From Search Engine To Sales Channel: AI & LLM ECommerce

LLMs are increasingly being used as search engines. Consumers are searching, comparing, discovering, and buying products through tools like ChatGPT, Gemini, or Copilot. E-commerce companies can benefit from this shift by presenting their products in a way that is optimized for AI-driven search engines.

In this blog, you’ll learn more about the opportunities LLMs offer as a sales channel and how to prepare your product data to make the most of them.

Quick Access

• Online Shopping Via LLMs
• Selling Products Through AI Shopping
• How To Make Sure LLMs Understand Your Product Data?

Online Shopping Via LLMs  

Until recently, consumers typically started their online shopping journey in search engines such as Google or Bing. Today, that starting point is increasingly shifting to Large Language Models (LLMs) like ChatGPT, Gemini, or Copilot. Instead of typing in loose keywords, users now ask complete, natural-language questions that reflect their needs and expectations, such as: “What is the best wireless headset for working from home?”

This way of searching feels more natural, faster, and more personal. Consumers receive exactly the product recommendations that match their requirements. LLMs already compare options and present a curated selection of recommended products, saving users the time and effort of reviewing and weighing specifications themselves.

For retailers, this means that LLMs are no longer just tools for orientation, they are evolving into an entirely new sales channel. An LLM’s recommendation can directly influence a consumer’s purchase decision.

LLMs As A New Sales Channel  

Rather than only advising on products and redirecting traffic to webshops, LLMs are increasingly taking on the role of seller. Checkout functionality within LLM platforms is being rolled out rapidly.

Within OpenAI’s ChatGPT, Microsoft’s Copilot, and Perplexity, users can already order and pay for recommended products directly without ever visiting the retailer’s own webshop. So not only is the way consumers search for products changing, but also how and when purchases are made. Retailers who invest in making their products easy for LLMs to find, understand, and recommend increase their chances of generating sales through these AI platforms.

Inspiration, exploration, comparison, decision-making, and checkout, all within a single platform, means fewer steps, less decision fatigue, and faster, better-informed choices for consumers. The entire customer journey, from question to purchase, can now happen within one conversational interface.

Selling Products Through AI Shopping  

LLMs are already being used for inspiration, research, advice, and even purchasing products. Their role is quickly shifting toward fully integrated shopping and transaction capabilities.

An important development is the rise of commercial features within LLMs. For example, ChatGPT Ads are increasingly discussed, allowing brands to become visible exactly when users actively seek purchase advice. Instead of generic advertisements, these are contextual recommendations aligned with the consumer’s needs and preferences.

For retailers, this means LLMs are becoming a direct sales channel where visibility and relevance are crucial. The right product needs to be recommended at the right time to support the consumer’s decision.

AI-driven shopping therefore requires a new way of thinking about marketing and e-commerce. It’s no longer just about driving traffic to your webshop, it’s about being present in the conversation where purchase decisions are actually made.

How To Make Sure LLMs Understand Your Product Data  

LLMs don’t recommend products simply because they are available, they recommend them because they understand what a product is and when it’s relevant.

They connect the context provided by the consumer, such as usage scenarios and preferences, with product information. The better that information is structured and enriched, the greater the chance that your product will be recommended at the right moment.

Product data is therefore no longer just input for webshops or SEO. It also feeds AI systems that advise and compare. Context, consistency, and semantics play a crucial role here.

Key Considerations For Product Data In AI Commerce  

• Categories and taxonomyA clear category structure provides context and helps LLMs understand relationships between products.
• Attributes and specificationsFactual characteristics form the foundation for comparisons and targeted recommendations.
• Product titles and descriptionsStrong product copy adds meaning. Who is the product for? When do you use it? What makes it stand out?
• Images and visual metadataFor multimodal LLMs that understand images as well as text, consistent and high-quality visuals help improve product recognition.

Structuring and enriching this product data for e-commerce at scale can be complex. An AI tool like PowerSuite.AI  helps retailers enhance and structure product data so it better aligns with how LLMs interpret and recommend products.

When product data is consistent, clear, and complete, LLMs can deliver more relevant recommendations to consumers.

LLMs are becoming both the starting point and the endpoint of online shopping. Make sure your product data is complete, structured, and consistent so these AI systems can effectively use it. This allows retailers to maximize visibility and relevance in AI-driven recommendation conversations, creating opportunities not only for inspiration and advice, but also for direct sales.

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