Imagine your best sales rep suddenly quits and joins a competitor. Bad enough. But what if that “rep” is actually ChatGPT—issuing millions of product recommendations every day and systematically favoring rival offerings? That shift is already underway in e-commerce: AI systems are becoming more influential in sales processes and are deciding, by their own logic, which products to recommend. And the transformation is moving fast: 89% of B2B buyers already use generative AI. The future of online commerce won’t be decided solely in search results, but in the direct answers from ChatGPT, Perplexity, and other generative AI systems—long before a customer ever sees a website or shop.

Enter Generative Engine Optimization (GEO), the next evolution of search engine optimization—built for a world where AI systems act as “answer engines” and can generate product recommendations directly. For e-commerce companies, this means product data and content must be optimized not only for Google, but also for the AI models increasingly shaping purchase decisions.

Traditional SEO optimizes for search algorithms; GEO optimizes for how AI language models understand content. The difference is fundamental: SEO focuses on keywords and backlinks, aiming for rankings that drive clicks to your site. GEO focuses on contextual understanding, optimizing for AI citations and aiming to appear within AI-generated answers. That also changes how success is measured. Primary GEO KPIs include Citation Rate (how often you’re cited in AI answers), Source Authority (trustworthiness as a source), and Context Relevance (the thematic contexts you appear in). Secondary metrics track AI-driven traffic, conversion quality from AI-referred visitors, and brand mention sentiment within AI contexts.

A practical example: A production manager asks ChatGPT for “industrial compressors for compressed air systems, 15 bar, oil-free, certified for the food industry.” The AI responds with three specific manufacturer recommendations, including vendor links and price comparisons. Does your product lineup make the list? Or does the AI recommend your competitors? These scenarios are already becoming reality.

Let’s look at a few current trends. While the numbers are still emerging, the direction is clear. E-commerce site operators report a 15% to 50% decline in classic search traffic due to AI-generated shopping recommendations. At the same time, traffic originating from AI tools is surging—especially in advice-heavy categories like electronics and software. AI search results, such as Google’s “AI Overview” shown directly on the results page, drive significantly higher click-through rates than traditional results when a brand is mentioned. Brands that historically underperform in classic rankings can also surface in Google’s AI overviews.

Consumer behavior is shifting as well. Among active AI users in Europe, 76% have used tools like ChatGPT at least occasionally for shopping research, and 17% do so most times or always. Shopping tasks where consumers use generative AI include conducting research (55%), getting product recommendations (47%), finding deals (43%), sourcing gift ideas (35%), discovering unique products (35%), and creating shopping lists (33%).

To compare options and make recommendations, AI systems need structured product information. GEO therefore starts with data quality. Product Information Management (PIM) systems form the foundation for executing successful GEO strategies. Why? PIM systems like Akeneo enable centralized management of all product-relevant information and create the consistency AI systems need for reliable recommendations. Clear separation of attributes, text, media, and translations helps AI models extract relevant information more easily. This cross-channel consistency benefits AI-driven applications across Google Shopping AI, Perplexity, and ChatGPT alike.

AI systems require structured product information at different levels of abstraction to generate sound recommendations. Think of the data architecture as an information pyramid: Base product data forms the foundation with technical specifications, pricing, and categories—the “what” of the product. On top sits the application context, which explains what the product is for and who it’s for: concrete use cases, target audience definitions, and system requirements.

The third layer comprises advisory content. This is where product data turns into real purchase guidance. Problem-solving approaches, comparison criteria, and decision aids help AI systems not just inform, but recommend. The tip of the pyramid is semantic intelligence: product relationships, brand positioning, and industry-specific terminology that enable AI models to understand and present the product in the right context. Together, these four layers function like a digital sales advisor: they deliver not only facts, but also an understanding of your product’s value and use cases.

Develop conversational product descriptions

AI language models are trained on billions of text samples, primarily natural, human communication. They understand contextual language better than bullet lists of technical specs. While traditional search engines evaluate keywords in isolation, LLMs analyze entire sentence structures and semantic relationships.

The principle: question–answer patterns mirror how people actually look for solutions. If your product descriptions already contain answers to common customer questions, you increase the likelihood that AI systems will identify them as relevant sources.

Instead of product copy like “Premium espresso machine with 15 bar pressure,” try: “This espresso machine is ideal for lovers of authentic Italian coffee. Its 15-bar pressure delivers the thick, velvety crema you’d expect from your favorite espresso bar.” The difference is intent orientation: the first version lists features; the second answers the implicit question “What’s in it for me?” and creates emotional associations that AI models factor into their recommendation logic.

Integrate FAQs with real buying guidance

Content that directly answers natural-language questions—and headings that align with user queries (e.g., “What is GEO optimization?”)—improves the chances of appearing in AI answers. Incorporate questions real customers actually ask. Again, this maps to question–answer patterns: when product pages already include the questions users pose to ChatGPT or Perplexity, the content is more likely to be recognized and cited as a relevant answer source.

• “Which graphics card do I need for 4K gaming?”

• “Does this sweater suit my body type?”

• “How long does the battery last under heavy use?”

Use structured data for AI comprehension

Schema.org is the markup standard supported by search engines like Google and Bing. What used to be primarily an SEO tool has become crucial for machine understanding in the AI era.

Whereas content was traditionally optimized for human readers, it now also needs to be readily interpretable by AI systems as primary consumers. Schema.org markup acts like “machine code” for AI systems, enabling precise interpretation of web content into knowledge graphs—the substrate for large language models. Use Schema.org markup strategically for:

• Product Schema: technical properties and specifications

• Review Schema: customer reviews and ratings

• Offer Schema: prices, availability, delivery times

• HowTo Schema: instructions and use cases

Create contextual product connections

AI systems learn by association—much like the human brain. Traditional search engines weigh individual keywords; large language models analyze semantic relationships between terms and concepts. These embeddings—mathematical representations of meaning—help AI models grasp connections that aren’t explicitly stated.

For e-commerce, that means building semantic links between products with phrasing like “Customers who buy this product also often need…,” “Perfectly paired with…,” or “An alternative for buyers with a budget of X….” These links act as semantic bridges, helping AI systems consider your products in relevant recommendation contexts—even when the user didn’t explicitly search for them.

A practical example: if you sell industrial compressors and explicitly note “Frequently paired with XY-series air dryers,” an AI asked about “complete compressed-air systems” can recommend both products as a total solution.

Adopt platform-specific GEO strategies

Different AI systems have different preferences worth factoring in. OpenAI’s ChatGPT favors fresh, trending content and weighs Reddit and social media heavily. Perplexity AI uses real-time web search with shopping features and offers a Merchant Program for improved visibility. Google Gemini integrates the Google Shopping Graph with over 50 billion product entries, while Amazon Rufus provides “Buy for me” functionality for external purchases as well.

GEO is more than another optimization tactic—it’s a response to a fundamental shift in B2B buying behavior. While SEO aimed to be found in search results, GEO aims to become the preferred answer to specific business problems. Studies already show a 40% increase in AI visibility through structured GEO measures—an indicator of the strategy’s potential.

The decisive difference lies in data architecture: instead of optimizing for keywords, leading companies structure their product information for machine understanding. PIM systems evolve from data warehouses into strategic competitive assets—enabling consistent, contextual product data across AI systems.

Of course, this is early days. In the medium term, companies will need to optimize for both SEO and GEO. Solid PIM systems can adapt well, incorporating new insights to help teams respond quickly and refine strategy.

What’s next? Multimodal AI systems will interpret not just text, but also images, videos, and technical drawings. Voice commerce will accelerate B2B procurement. And AI agents will increasingly make autonomous purchasing decisions—based on the data we structure today.

Companies that invest now in GEO-ready data structures are laying the groundwork for the next stages of development. The key question is: Are your product data ready for the changes AI is bringing to e-commerce?