How to succeed on agentic marketplaces and optimize for AI product discovery

Will AI shopping agents recommend your products?

Shoppers now ask assistants like ChatGPT and Gemini for product recommendations. If your listings are missing details like dimensions, materials, or compatibility, an agent may leave your product out of its answer. Worse, your marketplace dashboard won't flag an error, so you may never know which recommendations you’re missing out on.

In his Impact Cube session, Lucas Bassa, Product Lead at Channable, explains why agentic marketplaces require sellers to look beyond getting listings online. Drawing on his insights, this article explores GEO for marketplaces and AI discovery.

Key takeaways

• AI assistants now decide which products to recommend. Listings without dimensions, materials, or compatibility can be left out, so you may lose sales you never see.
• Agentic marketplaces work like large language models. They solve a shopper's problem instead of matching keywords, so only a few products make the shortlist.
• Ads do not buy a place in recommendations. ChatGPT selects products independently of ads, and a discount cannot prove a missing specification.
• A green dashboard shows your listing meets platform rules, but doesn’t show the ability of an agent to recommend it.
• The 4 Cs plan covers context, completeness, consistency, and capacity. Channable AI for marketplaces, through rules and enrichment, helps you prepare for the future of e-commerce.

What are agentic marketplaces and how do they change product discovery?

Agentic marketplaces are AI-powered marketplaces where AI agents search, compare, and evaluate products for the shopper, and increasingly buy on their behalf. The shopper describes what they need, and the agent works through the catalog to find a match.

Much like LLMs, Marketplaces are becoming problem solvers instead of keyword matchers.

• Lucas Bassa, Product Lead at Channable

How agentic AI transforms consumer trust and buying decisions

Shoppers are turning to AI for research faster than they are learning to trust it. Alchemer's 2026 retail report found that 48.5% of shoppers had used an AI tool to research a purchase in the past year, but only 15% of respondents trust AI recommendations more than other sources.

Source: Alchemer's 2026 retail report

Shoppers also gave specific reasons for their doubts:

• 35.1% worry about privacy and what happens to the data behind a recommendation
• 21.4% worry about fake reviews and AI-generated review content
• 16.5% suspect that AI steers them toward what the retailer wants to sell
• 42% suspect that the reviews they read are fake or created by AI

These doubts concern general AI tools, and the picture shifts when the agent is the shopper's own. In Accenture's 2026 Consumer Pulse survey, 74% said they would trust a personal AI agent more than their best friend to purchase on their behalf.

This suggests that shoppers are more willing to delegate when the agent acts on their instructions.

How much buyers delegate depends on the product. For example, when buying consumer electronics, a shopper can ask an agent to compare specifications and narrow down options while still making the final call.

Few shoppers trust an AI agent to reorder or buy for them yet, but the place where they buy is already shifting toward the conversation. Lucas expects that shift to reach every screen a shopper uses:

Whether you're doomscrolling on Social Media, chatting to your AI Shopping assistant, or casually playing games on your smart fridge: You'll be able to make a purchase directly from that platform.

Shoppers may not hand over every purchase, but they’ll be able to buy directly inside the apps and assistants where they already search. So let's look at how purchases in AI-driven marketplaces change the search results shoppers see, and which products make the cut.

The agentic eCommerce shift: How AI-driven purchases alter search results

Traditional marketplace search lets buyers scroll through products and compare the options themselves.

Lucas explains the difference when agents take over that filtering.

“In traditional search, you will see up to 50 products on a single page, giving room for lower-ranking products to emerge.” With agents curating the results, he says, “there will only be a few making it through to the recommendations.”

Your products have fewer opportunities to get noticed beyond that shortlist.

Sponsored listings vs. algorithmic verification: Where ad spend meets AI reasoning

Paid placements can put your products in front of buyers, but they can’t confirm a specification missing from your catalog.

So, if someone needs a monitor that connects to their laptop and charges it through one USB-C cable, a discount won’t establish whether your model supports video input and sufficient power delivery. The agent needs those technical details to assess whether it meets the request.

Google’s Gemini-powered Ad formats show how advertising and product assessment can work together. In May 2026, Google announced tests of AI Mode ads with an independent AI explanation alongside the advertiser’s creative. Gemini evaluates product information to explain its relevance, while the placement remains clearly labeled ‘Sponsored.’ Google also says highly relevant, high-quality ads are eligible for its Highlighted Answers format.

ChatGPT takes a different approach. According to official documentation, product results are selected independently of ads and commercial partnerships. Selection considers the buyer’s request, structured product information, and OpenAI’s safety standards and product policies. Paying for an ad doesn’t buy inclusion in those recommendations.

It’s clear that these platforms don’t apply one universal verification process. Increasing your ad budget won’t help an agent confirm whether your monitor meets the buyer’s requirements. You need to provide the missing specifications.

The silent risk: Marketplace search suppression and invisible exclusion

If you cannot convince an Agent that your product solves the shopper’s problem, you will be quietly omitted from the results.

Quietly describes what makes invisible exclusion difficult to detect. You receive no notification that your product was excluded, or which missing information prevented it from being recommended.

Suppressed listings vs. suppressed search: Why your dashboard stays green

Your dashboard tells you whether a listing meets the marketplace’s requirements—not whether an AI shopping agent has enough information to recommend it.

Here’s a look at the two reasons for losing visibility:

Suppressed listings	Suppressed search
The marketplace hides your listing from search or browse because it fails to meet platform requirements.	Your listing remains live, but an AI shopping agent may omit it from recommendations when the available product information doesn’t meet the shopper’s needs.

💡 On Amazon, a listing can look complete and still be blocked. Errors 8541 and 8542 appear when your product ID matches an existing ASIN but other attributes conflict with Amazon's catalog. Learn how to prevent Amazon product matching errors.

With suppressed listings, there is a defined platform issue to resolve. For example, Amazon provides a Seller Central workflow to identify suppressed listings and correct missing or noncompliant information.

With omission from an agent’s recommendations, the information gap may be more specific. A shopper might ask for a replacement part compatible with a particular appliance model. Your listing could satisfy the marketplace’s publishing requirements without clearly identifying that compatibility. The agent may then lack the evidence needed to include your product in its answer.

This is the difference between listing health and recommendation suitability. A green dashboard may indicate that your listing meets the platform’s requirements. But it doesn’t necessarily establish that your product information answers the shopper’s question.

💡 Can't see exclusion in your dashboard? Track new AI metrics instead! Read the complete list of agentic commerce marketing tips.

Selling to the algorithm first: Why backend structure matters more than keyword stuffing

Adding ‘backpack,’ ‘rucksack,’ and ‘daypack’ to a product title does not explain whether the bag meets a shopper’s specific needs. As Lucas puts it:

“Agents already know all the synonyms for backpack. They are looking for context to help them answer questions instead.”

He emphasises that discovery is shifting from matching words to understanding what the shopper wants to accomplish.

That context needs to establish product-solution fit. Take Lucas’s example of a shopper looking for a travel backpack that fits Air France’s overhead-bin limits and includes a dedicated 15-inch laptop compartment.

The relevant information isn’t another synonym for ‘bag,’ but the product’s dimensions and supported laptop size—specific attributes that help the system assess whether it meets the request.

The 4 Cs framework Lucas presents offers a practical way to put this shift into action.

The 4 Cs framework: Strategies to adapt your product data for agentic marketplaces

Lucas presents four priorities for preparing your product data for agentic discovery:

• Context: Connect product information to the questions shoppers ask
• Completeness: Fill the attribute gaps that prevent those questions from being answered
• Consistency: Keep product information aligned across channels
• Capacity: Build systems that can handle growing catalogs and changing channel requirements

Context: Shifting from keywords to intent-based attributes

Start your product feed audit with a buyer’s question. Lucas says:

“You have to audit your product content for the long-tail attributes that answer real buyer questions.”

These are the details that establish suitability for a particular use, not just the broad category a product belongs to. He points to both existing fields, such as dimensions, and newer opportunities to provide context, such as FAQs.

Put that into practice through intent-based mapping.

For example:

Buyer’s question	Product information to audit
Will this fit in the space I have?	Exact dimensions, units, and any installation clearance requirements
Will this work with my existing equipment?	Compatible models, connector types, and supported technical specifications
Can I use it in these conditions?	Verified operating limits, material properties, and relevant care or usage instructions

Completeness: Eliminating data gaps to secure recommendation eligibility

Once you’ve identified the attributes that matter, completeness means making sure those fields contain usable information, in line with product data quality standards. As Lucas explains:

“Gaps in these attributes mean you can’t answer these questions.”

Closing those gaps across thousands of products calls for more than individual listing edits.

To prevent that, use Channable’s advanced rule system and AI capabilities to fill and enrich your attributes automatically. Because doing this manually across channels is getting increasingly difficult.

A multi-channel platform like Channable combines rules and AI listing tools for marketplace sellers to make that work manageable. Rules apply repeatable changes using existing product data, while AI catalog enrichment for marketplace listings can extract missing values from information already present in your catalog.

Suggested values can then be reviewed and approved before they flow into your channel feeds. This helps turn useful information buried in product content into populated attributes, without manually processing every SKU.

Consistency: Unifying content across multi-channel retail platforms

“Agents will rely on multiple sources for their information. Making sure your content is consistent everywhere helps Agents to make recommendations with confidence,” says Lucas.

He also notes that content may serve as a source regardless of who is selling the product, making alignment across retail platforms an important part of managing your product information.

Channable’s integrations support the workflow Lucas recommends through its eCommerce integrations. Channable connects your eCommerce platform to marketplaces, comparison sites, and advertising networks, with integrations including Amazon, eBay, Google Shopping, and TikTok Shop.

Managing those connections centrally gives your team a shared starting point for distributing product information. As Lucas puts it, “Maintaining your entire catalogue and all of your channels in one place will therefore help you maintain that consistency.”

Capacity: Building scalable feed management systems

A workflow that works for a few hundred products can become a bottleneck across thousands of SKUs and multiple international marketplaces. Every price change, stock adjustment, and new channel requirement adds work if someone has to process it manually.

Lucas describes the need for “a system that scales with you,” one capable of handling “complex operations, growing catalogues and changing requirements.” He identifies marketplace order management, compliance, and price and stock management as critical processes to automate.

And Channable’s Marketplace Integrator addresses this operational workload through connected tools:

• Order connections route marketplace orders into your webshop and send shipping information back, reducing the need to switch between marketplace accounts to manage fulfillment.
• Stock synchronization keeps availability aligned between your webshop and connected marketplaces, helping reduce overselling. Update frequency depends on the connection and configuration, so it should not be treated as universally instantaneous.
• Dynamic Repricer automates price adjustments on Amazon and bol according to your strategy, helping you stay competitive while protecting margins.

💡 Comparing platforms? Read the Channable vs ChannelEngine comparison

Automating listing governance with feed management software

Marketplace management goes beyond publishing listings. Lucas connects that responsibility to the time marketplace teams need to grow the business:

Managing a marketplace today extends far beyond simple product listing creation and is involved in actively controlling your eligibility. To keep your business moving forward, having the time and headspace to concentrate on core drivers is essential.

Channable connects product-data management with performance-driven optimization:

• Rules and AI enrichment help maintain your catalog
• Channable Insights helps your team use product-performance data to segment catalog and inform feed rules and advertising campaigns

These features help you enrich your product data to better answer questions that drive discovery.

Try Channable for free