Generative AI is already woven into everyday life, and shoppers increasingly expect the same help from online retail. But the industry is learning that natural-language answers aren’t enough—AI needs the right shopping data and context to make recommendations
One click can turn shopping into a swamp. You search for a new pair of running shoes. and suddenly you’re staring at hundreds of results—styles you’d never wear. kids’ sneakers (even though you’re an adult). and options that don’t fit your budget. The worst part isn’t that there are too few choices. It’s that the choices stop feeling useful.
For many people, generative AI already feels like the fix. You ask it questions, it talks back, it helps you plan trips, troubleshoot problems, and make decisions. Now shoppers want that same experience in retail—guided, fast, and personal. The question isn’t whether AI should be in shopping. For more shoppers, it’s why it still isn’t better.
Constructor and Shopify data put the shift in context: nearly two-thirds of people have used tools like ChatGPT in their daily lives. up from 29% in 2023. Gen Z is even higher, with 78% having used GenAI. That familiarity is changing expectations across the board. Search bars and product pages are no longer just places to browse—they’re becoming places where people assume they’ll get conversation-like help.
But retailers are discovering something important: in shopping, the hardest problem isn’t understanding language. It’s making the right decision for a specific person.
Today’s AI can handle complex queries that would have sounded strange a few years ago. Shoppers can type things like “I’m planning a tailgate. what do I need?” or “Help me find new running shoes” and get recommendations that make sense at a basic level. The bigger test is whether those recommendations connect to real-world buying outcomes—whether the shoes. in this case. are the ones that person is most likely to buy.
That gap exists because shopping decisions don’t live in plain words. They’re shaped by what a shopper has done before—what they bought, nearly bought, and returned. They’re rooted in prior preferences. behaviors. and tastes that often sit scattered across retailer systems and. in many cases. remain proprietary.
General-purpose agents like ChatGPT and Claude don’t have access to the full trail. Without that context, they can sound confident while failing to narrow options the way a good in-store associate would.
Consider what kind of runner someone is. A serious runner might prioritize stability, toe box width, and whether a shoe works for trails or roads. They might prefer a particular brand or have liked the last version of a shoe they already own. A more casual runner may mainly want comfort for occasional jogs. When the input is vague—an “Ask me anything” prompt like “What are good running shoes?”—the system often can’t connect the dots. And if shoppers end up having to explain every preference and use case themselves, AI isn’t simplifying the experience. It’s just adding another layer.
Retailers, though, are starting to see progress where context is built in.
A context-based approach is gaining traction: some retailers have launched their own agents that combine their product and inventory data with shopper information. including real-time behavior on site. past purchases. and loyalty status. With that framework. when someone asks for guidance. the AI can move beyond generic suggestions and show items that person is more likely to want.
Not every shopper wants this kind of interaction, and adoption is still early. Even so, the impact described so far is hard to ignore. Amazon said shoppers who consult its AI shopping assistant are more than 60% more likely to complete a purchase during their session. and it reported usage rising with engagement up almost 400% year-over-year. Walmart has reported similar momentum: customers who use its Sparky AI have an average order value that’s 35% higher than other shoppers.
There’s also evidence tied to the heaviest shopping stretch on the calendar. On sites with AI agents during last year’s shopping period running Black Friday through Cyber Monday, more than 10% of revenue came from shoppers who used them, according to the data referenced in this reporting.
But the problem isn’t solved just because an AI agent exists. One personal example underlines why.
The next day after adding four pairs of shoes to my cart on a national department store site. I returned and asked the site’s AI agent for recommendations similar to what I’d been browsing. The response asked me a question that already felt like a reset—“To help me narrow this down. were you looking for men’s or women’s shoes?”.
It’s a small moment, but it captures the core frustration: AI can chat, yet without the right context at the right moment, it doesn’t help you move forward.
That’s why the next phase of AI in shopping is less about making models smarter at conversation and more about deciding when context matters most. Retailers are still experimenting to understand where conversational agents add the most value. So far. areas of high intent—retail search bars and chat—and key decision points like product pages are seen as strong fits. On product pages. shoppers often need quick answers to lingering questions such as “Do these run true-to-size?” or “Are these shoes good for wide feet?”.
As context improves, the expectation is that AI will become a more useful and more prevalent shopping companion. There’s also an implied shift in what “help” looks like: interfaces may ask clarifying questions as they learn and adapt. and agents may guide choices more directly rather than simply answering queries.
In the end, the future of AI in shopping will come down to whether it understands context well enough to help people act—with enough confidence to walk away knowing they chose the right product.
Kevin Laymoun, chief customer officer and chief revenue officer at Constructor, frames the direction in those terms: as context gets better, shoppers should be able to rely on AI not just to talk, but to help them finish the job.
AI in shopping generative AI ChatGPT Constructor Shopify Amazon AI shopping assistant Walmart Sparky ecommerce personalization retail search Black Friday Cyber Monday