Generative AI is consolidating the shopping journey into a single, intelligent conversation. Here’s how brands can stay visible when discovery goes conversational.
For the past decade, retail search has been a race for visibility. Brands learned how to climb SERPs, win bids in retail media, and fine-tune every product detail page for keywords and conversion — all to be found first when shoppers started Googling.
Now, retail search is quietly entering its next evolution, and it’s not happening on Google. Across major AI platforms like ChatGPT, Claude, and Amazon’s Rufus, consumers now generate more than 2 billion queries a day, and roughly four in 10 U.S. shoppers already use an AI assistant weekly for product discovery or advice.
GenAI search and, subsequently, Generative Engine Optimization (GEO) are still in their early days, and no one yet knows exactly which signals these systems will reward. But the direction is unmistakable: discovery is becoming conversational, and visibility is narrowing. So, what can brands do now to ensure they’re part of the solution?
How the shopper journey is collapsing into one conversation
For years, digital commerce followed a predictable rhythm. Shoppers moved step by step, from recognizing a need to browsing a sea of results, comparing features, reading reviews, and finally clicking “add to cart.” Each step offered multiple touchpoints for brands to intercept or influence the journey.
Generative AI collapses that process into a single interaction. Instead of typing “best protein powder for runners” and scanning 30 listings, a shopper now asks an AI assistant, “What’s a good protein powder for recovery after long runs?” and gets one or two tailored answers, often complete with reasoning and direct links to buy.
The result is a shorter path from intent to purchase, with far fewer opportunities for discovery along the way. Early data from AI-powered shopping environments indicate that customers arriving via generative queries are 10% more engaged and convert at a faster rate than those using traditional search methods. This fundamentally changes where and how influence happens.
The new rules of being found
For brands, the compression of the shopper journey is both a gift and a challenge. It makes the path to purchase frictionless, but also front-loaded. Once a model generates its answer, the window to appear effectively closes. Instead of optimizing for dozens of mid-funnel interactions, brands now have to win a single conversation.
While it’s too early to say exactly what these systems will prioritize, early patterns suggest four factors likely to shape which products surface first:
- Complete, structured data: Models rely on context, not just keywords. Products with detailed, well-tagged attributes (such as ingredients, use cases, benefits, and certifications) are easier for AI systems to understand and recommend.
- Credible sentiment: Generative AI places significant weight on trust signals. Fresh, consistent, and credible reviews are more likely to appear in the model’s responses, while low sentiment can quietly push a product out of view.
- Cross-platform consistency: Discrepancies between retailer PDPs, brand sites, and media copy can confuse AI crawlers and erode confidence in what’s authoritative.
- Conversational authority: AI models favor clear, natural language that answers a shopper’s question over keyword-stuffed or jargon-heavy product blurbs.
What brands can do now?
Generative search is still a small slice of traffic today, but the behaviors behind it (including faster decisions, fewer options, and higher trust thresholds) are already influencing how people shop. Here are three steps brands can take today to future-proof discoverability.
Be AI-visible
Though the algorithms behind GenAI search remain opaque, brands can still act on the fundamentals that have historically improved discoverability (and are likely to matter even more in AI-driven environments).
The first step is making product data intelligible to machines. That means ensuring every PDP includes attributes such as size, use case, benefits, and ingredients, with schema markup that allows models to understand the context, not just the text.
Because LLMs draw from multiple sources, ranging from retailer listings to brand sites, consistency across these platforms determines whether a product is even included in the recommendation set. Some tools can automate that consistency at scale, auditing PDPs, validating schema markup, and ensuring every SKU remains discoverable across channels.
Write for questions, not keywords.
Traditional SEO playbooks wax and wane about the importance of matching phrases for search visibility. GEO, however, depends on understanding intent. Product copy should read like an answer to the questions shoppers actually ask: “What’s the best protein powder for recovery?” or “Which moisturizer works for acne-prone skin?” Writing for that kind of natural language teaches the model which use cases a product solves, something keywords alone can’t do.
Leverage social proof as training data
Reviews, Q&As, and expert recommendations have always been important for persuasion, but in a generative search environment, they also matter for visibility. LLMs increasingly pull context and authority cues directly from these sources, learning not only what people buy but also why they trust it.
When feedback is recent, specific, and detailed (“it absorbs quickly without residue” or “my dog’s coat looks shinier after two weeks”), models can infer product benefits, use cases, and sentiment patterns. Those insights feed back into the recommendation logic and determine which products the AI considers “safe” to surface.
That makes review cadence and credibility management a new form of discoverability maintenance. A product with thin, outdated, or polarized reviews risks being overlooked entirely, even if it performs well on the shelf. Brands that regularly refresh reviews, close the loop on customer feedback, and encourage detailed commentary are effectively training the next generation of search.
Unify paid and organic signals
If current retail media trends are any indication, paid and organic signals will eventually converge as GenAI platforms learn to blend them into a single ranked recommendation. That means brands must ensure that every piece of creative, messaging, and attributes across channels reinforces the same structured, trustworthy story.
Performing all this manually across hundreds or thousands of SKUs is unrealistic. This is where technology, such as CommerceIQ, comes into play by helping brands align their creative, attributes, and spend, so paid and organic signals reinforce each other in a single performance view. The result is a feedback loop that turns retail data into a discoverability advantage.
