From Scraping to Syndication: What It Takes to Win at AI Product Discovery in 2026

Your next customer may never search for your product

Jordan has a problem: His new apartment gets almost no natural light, and he wants to add a few plants that won’t be dangerous around his cat. He doesn’t know much about plants, and he certainly doesn’t know which varieties to search for.

So he doesn’t search.

Instead, Jordan opens an AI assistant and describes what he needs: “I want a few low-maintenance plants for a dark apartment that are safe around cats. What should I get?”

Jordan hasn’t entered a product name, brand, or even a traditional search term. He’s described a need and expects AI to figure out which products fit.

For merchants hoping to reach shoppers like Jordan, that changes the challenge. Product information that works for a traditional product page may not provide enough context for conversational discovery. In an agentic world, the catalog becomes the language that AI systems use to understand and recommend what a merchant sells. That language has to communicate not just what a product is, but who it’s for and why it might be the right fit for a particular shopper. And it needs to do so in a way that stays true to the merchant’s brand voice.

What it takes to get discovered by AI

AI discovery starts with simply being there.

AI product discovery begins with a simple question: Can AI find your products?

As AI-driven discovery has grown, scraping has provided one way for AI systems to find product information available across the web. For merchants, that creates a path to discovery without requiring them to actively distribute product data to every emerging AI experience.

But being findable and being understood are two different things.

“We spent the last year teaching answer engines to find us. The real shift is teaching them to understand us — that's the difference between being scraped and being syndicated. Data is the new storefront, and the merchants who treat it that way are the ones showing up first.”

— Sharon Gee, SVP of Product, AI, Commerce

Think back to Jordan. A merchant might sell a plant that checks every box on his list. But if the product page simply identifies it as a “parlor palm” without clearly communicating details like light requirements, maintenance needs, or pet safety, an AI system may not have enough context to connect that product to what Jordan actually wants.

That’s one of the limitations of relying on scraping for discovery. Merchants have less control over what information an AI system finds, how it interprets it, how their products and brand are represented, and whether it has enough context to confidently recommend the product.

Those gaps become even more important as shoppers move beyond broad keywords and start asking detailed, conversational questions. AI systems work with the information available to them. A generic title, missing attribute, or description that lacks meaningful context can make the perfect plant for Jordan harder for AI to find and recommend.

Being there is the starting point. But merchants don’t have to leave discovery entirely up to what AI systems can find on their own.

Giving AI more to work with.

If scraping puts AI systems in the driver’s seat, the next step gives merchants a more active role in how their products show up.

Rather than relying solely on AI systems to find and interpret information from across the web, brands can proactively provide structured product data to answer engines, giving merchants a more intentional role in how their products are represented.

For Jordan, that could mean an AI assistant has more product information available when deciding which plants fit his needs. But access to that data doesn’t guarantee the AI has enough context to make a relevant recommendation.

If a merchant’s product data identifies a plant’s species, size, and price but leaves out whether it thrives in low light or is safe around pets, the same problem remains. The AI has data about the product, but not necessarily the context it needs to understand why that product fits Jordan’s request.

That distinction matters because conversational discovery asks more of a product catalog. Shoppers aren’t always looking for a specific item. They may describe a need, preference, use case, or constraint and expect AI to translate that intent into a relevant recommendation.

Getting product data in front of AI is progress. The bigger opportunity is making that data rich enough for AI to understand.

Better data leads to better understanding.

Giving AI access to product data solves one problem. Making sure that data tells the full story solves another.

Product data needs to communicate more than basic facts like a product’s name, category, size, or price. It needs the attributes and context that help an AI system understand what makes the product relevant to a shopper’s specific needs.

For Jordan, richer data can be the difference between getting a list of plants and getting a recommendation that actually makes sense. And the more specific the request, the more that context matters. A shopper might be looking for a waterproof trail shoe for wide feet, a sofa that can withstand pets, or a laptop powerful enough for video editing but light enough for frequent travel.

That’s where enrichment comes in. Solutions like Feedonomics Enrichment and BigCommerce Catalog Enrichment can help make product information richer and more useful, giving AI systems more context to understand what makes a product relevant to a shopper’s specific needs while helping merchants keep content aligned with their brand voice.

The goal is no longer simply to make products available to AI. It’s to give AI enough context to understand when those products are the right answer.

From waiting to be discovered to showing up where shoppers are.

Rich product data becomes even more valuable when merchants can put it to work wherever shoppers are expressing intent.

That’s where syndication comes in. And while syndication itself isn’t new, where that data can go is expanding. Merchants have long distributed product data across advertising channels, marketplaces, and social platforms. Now, AI shopping experiences are becoming another place where merchants can actively distribute enriched product data.

For Jordan, all of that work happens behind the scenes. He doesn’t need to know how a merchant’s catalog reaches the AI experience he’s using. He simply expects the products presented to him to match what he asked for.

For merchants, however, the distinction matters. Shopper discovery is fragmenting across search, AI conversations, marketplaces, social platforms, and merchant storefronts. The customer journey no longer has to begin on a traditional search results page or product page. That means merchants need to be ready to meet shoppers wherever discovery happens.

“One of the best statements I’ve heard in a long time is that the answer is the new ad. For years, brands focused on appearing on Google’s first page of search results. Now, you want your product to be the answer an AI system delivers. That single answer is the best possible ad at the moment.”

— Michael Scholz, VP of Marketing, Commerce

Commerce is helping merchants bring their product data to emerging AI shopping experiences with Agentic Shopping Channel: Google Gemini & AI Mode and Agentic Shopping Channel: OpenAI/ChatGPT, opening new paths to discovery.

But syndicating products to more places isn’t the end goal. What matters is connecting rich product understanding with the places where shopper intent forms, creating a path from discovery to decision to trusted transaction while keeping the merchant at the center.

And that raises a bigger question: If your products are showing up in AI experiences, how do you know that visibility is actually influencing what shoppers buy?

Showing up is only part of the equation

For Jordan, the journey seems simple. He asks a question, gets a recommendation, and decides what to buy.

For the merchant, there’s a much harder question hiding behind that experience: Did showing up in the AI response actually influence his decision?

As AI discovery evolves, it can be tempting to treat visibility as the finish line. If a product appears in an AI-generated answer or recommendation, something is clearly working. But a mention alone doesn’t tell a merchant whether the shopper engaged with the product, considered it over another option, or ultimately made a purchase.

“The real value of AI discovery goes beyond whether a product appears across answer engines. The bigger question is whether those moments actually influence what a shopper considers, chooses, and ultimately buys. For merchants, understanding that connection will be critical as AI becomes a bigger part of the buying journey.”

— Michael Scholz, VP of Marketing, Commerce

Exactly how that visibility translates into buying decisions is still taking shape. The evolution from scraping to syndication can help merchants become easier for AI systems to find and understand. But the next phase of agentic discovery will be about what happens after a product gets found.

The final word

The way shoppers discover products may be changing fast, but merchants don’t need to chase every new AI experience that emerges. Instead, they need to give those experiences something worth finding.

“Every merchant is somewhere on the same curve right now: crawl, walk, run. First you make sure you can be found. Then you make sure you're understood. Then you're not just discoverable, you're the default recommendation.”

— Sharon Gee, SVP of Product, AI, Commerce

That’s what effective product discovery looks like in the agentic era: making your products easier to find, understand, and recommend wherever shopper intent forms. Solutions like Feedonomics Enrichment and BigCommerce Catalog Enrichment can help build that foundation as discovery expands across new surfaces.

Because wherever shoppers go next, the goal is the same. Make sure your products are ready to meet them there.

Explore BigCommerce’s approach to agentic commerce and the solutions helping merchants adapt, grow, and stay ahead as ecommerce evolves.