If you're a payments manager at a retailer, agentic commerce has probably already landed on your desk, through conversations with your PSP, questions from leadership, or both. The questions tend to be the same:
When is this actually happening?
Are we ready for it?
What does it mean for how customers find our products?
AI platforms are still in the early stages of onboarding merchants, and fully automated checkout isn't here at scale yet. However, a big shift is already underway behind the scenes.
More shoppers are starting their product searches in AI tools rather than traditional search engines. This isn't just a new channel; these machine-led systems behave in fundamentally different ways from people. Most retail infrastructure wasn't built for that, which is why one question keeps coming up:
How do retailers get their products to show up in LLMs?
This article looks at what that actually involves, why it has landed with payments teams, and what businesses can do to prepare.
The article covers:
Why product feeds have landed on the payments team’s desk
Why having a product feed isn't the same as being ready
Three steps to getting a product feed ready
What to do if an MCP server has already been built
How Adyen can support retailers
Thinking through what agent-ready product data looks like for your business? Get in touch, or read Adyen’s guide to agentic commerce for retailers.
Why product feeds have landed on the payments team’s desk
Product data doesn't belong to one team. It sits across ecommerce, merchandising, logistics, compliance, and operations. So why are agentic commerce questions converging on payments?
Agentic commerce conversations tend to start with PSPs, card networks, and payments infrastructure. So the early guidance on what's actually possible, what's realistic, and what can wait typically reaches the business through payments first.
Plus, because product data touches so many departments, the payments team's cross-functional visibility becomes genuinely useful. Payment teams may not own the data, but they can see across ecommerce, finance, fraud, legal, and operations in a way most teams can't. That makes them well-placed to coordinate the right people around the right questions.
Having a product feed isn't the same as being ready
Most retailers already have a product feed. It might live in a PIM system, come from a commerce platform, sit in a Google Merchant feed, or be a mix of all three. And many businesses are already optimizing their catalogues for people and search engines, with clear titles, good descriptions, and well-organized categories.
But that doesn't make a feed ready for AI.
AI platforms follow strict product feed requirements such as specific fields, consistent formatting, and up-to-date data, which are often beyond what a standard ecommerce feed includes. Traditional ecommerce optimization is about making listings appealing to people. AI systems have different priorities, such as whether data is structured, complete, and machine-readable.
Unlike a traditional website journey, this doesn't just happen once at checkout. AI systems may check pricing, availability, and eligibility multiple times during a single interaction. So inconsistent or outdated data becomes visible quickly.
There's also a structural layer to this. Each AI platform has its own requirements, so retailers currently need to adapt their product data for each one separately. That means repeating similar mapping, formatting, and updating work across platforms, with ongoing maintenance each time requirements change.
Most enterprise retailers are somewhere in the middle of this journey. Some data is structured, some is spread across systems, and some isn't available in real time.
To understand where a business stands, Adyen suggests asking:
Can the feed connect to real-time inventory?
Does it reflect live availability?
Does it show accurate pricing and eligibility?
Can it be adapted to match different AI platform requirements?
Three steps to feed readiness
The gaps in a product feed setup will likely span teams and systems. So the work is as much about coordination as it is about technology.
1. Validate the existing feed against AI specifications
The first step is to check the feed against the platform's actual requirements, not assume it works because the catalogue works today. AI platforms are specific about what data they need, and fresh, accurate data is a key signal they use to decide what to show.
If a feed is missing fields, out of date, or inconsistent, products may show errors that undermine trust in the data. Or they may not appear at all.
2. Work with other teams to plug data gaps
The product information required is probably already somewhere in the business, just owned by different teams. So the first challenge is coordination.
Retailers need to:
Find what's missing: Look at the required fields and identify what isn't in the current feed. Common gaps include weight, dimensions, delivery timelines, returns policies, and regulatory details.
Work out where it lives: These fields are often owned by logistics, operations, finance, or compliance, not ecommerce.
Bring the right people together: The goal is not to rebuild systems, but to ensure the data can be accessed and used as AI platforms require.
In most cases, this means reconciling data across the PIM, commerce platform, order management system (OMS), inventory tools, marketplace feeds, and Google Merchant Center.
The key questions are:
Which system is the source of truth for pricing, availability, and eligibility?
Who is responsible for keeping that data accurate and up to date?
3. Decide who owns AI-ready product data
Regardless of how many systems product data spans, AI platforms expect it to be delivered in a single, consistent, up-to-date format.
Retailers are approaching this in a few different ways, each with trade-offs:
Extend the PIM: Adding fields and AI logic to a PIM keeps ownership close to catalogue and merchandising teams, but may require significant development work.
Use the commerce platform: Exposing product data through APIs and connecting directly to AI platforms can be quicker to set up, but may introduce dependency on the platform's roadmap.
Build a translation layer: Creating a service that pulls data from different systems, standardizes it, and formats it for each AI platform gives full control, but requires ongoing maintenance as requirements change.
Work with a payments or infrastructure partner: Letting a partner handle how data is prepared and shared with AI platforms can reduce custom development work, but means relying on an external layer.
There's no single right answer. The best approach depends on engineering capacity, how much control the business wants to retain, how many platforms it plans to support, and how frequently data changes.
Fast-moving sectors like travel, marketplaces, and food will need tighter real-time integration than more static catalogues.
In practice, this is less about picking a tool and more about deciding how systems work together to keep product data accurate, current, and ready for AI.
If an MCP server has already been built, what comes next?
Some AI platforms now let businesses create branded experiences within their interfaces. If a retailer has built an MCP server, it can be used to power one of these by connecting the server to the platform, building a conversational experience aligned with that platform's conventions, and letting users select the brand before interacting.
Retailers taking this approach tend to fall into two camps.
Some are using it to experiment with AI-native brand experiences. This tends to work best where customers already search directly for the brand, the journey benefits from step-by-step guidance, or the product is complex to configure.
Others are focused on ensuring their products appear in general AI searches alongside other brands when users ask broad questions. For most payment managers, this is the more immediate priority.
Which approach makes sense depends on the brand and its customers, and the two approaches are not mutually exclusive.
Whether a retailer is building a branded experience or optimizing for general discovery, the underlying preparation is the same. Structured, machine-readable product data, accurate pricing and availability signals, clear policies, and the ability to adapt as platform requirements evolve are all essential.
How Adyen is supporting retail payment teams
Based on its conversations with enterprise retailers and AI platforms, Adyen believes the most pressing challenge for agentic commerce is infrastructure.
The company is working with retailers and AI platforms to share what's being asked for today, help payments teams decide what’s worth doing now, and support preparation without pushing decisions that may be difficult to unwind later.
Adyen is also contributing to discussions on emerging standards , so retailers don't have to commit too early to any single approach.
At the infrastructure level, Adyen is focused on where AI interfaces meet real commerce systems. Its goal is to reduce fragmentation, support multiple standards, and help retailers avoid separate integrations for every new platform.
The company’s focus throughout is on helping retailers maintain control over their data and customer relationships.
Key takeaways for retail payments managers
Nobody expects payment teams to have agentic commerce fully figured out. But the conversations already happening with PSPs and partners provide a clearer picture of what's realistic than most teams currently have.
Adyen highlights several key points to keep in mind:
Having a product feed doesn't mean a business is ready. AI-led discovery requires explicit specifications, required fields, and data freshness beyond traditional ecommerce feeds.
The biggest blockers are fragmented data, unclear ownership, and assumptions about what already exists. These are solvable, but they require cross-functional coordination.
Retailers don't need to bet on a single AI platform to prepare. Clean, structured, machine-readable product data is reusable across AI assistants and discovery surfaces.
Focus on readiness without lock-in. Build foundations that can adapt as standards and AI platforms evolve, rather than committing too early to specific implementations.
Good product data is the starting point. But making it work across AI platforms also means keeping it up to date, translating it into different formats, and adapting it as requirements change. As a function that works across teams, payment managers are well-placed to help drive that transition.
Thinking through what agent-ready product data looks like for your business? Get in touch, or read Adyen’s guide to agentic commerce for retailers.
