OpenAI just announced that consumers can now buy products directly inside ChatGPT. At the same time, big-box retailers like Walmart are already seeing one in five referral clicks come from ChatGPT. In a matter of months, AI agents have gone from a fun novelty to a business necessity, marking one of the fastest shifts in consumer shopping behavior since the rise of the internet.
But ChatGPT shopping isn’t just a new sales channel—it’s a stress test that exposes a hard truth: AI runs on data, and most retailers’ data isn’t ready.
Shoppers now expect an AI agent to answer questions like, “Who can deliver a patio heater by Saturday?” Or “Where’s the best price on a Dyson vacuum today?” Those answers don’t come from thin air, they come directly from a retailer’s own systems. If inventory is siloed, prices don’t match across channels, or fulfillment timelines are outdated, the agent will provide the wrong answer. And when the promise is broken, it doesn’t just cost a sale. It erodes trust in both the retailer and the AI itself.
Walmart is one of the exceptions that proves the rule. I work for a company that helps Walmart to automate and unify their data systems. Through years of supporting their integrations and data operations, I’ve seen firsthand how the retailer has invested in making data a true competitive advantage. They’ve prioritized connecting ERP, inventory, and fulfillment systems—infrastructure that now allows AI agents to surface Walmart’s product information with confidence. That’s why they are seeing a surge in ChatGPT referral traffic. For most retailers, the same query would expose outdated prices, phantom stock, or missed delivery windows.
This isn’t about building a flashy chatbot or trying to “own” the front end of AI shopping. Leave that to the AI platforms. The real work is plumbing: clean, real-time, end-to-end data. If your systems can’t deliver reliable answers, AI will simply skip you. In a world where consumer habits form quickly, disappearing from the funnel now could mean disappearing for good.
So how should retailers respond? First, by treating data as a strategic asset, not an IT afterthought. That means hiring leaders who understand integration as a growth lever, not just a cost center. It means reorganizing so that operations, digital, and merchandising teams are working from the same data, not fighting over conflicting versions of the truth. And it means investing in systems that update in real time, rather than batch processes that were designed for yesterday’s pace of commerce.
Second, retailers need to build technical resilience. That doesn’t just mean buying new tools; it means designing architectures where pricing, inventory, and logistics are connected tightly enough to withstand the demands of AI-driven discovery. Retailers that continue to treat these functions as separate domains will find themselves invisible in an AI search world.
These latest shifts continue the transformational journey that retailers have undergone moving from brick and mortar stores to online sales, then to mobile sales, and on to social media. Now they face a new challenge as the concept of omnichannel sales continues to expand.
The AI commerce era is here, and it’s moving faster than most executives expected. The retailers that survive won’t be the ones with the biggest marketing budgets, but the ones whose systems can actually talk to AI. Success will come to the companies that align technology and organizational priorities around one goal: making their data trustworthy, current, and connected. Those that don’t risk falling out of the conversation entirely.
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