E-commerce is entering a new phase: consumers are no longer just exploring pages, they are gradually delegating part of their decisions to conversational agents. These systems — capable of analyzing, comparing, filtering and recommending in real time — create a new acquisition channel, still discreet but in strong expansion. This transformation requires brands to rethink how they work on product data, visibility, and user experience.

1. A new reality: AI is really influencing purchases

1.1 From experimental use to an acquisition lever

For a long time, chatbots were a gimmick. But the improvement of multimodal models, integration with trading platforms and the ability to orchestrate several successive actions have changed the situation. More and more purchasing processes now go through a “pre-qualification” phase carried out by AI:

• the user formulates a need;
• the agent identifies compatible products;
• compare the options according to the criteria expressed;
• then suggests a selection that has already been filtered.

This transformation reduces the number of steps required to go from need to purchase. It also creates a new form of traffic: A traffic that is already convinced, resulting from a decision process outsourced to AI.

1.2 A still low weight but growing rapidly

Even though the share of orders attributed to AI agents remains modest in most sectors, the trend is clear:

• the growth is steady,
• seasonality does not interrupt the phenomenon,
• conversion rates are often higher than those from traditional sources.

This behavior is explained by the very nature of the traffic generated: a user who uses a conversational agent to define his needs arrives on the site with a higher level of confidence and clarity. The preliminary route has already served as a sorting process.

1.3 Signals of an emerging channel

The history of e-commerce is full of similar examples: Google Shopping, Pinterest, TikTok Shop or Amazon recommendations. All of them initially emerged as weak signals, before becoming major acquisition drivers.
The common point:

• slow adoption,
• then an acceleration phase as soon as use becomes natural in consumers' daily lives.

The AI agency seems to be entering this second phase.

2. What is an AI agency?

2.1 An AI that no longer only responds: it acts

The AI agency goes beyond initial conversational models. It doesn't just generate a response:

• she reasons,
• plan,
• performs tasks,
• and makes decisions based on the objective expressed by the user.

An agent can thus:

• identify a need (e.g. trail shoes for wet winters),
• filter a complete catalog,
• eliminate bad candidates,
• propose relevant alternatives,
• then direct towards a purchase in line with the preferences expressed.

2.2 Key characteristics

Autonomy in reasoning.
The agent does not expect specific instruction; they infer, explore, and navigate through options without human intervention.

Deep understanding of product data.
Current models prioritize:

• data structure,
• the consistency of the attributes,
• the ability to understand the key characteristics of a product

Continuous personalization.
The recommendations are no longer based on the average profile, but on each expression of intent. The same request may produce different responses depending on the conversation history.

Responsiveness in real time.
Agents adapt their suggestions as soon as a new constraint appears: budget, availability, use, aesthetic preference.

Execution becomes a mixture of linguistic understanding, multimodal interpretation, and goal-oriented reasoning.

3. How the AI agency influences shopping

3.1 Towards frictionless conversational commerce

One of the most significant developments concerns the elimination of traditional funnel steps. With direct integrations into e-commerce platforms, it becomes possible to:

• to discover,
• compare,
• select,
• and buy a product
  without leaving the conversational interface.

This fluidity transforms behaviors: fewer clicks, fewer pages, more conversions.

The role of e-commerce sites is not removed, but moved: they become environments for validation, not discovery.

3.2 The emergence of new signals of trust

In the same way that the “Amazon's Choice” or “Google Top Seller” labels influenced choices in previous years, AI is introducing its own implicit signals:

• be recommended in a conversational exchange,
• appear in a short list,
• be cited as “relevant according to your criteria.”

These signals rely almost exclusively on the quality of product data. Incomplete or inconsistent attributes reduce the relevance score in the models.

Experience.
Merchants are finding that poorly informed products, despite excellent sales on traditional channels, are often missing from the responses generated by AI. On the other hand, better structured products gain visibility, even if they left with less volume.

3.3 Higher conversions

As the conversational journey pre-selects the options, the user arrives on the page with a very high degree of intention. Conversion rates from AI agents often exceed those from other sources, including:

• generic searches,
• display campaigns,
• traditional SEO.

This performance is due to the reduction of decision-making friction: the user no longer has to choose among 200 options but between 3 that correspond to his constraints.

4. The implications for e-commerce SEO

4.1 SEO is no longer just about ranking: it's about being cited

Traditional SEO is based on position in the SERPs.
In an environment dominated by AI agents, a new criterion is emerging:
the ability of a product to be selected, understood, and cited in a generated response.

SEO is no longer just a competition between URLs, but a competition between entities:

• products,
• brands,
• characteristics,
• profits,
• social proofs.

4.2 Product data is becoming the raw material for SEO

To be selected by AI agents, a product must have:

• full attributes,
• structured descriptions,
• contextualized visuals,
• up to date information,
• consistency between flows, PDP, reviews, creations and market data.

AI models analyze structured data directly, not obfuscated text.

Here is where the concept of Product intelligence makes perfect sense:
centralize, standardize and activate product data for all use cases — Shopping Ads, SEO, creation, discovery AI, landing pages, etc.

4.3 The importance of authority signals

AI agents give more weight to:

• the quality of the reviews,
• the density of technical information,
• verified feedback,
• coherence between several sources.

A product with 20 solid reviews, a complete description and well-informed attributes is more likely to be selected than a product with 500 generic reviews and a flawed sheet.

This logic reinforces the demand on product data, much more than on traditional marketing content.

4.4 Towards double optimization: blue links + AI

Sites must now optimize:

1. for classic engines (Google, Bing, organic SEO),
2. for AI models (citation, semantic understanding, data quality).

The two worlds converge but do not replace each other: one remains the main channel, the other becomes an amplifier of conversion and intention.

5. How to prepare for the agency era

5.1 Prioritize data structuring

The first step is simple: turning data into a strategic asset.
Concretely:

• harmonize attributes,
• enrich the missing characteristics,
• optimize titles and descriptions,
• standardize formats for GMC and social platforms,
• align flows, PDP, creatives, and market data

This rigor in product flows is essential to be “readable” by AIs.

Tools based on multimodal and multi-model architectures, such as those dedicated to the enrichment of flows and the generation of structured assets (e.g. Smart Asset of Dataïads), have demonstrated their ability to improve:

• the quality and consistency of product data,
• the structuring of attributes,
• adapting content to the constraints of LLM models,
• the machine readability required to power the AI recommendation engines.

These technologies facilitate alignment between source data, the needs of discovery engines, and the requirements of conversational interfaces. They allow product catalogs to fully exist in an environment where semantic understanding, precise qualification and multimodality (text + image + context) condition visibility.

5.2 Reinforcing authority signals

It's about building trust from the point of view of models:

• reliable technical descriptions,
• authentic and detailed reviews,
• visible social proofs,
• consistency between the different platforms.

Signals should be structured and not anecdotal.

5.3 Preparing AI-readable content

The models prefer:

• the clear descriptions,
• attribute lists,
• explicit user benefits,
• the answers to the implicit questions (“is it suitable for sports use?”).

The logic is no longer that of the “landing page optimized for keywords”, but that of the form that is perfectly understandable by a machine.

5.4 Work on personalization and context

AI agents don't recommend the same thing twice: they adapt the response based on history.
Brands must therefore:

• segment their catalogs,
• offer clear ranges,
• facilitate the understanding of uses,
• contextualize their recommendations.

AI excels in personalization... provided you have the data.

5.5 Do not eliminate the human

The models are efficient in analysis, but credibility is still linked to:

• the transparency of information,
• experiential content,
• concrete use,
• product storytelling.

Expert content, field feedback and business knowledge remain essential levers.

Conclusion

The evolution of e-commerce is no longer just about the SERPs or the optimization of campaigns. It is now based on the ability to become intelligible and recommended by AI agents.
Brands need to incorporate this logic: structure their product data, strengthen their authority, create consistent post-click experiences, and adapt their content to conversational models.

The AI agency does not replace existing strategies: it amplifies their impact for actors capable of making their catalog readable, relevant and actionable in real time in all contexts.

‍