How Artificial Intelligence collects and processes information

How artificial intelligence derives information

Artificial intelligence doesn’t «know» the world the way a human does. It doesn’t have personal experience, business judgment, or a true perception of product quality. What it does is predict, synthesize, and respond based on data it has seen, data it can retrieve in real or near real time, and information provided by external systems. Ahrefs’ article on how AI finds its information explains that modern AI systems rely primarily on three broad categories: training data, i.e. huge sets of texts used to train large language models, live or semi-live search via web crawlers and search indexes, and techniques like RAG, i.e. retrieval augmented generation, where the model retrieves relevant documents before composing the answer.

For an e-commerce store owner, this distinction is critical. If your product, brand, categories, buying guides, and reviews are not easily discoverable, understandable, and trustworthy on the web, then your chances of being leveraged by tools like ChatGPT, Perplexity, Gemini, or AI Overviews are reduced. AI can glean general knowledge from older training datasets, but when a user asks «best men’s sneakers for walking,» «organic dog food for sensitive stomachs,» or «which CRM is best for a small e-commerce store,» the answer will be influenced by what they can find, trust, and associate with specific entities. This is where the new era of ecommerce SEO begins: it’s not enough to just rank on Google; you need to be readable by AI systems.

The scale of this shift is already evident in the adoption of generative AI. According to McKinsey, the percentage of organizations regularly using generative AI has increased from 33% in 2023 to 65% in 2024. This isn’t just a technology trend; it’s a behavioral shift in search, product research, content production, and purchasing decision-making. As the chart below shows, adoption has doubled in a short period of time.

Why this changes SEO for e-shops

Until recently, the basic question for an e-shop was relatively simple: «how do I appear high on Google?» Today, the question is becoming more complex: «how will search engines and AI tools understand who I am, what I sell, why I am trustworthy, and what buying needs I answer?» AI search moves the experience from a list of ten blue links to answers that summarize, compare, and recommend. This means that the user can get a first answer without directly visiting a website, so zero-click search becomes even more important for brands that rely on organic traffic.

In classic SEO, product pages, categories, and blog posts are optimized for keywords, technical speed, internal linking, and backlinks. In the AI-driven environment, these remain essential, but new layers are added: entity SEO, clear brand identity, structured data, trusted sources, product comparisons, author expertise, return policies, availability, prices, features, and reviews in a recognizable format. Artificial intelligence tries to answer real questions. If your content is simply descriptive, repetitive, or copied from suppliers, it will hardly become a preferred source.

Semrush recorded that AI Overviews appeared in 6,49% of queries in January 2025 and 13,14% in March 2025, showing how quickly AI answers are entering search results. For an e-commerce store, this means that informational searches at the beginning of the customer journey, such as selection guides, comparisons and «what to buy» questions, may be increasingly influenced by AI-generated answers. The graph below shows the increase in the presence of AI Overviews over three months.

Where do AI systems get data from?

The first key source is training data. Large language models are trained on vast amounts of text from the public web, books, code, articles, documentation, and other datasets. But this knowledge has limits: it may be older, may not include your new brand, may not know if you changed prices, or if you launched a new collection. Therefore, when a user asks something commercial and topical, the models need access to more recent information. That's where search engines, web crawlers, APIs, and RAG techniques come into play.

The second source is the search index. Engines like Google and Bing already have huge crawling, indexing, and ranking infrastructures. When AI is integrated into search engines, it doesn’t operate in a vacuum; it uses existing content discovery and evaluation systems. This is why classic SEO isn’t dying, but evolving. If your pages aren’t crawled, if they have the wrong canonical, if they’re blocked by robots.txt, if they don’t have a clean architecture, or if they load slowly, then you’re making it difficult not only for Google but also for the AI layers that are built on top of search.

The third source is RAG, or retrieval augmented generation. Simply put, the system first searches for relevant documents and then generates an answer based on them. This reduces the chances of incorrect answers, but it creates a new requirement for brands: to have content that answers specific questions clearly, completely, and in a documented manner. For example, a category «child car seats» should not only contain products. It needs a selection guide by age, weight, safety standards, frequently asked questions, comparisons, installation tips, and structured data. This way, the AI has more reliable points of reference to understand the depth and usefulness of your content.

The fourth source is structured data and product feeds. Schema markup for Product, Offer, Review, FAQPage, Organization, and BreadcrumbList helps engines read not only the text but also the structure of the information. If you sell products with multiple variations, colors, sizes, availability, and discounts, proper markup reduces ambiguity. Product feed optimization in Google Merchant Center, marketplaces, and price comparison engines now acts as a data foundation for visibility in more contexts, not just advertising campaigns.

The data that AI machines must "read" correctly

Artificial intelligence favors clear, consistent, and verifiable information. For an e-shop, this starts with the product page itself. The title should accurately describe the product, not be filled with keywords. The description should explain use, materials, dimensions, compatibilities, advantages, and limitations. Photos should have proper alt attributes where necessary, not generic file names. Reviews should be authentic, with the ability to understand the rating and number of reviews. The shipping and return policy should be accessible and clear, because it is a sign of trust for the buyer and indirectly for the systems that evaluate reliability.

Content authority is equally important. An e-shop selling nutritional supplements, cosmetics, electronics or baby products cannot rely on 300-word rough texts. It needs thematic sections, guides, comparisons, expert input, up-to-date sources and writers with clear experience. EEAT is not a decorative acronym; it is a practical framework of credibility. When the AI composes an answer, it looks for patterns of trust: who says it, how thoroughly it documents it, whether there are contradictions, whether the site has a history of quality and whether other sources recognize it.

The cost and complexity of developing large models shows why AI platforms rely so much on organized data, search, and external information retrieval. According to the Stanford AI Index 2024, the estimated training cost of Gemini Ultra was $191.4 million and GPT-4 was $78.4 million. The technology is expensive and complex, so the quality of data available on the web becomes a competitive advantage for any brand that can leverage it. The chart shows the cost scale for two leading models.

Step-by-Step guide to AI-ready e-shop

Step 1: Perform a technical indexability check. Check robots.txt, XML sitemap, canonical tags, status codes, pagination, faceted navigation, and rendering. If important categories and products can’t be crawled properly, no AI layer will be able to effectively leverage them. Use tools like Google Search Console, crawl software, and server logs to see which pages bots are actually visiting.

Step 2: Build category pages with real informational value. Don’t let the category be just a grid of products. Add a short introduction, filters that don’t clutter the index, a buying guide, FAQs, and internal links to related articles. For example, a «running shoes» category could link to guides on pronation, distance, running surfaces, and material comparison.

Step 3: Optimize your product pages with schema markup. Add Product, Offer, AggregateRating where allowed, GTIN, brand, availability, price, shipping details and return policy when technically possible. Structured data helps the engine distinguish what is a product, what is a price, what is a rating and what is a feature. This is fundamental for ChatGPT SEO and for future search experiences that will compare products directly within the response.

Step 4: Create thematic content hubs. If you sell garden tools, create hubs for lawn care, pruning, irrigation, and seasonal equipment. If you sell fashion, create hubs for materials, applications, capsule wardrobes, and combinations. Hubs help AI understand that you’re not just a store, but a trusted source around a commercial topic.

Step 5: Clean up and sync your feeds. Product feed optimization should include correct titles, categorization, images, prices, availability, identifiers, and custom labels. If the information on your site, Merchant Center, and marketplaces differs, it creates inconsistency. AI doesn’t like ambiguity. Neither do shoppers.

Step 6: Strengthen trust signals. Add a detailed «About Us» page, prominent contact information, policies, real photos where possible, certifications, media mentions, and partnerships. Reviews should be collected systematically and responded to in a human way. Trustworthiness is not just a ranking factor; it is a reason for selection.

Step 7: Measure AI visibility. Monitor whether your brand appears in AI Overviews, AI search tool answers, and conversational queries related to your categories. Keep a list of high-intent questions and periodically check which sources are mentioned. If competitors appear, analyze what content they have that you don’t: comparative guides, better structure, more reviews, clearer data, or stronger backlinks.

Practical conclusion for e-commerce owners

Artificial intelligence does not replace the need for a good website, a clean technical base and useful content. On the contrary, it makes them more important. AI systems need reliable sources to answer user questions. The better organized, documented and traceable your e-shop is, the more likely you are to become part of those answers. The future of search will not necessarily belong to those who write the most articles, but to those who provide the cleanest, most complete and most reliable information about their products.

For TWO DOTS, the strategy is clear: a combination of technical SEO, structured data, content with commercial intent, entity SEO and systematic improvement of product feeds. An e-shop that wants to remain visible must think like a publisher, a data provider and a trusted brand at the same time. Artificial intelligence will continue to extract information from the web, from indexes, from feeds and from documented sources. The question is whether your e-shop will be among them.

Sources: Ahrefs: How Does AI Get Its Information? | McKinsey: The State of AI in early 2024 | SEMrush: AI Overviews Study | Stanford AI Index Report 2024 | Google Search Central: Structured Data | Google Merchant Center: Product Data Specification