Agentic commerce: your products must be more than just visible—they must be highly recommendable

For twenty years, a major part of e-commerce was built around a relatively straightforward playbook: get indexed, drive traffic, convert consumers on product detail pages (PDPs), and optimize conversion rates.
The rise of AI agents is beginning to disrupt this funnel.

In its 2026 study on agentic commerce published with KPMG, FEVAD describes a new layer of intermediation between consumers and merchants. Agents can already understand intent, compare offers, and prepare transactions. Eventually, some will execute purchases autonomously on behalf of the user.
This marks a massive shift: before a consumer even visits a product page, an AI may have already shortlisted the products it deems relevant.

From search engines to decision engines

While search engines traditionally deliver a list of results, AI agents go further. They parse and understand complex natural language requests:

"I’m looking for a headset for open office work, with good noise-canceling, comfortable for multi-hour wear, and compatible with both my PC and phone."

The agent then analyzes multiple products, compares specs, and surfaces only a handpicked selection of options.
FEVAD and KPMG summarize this transition perfectly: the goal is no longer just to win the click, but to be understood, shortlisted, and recommended by a new generation of AI intermediaries.

In other words, the winning product is no longer just the one with prime real estate on a search engine results page (SERP).
It is the one whose data allows the AI to clearly understand:
what the product is, who it is for, the contexts in which it excels, its specs, its limitations, and exactly why it solves the user's intent.

The shift is already underway

This is no longer a futuristic scenario.
According to data published by FEVAD and KPMG, 31% of online shoppers already use generative AI for their e-commerce journeys. Among regular AI users, this number jumps to 73%. Authors estimate that if this trend continues, nearly half of French consumers could use agentic commerce by 2030.
This is taking place in a French e-commerce market that reached €196.4 billion in 2025, growing 7% across 3.2 billion transactions.

The question is not whether traditional commerce is disappearing.
It is understanding where the purchase decision is shifting.

Product data quality becomes a strategic moat

This shift elevates the product catalog to a strategic priority.
Human buyers can easily decipher imperfect product pages. They look at images, scan reviews, or intuitively grasp that a product likely fits their needs.
A machine, however, must be able to parse data points, map them to user intent, and assess their reliability.

An absent, ambiguous, or contradictory attribute carries far heavier consequences than in traditional SEO: the product doesn't just slip a few ranks.
It gets completely excluded from the agent's consideration set.
Consequently, FEVAD explicitly identifies product data quality, visibility in generative AI responses, and merchant infrastructure adaptation as the core pillars of agentic commerce readiness.

SEO, AEO, and GEO: Three complementary frameworks

SEO remains essential. Search engines aren't going away, and digital storefronts remain central to the customer journey.
However, two new disciplines are rapidly gaining traction.

AEO (Answer Engine Optimization) focuses on structuring information so clearly that answer engines can parse it and feed it directly into their responses.
GEO (Generative Engine Optimization) is a broader strategy designed to optimize content, brands, and catalog data for generative AI systems to ingest and recommend.

In the context of agentic commerce, these methodologies extend far beyond editorial site content.
They apply directly to product data.
A clear product title, fully populated attributes, precise descriptions, compatibility details, use cases, dimensions, materials, and box contents are now critical data points an agent leverages to decide if a product matches a user's intent.

Visibility is no longer enough

This is perhaps the most critical paradigm shift.
For years, brand strategy centered on visibility:
- Does my product show up?

The question is rapidly becoming:
- Does an AI understand my product well enough to recommend it?

These two questions demand different standards. A product can be perfectly indexed, yet completely un-recommendable if its data is fragmented, inconsistent, or lacks context.
"Recommendability" is emerging as the new KPI for product performance.

How brands can prepare today

While rebuilding an entire e-commerce strategy around fast-evolving AI agents is premature, building on these core fundamentals today will future-proof your business regardless of how the technology matures:

1. Ensure product data integrity: Deliver complete, consistent, and machine-readable specifications.

2. Structure content around buyer intent rather than just search keywords.

3. Eliminate ambiguity between product titles, descriptions, attributes, technical specs, and rich content.

4. Measure recommendability, assessing how effectively AI engines can interpret your product and identify the exact contexts in which it should be suggested.

5. Monitor real-time updates to content, pricing, and stock status, as a highly relevant but out-of-stock or poorly described product will be instantly dropped from AI recommendations.

Crucially, this optimization pays immediate dividends. It upgrades catalog quality, enhances customer UX, boosts internal site search, and feeds the AI shopping assistants already deployed by major retail platforms.

Designing commerce for machines

Agentic commerce will not replace digital storefronts, SEO, or traditional marketing.
Instead, it adds a new audience segment.
Alongside human consumers, brands must now optimize for the machines that scan, compare, and filter products on their behalf.

FEVAD and KPMG highlight a value shift toward this new intermediary layer. "Agent-ready" infrastructures, robust APIs, and highly structured product catalogs will become increasingly critical components of the digital commerce value chain.
For brands, the takeaway is simple:

In the near future, before you can convert a buyer, you must first get approved by their AI agent.

At OKTee, this is exactly why we distinguish between visibility and recommendability: being listed in a catalog no longer guarantees that your product is understood, selected, and suggested at the right time.
The age of agentic commerce is just beginning, but the foundational data quality needed to win in this new era can—and should—be built today.

Primary Source: FEVAD × KPMG 2026 Study, Agentic Commerce: What’s at Stake for E-commerce?, published September 17, 2026: https://www.fevad.com/etude-fevad-kpmg-commerce-agentique/