Why 2026 Will Reshape Commerce: The Data Shift Retailers Can’t Ignore

There’s a major shift happening in commerce, and it’s hiding in plain sight. You can see it in how people are starting their buying journeys and in how often they’re already using answer engines to make product decisions. OpenAI’s own data shows that more than 55 million messages every day in ChatGPT are about items people want to purchase. That’s the early shape of a new discovery channel.

Even with conservative assumptions, those queries represent more than $2 billion in immediate annual payment revenue for anyone capable of powering checkout inside those interfaces. With higher adoption, stronger buying behavior, and conversion rates that reflect more confident decision-making, the opportunity expands to more than $67 billion. And all of that is driven by one simple pattern: consumers are asking artificial intelligence what to buy.

For retailers, this shift means discovery is increasingly happening somewhere other than their storefront. To participate without losing their identity (as they already have to the Amazons of the world), brands need the kind of product and customer data that answer engines can understand and trust. Well-structured catalogs, clean identity data, and clear signals about what makes a product right for a specific customer in a specific moment.

That’s the opportunity in front of us. As discovery moves upstream, retailers that modernize their data foundations will put themselves in a stronger position than they’ve had in years: able to show up in the answer, own the transaction, and build the long-term customer relationships marketplaces have long since eroded.

Discovery is Becoming Answer-Led

Most B2B retailers have probably experienced a version of this scenario: A buyer lands on a site, hits the search bar, filters the catalog results to narrow the options, cross-references a PDF, and eventually calls or emails because they still aren’t confident they’ve identified the right product. For all the investment in digital channels, many purchases still depend on a human expert stepping in to confirm fit, compatibility or lead times. Buyers are looking for certainty.

Answer engines are simply compressing that process. Consumers can now ask for the specific running shoe that won’t aggravate a knee injury, or the exact part that fits a particular machine model, or the sofa that can be delivered this weekend. These are intent-rich prompts, and AI systems are getting better at interpreting them.

This change shifts the burden upstream. To participate, merchants need to expose structured, complete, machine-readable product data. This means real product information, including attributes, variations, related content, compatibility, logistics constraints, and differentiators.

Many retailers still rely on catalog structures built around legacy platform constraints. Those constraints simply won’t hold up in an environment where the quality of your product data directly determines whether you’re included in the answer at all.

Retail Has More Customer Data Than Ever, But Far Less Value Than it Should

On the customer side, the story is similar. Retailers have collected mountains of data over the last decade. Loyalty programs, on-site behavioral analytics, third-party enrichment, internal BI projects — the quantity is staggering. However, most struggle to turn it into something genuinely useful.

Customer data platforms (CDPs) promised a single source of truth. In reality, many organizations found themselves overwhelmed by identity resolution challenges, siloed data, inconsistent integrations, and the sheer manual labor required to make any of it actionable. The idea was sound, but the operational lift was not.

AI has the potential to change the equation by making these systems more sustainable. AI can help reconcile identities, surface patterns, generate real-time insights, and eliminate the operational overhead that kept many CDP deployments from truly delivering value.

What matters most in an answer-driven world is the connection between the product a customer is looking for and the corpus of data you hold about that customer — what they've bought, what they’ve returned, what they favor, how they shop, and which experiences lead to a second purchase vs. a one-time order. When product and customer data work together, the retailer can deliver a more confident, more relevant experience at exactly the moment the customer needs it.

That’s the piece retailers are missing today, and the piece the next era will require.

AI or Not: Retailers Run the Warehouses

Marketplaces changed the industry by controlling the customer relationship. Their scale is undeniable. Their logistics networks are extraordinary. But in exchange for that infrastructure, retailers gave up something fundamental: direct access to customer data and the ability to build meaningful loyalty.

Answer engines have the potential to introduce a very different dynamic. They want to understand the question, parse the intent, and deliver the best possible recommendation. However, they aren’t going to run warehouses. They won’t deliver to your door in the next four hours, manage returns, staff customer service, or be the ones responsible for damaged items, shipping delays or fraud investigations.

In other words, they don’t want to be the merchant of record. And that creates a unique opportunity. Retailers that show up as the “right answer” can still own the transaction, the fulfillment, and the post-sale experience. They get the data. They get the margin. They get the customer relationship. And they get the second order, which is where real lifetime value gets created.

The companies that modernize their data foundations now will be the ones positioned to benefit when answer-driven commerce becomes the norm.

A Strong Foundation Matters More Than a Shiny Front-End

Retailers have spent the last decade polishing storefront experiences, redesigning PDPs, and optimizing checkout flows, which is all important work. However, as discovery moves outside the storefront, the most important work will happen underneath the surface.

The retailers that will win in 2026 and beyond will be the ones that:

• Treat product data as a first-class strategic asset.
• Rebuild catalog structures to be machine-readable and deeply descriptive.
• Connect customer data into usable, AI-ready pipelines.
• Invest in orchestration and automation that make fulfillment and post-sale service consistent.
• Focus on lifetime value, not just the initial conversion.

2026 is the Year Retailers Start Taking Back Control

The last decade pushed retailers deeper into dependency on search, marketplaces, and complex martech stacks. The next one offers a chance to reverse that trend by modernizing the fundamentals that determine whether a retailer can participate in the next phase of digital commerce.

If 2023-2025 were the years AI entered the conversation, then 2026 will be the year retailers learn how to operationalize it. If done well, AI will reinforce the merchant’s role in the transaction. It will help retailers get closer to their customers. And it will give them the tools they need to deliver consistent, high-value experiences that make customers stick around.

The inflection point is here and the question for retailers is simple: Will you own the data that drives your business forward or let someone else do it for you?

Bryan House is the CEO of Elastic Path, a composable commerce solution.