Imagine a shopper asking an artificial intelligence assistant to find a wool coat under $200, crafted with traceable fibers, and guaranteed to ship before Friday. The automated agent efficiently compares options, verifies technical specifications, and recommends a curated handful of coats that match every element of the request.
But what happens if the shopper did not even know they wanted a wool coat until they saw it worn by someone else?
This shift represents a fundamental evolution in consumer behavior. Earning a prominent place among AI-driven product recommendations has quickly become a critical priority for modern retailers. Yet, brands also possess a powerful, broader opportunity: to actively influence what shoppers desire long before they ever type a query into a search bar. A compelling image, a striking idea, or an unexpected moment of discovery can inspire an individual to visualize a product in their own life, sparking a purchase they had never initially planned to make.
"Commerce is moving upstream, into the moments when people are still figuring out what they want," says 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 expect, consumer research highlights a generational shift in how artificial intelligence is being adopted for everyday purchasing decisions. Data from a recent Pacvue report indicates that Millennials lead daily usage of AI tools for shopping research at 39 percent, which is nearly double the 21 percent reported by Generation Z. Furthermore, more than half of all consumers in the United States now rely on artificial intelligence to find, evaluate, and compare products.
AI shopping agents operate by narrowing down vast oceans of inventory—engaging in a conversational dialogue, asking clarifying questions, and ultimately building a targeted shortlist of products tailored precisely to a shopper’s preferences. However, once multiple products clear these rigorous digital filters, the agent still requires a distinct rationale to select one over the others.
A product described entirely in generic category language quickly becomes interchangeable with its competitors. The unique brand positioning that has traditionally shaped marketing strategies now serves a new purpose: it gives an AI agent a logical reason to choose a specific brand, and gives the shopper a concrete reason to care.
Industry experts emphasize that this distinctiveness works best when translated into tangible attributes that automated agents can easily access, evaluate, and compare. Metrics such as fabric weight, exact fit profiles, and specific fiber origins provide the objective data points algorithms crave. Research and advisory firm BCG advises retailers to make their product data authoritative, semantically rich, factual, and machine-readable to ensure visibility in agentic commerce.
Maintaining this data integrity must extend far beyond a brand’s proprietary website. According to an analysis by McKinsey, a staggering 99 percent of the sources cited by large language models (LLMs) for consumer goods brands originated from outside brand-owned websites. When descriptions found across external digital ecosystems conflict or lack necessary detail, AI models become significantly less likely to include a brand—or they risk representing it inaccurately.
Precision, however, cuts both ways. The exact detail that successfully matches a product to the right request will simultaneously rule it out of incorrect ones. A shopper who instructs an agent to locate a 100-percent wool coat will never be shown a garment listed as 60 percent wool and 40 percent polyester, regardless of how complete or polished the rest of 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 exact consumer requests you can successfully satisfy is ultimately worth far more than appearing in irrelevant searches you cannot fulfill.
Data can only express differences that already exist in the marketplace. "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 when an AI assistant successfully narrows down the field, the final recommendation must still win over the human consumer. According to findings from Salsify, 61 percent of shoppers report that high-quality product images and videos are the single most important element on a product page when deciding whether to make a purchase, ranking above written descriptions, customer reviews, and even price points.
Consistency across digital touchpoints is equally vital. If a product landing page tells a conflicting story compared to what the AI agent promised—such as a different price tag, an unlisted size, or a vague material claim—the shopper’s confidence instantly plummets at the exact moment it matters most. Abhay Singhal offers clear advice for keeping this digital handoff intact: link directly to the exact product variant and ensure promotional messaging remains uniform across platforms.
Consumer confidence also stems from a deeply personal consideration: understanding how a product will actually look, feel, fit, and function in their day-to-day lives. For apparel and lifestyle purchases, answering this final lingering question is precisely where advanced visualization technology is beginning to play a transformative role.
Despite the rapid rise of automation, skepticism remains high. Only 46 percent of consumers fully trust the recommendations provided by artificial intelligence, and 89 percent routinely double-check AI-generated information before completing a purchase, according to a study by the Interactive Advertising Bureau (IAB).
This reality transforms the traditional product page into a high-stakes 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.
To bridge this gap, platforms like InMobi’s Glance are bringing inspiration-led discovery to life through AI-powered personal shopping experiences spanning smartphones, connected TVs, and newly announced expansions into PCs through partnerships like Lenovo and Motorola Qira. Operating across more than 60 million devices, Glance introduces relevant products during natural moments of everyday media consumption. Through features like Visual Dialogue, shoppers can upload a selfie, visualize how different apparel options might look on their own frame, and refine recommendations through conversation before seamlessly transitioning to a retailer’s website to buy.
To further minimize friction, Glance is integrating True Fit’s fit intelligence infrastructure. This partnership pairs AI-generated visual simulation with sizing recommendations backed by massive pools of historical purchase and return data. By addressing the two questions that most frequently dictate whether an apparel purchase succeeds—how an item looks on the individual and whether it will fit correctly—retailers hope to build vital consumer confidence while simultaneously curbing costly fit-related returns.
As the retail landscape enters its next phase, AI-powered commerce will continue to grant brands unprecedented opportunities to influence what consumers discover, evaluate, and ultimately purchase. Organizations that structure their product data for machine comprehension while crafting visual narratives that inspire human imagination will be best positioned to earn algorithmic recommendations, drive organic demand, and successfully convert passive discovery into lasting sales.