How e-commerce sites need to change for the AI era

At the same time, generative AI models are constantly upping the amount of context they can parse.

“As these models are used more, they process these longer prompts through their world model, which helps them understand the context behind the question that’s asked with all of the knowledge they’ve built up until now,” says Max Sinclair, CEO of Azoma AI, which helps brands with their AI search strategies. While Google has collected user data for 20 years — age, location, preferences — this will now all feed into AI search, which understands the searcher’s intent and context, based on this existing data.

Gone are the days of traditional keyword hacking, where brands would scope out the keywords that were easiest to rank on and build their website copy and product listings around. Instead, brands must now focus on writing intent-based content that talks about product categories and user problems more, as well as FAQ and how-to-style conversational content that pre-empts user queries. “A one-liner for a product description is no longer OK,” says Hildon. “Now, multiple pages of detail about a product can feed into the AI: the more you can expose it to data, the higher the chance of conversion.”

Data points that LLMs latch onto are details like defining who an item is for, talking about its functionalities, its use cases and what events a customer may wear it to.

For a luxury handbag, for example, a brand may have previously included a line in their product details such as “100 per cent Italian leather handbag.” Now, they may describe the product as a “100 per cent Italian leather handbag with space for a laptop that would suit a woman in her 20s or 30s, who can take it straight from work to an evening dinner”.

As well as curating more detailed product descriptions on their own websites, experts say that brands also need to think more holistically about every word relating to their brand that sits online.

“The question of brand health is now more relevant than ever, as LLMs are indexing the internet for all the unaided awareness and opinions about products and brands that exist,” says Sam Shapiro, partner at venture capital firm VMG Technology.

The more product imagery, the better

Another big shift with AI search is the move to “multimodal” search, where the question of context now extends to product imagery, as well as written content. Industry experts say the better a brand’s product images align with their written descriptors, the more likely they are to be parsed well and suggested to users by AI models.