Your customers aren’t searching anymore, they’re asking questions, says Alex Moseman, Retail and Tech Expert at PA Consulting.

“Find me waterproof hiking boots that won’t hurt my plantar fasciitis” has replaced “hiking boots waterproof” – and the difference matters more than you think. While you’ve spent a decade perfecting SEO, shoppers have moved on to AI assistants that answer questions, compare options and even complete purchases.

The shift is happening faster than most retailers realise. Generative Engine Optimisation (GEO) means last month, millions of shoppers asked ChatGPT, Claude or Gemini for product recommendations. Shopping agents are now browsing your catalogue, reading your product pages and deciding whether to recommend you – but the rules you mastered for Google don’t work here.

What’s actually different

Think of AI assistants as your smartest customer service employee, but scaled infinitely. They translate “I’m hosting 8 people on a tiny balcony” into a shortlist of compact barbecue grills. But here’s the catch, if your content doesn’t address the scenario, you’re invisible. The AI assistant won’t guess what your product does; it needs explicit information.

SEO taught you to optimise for snippets but GEO demands complete answers. When someone asks about durability, AI needs specific material details, warranty terms and care instructions. When they wonder about fit, it needs measurements, sizing notes from reviews and comparison to similar items, not general claims about being “true to size.”

You need to be consistent too, if your website says one thing, your app another and Amazon a third, AI assistants back away. They can’t risk recommending something with conflicting information.

What omnichannel retailers must do

Start by answering the questions shoppers actually ask. “How does this fit?” or “will this work with…?” is missing information that keeps AI from recommending you. Address the scenario, not just the spec, don’t just say “waterproof rating IPX7” but explain “keeps contents dry in rain, but don’t submerge.”

Choose the specifications that actually matter in your category and present them consistently everywhere. If a warmth rating drives jacket sales, every jacket needs one. If battery life determines tool choice, state the runtime, not just voltage. Answer the five questions everyone asks, based on reviews and customer service inquiries, with specifics not marketing content. Real-time local inventory, accurate pick-up windows and shop specific promotions are also key selling points.

The images need to work harder too. Show products in context with clear scale references. Add detailed shots of the features people ask about. The technical details don’t matter, what matters is making information obvious and accessible.

Setting your data foundation

It’s important to choose one system as the source of information about your product. Every channel should use that data with no exceptions, no “special versions” for different marketplaces. That means defining your vocabulary once: what’s a bundle, what’s a variant, what does “ships in 24 hours” really mean?

This requires ruthless standardisation, with the same units, same colour names and same size charts everywhere. This feels tedious until you realise inconsistency is why AI won’t recommend you.

Date-stamp your updates so AI knows your content is current. AI assistants weight recent information more heavily – “Updated yesterday” beats “posted in 2019” every time. Start with 50-100 products in one high-intent category where you have strong expertise. Make them “AI-ready,” then measure what happens before expanding.

Measuring what matters

You should then check how often you appear in AI responses for category queries and when AI chooses you over your competitors. Watch the conversion rates from chat, voice, in-app assistants and pay attention to reasons for returns that reflect missing information, then fix the content.

Companies doing this well are seeing their products appear in three times more within AI-driven recommendations. Their return rates are dropping because customers get accurate information upfront. Their stores benefit because local inventory can be checked in new ways.

The bottom line

We’re watching the biggest shift in product discovery since the invention of search. Shoppers are moving from hunting for products to having products suggested by trusted AI advisers. The retailers thriving in five years won’t be the ones with the best keywords, they’ll be the ones AI assistants trust to recommend.

The question isn’t whether to prepare for GEO – it’s about whether you’ll be ready when your customers stop searching and start asking.