator, but also a content selection criterion for AI.

Optimizing Core Web Vitals is a complex process, but there are several actions worth implementing at the online store level. Here are the most important ones:

• Use modern graphic formats, such as WebP instead of PNG or JPG. They provide a much smaller file size while maintaining high image quality.
• Match image sizes to actual display – avoid loading graphics at 2000x2000 px if they are displayed at 600x600 px on the site.
• Compress graphic files – reducing image weight by up to 90% without noticeable quality loss significantly speeds up page loading.
• Use alt attributes – this not only improves site accessibility, but also its semantics, which is important from the AI perspective.
• Optimize HTML, CSS, and JavaScript code – by removing unused styles and scripts and combining files, you can significantly reduce resource loading time.
• Gzip compression – a compression method that reduces the size of files sent from the server to the browser. It works mainly on text files such as HTML, CSS, JavaScript. As a result, files are smaller, load faster, and data transfer usage is reduced.

With such changes, the store not only gains in performance, but also becomes more “readable” for AI algorithms, increasing the chance of appearing in answers generated by chatbots.

Every website competes for the algorithm's attention with hundreds of thousands of other sources. If your site loads quickly, maintains a stable content layout, and responds instantly to interactions, it becomes more accessible, readable, and – importantly – preferred by AI.

Loading speed is no longer just a matter of UX and classic SEO. In the context of GEO and AEO, it is one of the key elements that determines whether content will even be noticed and used by language models.

AI chatbots not only analyze, but also understand and cite content. That is why language matters more than ever before. Language models do not function like traditional search engines. They do not just scan text. They interpret meaning.

This means that content must be designed to “speak” to the algorithm. Take care of its clarity, logic, and good contextual placement. Proper language construction significantly increases the chance that the site will be indicated by AI as a source, with a quote and address.

To increase the chance of being cited by AI chatbots, the text must above all be understandable. Simplicity of message matters – clear language makes it easier for models to interpret content.

Logical text structure is equally important. The layout should be organized and clear, which helps algorithms understand the content and assign the right meaning to it.

It is also worth taking care of language richness. The text should use contextual vocabulary, such as synonyms, thematically related concepts, or words from the same semantic family. Also, adjust the tone and language to the specifics of the industry in which the brand operates. This approach promotes better understanding of the content's meaning, both by users and AI algorithms, which may consider it more valuable.

Creating content for language models does not have to mean losing quality. On the contrary – it is still about writing substantively correct texts in a thoughtful, clear, and precise way. Here are the key rules:

• Use simple grammatical constructions – avoid complex sentences and specialist jargon. The content should be understandable to both the algorithm and the user.
• Enrich the text with synonyms and related concepts – this makes it easier for chatbots to grasp the broader context and improves answer relevance.
• Avoid generalities – phrases like “best quality” or “great service” are hard to verify and rarely used by AI as quotes. What matters is specifics and measurable value.

Bad:
“Our sports shoes are a great choice for everyone. We have good quality and affordable prices.”

Good:
“Men's Nike Air Max sports shoes, size 43. Available with free shipping in 24h. Perfect for running and everyday use thanks to Air cushioning and mesh upper.”

The second version provides more specifics: it contains the full product name, model details (size, cushioning, upper construction), a personalized message for the target audience (men looking for running shoes), and information about delivery conditions. Importantly, it uses phrases that are readable for AI (e.g. men's sports shoes) and can be directly quoted as an answer to a user's query.

Create content that retains its meaning even when quoted outside the original context. Combine natural language with technical details. This not only makes the text easier to read, but also increases the chance of it being quoted by AI.

Also, make sure to create unique content. Copying descriptions from other sources (e.g. from the manufacturer's website) lowers their value in the eyes of algorithms. The foundation of visibility is both high text quality and content originality.

When writing, try to anticipate potential user questions. Focus not only on the dry information you want to convey. Consider why someone would be looking for a given item or service. The ability to sense query intent becomes a key success factor in the era of conversational search.

Working on visibility in AI-generated results does not end with the website. Contextual signals from outside, such as inbound links, citations, user reviews, or social media activity, are equally important.

In the eyes of AI algorithms, you are not an isolated entity, but part of a broader information ecosystem. The stronger and more valuable the connections with other sources, the higher your site is rated as a reliable source of knowledge.

Why are external links important for chatbots and AI search engines?

• Domain authority – links from reliable sources, such as articles in industry media, university publications, expert blogs, or news portals, signal that your site is valuable and trustworthy.
• Semantic context – AI analyzes thematic connections between sites. Links from content similar in context increase the chance that your information will be properly interpreted and used in answers.
• Credibility and ranking – chatbots use data from sites that are themselves frequently cited and recommended. In this sense, external links act as a form of collective verification of content quality.

There are many ways to acquire external links, but not all have the same value from the perspective of visibility in AI-generated results. Below we present those that play an important role:

• Publications in high-authority sources - Presence in reputable industry media, expert services, or on opinion leader blogs is a clear signal to AI that the brand is recognizable and worth attention. Such citations increase domain credibility.
• Citations in reviews and comparisons - If your products are mentioned in rankings, comparisons, or review articles (e.g. “Top 10 best shoes for summer”), you build a reputation as a source. For AI, this is valuable information that supports the decision to cite your site.
• Thematic links - Context matters. A link from a healthy lifestyle blog to a supplement store will be more valuable than a link from an unrelated site. AI considers the thematic relevance of sites when assessing source accuracy.
• Links from reliable sources - It is important that links meet the requirements set by both search engines like Google and chatbots (including good optimization, no duplicate content, proper ratio of outgoing to incoming links).

Not all external links will benefit your site. As in classic SEO, there are sources that can lower its credibility in the eyes of language models.

This applies especially to links from mass, low-quality sources, such as bulk-generated directories or so-called link farms. Such content is recognized as spam and filtered by both chatbots and search engine algorithms, which may result in your site being omitted from results.

The conclusion? The value of links does not come from their number, but from the quality, context, and credibility of the source.

The more valuable links, citations, and positive mentions of your brand, the greater the chance that AI chatbots will use your content as a reliable source of information.

For language models, it is these signals – consistent, repeatable, and from trusted places – that prove your site is worth citing and can be recommended to users as a reliable source of information.

The development of artificial intelligence is gaining momentum, and its impact on e-commerce is becoming increasingly visible. Already today, the first solutions called AI Agents (e.g. Operator from OpenAI) are emerging. They not only support users in choosing products, but in principle can also take over part of the decision-making in the entire shopping process.

If the current pace of AI development continues, it can be expected that more and more stages of the shopping path will be automated. For online stores, this means an even greater need to be “ready to talk” with algorithms, both in terms of content and technical aspects.

There are many indications that artificial intelligence will increasingly automate everyday consumer activities. AI models may gain not only the ability to analyze offers, but also, under certain conditions, to suggest and even make purchasing decisions on behalf of the user.

Example areas of application include:

• selecting products based on search history, preferences, and user reviews,
• analyzing the completeness of product information and offer availability,
• assessing delivery time, costs, and return conditions,
• making decisions according to previously established shopping priorities.

It can be assumed that AI Agents will become increasingly integrated with the entire shopping ecosystem – not only as intelligent advisors, but also as conversational interfaces guiding the user from question to transaction completion.

Such a scenario means one thing: the credibility and quality of the data on which these systems base their decisions will become more important.

Although the pace and direction of artificial intelligence development are not fully predictable, there are specific areas worth investing in now. If language models and AI systems are indeed to play an increasing role in shopping processes, a well-prepared eCommerce infrastructure will become a real competitive advantage.

It is worth taking care of:

• complete and up-to-date product data – lack of information may cause the offer to be omitted by AI,
• intuitive and consistent UX – both users and algorithms value clarity and logical navigation,
• smooth system integration – inventory, delivery, and payment information should be available in real time,
• content structure and quality in line with SEO, GEO, and AEO requirements – well-optimized pages have a better chance of appearing in results recommended by AI.

Preparing for change is not about predicting every scenario. Instead, it is worth investing in building a flexible, development-ready, and technically prepared platform.

Although it is difficult to say unequivocally today that GEO and AEO will become the foundation of visibility in eCommerce, there are many indications that their importance will systematically grow, especially in the context of conversational search and recommendations generated by language models.

Stores that are already investing in semantic SEO, consistent data structure, up-to-date information, and content credibility are gaining an advantage in the digital eyes of AI systems analyzing web content.

In the long run, it is possible that the competition for customer attention will move beyond traditional channels – advertising or classic positioning – and include the way algorithms understand, interpret, and recommend specific offers.