Imagine a shopper asking an artificial intelligence assistant to find a wool coat priced under $200, crafted with traceable fibers, and guaranteed to ship before Friday. The digital agent instantly compares dozens of options, cross-references detailed material specifications, checks logistical timelines, and recommends a carefully curated shortlist of coats that precisely match every single parameter of the request.
This scenario illustrates the growing reality of agentic commerce, where automated assistants do the heavy lifting of product research. Yet, a fundamental question emerges for brands navigating this automated landscape: What happens when the shopper didn’t even know they wanted a wool coat until they happened to catch a glimpse of it on someone else?
Earning a coveted spot among AI recommendations has rapidly become an urgent priority for modern retailers and consumer brands. However, this technological shift also presents a profound opportunity to influence what shoppers desire long before they ever type a query into a search bar or prompt an assistant. A striking image, a compelling concept, or an unexpected digital discovery can easily inspire a consumer to imagine a product seamlessly fitting into their own daily life, sparking a spontaneous purchase they had never originally planned to make.
"Commerce is moving upstream, into the moments when people are still figuring out what they want," observes Abhay Singhal, co-founder of InMobi and CEO of InMobi Advertising. "Sometimes that starts with a search, but often it starts with seeing something that feels right, imagining it on yourself, or picturing how it might fit into your life."
Standing Out on the Shortlist
Contrary to what many traditional retailers might initially expect, consumer demographics show that older digital users are leaning heavily into these new technologies. Millennials currently lead daily usage of AI tools for shopping research at 39 percent, a figure nearly double Gen Z’s 21 percent adoption rate. Furthermore, more than half of all U.S. consumers now utilize artificial intelligence to discover, evaluate, and compare products, according to recent findings from Pacvue.
AI shopping agents operate by narrowing choices down—first by asking clarifying questions, and then by building a tight shortlist of products that align with the user’s specific preferences. Once several competing products clear those initial algorithmic filters, the agent still requires a definitive reason to select one over the others. A product description written in the same generic category language as all of its competitors quickly becomes interchangeable. The unique distinctiveness that has traditionally shaped brand positioning now gives an AI agent a concrete reason to choose a brand, and gives the shopper a compelling reason to care.
That distinctiveness works best when expressed through concrete, tangible attributes that automated agents can easily access, evaluate, and compare, such as fabric weight, precise fit profiles, or the verified geographic origin of fibers. Industry analysis from Boston Consulting Group (BCG) advises brands to make their product data authoritative, semantically rich, factual, and machine-readable to ensure optimal visibility.
Crucially, this data must hold up far beyond a brand’s proprietary website. In a detailed analysis conducted by McKinsey, researchers found that just 1% of the sources cited by large language models for consumer goods brands were actually brand-owned websites. When product descriptions across the broader web conflict with one another or lack necessary detail, AI models are significantly less likely to include a brand in their recommendations or represent its offerings accurately.
At the same time, precision cuts both ways. The exact same data detail that matches a product perfectly to the right request will simultaneously rule it out of mismatched queries. A shopper who explicitly instructs an AI agent to find a 100% wool coat will not be shown an item listed as 60% wool and 40% polyester, no matter how comprehensive or otherwise attractive the listing might be.
"If somebody can suddenly ask a very detailed question about your product, the answer may be that your product isn’t what they want," notes Sucharita Kodali, vice president and principal analyst at Forrester. "Being reliably matched to the requests you can satisfy is worth more than appearing in ones you can’t."
Ultimately, data can only express differences that already exist in the physical products themselves. "You need products people want," Kodali adds. "The interfaces people use to find those products may change, but having something worth finding is still the most important part."
The Recommendation Still Has to Win Over the Shopper
Even after an AI agent selects a product, the recommendation still has to win over the human consumer. According to data from Salsify, 61% of shoppers report that high-quality product images and videos are the single most important product page element when deciding whether to finalize a purchase, ranking even higher than written descriptions, customer reviews, and price points.
Consistency across digital touchpoints matters just as much. If the final landing page tells a different story than the AI agent did—such as displaying an unexpected price, a missing size selection, or a vague material claim—the shopper’s confidence instantly drops at the exact moment it matters most. Singhal’s primary advice for keeping the handoff intact and trustworthy is straightforward: link directly to the exact product variant and keep promotional pricing entirely consistent across platforms.
Confidence also stems from something deeply personal: understanding how a product will actually look, fit, and function in the shopper’s everyday reality. For fashion and lifestyle purchases, answering that final question remains critical, which explains why advanced visualization is playing an increasingly central role in digital retail.
Even when an AI agent’s pick fits the technical request, modern shoppers still want to verify it for themselves. Only 46% of consumers fully trust the recommendations provided by artificial intelligence, and a striking 89% routinely double-check AI-generated information before completing a transaction, according to a study by the Interactive Advertising Bureau (IAB).
This dynamic transforms the traditional product page into a critical confirmation step. "The AI-assisted shopper may arrive at a product page with much of the research already done and a clear candidate in mind," Singhal explains. "At that point, the page has a different job: confirm the decision. That means showing the product in real context, answering the last questions on fit, scale, and specifications, and giving the shopper credible signals such as current reviews."
Driving Discovery and Desire Before Intent
"AI gives brands a new way to participate in those moments by bringing together context, deep product intelligence, and visualization," Singhal says.
Ecosystems are rapidly evolving to bring this vision to life. For instance, InMobi’s Glance provides an AI-powered personal shopping experience spanning smartphones and connected TVs, with ongoing expansions into personal computers through collaborations like Lenovo and Motorola Qira. Operating across more than 60 million devices, Glance introduces relevant products during natural moments of everyday discovery. Through conversational visual features, shoppers can upload a selfie, visualize how different looks might appear on them, and refine recommendations through dialogue before transitioning directly to the retailer’s website to complete a purchase.
To further bridge the gap between imagination and reality, platforms are integrating advanced sizing technologies. Incorporating fit intelligence solutions directly into agentic commerce pairs AI-generated visual previews with size recommendations informed by historical purchase and return data. This integration tackles two fundamental questions that frequently decide whether a consumer hits the buy button: How will this garment look on me, and will it actually fit? For apparel retailers, that added layer of digital confidence holds the potential to significantly curb costly fit-related returns.
The next phase of AI-powered commerce will undoubtedly provide retailers with expanded opportunities to influence what consumers discover, evaluate, and ultimately purchase. Brands that structure their product data so it is easily understood by machines while remaining compelling enough for people to imagine owning will be best positioned to earn prominent AI recommendations, inspire organic demand, and successfully convert early discovery into lasting sales.
