Amazon Rufus: How We’re Preparing Brands for the Shift to AI Search

Over the past few years, ecommerce has been undergoing a revolutionary change in the way shoppers discover, evaluate, and buy their products online. 

Amazon Rufus, the ecommerce giant’s own generative shopping assistant, is one of the biggest driving forces at the centre of this change. 

As shoppers increasingly turn to AI discovery, rather than traditional search engines, visibility in tools like Rufus is poised to become just as important as winning the Amazon buy box. 

In this post, we’ll take a closer look at AI’s impact on the online shopper experience, how the AI-first future of ecommerce is beginning to emerge, and the best practices we’re using to optimize product listings for Amazon’s new discovery channel. 

Amazon Rufus and The State of AI-Assisted Shopping 

Since ChatGPT launched in 2022, AI has gone from being a novelty to a big part of countless online experiences, and ecommerce is no exception. 

According to one 2025 Adobe survey of 5,000 US consumers, just over half (53%) of consumers now use AI tools in their shopping journey, with 47% of these respondents using them for product discovery and recommendations. 

This has been the result of a period of rapid growth, with ecommerce traffic from AI assistants doubling every two months since September 2024. This includes a 1,300% YoY increase in traffic between November 1st and December 31st, and a 1,950% YoY increase on Cyber Monday alone. 

Meanwhile, Amazon has been investing heavily in AI, determined to position it as its main product discovery channel. 

First launched as a beta version in early 2024, by the following October Amazon Rufus was handling some 274.3 million daily queries. That’s about 13.7% of total Amazon searches. Some projections predicted that this proportion would grow to up to 35% of total Amazon search volume by the end of 2025. 

This meteoric rise of AI in online shopping isn’t expected to slow, and is sending brands a clear message: this channel is here to stay. 

While optimizing for AI shopping assistants is still a young science, brands that are early adopters stand to gain a lot as AI shopping becomes more normalised and popular with the average buyer.

Amazon Rufus and the AI-First Future of Ecommerce 

It’s clear that the spread of AI shopping assistants is already changing shopping behavior, and what’s now a few ripples is set to become a tidal wave. 

Here’s some of the key changes to organic ecommerce discovery as AI shopping becomes the norm. 

Faster and More Compressed Funnels 

Part of why Amazon Rufus and other AI assistants are growing so rapidly is their ability to reduce friction at each stage of the ecommerce journey. 

Traditionally, ecommerce consumers would move through a linear conversion funnel that looks something like this: 

1. Broad keyword searches for discovery. 

2. Product category pages for comparison. 

3. Individual PDPs for final evaluation. 

Each step in this process required some effort on the part of the shopper – scrolling, filtering, opening and managing tabs, then manually weighing up the USPs and trade-offs before making a final decision. 

With tools like Rufus on Amazon, this entire process is condensed into an easy, conversational exchange. 

Shoppers can give their preferences and constraints up-front, such as: 

• Price range
• Use cases
• Care requirements

And then receive a tailored shortlist of products in seconds. 

This means that instead of spending time browsing dozens of products and deciding what’s best for their needs, the shopper can have a more conversational shopping experience – focusing on a few highly-relevant options that meet their criteria in record time.

With this rapid, compressed funnel, brands will have fewer chances to get the attention of its audience compared to traditional organic search. 

If your product doesn’t show up in the first few responses from an AI tool, it may not be considered by the audience at all. 

In an AI-first ecommerce arena, organic success will be less about earning broad visibility, and more about being relevant to specific buyer intents. 

Contextual Trust 

As AI becomes more and more integrated in online shopping experiences, the primary touchpoint for customers will stop being the platform, and focus will move to the AI itself. 

Unlike the A10 search algorithm, Rufus doesn’t simply list products, but explains why certain options are the better choice for the user, referencing reviews, product details, and real-world use cases. 

This enhanced context is critical to the AI shopping experience. Product recommendations that explain their reasoning and match products to a shopper’s unique needs will feel far more credible than a page of search engine results with no evaluation past the star rating. 

However, this contextual trust is conditional. AI assistants like Rufus will only cite products when the listings cite strong and consistent signals when it comes to relevance and quality. 

When you’re able to clearly articulate use cases, address common concerns, and reinforce claims with detailed visuals and reviews, you’ll make it much easier for an AI shopping assistant to justify its recommendation. 

On a post-AI Amazon, trust in your listing isn’t just earned by the right keywords appearing in the title. Instead, you need to ensure consistency, clarity, and context in every aspect of the listing. 

Increased Price Sensitivity and New Discovery Paths 

Another change on the horizon will come from the new layers of price awareness that AI assistants introduce to the shopping experience. 

Under the traditional model, shoppers have to do their own price comparison, deals tracking, and timing of their purchases to align with promotions. Soon, AI assistants will be adept at doing this on the shopper’s behalf.

Amazon has already released statements suggesting that Rufus can be used for setting price alerts and highlighting better-value alternatives, or even automatically making a purchase when a product drops below a certain level. 

This opens up new product discovery paths capable of bypassing Amazon’s search function completely. 

For ecommerce brands, this means pricing strategy and value communication will be even more important. AI tools won’t just be evaluating your listings based on their price point, but also their overall value compared to alternative products. 

If your listings make a point of justifying price with durability, features, and long-term savings, they’ll be more likely to be recommended by shopping assistants like Rufus. This is true even in price-sensitive scenarios. 

As AI shopping blurs the lines between comparison, discovery, and purchasing, brands will need to anticipate their products being discovered (and excluded!) based on pricing logic contained in chats between shoppers and the AI, and not immediately obvious by simply comparing your pricing with competitors’. 

How Brands Can Optimize Listings for Amazon Rufus 

To optimize for Amazon Rufus, every brand and Amazon marketing agency will need to undergo a fundamental shift in how they approach listing optimization. 

While the A10 algorithm assesses listings by the presence or absence of keywords, Rufus is capable of understanding them as more complex sources of information, capable of answering questions, removing common objections, and earning recommendations from a trusted AI tool. 

Here’s a step-by-step workflow to build Amazon listings that are easy for Rufus to interpret, contextualise, and recommend in interactions with Amazon shoppers. 

1. Use Amazon Rufus for AI-First Competitor Research 

Start the process by using Rufus as a research tool. 

Open Amazon with a new dummy account, and use it exactly as someone your audience would, asking natural, personalized questions to explore your product category, price range, and use cases. 

Throughout this process, take notes on things like:

• The brands and ASINs that Rufus consistently recommends. 
• The product attributes that Rufus highlights (e.g. certifications, ease of use, materials and their benefits, and overall value). 
• How Rufus highlights comparisons and trade-offs between different products (e.g. price vs quality, good for beginners or advanced, compact or complex). 
• How adding contextual variables change Rufus’s product recommendations, e.g. household size, lifestyle, and what exactly you need the product for. 

Carrying out research in this way should help you build a complete picture of how Rufus understands the product category, and hopefully identify gaps where: 

• Product use cases are underrepresented. 
• Competitor listings are lacking in clear explanations or reinforcement using visual content. 
• AI responses tend to focus on a select handful of brands, due to those sellers having AI-friendly content. 

These kinds of gaps in competitor visibility can present opportunities you can target to make your listings stronger candidates for recommendations, and reap a higher proportion of clicks and orders. 

2. Turn Buyer Personas Into Conversational, Intent-Focused Questions 

Tools like Amazon Rufus are designed to serve shopper intent rather than more general keyword matches. Therefore, your content needs to reflect how real shoppers think and ask questions. 

Start by ensuring your buyer personas are defined in detail, including: 

• Key pain points and motivations. 
• Level of experience (e.g. first-time buyer vs expert in a certain product category.) 
• Budget sensitivity and tolerance for risk. 
• The lifestyle scenarios and environments where a given product is used. 

From there, you’ll be able to match each persona to chat-friendly queries they’re likely to ask Amazon Rufus, including:

• Exploratory questions, e.g. “What’s a good fishing rod for beginners?”
• Validation questions, e.g. “Is this product safe for children and pets?”
• Comparison questions, e.g. “What’s better for small spaces, X or Y?” 

This kind of exercise can inform both copywriting and content prioritization. If there’s a logical route to Amazon Rufus showing your product as an answer to a certain question, then your listing must clearly address that question, whether in the copy, images, or A+ content. 

3. Optimize Standard Text Fields to Reinforce Context, Intent, and Recommendation Signals 

Once you’ve mapped audience intent, the next step is to optimize the listing’s standard text fields to communicate relevance and maximize visibility on Rufus. 

For now, Amazon users are still using the standard search function alongside Amazon Rufus product discovery. With this in mind, it’s best to balance standard, SEO-oriented keyword optimization with tweaks designed to align your listing with AI product discovery. 

Some Rufus-specific improvements to prioritize include: 

• Refining titles to emphasize the main use cases and buyer context, not simply the item’s product specs. 
• Rewriting bullet points to explicitly connect product features to real-world uses. 
• Expanding your product description to cover more decision-making factors, niche use cases, or common concerns in the product niche. 

These tweaks to the crawlable text will strike a balance between hitting the keywords you want to rank for in standard Amazon search, and the consumer questions that the listing answers confidently. 

By achieving this, you’ll help Rufus: 

• Understand when to recommend a product. 
• Distinguish your product from similar alternatives. 
• Justify the recommendations it gives customers with supporting context.

4. Create Visual Assets That Communicate Use Cases and Decision Factors 

Images in your product gallery and A+ content play a major role in AI product discovery, as they reduce ambiguity. 

When your listings are accompanied with clear, contextual visual content, you can help both AI systems and shoppers to efficiently understand both product functions and relevance. 

The good news is that this doesn’t have to change Amazon image best practice that’s been in place for years before Rufus was rolled out. The same kind of images that help human customers understand your products will serve the same purpose for AI discovery tools. 

Some Rufus-friendly image creation strategies include: 

• Using lifestyle images that show the product in use across several different scenarios, tied to your buyer personas. 
• Using more minimalist infographics to display product dimensions, durability, compatibility, and other variables. 
• Using text overlay to show outcomes instead of just features, e.g. “fits easily on most desks” instead of just “compact size”. 

When carefully-selected visuals reinforce the same intent signals as your copy, you’ll build a more consistent listing that raises the confidence of both AIs and human shoppers when understanding your product. 

5. Build A+ Content That Answers Questions and Removes Uncertainty 

A+ content gives you the opportunity to upgrade your listing from just being relevant to being truly authoritative. 

From an Amazon Rufus perspective, this part of the listing is a deep knowledge base that creates a more complete picture of the product and supports its recommendation logic. 

To maximize Rufus visibility, your A+ content should: 

• Directly answer the key questions you highlighted during buyer persona research. 
• Explain how the product’s features solve specific challenges, or outperform competitor ASINs. 
• Address common objections regarding price, durability, learning curves, etc.
• Include comparison charts and use-case information for clearer product positioning. 

While pre-Rufus A+ content focused on more general brand storytelling, your current and future pages should place more focus on decision clarity. 

The more your listing resolves uncertainty for your target market, the easier it will be for Rufus to confidently recommend your product as the “right fit” for a shopper’s queries. 

FAQs 

Future-Proofing Your Organic Discovery 

Though Rufus hasn’t totally replaced traditional Amazon search, Amazon is all-in on making AI a key part of product discovery. 

If you’re able to get ahead of this emerging trend, and optimize your listings for the nuances in how AI crawls products, you’ll be able to position your brand for stronger organic visibility and drive more sales in the AI-first future.

The post Amazon Rufus: How We’re Preparing Brands for the Shift to AI Search appeared first on Seller Labs: Amazon Seller Software and Platform.