Your eCommerce Website Was Built for Humans. Now It Needs to Sell to AI Agents Too

For years, we have been building eCommerce websites around one person: the shopper.

We think about how they search Google, land on a category page, filter products, open a product page, compare options and eventually make a purchase.

That journey is changing.

Across the eCommerce businesses we work with, we’re seeing more visitors shift part of their traditional Google search behaviour into AI platforms. People are using AI to research products, compare their options and narrow down what they want to buy.

At the same time, AI systems are crawling product pages and trying to understand the information retailers publish.

This creates a simple problem for eCommerce businesses.

Your website is no longer being read only by humans.

You have another audience to think about: AI.

And this isn’t something retailers need to prepare for in five years. The search behaviour is already here.

Your supplier’s product data isn’t enough

This becomes particularly interesting when you have a large catalogue.

Imagine you have 10,000 products.

Like many retailers, you’re probably receiving at least some of your product information directly from manufacturers or suppliers.

The problem is that your supplier isn’t giving this data only to you.

They may be supplying exactly the same title, description, specifications and images to dozens or even hundreds of other retailers.

You import it. Your competitor imports it. Another marketplace imports it.

Suddenly everyone is selling the same product using essentially the same information.

There are also often significant gaps in supplier data.

Useful attributes may be missing. Products may not be properly categorised. Important information might exist only inside an image. The supplier might provide one basic product photo and nothing else.

Reviews are another missed opportunity.

If a product is sold through multiple channels, there may already be hundreds of customer experiences associated with that specific product or MPN. Understanding what customers repeatedly like, dislike or ask about can tell you far more about how a product should be presented than a manufacturer’s specification sheet alone.

Retailers need to start treating their product catalogue as an asset rather than simply data that needs importing into Magento, Shopify or WooCommerce.

Product data needs to explain the outcome

There is another problem with traditional product descriptions.

They are usually very good at explaining what the product is.

They are often much worse at explaining why somebody should buy it.

Take a beauty product.

The manufacturer may give you a long list of ingredients. Those ingredients matter, and for some shoppers they matter enormously.

But another shopper might simply be trying to understand: Is this suitable for my particular skin concern?

That’s a completely different question.

A technically correct product description doesn’t necessarily answer the questions people actually have when they’re deciding whether to buy.

The same applies across eCommerce.

Which model is right for my situation? What’s the difference between these two products? Is the more expensive version worth it for what I need? Will this fit? What else do I need with it?

These are the questions that good salespeople answer every day. Increasingly, they are also the questions customers are asking AI.

Your product information therefore needs to do more than list specifications. It needs enough context to help both a person and a machine understand where the product fits, who it is relevant for and how it compares with the alternatives.

Comparison tables can be incredibly useful here, particularly for retailers with large catalogues containing many similar products.

Structured product data and schema also become increasingly important because you want to make the underlying information as clear and accessible as possible.

The product page is becoming a landing page

Retailers traditionally put enormous amounts of effort into their collection and category pages. That still matters.

But think about what happens when product discovery starts somewhere else.

Someone has a conversation with an AI assistant. The AI helps narrow down the options and eventually surfaces a particular product.

Where does that person go? Potentially straight to your product page.

The PDP now has to do a lot of the work that previously happened earlier in the customer journey.

It needs to establish confidence, communicate the value of the product, answer questions, provide useful imagery and comparisons, and ultimately convert the visitor.

AI may drive the traffic. Converting that traffic is still the merchant’s problem.

And this is where the next stage becomes particularly interesting.

Don’t build another support chatbot. Build an AI sales consultant.

Even with the world’s best product page, customers are going to have questions. That’s normal.

The problem is that many businesses send those questions straight into customer support.

The support team is already dealing with order questions, delivery issues, returns, complaints and existing customers.

A visitor asking, “I’m buying this for X. Will it work?” isn’t necessarily looking for customer support. They’re trying to make a buying decision.

The same is true of: “Why should I buy this model rather than the cheaper one?” That’s a sales conversation.

This is why embedding AI sales consultants into eCommerce websites is a logical next step.

The important part isn’t putting another chat bubble in the bottom-right corner of the website.

The AI needs to understand the product catalogue and have access to the relevant knowledge held by the business. Then it can have a practical conversation with the shopper.

According to Paul Ryazanov, founder of Comerix.ai, this is where retailers should start thinking beyond the traditional support chatbot. An AI sales consultant should be able to use product and business knowledge to answer the practical questions that stand between a shopper and a purchase.

If someone asks, “Will this work for what I need?” or “Why should I buy this model rather than the cheaper one?”, the goal isn’t simply to point them towards an FAQ. It’s to understand the question, use the available business knowledge and help the customer make a confident buying decision.

But making the sale isn’t the end of the problem.

What happens when the shopper doesn’t buy?

Most visitors don’t buy during their first interaction with an eCommerce website. And that’s perfectly normal.

People get distracted. They look at social media. They compare products somewhere else. Their children need something. They get a phone call. They decide to think about it and come back tomorrow.

But imagine somebody has just spent five minutes talking to your AI sales consultant.

They’ve told you what they’re trying to achieve. They’ve looked at three products. They’ve explained what matters to them. They may even have told you why they’re uncertain about buying.

Then they close the website. What happens to that knowledge?

For many retailers, effectively nothing. That’s a huge missed opportunity.

We’ve spent years optimising abandoned-cart emails, but much of that communication is still generic: “You left something behind.”

The next stage should be far more personalised.

If the business understands what the customer was trying to achieve, which products they considered and what questions they asked, the follow-up can actually help them continue their buying journey.

Eventually, this personalisation shouldn’t be limited to email.

It can influence product recommendations, website messaging, the returning customer experience, future AI conversations and potentially even the checkout experience.

Every customer interaction can become useful context for the next one.

Each system knows something about the customer. Very few know the customer.

The problem becomes even bigger after somebody buys.

Look at the average eCommerce technology stack.

Shopify or Magento knows what the customer ordered.

Zendesk, Gorgias or eDesk might know about their support tickets.

Your email platform knows which campaigns they opened. Your chatbot has another conversation. Analytics contains behavioural information. Your inventory and fulfilment systems contain another part of the picture.

There might also be direct emails with members of your team.

There is plenty of data. The problem is that it’s distributed across different systems.

Each system knows something about the customer. Very few know the customer.

Six months later, that person returns to your website and the business may have limited knowledge of the relationship that already exists.

The goal shouldn’t simply be to acquire customers with AI.

Retailers need to preserve customer relationships so that the knowledge created through discovery, conversations, purchases and support interactions isn’t lost.

Remember the customer, not just the order

There is also a difference between storing transactional data and actually knowing something useful about a customer.

Here’s a very simple example.

Imagine you sell pet products.

Knowing that a customer bought a particular bag of dog food six months ago is useful.

But why stop there? Ask them what breed their dog is. Ask the dog’s name.

Suddenly, instead of knowing only that someone purchased Product X, you know that they own a Labrador called Max.

As the relationship develops, you can learn which products are relevant to Max, what the customer prefers, what they have previously asked about and what they might need next.

That can influence the products you recommend, the emails you send, the experience they have when they return to the website and the conversations they have with your AI sales consultant.

This doesn’t mean collecting data for the sake of collecting data.

The goal isn’t necessarily to know more about the customer. It’s to remember the right things.

And then actually use that knowledge to make their experience better.

This is how business knowledge starts turning into sales.

From an eCommerce website to an AI-powered customer journey

When you put all of this together, the change is much bigger than adding ChatGPT-style functionality to an online store.

The journey starts before the customer reaches your website.

An AI platform discovers and understands your products. That requires better product data.

The shopper arrives directly on a product page. That page needs to work as a landing page and convert them.

They still have a question. An AI sales consultant uses your product and business knowledge to help answer it.

The customer doesn’t buy immediately. The knowledge from that interaction helps personalise what happens next.

Eventually they purchase. Instead of forgetting everything except the order, the business preserves the relationship.

When the customer comes back, you don’t start from zero.

That’s where eCommerce is heading.

It also comes back to three principles behind the approach being developed at Comerix.ai:

Preserve customer relationships.

Transform business knowledge into sales.

Build AI-powered sales and customer engagement teams.

For retailers, this doesn’t mean throwing away Magento, Shopify, your CRM, helpdesk, email platform or the rest of your existing technology.

Those systems still have important jobs to do.

The opportunity is connecting the knowledge sitting between them and making it useful.

Because the future of eCommerce isn’t simply about putting AI on your website.

It’s about building a business where your product data, customer knowledge and customer interactions can work together – whether the next person interacting with your business is a human shopper or an AI acting on their behalf.