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AI shopping is quickly gaining momentum: in 2025, 26% of US adults used AI for product discovery and recommendations. The next phase of this future reality is far more transformative: agentic commerce, where AI agents go beyond helping buyers find and filter products (by price or date of delivery, for example), and complete purchases on their behalf or even think about making autonomous orders based on past behavior.
ChatGPT can now buy products directly within a conversation, and Google is rolling out Agent Payments. The race to enable AI-driven transactions is accelerating rapidly. Most of the vendors looking to get into this space might not realize all the problems they might face between checkout and fulfillment.
For agentic commerce to work at scale as a reliable, everyday shopping experience, four critical elements must fall into place. And each one presents challenges that reveal why order operations infrastructure will determine which brands win in the AI commerce era.
Pillar 1: Product Discoverability: Making Your Catalog AI-Readable
An AI agent can’t recommend or purchase what it can’t find.
Unlike traditional search engines that crawl websites, AI agents need structured, real-time access to product catalogs. They need to understand your products’ detailed attributes: materials, sizes, colors, compatibility, use cases, and more. This data must be formatted in a way that AI can parse, understand, and match to consumer intent expressed in natural language.
The problem? Most commerce systems weren’t built for AI consumption. Product data lives in fragmented silos: your Shopify store, your marketplaces, your ERP, your PIM system. Each with different schemas, formats, and update cycles.
Pillar 2: Real-Time Inventory Accuracy: No Phantom Products
AI agents won’t surface products that can’t be fulfilled.
This is where most agentic commerce implementations break down. An AI agent asks: “Can you deliver this product to my user within their required timeframe?” If your inventory data is synced daily (or worse, manually updated), the answer is often wrong. Stockouts that haven’t propagated. Inventory allocated to other orders but still showing as available. Products sitting in the wrong warehouse for the delivery promise you’re making.
AI agents are ruthless about reliability. If a brand repeatedly offers products that turn out to be unavailable, the AI will deprioritize that merchant entirely. Consumer trust and AI ranking both depend on inventory accuracy.
Another problem to mention is that there are too many layers between the 3PL or WMS and the actual selling channel. Too often today you have to go from 3PL or WMS to OMS to commerce platform to selling channel, and that is too many hops. That’s why there’s a need for standards such as onX, which allow you to go directly from an AI selling channel down to 3PL or WMS.
Pillar 3: Transparent and Optimized Delivery Expectations: No Surprises After Purchase
Consumers need to know when their order will arrive before they commit.
In traditional ecommerce, you might bury shipping estimates on a separate page or reveal them only at checkout. AI agents don’t work that way. When a consumer asks, “Can I get this by Friday?” the AI needs an immediate, accurate answer. If the delivery SLA isn’t clear to the buyer either offering only products that meet their requirements, highlighting the commitment proactively before the order, or confirming it through a dialogue; the transaction might end up disappointing.
This requires understanding carrier capabilities, real-time capacity constraints, cut-off times, regional differences, and the ability to promise delivery windows with confidence. All of this on top of knowing inventory location.
Pillar 4: Seamless Order Handoff: From Intent to Fulfillment
The AI agent closes the sale. Now what?
This is where theory becomes reality and where you might experience the first instances of operational hiccups if you don’t have the right tools. That order, captured through a conversational AI interface, needs to flow into your WMS, OMS, trigger fulfillment, update your ERP, sync with your 3PL, generate shipping labels, handle exceptions and integrate with every other system in your commerce stack.
Most commerce operations weren’t designed for this. They expect orders to arrive through their own checkout interface, formatted their way, with their data structure. An order from an AI agent looks different. It might have different fields, capture intent differently, or require different validation rules.
Why This Matters Now
The brands that adapted quickly to Amazon captured market share. The brands that moved early on Amazon and TikTok Shop built considerable advantages. Agentic commerce is following the same pattern, just faster.
The difference is that agentic commerce requires more than marketing agility and channel strategy: it requires operational excellence. There is no “Fulfilled by AI” to set the operational side to easy-mode. AI agents won’t hide poor inventory management, slow fulfillment, or fragmented systems. They’ll amplify those weaknesses by routing consumers to competitors who can deliver.
The four pillars are the basis for participating in the next wave of commerce. And the time to build that foundation is now: before agentic commerce moves from 26% adoption to the de-facto channel.
Want to see how your commerce operations stack up for the agentic era? Book a demo to see how brands are already enabling AI-powered shopping experiences.
