If digital, mobile, and marketplaces defined the last decade, then AI will reshape how products are discovered, recommended, and purchased as we enter the next era of commerce.
Going forward, brands can’t only focus on the digital shelf; they’ll also need to prepare for an agentic shelf that’s steadily driving product discoverability and brand perception.
That was at the heart of the discussion at the Salsify 2026 Digital Shelf Summit (DSS). During the session, “Architecting Growth: The New Fuel for Personalized Shopping Experiences,” ecommerce leaders from PC Express, Grainger, Southern Glazer's Wine & Spirits, and VML shared their perspective on the future of ecommerce.
Here’s their take on how brands can better prepare for this new AI-driven shopping era, the growing importance of collaboration with suppliers and retailers, and why trusted product data is non-negotiable for today’s agentic commerce experiences.
For years, shoppers have entered relevant keywords into search engines to find products. But in the near future, most shopper journeys won’t begin with keywords, but with questions.
With the rise of AI agents and generative AI engines like ChatGPT, Gemini, and Claude, consumers have another channel that will shape how they discover products and make buying decisions.
AI agents are distinct from other shopping channels in that they understand context, make hyper-personalized recommendations, and give consumers a curated set of choices that shrinks consideration down to only a handful of products.
“The agentic world, or agents, specifically, will be responsible for this future — this future motion where we're moving through our lives and the things that we need and value show up for us,” Molly Schonthal, managing director, agentic commerce transformation at VML and WPP Enterprise Solutions, says.
AI agents are shifting commerce away from an endless shelf that gives brands ample opportunity for consumers to find them. Instead, it’s creating a dynamic, intelligent shelf where context and relevance will make or break whether a brand gets discovered at all.
Brands can win on this new, dynamic digital shelf with one key asset: richer product data.
Rather than relying on basic metadata like titles and descriptions, brands must provide highly accurate, structured, and contextual information to AI engines to influence their answers. AI agents love details, so the richer the product content, the higher the likelihood an AI agent will recommend a brand.
Jacqui Nie, product information management lead at PC Express, gave the example of a consumer going to an AI agent for a pasta recipe. One pasta product has basic metadata, but the other has keywords and details such as gluten-free, high protein, weeknight meal, quick preparation, and ready in five minutes.
“It's a no-brainer which product this agent is going to recommend,” Nie says.
Nie added that data attributes such as robust product features and benefits, dietary labels, nutrition information, and AEO (answer engine optimization) keywords will become increasingly important for brands to build their competitive advantage on the new digital shelf.
With agentic commerce, context matters.
When brands were competing on an endless aisle, success largely depended on showing up repeatedly. On the agentic shelf, fit and relevance define success.
“We're underprepared to show up in context to a question being asked,” Schonthal says.
Brands now need to restructure their product and content schema to make it more contextual and relevant to AI agents, and by extension, shoppers.
“You have to start with product information and a product information schema that itself could be competitively advantaged because you have access to things that customers want that not even the manufacturers know about,” says Greg Carlson, senior director, merchandising content and search, at Grainger. “If you're really good at defining what that schema is and you're really good at gathering [it] and you're also really good at creating [it], you can build that out just like data and be just as relevant.”
The implication here is that flowery marketing copy won’t win in the age of agentic. Brands need to focus on specificity and depth to build credibility with AI agents and consumers.
Agentic commerce makes seamless collaboration between brands and their supply chain and retail partners more vital for delivering compelling, conversion-driving product experiences.
Yana Kaszubska, ecommerce lead at Southern Glazer’s Wine & Spirits, says as a distributor, her company spends a lot of time educating its suppliers about the value of creating quality product content. She says agentic commerce has strengthened the business case for suppliers to improve product data and develop a more robust product detail page (PDP).
“The conversations are starting to drive the point home of, ‘Okay. I cannot afford not to have something on the product page,” she says.
Brand-retailer collaboration is just as important, especially with the emergence of retailer agents such as Amazon Rufus (now Alexa for Shopping) and Walmart Sparky.
Retailer agents use various inputs to inform product recommendations, including PDPs and customer reviews.
To better support a dynamic digital shelf, brands, retailers, and distributors will have to work together to fill data gaps, continuously update product information, and develop better governance to ensure they feed AI agents accurate, relevant data.
“For example, what are some data you have that you think will really drive agentic commerce on our site but that we're not using?” Nie says. “Let us know. We want to learn from you. Conversely, we may ask, ‘We're looking for this data. Do you have it? If not, can you get it?’ How can we work together, share these ideas, and get better together?”
Get Ready for the Agentic Era
AI is already transforming commerce, but the fundamentals still matter. Brands need high-quality and contextual product data to support AI-driven shopping experiences, increase their visibility in this new channel, and show up in the relevant shopping moments.
No matter the era, the path to better product experiences always has to start with better product information management.
“At the heart of any AI agent is product data,” Kaszubska says. “We can't escape this. We have to make sure that this is not only accurate, but it's also complete.”