OpenAI and Pine Labs Bring Agentic Commerce to Payments

Agentic commerce enters Indian payments

Agentic commerce is moving beyond chat experiences into real payment workflows. Today, OpenAI and Pine Labs announced a partnership aimed at bringing AI agents into commerce transactions in India, positioning Pine Labs as a key payment partner for ChatGPT in the market. The signal for retailers is clear: AI is starting to execute, not just advise.

This shift matters because payments are where customer trust is earned or lost. When AI can initiate or complete a payment, the quality of data behind the transaction—product attributes, pricing, taxes, and fulfillment rules—becomes the difference between a seamless experience and a costly failure.

What OpenAI and Pine Labs are actually building

The announcement is less about a chatbot and more about the infrastructure around it. Coverage of the partnership emphasizes that Pine Labs will integrate with OpenAI to enable agentic workflows across the payment stack, including areas like invoicing, settlement, and reconciliation for enterprises. The intent is to connect AI decision-making with the systems that move money.

For e-commerce operators, that means AI assistants will increasingly interact with:

• Payment options and routing
• Refund and dispute workflows
• Invoicing and order-level documentation
• Reconciliation tasks across marketplaces and PSPs

The closer AI gets to the money, the stricter the data requirements become.

It also reframes where innovation happens. Instead of just optimizing on-site search or product recommendations, teams will need to think about decision automation at checkout: routing the right payment method, handling cross-border tax nuance, or catching a mismatch between a refund and its underlying order. These are traditionally finance-led processes, but agentic workflows will pull them closer to the product data and merchandising stack.

Why this matters for e-commerce ops teams

AI-driven payments change the risk surface. A helpful assistant can now become a transactional actor, which introduces new operational questions:

• Can your system validate SKU-level tax rules before a payment is triggered?
• Are discount and promotion rules consistent across channels?
• Can an agent safely approve a refund without manual review?
• Do you have structured data for localized compliance and invoicing?

If those answers are unclear, you are not alone. Many retailers still manage product data in fragmented spreadsheets and vendor feeds. That makes “agentic” workflows brittle. Before AI can take action, it needs a single source of truth.

Another implication is control. When an agent can take action, you need clear guardrails: which scenarios are auto-approved, which require a human review, and which are blocked outright. That control logic has to be deterministic and auditable, which is hard to achieve if the underlying data is inconsistent or missing. Getting the data layer right is the lowest-risk way to prepare for more autonomous workflows.

Product data is the hidden dependency

Agentic commerce only works when the data behind it is structured, complete, and fresh. That includes more than product descriptions; it includes logistics attributes, category taxonomies, legal restrictions, and regional pricing logic. If your product feeds are inconsistent, an agent can trigger the wrong payment, recommend the wrong shipping option, or generate a faulty invoice.

This is where product data platforms start to matter. Tools like Lasso help teams normalize messy supplier feeds, enforce attribute schemas, and keep catalog updates synchronized across channels. If you want AI to act safely, your product data needs the same rigor as your payment stack.

Think of it as a maturity ladder. First you standardize. Then you enrich. Then you monitor. Only after that can you confidently allow AI to execute transactions. Each step reduces downstream exceptions—manual refunds, failed invoices, and chargeback disputes—that erode margin and trust.

If this topic is relevant, revisit our product feed management guide and the broader features overview to see how structured data workflows map to AI automation.

How to prepare: five practical steps

You do not need to wait for agentic payments to reach every market to start preparing. Focus on the fundamentals that make automated commerce reliable:

• Audit your core product attributes (price, tax class, category, brand, GTIN) and fix gaps.
• Standardize schemas across channels and suppliers to reduce mapping drift.
• Separate marketing copy from transactional fields so AI doesn’t confuse them.
• Track inventory and availability at SKU level, not just category level.
• Document approval rules for refunds, discounts, and exceptions.

If you’re unsure where to begin, it helps to benchmark your workflows against real use cases. Start with our use cases page, and align the work with the teams responsible for payments and catalog operations.

Finally, add measurement. Define a short list of metrics that reflect readiness for agentic commerce: percent of products with complete attributes, refund rates tied to catalog errors, and time-to-reconcile per channel. If those numbers move in the right direction, you are building a foundation that future payment agents can trust.

Getting started with agentic commerce

The OpenAI and Pine Labs partnership shows where the market is heading: AI agents that can execute transactions, not just recommend them. That raises the bar for data quality and operational governance.

In practice, the first wins will be narrow: automating a subset of refund approvals, improving invoice accuracy, or eliminating manual reconciliation steps. Teams that already treat product data as a first-class asset will be able to move faster when these tools become widely available.

Lasso can help you prepare by cleaning and enriching product data, keeping schemas consistent, and making it easier to connect catalogs to the systems that power checkout. If you’re planning for AI-driven commerce, explore pricing and book a demo via contact to see how it fits your stack.

For more practical guidance, browse the blog and keep an eye on how agentic workflows evolve across payments, customer support, and merchandising.