Agentic Commerce: Your Products No Longer Just Need to Be Visible. They Need to Be Recommendable.

For more than twenty years, e-commerce has largely followed the same logic: get indexed, generate traffic, bring shoppers to a product page and optimize conversion.
AI shopping agents are beginning to change that sequence.

In its 2026 study on agentic commerce, FEVAD and KPMG describe a new layer of intermediation between consumers and merchants. AI agents can already understand intent, compare offers and prepare a purchase. Over time, some may also carry out actions on behalf of the shopper.
The shift is significant.
Before a consumer even visits a product page, an AI system may already have decided which products are relevant enough to show or recommend.

From search engine to decision engine

A traditional search engine returns a list of results.
An AI shopping agent can go further.
A shopper might ask:

“I need headphones for working in an open office, with strong noise cancellation, comfortable enough to wear for several hours and compatible with both my laptop and my phone.”

The agent can interpret this intent, compare available products and narrow the selection down to just a few options.
FEVAD and KPMG highlight exactly this shift: merchants are no longer speaking only to consumers. Their catalogs must also be understandable by machines that read product data, compare products and verify conditions.

That means the winning product may no longer simply be the one ranking highest in a traditional search result.
It may also be the product whose information makes it easiest for an AI system to understand:

• what the product is;

• who it is for;

• which use cases it fits;

• what its key specifications are;

• what its limitations are;

• and why it matches the shopper’s intent.

Agentic commerce is already becoming part of the buying journey

This is no longer purely theoretical.
FEVAD reported in February 2026 that 31% of French online shoppers already use generative AI when shopping online. Among frequent AI users, that figure reaches 73%. AI is used primarily before purchase, particularly to save time, compare products, gather information and narrow down choices.

That distinction matters.
The immediate transformation is not necessarily that AI agents will autonomously buy everything for consumers. The first major shift is that AI is increasingly helping decide which products deserve consideration in the first place.

Product data quality becomes strategic

This gives product information a much more important role.
A human shopper can sometimes compensate for an imperfect product page.
They may interpret a photo, browse reviews, open several tabs or infer missing information from context.

An AI agent needs structured, explicit and reliable information. If a key specification is missing, ambiguous or contradictory, the consequence may go beyond losing a few positions in search results.
The product may simply fail to enter the agent’s shortlist.
FEVAD and KPMG specifically identify product data quality, visibility in generated answers and infrastructure readiness as priorities for merchants preparing for agentic commerce.

SEO, AEO and GEO now work together

SEO remains essential.
Search engines are not disappearing, and retailer and brand websites will continue to play a major role in discovery and conversion.
But two additional disciplines are becoming increasingly relevant.

AEO - Answer Engine Optimization aims to make information clear and structured enough to be understood and used by answer engines.

GEO - Generative Engine Optimization focuses more broadly on improving how content, brands and offers are interpreted and surfaced by generative AI systems.

In agentic commerce, these concepts go beyond editorial content. They increasingly apply to the product itself.
Clear titles, complete attributes, precise descriptions, compatibility information, dimensions, materials, intended use and package contents can all become signals used by AI systems to determine whether a product matches a shopper’s request.

Visibility is no longer enough

This is probably the most important conceptual shift.

For years, brands have mainly asked:
“Is my product visible?”

The question is gradually becoming:
“Does an AI system understand my product well enough to recommend it?”

These are not the same problem. A product may be perfectly indexed and still be difficult to recommend if its content is incomplete, inconsistent or too vague.
That is why product recommendability is becoming a new dimension of digital shelf performance. Visibility gets a product into the conversation.

Recommendability helps it stay there.

What brands can prepare today

It would be premature to redesign an entire e-commerce strategy around autonomous agents that are still evolving.
But several fundamentals already make sense today and are likely to remain useful regardless of how the technology develops.

1. Improve product data quality
Make specifications complete, consistent and machine-readable.

2. Structure content around shopper intent
Do not optimize only for keywords. Explain clearly which needs, situations and use cases the product addresses.

3. Remove contradictions and ambiguity
Titles, descriptions, attributes, specifications and enhanced content should tell the same story.

4. Measure product recommendability
Evaluate whether an AI system can understand the product, distinguish it from alternatives and identify when it should be recommended.

5. Monitor changes continuously
Content, availability, price and offer quality can all influence whether a product remains relevant for a recommendation.

These improvements are not useful only for future AI agents.
They also improve catalog quality, retailer search engines, current shopping assistants and the overall customer experience.

Commerce is becoming machine-readable

Agentic commerce does not mean the end of websites, SEO or traditional e-commerce.
It adds a new audience.
Alongside consumers, brands now need to think about the systems that read, interpret and compare their products on the consumer’s behalf.
FEVAD and KPMG describe agentic commerce as a shift in the buying journey and in how value may be distributed across the e-commerce ecosystem. Preparing catalogs, infrastructure and product information for AI agents is therefore becoming a strategic issue rather than a purely technical one.

For brands, the change can be summarized in one sentence:

Before convincing the shopper, you may increasingly need to be understood by their AI agent.

At OKTee, this is why we distinguish between visibility and recommendability.
Being present in a catalog is no longer enough to guarantee that a product will be understood, selected and recommended at the right moment.

Agentic commerce is only beginning.

But the product data needed to prepare for it can already be improved today.

Primary source: FEVAD × KPMG 2026, Agentic Commerce: What Are the Challenges for E-commerce?
https://www.fevad.com/etude-fevad-kpmg-commerce-agentique/