Your website might make perfect sense to a person. Does it make sense to an AI agent?

One of the most interesting things we keep learning at eLLMo is how differently AI shopping agents and people experience the same store. Some of the differences are surprisingly small, and they still decide who gets the sale.

1. What people work around, agents get stuck on

People are very good at using imperfect websites. We read visual cues, scroll past clutter, make reasonable guesses and piece facts together from different parts of a page.

AI agents work differently. How a page is structured, whether product details are written out clearly, and how the information is organized all change whether an agent can understand your product, compare it and recommend it with confidence. A product can be an excellent match for what the shopper asked for. If the agent cannot work out why, that product may never make the shortlist.

2. Being found is not being chosen

Say an agent finds your product. Great. Now can it tell how your product differs from a competitor's? Can it work out the cost of owning it, check a certification, understand where you can and cannot ship, or tell which location offers a particular service?

Being found is only the start. An agent needs enough reliable information to make a decision, not just enough content to find you.

Agents need enough to decide, not just enough to find you.

3. Two kinds of shopper, one journey

This is my favorite part of what we are building. We look at how AI agents find and compare products. We also send simulated buyers through the same pages, each with their own needs, habits and reasons for buying.

That lets us ask both sides the same questions. What does an agent need to recommend a product with confidence? What does a person need to feel good about buying it? Where do the two overlap, and where do they split?

Sometimes the problem is technical: an agent cannot read information that is already on the page. Sometimes it is human: a buyer does not see the value, does not trust a claim or cannot justify the price. They are different problems, but they sit on the same path from first look to checkout.

4. The exciting part: live A/B tests

A simulation tells you where buyers are likely to stop. We have always believed it should be checked against what real shoppers actually do. So we now run A/B tests straight from eLLMo: a finding from a simulation becomes a proposed fix, the fix goes live as an A/B test, and we measure what happens.

In one recent 17-day A/B test, making certification information that was already on the page easier to find produced a 32% lift in revenue per visitor. We did not add a single new product claim. We only made the existing facts easier to understand.

Lately our A/B test dashboards have been showing a lot of green, and it is not even St. Patrick's Day.

What if something a simulation suggested does not hold up with real shoppers? That is exactly why the live test matters. Every result is a chance to question our assumptions, compare what we expected with what buyers actually did, and get better at reading the next generation of shoppers.

What I take from all this

The next step in selling online is not only about getting mentioned in an AI answer. It is about the whole journey, from being found to being considered to being bought. We are learning how agents choose, how people decide, and how to make a business easier to choose for the right reasons.

We are only starting to see how those two worlds fit together. If you want to dig into the agent side, start with what makes an AI agent pick your brand and the product pages AI cannot read. For the people side, see the information was there, but nobody found it. To see both on your own store, run a simulation or A/B test the fix.

Armando. Always at your service!

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