AI Shopping Could be the Best Thing That's Happened to Challenger Brands on Amazon

The life of a challenger brand trying to get started on Amazon is more than a little tough.

Promising new brands come to Vendor Central with a great product, developed around a specific audience segment, but find their listing is nowhere to be seen on organic search results. Meanwhile, the same small group of bestsellers sit at the top of the page, enjoying a steady stream of organic traffic and the baked-in trust that comes with occupying the top spots of a popular SERP.

For years, this has been a standard feature of selling on the world’s biggest marketplace. Under the Amazon Flywheel, popularity begets popularity, and challenger brands have had to pour big budgets into their advertising to get that essential first surge of momentum.

Now, however, there’s a change in the air. The way that Amazon retrieves and recommends products is beginning to shift with the proliferation of AI tools and their role in the shopper experience. This change is hinting at a future of ecommerce search that’s less about matching keywords, and more about understanding the meaning in conversations between users and AI agents.

As AI models become better at understanding what a shopper wants, there’s a strong, evidence-backed case that the playing field could widen, and create new opportunities for challenger brands who have previously been living in the big players’ shadow.

This isn’t a guarantee, and we’ll get to the caveats a little later. However, the shift on the horizon is worth understanding now, while there’s still time to adapt to it.

Amazon Search Has Always Rewarded Popularity

Traditional Amazon search rankings are governed by a feedback loop that rewards brand recognition and time in the market.

A product that ranks well is seen by more shoppers. Better visibility means more clicks. More clicks means more sales. More sales generate more reviews. More reviews and sales history feed right back into Amazon’s ranking algorithm, and keeps a successful product at the top of the rankings.

This isn’t some big conspiracy formulated to favor big brands (even though that’s exactly what it feels like at times). It’s just a natural consequence of a set of long-established ranking signals that have always been a core part of Amazon SEO, including:

Sales Velocity: How quickly a product is selling, and how this compares to similar listings.

Conversion Rate: The proportion of shoppers who see a listing and go on to buy it.

Review Volume: The number of reviews, and how recent they are.

Historical Performance: The track record of sales and engagement built up over time.

Advertising Investment: Amazon Advertising investments that put a product in front of more shoppers, contributing to the cycle we outlined above.

Amazon has never published the exact mechanics of its ranking algorithm, and it’s not going to any time soon. However, the cumulative effects of these signals is something Amazon marketing specialists have observed for years: a clear reinforcement for any brand that’s already winning.

A listing with strong historical sales data will have an automatic advantage over newer, smaller competitors, even if that newer product is a better fit for a given shopper’s needs. This “winner takes most” dynamic is exactly why many great products struggle to break into their markets, no matter their quality or how well they meet the needs of a specific audience segment.

AI Changes the Retrieval Problem

The recent debut of AI shopping is where things get interesting, and also where Amazon’s own research is making a major contribution to the story.

Traditional Amazon search essentially works like this:

1. A shopper enters a query.
2. The algorithm matches the keywords in the query to those that appear in the product copy.
3. Performance factors are considered and the algorithm serves a list of results.

To oversimplify, if a shopper types in “quiet blender”, the algorithm looks for product listings that contain those words, and serves up a ranked list based on a variety of other performance signals.

AI retrieval, on the other hand, works differently. Instead of simply matching search query text to a product detail page, shopping is done in a more conversational and semantically-driven way. It works to understand your actual buying intent, then finds the best match for that intent, even if the exact words entered don’t appear anywhere in a given listing.

This product discovery process looks more like:

1. The shopper asks the AI a question.
2. The AI works to understand the intent behind this query.
3. Products are analysed semantically.
4. Candidates are shortlisted.
5. The shopper receives product recommendations.

Amazon’s internal research teams have been doing some interesting work in this area. In one June 2025 blog post on Amazon Science, researchers explain that for most of the past decade, machine learning has been relying on embeddings: models that convert data into vectors positioned in a shared and representational space, ensuring that items with similar meanings end up sitting together geometrically.

Historically, this meant that a query would be embedded, and the system would go hunting for a response whose embedding sat near it, whether that particular response was text, an image, or some other form of data.

Amazon’s researchers took this a step further, presenting a model called GENIUS at CVPR 2025. Rather than comparing an entered query to every possible candidate in a given catalogue (a very slow process given the sheer scale of Amazon), GENIUS takes a query as input, and generates a single defining query as an output. According to the blog post, this approach improved upon best-performing prior generative retrieval methods by between 22% and 36% across several benchmarks.

You don’t need to be an experienced software engineer to understand the significance here. The headline is this: ecommerce product listings no longer have to contain exact-match keywords to be considered relevant.

Retrieval systems built around semantics (which is exactly what AI shopping enables) look for meaning, not simple string matches. This is a fundamentally different mode of search compared to the one Amazon search has been governed by for most of its existence.

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Why This Could Help Smaller Brands

The shift towards AI shopping is not only starting to build a better shopping experience for the end customer, but could also be opening up all new opportunities for challenger brands.

Imagine if a shopper on Amazon asks Rufus “What’s a quiet blender that fits in a small kitchen and is easy to clean?”

Under standard, keyword-driven search, the results would probably be dominated by:

• The best-selling blenders in their category, regardless of noise level.
• The highest-reviewed blenders, again with little consideration of whether they’re fit for purpose.
• Products that have listings optimised for terms like “quiet blender” or “blender for small kitchens”.
• Whichever products happen to be bidding on “quiet blender” as an advertising keyword.

None of these results are directly and effectively answering the question the shopper asked.

A semantic retrieval system, on the other hand, has potential to weigh a much richer set of signals in order to find the best match for the shopper’s intent, for example:

• Compact product dimensions.
• Noise-related claims made in the listing copy.
• Customer reviews that specifically mention the blender’s quiet operation.
• Language or described features around the ease of cleaning.
• Structured product attributes.
• Sophisticated image understanding, for example recognising the small footprint of a blender on a countertop in an Amazon product image.

Under this model, a more niche product specifically designed for a small kitchen and quiet operation could be the stronger semantic match than the category bestseller, and selected as the best response to the question that was actually asked.

Note that this isn’t a suggestion that AI shopping ignores popularity or velocity signals altogether. Sales history, reviews, and other measurable variables all remain valuable data points for any retrieval system.

Rather, the movement towards AI shopping has the potential to increase the pool of relevant candidates, before recommendations and rankings take place. This gives lesser-known, but well-matched niche products a route into consideration that traditional product search might have denied them in the past.

There's Evidence Shoppers Are Asking More Complex Questions

None of this would matter much if shoppers were still typing 2-3 word searches into a search bar every time they wanted to look for a product. However, we’re seeing evidence that this behaviour is gradually changing.

The 2026 annual retail report by fintech platform Adyen revealed that the use of AI assistants by UK shoppers has more than doubled year-on-year, rising from 12% in 2025 to 28% in 2026.

Query data looking specifically at AI-driven shopping is sparse, but a 2025 report from The Growth Memo found that the average query length in ChatGPT is 23 words, while for traditional search it’s just 3.37 words.

These statistics and many more from across the AI and ecommerce space tells a story of AI rising out of its original status as a novelty, and steadily growing to become an embedded part of how people shop.

Another 2025 report by the Interactive Advertising Bureau, built on more than 450 digital ethnographies and a survey of 600 US consumers, suggested that shoppers aren’t using AI to completely replace their shopping habits, so much as add a new layer to them.

The study, titled When AI Guides the Shopping Journey, suggested that AI is most useful to shoppers at the point where complexity peaks. This is where AI offers shoppers an intuitive way to narrow down their options and build confidence, while also noting that AI doesn’t shorten the path to purchase as much as reshape it, opening up new touchpoints where brands can build trust. Shoppers also frequently hit points of friction when using AI tools, and many will go on to verify recommendations based on reviews, product pages, and other materials before they commit to a decision.

To put it simply, shoppers are increasingly using AI to help them work through complex, multi-part decisions rather than entering a single, compressed query into a search bar and hoping for the best. This is precisely the kind of behaviour that AI’s semantic retrieval is designed to serve.

What This Means for Your Amazon Listings

If AI-powered product searches continue to grow in popularity, and AI retrieval genuinely places more weight on semantic understanding, the practical implications for challenger brands could be huge.

Up until now, Amazon SEO was focused on questions like “how do I rank for the search term ‘standing desk”.

Increasingly, brands may find themselves having to answer “can an AI understand exactly who this product is for and the problem it solves?”

If and when it becomes the norm, the reframing could change exactly what good listing content looks like:

Note that none of this means you should abandon traditional Amazon SEO. Exact-match keywords still matter for standard search, and this norm isn’t going anywhere for the foreseeable future.

However, it does mean that challenger brands now have a good reason to monitor the development of AI-assisted shopping in the near future, and invest in thorough, benefit-led, and context-rich content. While this has been best practice for Amazon CRO for years, a lot of brands have had a tendency to treat it as an extra “nice to have”, when it could soon become a ranking necessity.

Disclaimer: Don't Mistake Likelihood for Certainty

Before you go all-in on optimising your content for AI discovery, it’s crucial that we emphasise the limits of the evidence.

Amazon hasn’t disclosed how Rufus, or any future AI shopping experience they have in the works, actually weighs semantic relevance compared to behavioural signals such as sales velocity or conversion rate. There’s a high likelihood that variables like price, availability, fulfilment speed, and review history will all continue to carry real weight in the next iterations of Amazon search, whether it’s AI-powered or not.

Top brands didn’t achieve their advantages by accident. The introduction of semantic search is certainly going to be felt, but it isn’t going to upend years of accumulated performance data overnight.

The message behind the developments we’re seeing is less “AI will level the playing field”, and more “AI retrieval may reduce the performance penalties for not being the biggest brand,” namely by improving the changes that a genuinely well-matched and relevant product will get surfaced when shoppers express nuanced, specific needs, rather than generic product searches.

Even if performance data remains a powerful factor in organic visibility, the nuance introduced by semantic retrieval is important, and one which some early-adopter challenger brands could well benefit from in the near future.

A Big Future for the Little Guy

For many years, challenger brands have had to compete with big category leaders in the exact same arena: high-volume, high-competition keywords that the biggest players can afford to dominate through historical sales velocity and the sheer scale of their advertising spend.

Though nothing is certain for now, evidence of changes in shopper behaviour and Amazon’s investment in AI shopping could be poised to change what works for organic discovery.

Some time soon, challenger brands that consciously position their products as the ideal fit for a specific use case could be enjoying a newfound boost in organic visibility. This advantage will be rooted in the real-world merits of their product, rather than the depth of their pockets, and will have the biggest impact for plucky brands that keep their finger on the pulse and adjust course based on the evolving role of AI in ecommerce.

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