To sway the human shopper, product discovery funnel relied on traditional marketing visuals and copy. Differentiation was shaped by branding and UX, while conversion often hinged on persuasion.
But new findings from the PYMNTS Intelligence November 2025 Payments Optimization Tracker® Series reveal that as agentic artificial intelligence (AI) systems mature, the traditional eCommerce model is beginning to fracture and can no longer be taken for granted.
The report highlights how agentic AI marks a sector-wide shift from assistive to autonomous commerce.
Assistive AI like chatbots, recommendation engines and search optimization each support human decision-making. However, Agentic AI represents an entirely new paradigm by potentially replacing elements of that same decision-making process, all the way to its execution.
Instead of nudging a shopper toward a product, an AI agent may be instructed to “reorder household staples under $50,” “find the best-value laptop meeting these specs,” or “book the cheapest flight that arrives before noon.” The agent evaluates options across merchants, selects the optimal one based on predefined criteria and completes the transaction.
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While still early, their emergence is beginning to raise practical questions for retailers about how products are discovered, selected and paid for when the “customer” is no longer a person at a screen.
What It Means to Be Machine-Readable
For many eCommerce retailers, the agentic AI commerce journey might not sound like capabilities their storefronts were designed to support. But the PYMNTS Intelligence report found now is the best time to get started. Becoming “agentic-ready” is not a single upgrade or API integration. Rather, it is a structural transformation that touches product data, pricing logic, payments, risk systems and governance.
The first requirement for agentic commerce is visibility. An AI agent cannot buy what it cannot understand.
For retailers, that means transforming product catalogs from marketing assets into structured, standardized, machine-consumable data sets. Descriptions optimized for human persuasion—rich imagery, narrative copy, lifestyle framing—must be complemented by precise, unambiguous metadata: specifications, dimensions, compatibility, warranties, return policies and availability in consistent formats.
Retailers whose digital presence has been optimized for visual appeal or narrative persuasion are discovering that those qualities translate poorly to machine-driven selection.
As a result, some retailers are investing in data normalization efforts that would once have been considered internal cleanup projects. Product catalogs are being restructured to align with common schemas. Inventory systems are being connected more tightly to front-end listings. The goal is less about presentation than interpretation: ensuring that a machine can accurately understand what is on offer.
Read the report: Agents of Change: How Agentic AI Is Redefining Commerce
Payments and Delegated Authority
If discovery presents technical challenges, payments raise more fundamental questions.
Allowing an AI system to initiate a transaction requires some form of delegated authority, or trust. Consumers must decide how much control to grant, under what conditions and with what safeguards. Retailers, meanwhile, must determine how to recognize and process payments initiated by software rather than people.
Existing payment flows are built around explicit user confirmation: clicking a button, entering a password, approving a charge. Agentic commerce may strain that model.
Retailers that have treated payments as a standardized backend function are finding that agent-initiated transactions surface new operational and liability questions. Who is responsible if an agent exceeds its mandate? How are disputes handled when the “buyer” is an algorithm? How is consent demonstrated after the fact?
These issues are pushing retailers to work more closely with payment providers and networks as they test agent-driven use cases. In many cases, the pace of experimentation is constrained less by technology than by unresolved questions of responsibility and risk.
Some retailers are beginning to design customer support processes with autonomous transactions in mind, anticipating questions that focus less on intent and more on outcome: what happened, why, and how it can be fixed. The ability to explain agent-initiated decisions in understandable terms may become a competitive differentiator.
The shift is gradual, not abrupt. But for retailers, the work of becoming machine-readable, and trustworthy, is already beginning.
At PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you’ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.
