When dozens of sellers offer the same product: what makes AI pick you?

Everyone is talking about AI agents as the next major sales channel. Any store focused on growth is trying to get onto the waitlist for Instant Checkout and similar programs.

Those who get in early will see the upside quickly. A buyer asks an assistant: “Find me a coffee machine under $300 for home, simple and nice to look at.” The assistant finds a good match, everyone wins.

But once AI shopping is open to more merchants, it won’t be a few dozen “good coffee machines” — it’ll be thousands. At that point you are just one of many sellers of the same product. The assistant will choose between you and your competitors according to its own rules, and your task is to give it enough reasons to choose you.

That is where things get interesting.

How the assistant looks at identical products

For the assistant it is not the seller who comes first. It is the product: a specific coffee machine with specific characteristics. Whether it fits what the buyer asked for is determined by product data, not landing-page copy.

Before your offer ever shows up in checkout, it has to win a place in the shortlist. At that stage the assistant leans heavily on how complete and structured your feed is, and whether your delivery options make sense for this particular buyer: the right region, the right speeds, the right methods.

Once the product is chosen, the next question is who should fulfill the order. At that stage, the agent optimizes for one thing: minimizing the risk of a bad customer experience.

The assistant will look for signals like:

• does the price in the description match the price at checkout
  
• is the product actually in stock
  
• how often things break or get cancelled on the seller’s side, and what the return rate looks like
  

Checkout only happens after your offer has already won that round of competition. If the price, availability or terms don’t match what was promised, you may still get this order, but your reputation as a seller goes down and you’re less likely to be surfaced next time.

AI agents won’t “give everyone a chance” one by one. They have their own reputation at stake: products are offered in their name.

What a seller can do about it

Even though the final decision rests with the assistant, sellers can still influence how often their products get shown.

Your product feed stops being an internal spreadsheet and becomes your storefront for the assistant. Required fields make sure price, availability and checkout status are correct. Recommended attributes like richer media, reviews and performance signals help with ranking, relevance and trust. When prices and availability update on time, attributes are mapped to real use cases (“for a small kitchen”, “for pods”, “with auto-clean”), and the description matches what happens in checkout, you immediately look more reliable than much of the market.

Transparency matters just as much. Delivery terms, returns, typical timelines, clear copy on the site — all of this is now parsed by assistants - not just read by people. Where one seller has to explain everything manually in support, another seller benefits because the assistant already understands what the buyer should expect — and is less likely to overpromise.

Technical predictability is another strong reason the assistant will prefer you. Before you even sign up for an AI program, it is worth making sure your data and checkout are not behaving inconsistently and orders are not being cancelled at the last step. Every failed order here is a signal that your offer is risky.

Why an infrastructure layer shows up in this setup

Between the seller and the assistant a new level is already emerging: infrastructure players like SellerAI. Their job is not to replace the store, but to take on the part of the work that is too expensive for each seller to do on their own.

For smaller stores this means that:

• the catalog is brought into the format assistants expect
• checkout is implemented according to the standard and behaves predictably
• payments and order statuses move through a predictable flow, without surprises
• feed quality is checked and scored so you do not end up with a half-empty feed that looks weak to assistants
  

For larger sellers and platforms it is mostly a question of speed: instead of digging through specifications and building their own integration from scratch, they use an off-the-shelf layer and keep control over funds and roles.

In both cases the goal is the same: when the assistant sees dozens of identical products, you make it into the shortlist of ‘safe’ candidates, instead of getting filtered out as risky.

Why these changes are ultimately for the better

Yes, the role of the seller changes as AI enters commerce. Now the main focus is not on being seen and loved by the buyer, but on looking like the safest and clearest option in the assistant’s eyes. This is not a catastrophe. It is three new questions you need to be able to answer:

• is it clear from your data what you sell and on what terms
  
• does what you promise in the description match what actually happens at the moment of payment
  
• do you have infrastructure (your own or through partners) that can handle growing AI traffic
  

If the answer is “yes”, AI shopping will not just become viable for you, it will also protect you from an endless race for the buyer’s attention where everyone shouts louder and nobody wins.