2026 will accelerate a shift that has already begun: e-commerce will no longer be played out solely on “scrolling” pages, but increasingly through conversational interfaces and agents (ChatGPT, Gemini, native assistants) that recommend, compare, and can execute certain actions (add to cart, order, follow-up, after-sales service) with user mandate or confirmation.
For merchants, the stakes are getting higher: become a reliable source “consumable” by agentsor lose visibility to players whose data is more structured, up-to-date and actionable.
The transformation of the product catalog: from text to machine-readable entity
For an AI agent to be able to recommend, and above all secure, a transaction, your product flow must go beyond marketing description. It must be transactional, structured and reliable: identifiers, variants, prices, stock, lead times, policies, compatibilities.
Towards variant-ready granularity (SKU / declination) when critical
In many catalogs, a “parent” product page groups together sizes/colors via drop-down menus. It works for a human. For an agent, it all depends on your ability to clearly expose the purchasable variation.
What becomes decisive in 2026, especially for sensitive categories (sizes, compatibility, availability, express delivery), is to make each variant addressable and explorable:
- Data individualization: each variant (SKU) must exist as an autonomous data object (or as a perfectly modeled variant) with its own attributes.
- Direct addressability: price, stock, GTIN/UPC/EAN, delivery times, restrictions (e.g. zone, carrier) must be retrievable for a specific variant.
- Machine readability: if your options remain “hidden” in the UI (menus, scripts, front-end logic), an agent may misinterpret availability and prefer a more explicit competitor.
Remember: it’s not always a question of creating “15 pages = 15 SKUs”, but of guaranteeing access granularity at variant level, via flows, APIs, schemas or endpoints.
Formats and distribution: CSV, JSON, API… choose according to complexity
- CSV/TSV: very good for “simple” catalog feeds (flat attributes, periodic updates).
- JSON/JSONL: useful whenever you’re transporting rich structures (nested variants, delivery rules, bundles, policies, compatibilities) or feeding IA/data pipelines.
- API / webhooks: indispensable when freshness is critical (stock, price, ETA delivery) and when you want to serve “SKU X now” unit requests.
Semantic layers: structured + text + embeddings (not one against the other)
Modern agents and engines combine :
- structured data (attributes and identifiers),
- content (description, uses, FAQ),
- semantic signals (search/embedding on the platform or merchant side).
You don’t need a “vector base” to exist in AI, but you do need to provide data that enables unambiguous intention/product matching (e.g. “high mountain bivouac” ⇒ comfort temperature, weight, volume, resistance, standards).
Logistics reliability: uncertainty pays off in recommendations
An agent prefers a sure answer to a vague promise. Cards or flows with uncertain stock, inconsistent pricing, vague deadlines or untraceable policies are less advisable and riskier in execution.
Objective: expose critical fields in near-real time (API / webhooks / frequent sync): availability, lead times, fees, options, returns.
Structured content and reassurance: make your product sheet “answer”.
Don’t settle for attributes. Add a context of use and trust:
- benefits and limitations (for whom / not for whom),
- opinion synthesis,
- warranties, certifications, after-sales service, returns,
- size/compatibility guide.
The format (Markdown, HTML, CMS blocks) is less important than the structure (headings, lists, FAQ) and stability of the information.
Infrastructure: mastering new purchasing protocols
The risk in 2026 is not just of “not being crawled”, but of not being interoperable with standards for exchange between agents and merchants.
The standards war: ACP vs UCP
The market is converging on two major philosophies. A robust strategy is to be compatible with both, as they address different uses.
1) ACP (Agentic Commerce Protocol): the fast way to transactions
ACP, supported by Stripe and OpenAI, aims for a programmable, secure, low-friction transaction.
- Philosophy: maximum friction reduction.
- How it works: structured exchanges + security + payment execution when the user has given a mandate/authorization.
- Target: recurring purchases, restocking, convenience, impulse (when the decision is simple).
2) UCP (Universal Commerce Protocol): a full-stack commerce conversation
UCP, supported on the ecosystem side by Google and Shopify (and joined by other players), aims for a broader layer of communication than payment: product information, policies, post-purchase, and so on.
- Philosophy: cover more of the life cycle (before, during, after purchase).
- How it works: more complex queries (returns, compatibility, tracking, after-sales service, FAQs, etc.), with structured answers that can be used by the agent.
- Targets: complex purchases, technical categories, consulting needs, trade-offs (performance, size, compatibility, constraints).
Orchestrating the product catalog rather than rebuilding it
The pitfall on the merchant side: coding “one-off” integrations for each agent/protocol. Sustainable strategy rests on two pillars:
Source data hygiene
GTIN/EAN, attributes (material, origin, dimensions, weight), compatibilities, declinations: the wrong data breaks everything, protocol or not.
Orchestration by middleware/platform
Avoid touching the heart of the e-commerce IS if you don’t have to. Rely on :
- integration layers (middleware),
- or your platform’s native capabilities, to “translate” your catalog and endpoints to the standards expected by agents.
Affiliation: the pivotal role of Effinity’s Feed Center
Affiliation (comparators, shopping guides, content) is also evolving: product feeds are no longer just lists of offers, but feeds that can be exploited by recommendation engines and agents.
With this in mind, Effinity is upgrading its Feed Center to more “AI-ready” feeds, capable of displaying :
- more complete variants and attributes,
- more frequent availability/delivery signals,
- semantic fields useful for next-generation comparators, to increase the “consumability” of data by recommendation and response engines.
