Global e-commerce exceeds $5 trillion in volume, marketplaces capture an ever-increasing share of transactions, and a new sales channel is emerging: AI agents that purchase on behalf of the customer. For entrepreneurs managing an online business, these changes are no longer speculative. They are already reshaping the rules of acquisition, loyalty, and margin.
Agentic commerce: when AI buys on behalf of the customer
Most guides on online business discuss SEO, social advertising, or content marketing. These levers remain active, but a parallel channel is gaining traction without many e-commerce merchants preparing for it.
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AI agents, integrated into voice assistants or shopping applications, compare offers, check reviews, and finalize purchases on behalf of the user. According to data reported by Ellabbe9, retail spending driven by AI agents is expected to reach $20.9 billion by 2026, nearly four times the volume of the previous year.
What is changing concretely is the nature of the “customer” to convince. An AI agent does not respond to an advertisement banner or brand storytelling. It leverages structured data: complete product sheets, machine-readable prices, explicit return conditions, verifiable reviews. Merchants who publish clean data feeds and detailed schema.org tags increase their chances of being selected by these agents.
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Field reports vary on the exact conversion rate of these agents compared to traditional channels. What emerges from several analyses is that AI agents convert better than paid search or social ads in categories where choice is based on objective criteria (price, availability, delivery time). For products with a strong emotional or aesthetic dimension, the dynamics remain less clear.
The information available on Full Press’s business portal allows tracking these digital market developments over the months, with an operational rather than speculative angle.
Dependence on marketplaces: a double-edged growth lever
Creating one’s own online store remains the most common advice. The reality of the market tells a different story: the share of marketplaces in global e-commerce revenue grows every year. For many sellers, Amazon, Cdiscount, or Etsy represent the majority of their sales.
This concentration poses a problem of commercial sovereignty. Ranking rules, commissions, and return policies change without notice. A seller who generates more than half of their revenue on a single platform exposes themselves to structural risk.
Diversifying distribution channels does not mean leaving marketplaces, but balancing. The most robust strategy combines a marketplace presence (for volume and visibility) with a proprietary site (for margin and direct customer relationship). The proprietary site also serves as a foundation to feed AI agents with structured data, an advantage that marketplaces do not always share with third-party sellers.
Three concrete levers to reduce dependence
- Collect marketplace customer email addresses through physical inserts in packages (thank-you card with QR code to the proprietary site), respecting the conditions of each platform.
- Publish specialized content (buying guides, technical comparisons) on one’s own domain to capture organic traffic that marketplaces do not provide.
- Test live shopping on social networks, a rapidly growing format that allows selling without intermediaries while creating a loyal community.
Structured product data: the often-overlooked technical foundation
Whether traffic comes from a search engine, social network, or AI agent, the quality of product data determines visibility. Poor product sheets (generic title, short description, no technical specifications) penalize natural referencing and exclude the merchant from the feeds utilized by automated tools.
An optimized product sheet contains at least a descriptive title with the category and brand, standardized technical attributes (size, weight, material, compatibility), images on a neutral background with multiple views, and a schema.org markup of type Product with price, availability, and aggregated reviews.
This structuring work takes time, especially for large catalogs. PIM (Product Information Management) tools allow centralizing and enriching data before distributing it to each channel. The return on investment is measured on two axes: better indexing by search engines, and eligibility for AI agent feeds and comparators.
Online marketing strategy: what expert content changes in 2026
Generic content produced in bulk is losing effectiveness. Search engines increasingly value expertise signals (identified author, cited sources, depth of treatment). For an online business, investing in targeted expert content yields more than multiplying superficial articles.
Several monetization models are emerging around this logic:
- Paid newsletters specialized in a specific sector (regulation, technology watch, market analysis), which build a loyal audience.
- Short online courses, tailored for field professionals who have little time, with formats of a few minutes per session.
- AI-powered business automation tools that transform sector expertise into a recurring service (automated audits, personalized reports).
The underlying trend is the shift from volume marketing (reaching as many people as possible) to precision marketing (reaching the right people with the right depth). Social networks remain a discovery vector, but conversion increasingly hinges on the quality of content and the fluidity of the purchasing journey.
The online business market in 2026 rewards players who master their data, diversify their channels, and are willing to structure their offerings for buyers who are no longer solely human. The tools exist, and market data is accessible. What makes the difference is the ability to assemble them into a coherent strategy rather than chasing each novelty in isolation.