Digital Front Door

When AI Becomes the Shopper

• Scott Benedict

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0:00 | 8:05

Shoppers aren’t always the ones doing the shopping anymore. We unpack the accelerating rise of AI agents as product researchers and decision-makers, and why that flips long-held e-commerce priorities on their head. If a machine is choosing what to recommend, the inputs it reads, structured product data, real-time pricing, accurate inventory, and truthful delivery promises, matter far more than hero images or clever copy. That’s not a distant future; it’s the competitive landscape right now.

I walk through how agents actually shop: they parse intent, query catalogs through APIs, filter by constraints like budget and delivery dates, and present tight, defensible short lists. There’s no polite feedback when your feeds are stale, or your systems disagree; you simply disappear from the candidate set. That’s why this shift is different from mobile or omnichannel. It’s not another surface to polish; it’s a different decision-maker that rewards reliability over rhetoric and execution over aesthetics.

We break down what machines value and what they ignore, then translate those insights into a practical playbook. Think AI-first: treat product information, pricing, availability, and fulfillment as mission-critical infrastructure. Enforce attribute completeness, unify schemas, and expose robust, low-latency APIs. Measure success with machine-aware KPIs, freshness SLAs, discoverability in agent-guided flows, inclusion rates on constrained queries, alongside traffic and conversion. This is not just an IT problem; it spans merchandising, operations, and leadership, reshaping incentives and investments across the org.

If you want your products to show up when agents decide, build trust at the data layer and keep it fresh. When AI becomes the shopper, execution becomes the brand. Subscribe for more practical strategies on retail’s next turn, share this with a teammate who owns your catalog or inventory pipeline, and leave a review with the one metric you’d change first.

SPEAKER_00:

Well, hello and welcome to Scott's Thoughts. I'm Scott Benedict. I've been reading a lot recently about how quickly the role of AI in retail is changing and how many leaders are still underestimating what may be happening and what the impact is on their business. Now, recently I spent some time reviewing a piece of research from a company called Miracle. They are leading provider of e-commerce software solutions. It was titled Winning in the Agenic Era, the E-commerce transformation roadmap, which sounds a little bit sterile. And it was really written from a technology provider's perspective, but the ideas in it reinforced something I've been seeing firsthand across retail, consumer brands, and marketplaces. Now, for years, we've built digital commerce shopping experiences with one primary audience in mind, the shopper, the human shopper, if you will. We've optimized search, navigation, product pages, promotions, and checkout flows all for humans. We've debated layouts, hero images, and copy in the tone of copy, if you will. And all of that still matters, mind you, but it's no longer, in my view, the whole story. And that's what the research is basically telling us. Because increasingly, shoppers aren't doing the shopping for themselves. They're asking AI to do it for them. Tools like ChatGPT, Gemini, and others are becoming everyday shopping companions or agents, if you will. Consumers are using them to research products, compare prices, check availability, and narrow down choices before they even ever visit a retailer's website if they visit the website at all. Miracles research frames this not as a feature scenario, but something that is already happening, is already underway. And that's a critical shift that is being missed, in my view. These AI agents don't experience your brand or your product the same way a human does. They don't browse, they don't scroll, they don't forgive inconsistencies and information. They shop based on data. When someone asks an AI agent to find a product, say something under a certain price or available by a specific date, the agent follows a very logical process. It interprets intent, pulls product information, filters by inventory and delivery constraints, and then presents a short lift list of recommended products, often with the explanation of why those products made the cut, why they were recommended to the shopper. Typically through APIs rather than through traditional web pages. Now, if it's not, the AI doesn't complain. It doesn't provide feedback, it simply ski over you or your product or your retail website. That's why this moment feels fundamentally different from past shifts like mobile or omnichannel. This isn't just another channel to optimize, it's a change in how purchase decisions are being made. And we're moving from shopper-led discovery to machine-mediated decision making. And it's a pretty interesting and fascinating process. In this environment, accuracy beats persuasion, reliability beats credit creativity, and availability defeats uh aspiration. Execution, proper execution, becomes the experience. One of the most interesting and useful aspects of Miracle's research is how it clearly outlines that AI agents actually care about or what they care about. At its core, it's very practical, structured product data, real-time pricing and promotions, accurate inventory and delivery promises, credible brand context, and social proof, and all that fast, quickly, reliable and accessible to the shopping agents. What's striking is that it doesn't show up as is what doesn't show up on that list. Beautiful layouts, clever creative copy, or perfectly staged lifestyle imagery. Those things still matter, obviously, to human shoppers, but they don't matter to the machines that are increasingly influencing what gets recommended. And that leads to a hard truth for many retailers and for many brands. You can look digitally sophisticated on the surface, uh, but you still will be effectively invisible to an AI. Batch data feeds, pricing that's out of sync, optimistic delivery dates, systems that don't talk to each other, these are all hit issues that humans may tolerate, but AI won't. Miracle makes the point in their research that once an agent decides that your data can't be trusted, it stops recommending you or your product. And that's a much harder problem to fix than a broken search result or a low converting product page. So, what should leaders in retail and consumer brands be doing now? Well, I think the takeaway from this research and from what I'm seeing in the marketplace is that the answer isn't chasing the latest AI feature or launching another chatbot. It's adopting an AI-first mindset across your organization. Treat product data, pricing, inventory, fulfillment as mission critical infrastructure for your business. Design systems assuming AI and not people, or not just people, will increasingly be the primary consumer of the product and service information that you provide. Start measuring success not just by traffic and conversion rate, those are still important, but also by discoverability and performance through AI-guided journeys. This is an IT initiative, I can tell you. It covers merchandising, operations, and it's a leadership challenge, quite frankly, for our industry. It touches how teams are structured, how success is measured, and how investment decisions are made. Because in the Agentic era, something miracle describes very clearly in this research that I'm referencing is that retailers that win won't be the ones with the best looking websites per se. They'll be the ones whose data is trusted, timely, and visible or readable by a machine. When AI becomes the shopper, execution becomes the brand. That's what I've been thinking about. I'm Scott Benedict.