Jan 20, 2026 | 5 minute read
written by Brianne Thomas
Summary: AI-driven buying experiences promise to remove friction from commerce, but protocols alone aren’t enough. As discovery and purchasing move into AI platforms, businesses lose control of the context in which products are evaluated and bought. This post explains why product data is the real prerequisite for AI readiness, and outlines practical steps to prepare: assigning ownership, using structured data, decoupling product data from channels, modeling product relationships, and reviewing product feeds. The future of AI commerce depends less on new features and more on the quality of the product data underneath them.
We can all agree on the goal to remove friction from the purchasing process. Emerging protocols like UCP and ACP are designed to do exactly that. At their core, these protocols enable AI systems to not just recommend products, but ultimately take action on behalf of buyers, dramatically reducing friction across discovery, evaluation, and purchase.
There’s been no shortage of coverage on the launch of these protocols. What’s missing, however, is a serious discussion of what’s required to make them work effectively in real-world commerce environments. Yes, your commerce platform should work toward supporting these protocols. But even if they were fully supported tomorrow, most organizations still wouldn’t be ready.
We’ve seen this movie before. Just look at what happened with Amazon’s Shop Direct program.
So how do you actually become ready? The answer isn’t another AI feature or protocol. It’s product data.
Product data isn’t as “fun,” “new,” or “buzzy” as AI-powered assistants or autonomous checkout flows, but it’s a prerequisite. And while you already have product data supporting your current selling channels, that data is rarely clean, well-structured, or designed for reuse. In practice, inconsistencies, duplication, and implied logic are common.
Historically, this hasn’t been a critical issue because products were served within the controlled context of owned experiences. Frontends, sales teams, and custom logic compensated for gaps in the data. But as buying moves into AI-driven environments, those buffers disappear. Your products can be discovered, evaluated, and purchased entirely within AI platforms like ChatGPT or Gemini, in a context you don’t own and can’t fully predict.
When you don’t control the context, you have to take control of what you can. And that’s your product data.
Here’s what you can do today:
Product data is a strategic asset and should be treated as such. Assigning an owner for product data strategy creates clear accountability for how product data is modeled, governed, and reused across the business.
With a dedicated owner, organizations establish consistent standards for field usage, relationships, and data quality, rather than allowing each team to define products differently. This centralized oversight reduces duplication and ensures product data can scale as new channels, experiences, and AI-driven workflows are introduced.
The result is higher-quality product data, faster execution, and a catalog that supports growth instead of constraining it.
Structured data in your catalog matters because AI can’t reliably reason over ambiguity. The more your product information is implied or dependent on human interpretation, the more AI has to guess. And guessing is where accuracy, trust, and conversion break down.
Structured data turns product information into clearly defined facts using attributes, constraints, relationships, and rules. Because this data is independent of presentation logic, it can be consistently consumed across channels, systems, and AI-driven experiences, enabling more accurate discovery, reasoning, and transaction.
Decoupled product data is critical because AI systems don’t experience your storefront, they consume data. When product data is tightly bound to channels, it becomes brittle, ambiguous, and difficult for AI to reason over, especially when the same product exists in multiple, slightly different forms across channels.
Decoupling creates a single source of truth for core product data. It allows the same product to appear consistently in a B2B portal, a D2C site, a partner marketplace, or an AI assistant without being redefined each time. That consistency gives AI systems a clear, unambiguous understanding of what a product is, reducing the need for inference and guesswork.
As a result, decoupled product data turns products into reusable, machine-readable building blocks that AI can reliably discover, reason over, configure, and transact with across any channel, present or future.
Product data should also be evaluated for completeness. Are all attributes defined and populated? Does your product data include FAQs, images, and videos?
Product relationships are links between products that define how they interact or should be considered together. Instead of treating products as isolated records, relationships express intent, constraints, and commercial logic.
Examples include: product compatibility, required or optional components, service or warranties, product configurations, and bundles. When these relationships are clearly defined in product data, AI systems can reliably surface valid options, guide buyers to the right configurations, and avoid guesswork during discovery and purchase.
Reviewing your product feed matters because it’s often the version of your catalog that machines actually see. As commerce shifts toward AI-driven discovery, your product feed increasingly is your storefront.
Reviewing the feed shows you what downstream systems actually consume, not what you hope buyers understand. It helps you identify duplicate products created to support specific channels or merchandising needs, confirm that data is properly structured, and ensure product relationships aren’t enforced only through frontend logic or buried in PDFs.
AI-driven buying experiences promise less friction, faster decisions, and better outcomes. But those outcomes aren’t unlocked by protocols alone.
They’re unlocked by product data that is owned, structured, decoupled, and explicit.
AI will change how buyers buy.But your product data will determine whether they can.
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