How AI Shopping Assistants Are Changing How We Buy — And What Brands Must Do
- Anil Gandharve
- May 12
- 15 min read
Updated: May 29
Table of Content
AI shopping assistants aren’t coming — they’re already here. And they’re flipping the script on how people search, compare, and decide what to buy online.
Take Amazon’s Rufus AI: it already powers around 13.7% of all Amazon searches, handling a staggering 274 million queries per day. That’s not a pilot — that’s prime-time traffic. And it’s growing fast. Projections show Rufus could account for 25% to 35% of Amazon searches by the end of 2025, making AI-driven discovery a central feature of the customer journey.
It’s not tucked away in some obscure tab either — Rufus is now wired directly into Amazon’s main search bar. What used to be a keyword match game is now a full-blown conversation powered by generative AI in eCommerce, where context, intent, and natural language rule.
If you’re a brand selling on Amazon, Walmart, or Target, this shift means one thing: adapt fast, or risk fading into digital obscurity.
Let’s unpack what this means for your marketplace strategy.
Why AI Shopping Assistants Are Gaining Traction Fast
Shoppers aren’t just trying AI — they’re embracing it. Surveys consistently show that people who use AI shopping assistants report higher satisfaction with the experience and are more inclined to use them again, especially for complex or high-consideration purchases. That’s a big signal: we’re not looking at a novelty, we’re looking at a shift in default behaviour.
A major driver behind this surge is Gen Z. This generation expects speed, relevance, and a frictionless path to purchase — and AI assistants deliver.
But it’s not just about convenience. Gen Z loves control and clarity. They lean heavily into visual search and conversational interfaces — not rows of filters or clunky menus.
And they’re not just using AI for basics. More than half already use it to hunt deals, brainstorm gift ideas, or even create personalised presents during the holidays. This behaviour is setting the tone for the next wave of ecommerce — one where product discovery is smart, social, and seamless.
An AI shopping assistant isn’t a fancy search box. It’s more like that store associate who knows what’s in stock, gets your preferences, and doesn’t waste your time. It listens. It learns. It recommends — fast.
This isn’t keyword search. It’s a conversation. And it’s changing everything.
What Is Rufus AI — And How It’s Changing the Rules of Marketplace Search
Rufus AI is Amazon’s generative AI-powered shopping assistant — and it’s not just a feature, it’s a full-on shift in how search works on the world’s biggest online store.
Built into the Amazon app and website, Rufus lets users ask detailed, open-ended questions about anything from “best low-carb snacks for travel” to “gift ideas for a six-year-old who loves dinosaurs.” It then responds in a natural, conversational tone — giving not just links, but actual answers. Think product comparisons, tailored suggestions, and even follow-up clarifications based on your last question.
You can talk to Rufus via voice or text, and it works fast — thanks to custom AWS hardware built to handle huge volumes of real-time queries. It’s trained on Amazon’s entire catalogue, customer reviews, Q&A sections, and even relevant info from across the web.
In short, Rufus doesn’t just search — it helps you decide. It's your true AI Shopping Assistant.
How is Rufus AI different from the Amazon A10 algorithm?
To understand Rufus’s impact, you need to know what it’s replacing (or at least reshaping): the Amazon A10 algorithm.
A10 is Amazon’s long-standing product ranking engine. It determines which products show up first when you type in a search — and it's based on traditional ecommerce signals like:
Keyword relevance (title, backend fields)
Conversion rates and sales velocity
Review ratings and customer satisfaction
Seller performance and authority
If you’ve ever spent hours trying to get your product to rank for “organic trail mix,” you’ve been playing by A10’s rules.
But here’s the key: A10 delivers results. Rufus delivers answers.
Feature | Rufus AI | Amazon A10 |
Purpose | Generative AI assistant to guide shopping | Ranking engine to list products by relevance |
Tech | Trained large language models (LLMs) | Machine learning with structured data |
Personalization | Deep — follows context, understands follow-up questions | Basic — past search and purchase history |
User Interaction | Conversational Q&A, recommendations | Keyword-driven product list |
Impact | Recommends based on content depth, sentiment, use-case fit | Ranks based on algorithmic score and performance |
The shift is big. With Rufus acting as a gatekeeper, your product might bypass traditional search rankings entirely and land directly in a recommendation — if your content matches the user's intent well enough.
But the reverse is also true: If your product page lacks detail, clarity, or conversational cues, Rufus won’t pick it. Ask yourself — does your listing answer “Is this snack gluten-free?” or “Is it good for toddlers with allergies?” If not, you’re invisible in AI-first discovery— and that’s where smart eCommerce automation can make all the difference.
This is more than an algorithm tweak. It’s a redefinition of the digital shelf. And it rewards brands who treat their product pages like customer conversations — not keyword stuffing exercises.
Want to learn how to do this better? Check out our blog on eCommerce keyword research for marketplace success.
Voice Search Is Becoming the New Normal in Ecommerce
Voice search is becoming a common way to shop - it's your easiest way to access AI Shopping Assistant. An estimated 153.5 million Americans use voice assistants, with 38.8 million using smart speakers for shopping tasks. By 2024, about 27% of U.S. consumers had made payments via voice commands (reference).
Over half of smartphone users now use voice to search for product info. On smart speakers, it’s even more embedded in everyday life — from adding items to a cart to reordering essentials. In fact, voice commands are already driving a meaningful chunk of retail traffic, especially in categories like snacks, groceries, and home essentials.
The big change? Shoppers aren't thinking in search terms anymore. They’re thinking out loud — literally. And they expect platforms to understand them.
This shift is why platforms like Amazon and Walmart are pushing hard into voice-enabled discovery. Rufus is already taking millions of spoken questions per day. As AI assistants become more conversational and more integrated into daily routines, the expectation will be: speak, get the answer, buy — no scrolling, no filters.
Why you need to optimize for voice search before competitors do
With voice search, you don’t get 50 results. You get one or two. That’s it. If your product isn’t in that tight shortlist, you’re out.
Unlike traditional search where your listing might still show up on page 2 or 3, voice discovery is ruthless. The assistant will recommend a top choice (or maybe a small selection), and move on.
This is especially true in mobile and smart speaker environments, where screens are limited or non-existent.
The opportunity? If your content is structured right, you don’t just compete — you win by default. Your product becomes the answer, not one of many options.
Differences between voice search optimisation and traditional SEO
Traditional SEO is about keywords and ranking. AI shopping assistants and Voice optimization is about understanding and intent.
Traditional keyword SEO alone is losing primacy. AI-powered search is more semantic and contextual, focusing on user intent and broader signals. Amazon’s Rufus AI, for instance, considers not just on-page keywords but also related context (e.g. understanding that a query about “party snacks” might involve chips, popcorn, and pretzels even if “party” isn’t in the title) and even pulls information from brand websites and the wider web. As a result, product ranking in an AI-driven interface may not mirror classic search results – relevant products with rich content and positive user feedback can outrank keyword-stuffed listings.
Here’s how to shift your approach:
Write how people talk: Use natural phrasing. Instead of “low-calorie snack bars,” try: “Good snacks for dieting without feeling hungry.”
Structure for questions: Think FAQ format. “Is this gluten-free?” “Can kids eat this?” “Does it stay fresh after opening?” These are the questions AI is trying to answer — so give it the answers, clearly and early.
State product benefits explicitly: Don’t assume the AI will piece things together. If your bar is vegan, say so — don’t bury it in an ingredient list and hope it gets picked up. AI doesn’t guess. It looks for facts.
Context > keywords: It’s not about repeating “protein snack” five times. It’s about making sure your content reflects use-cases. “Perfect for post-gym recovery” or “Ideal for busy mornings” is what voice queries latch onto.
Voice is changing the rules — but it’s also simplifying the game. Brands that think like their customers talk will stay one step ahead.
Read more about "Mastering SEO E-Commerce Category Pages" and Speak with a Digial Shelf Strategist.
From Keyword to Intent — Search Is Getting Smarter
Search isn’t just about what people type — it’s about what they mean.
We’re entering an era where AI assistants like Rufus interpret full thoughts, not just fragments. Shoppers might not know the exact product name. They just describe the job it needs to do: “What’s a good snack for hiking in hot weather that’s not too sugary?”
Old-school search would trip over that. But intent-led search — powered by AI — gets it.
Coexistence of Two Types of Users: Keyword-Driven and Intent-Driven
Right now, both types of users are active. The keyword crowd still types in terms like “vegan trail mix” or “best budget earbuds.” These are the traditional searchers — and they haven’t gone away.
But right next to them, a growing group is talking to AI tools. They ask things like, “I need snacks that won’t melt in my bag,” or “What’s a good Bluetooth speaker for outdoor use?”
You have to serve both audiences. If your content is too AI-optimized, you might lose search visibility. If it’s too keyword-stuffed, the AI won’t recommend it.
How Brands Need to Shift: New Formats, Smarter Content
It’s time to treat your product detail page (PDP) like a hybrid machine. One engine for keyword relevance. One for intent clarity.
What this looks like in practice:
More FAQs: Not just generic ones. Real questions you hear in reviews, customer service chats, or community forums.
More natural phrasing: Write how people talk. “Great for post-workout snacks” beats “high protein, low carb.”
More explicit claims: Don’t assume the AI connects dots. If it's gluten-free, vegan, nut-free — say it clearly and early.
Balancing Act — Serving the “Old School” User While Adapting to the New
Yes, your title still matters for ranking. But your bullets? They should double as responses to voice queries. Your description? It should read like a conversation starter, not a feature dump.
One part of your PDP speaks to Amazon’s A10. The other whispers to Rufus.
Both matter.
Tactics for Optimizing Content to Match User Intent on Marketplaces
Use conversational keywords: Phrases like “What’s the best...”, “Good for kids who…” or “Something that won’t melt” help AI understand context. They can live naturally in bullets or descriptions.
Add FAQs based on real shopper behaviour: If customers keep asking, “Does this contain peanuts?” — build that into a Q&A or bullet. Don’t make the AI (or the shopper) guess.
Update copy based on review trends: Are reviews saying, “super crunchy” or “perfect for hiking”? Weave those exact words into your copy. They’re the language AI will favour when summarising or recommending.
This isn’t about rewriting everything. It’s about tuning your content for both how people search — and how they speak. The brands that get this right will dominate both search lists and AI shortlists.
What Brands Must Do Right Now to Serve AI Shopping Assistants
What Brands Must Do Right Now
AI shopping assistants aren’t just changing how customers discover products — they’re changing what kind of content gets seen, trusted, and chosen. If your product listings are stuck in the old model of SEO, you’re missing the new front door.
Here’s how to get current — and stay ahead.
Build for Voice-First, Not Screen-First
When someone asks, “Is this good for school lunches?” they’re not looking at a screen. They’re listening.
That means your product descriptions need to work when spoken aloud. They should sound like clear, confident answers — not marketing copy. Say what the product does. Say who it’s for. Say why it matters.
Ask yourself:
Would this sound natural if Alexa read it?
Is the first bullet enough to answer a simple shopper question?
Could Rufus pick out the use-case instantly?
If not, it’s time to rework it.
Automate SEO to Keep Pace with AI-Powered Discovery
The pace of change is brutal. Consumer questions evolve daily. Voice queries aren’t static — they reflect real-world shifts: seasons, trends, even TikTok moments.
You can’t afford to rewrite listings by hand every quarter. That model’s broken.
To win now, brands need SEO that:
Adapts fast to new shopper queries
Reflects voice-first behaviour and language
Works across platforms like Amazon, Walmart, and Target without manual rework
Preparing Your Product Listings for AI Shopping Assistants Like Rufus
Here’s what AI like Rufus is doing: it’s scanning your PDP like a human would — but with less patience. If it can’t find the answer in a few seconds, it moves on.
That means your listing needs to be:
Structured: Use titles, bullets, and Q&A to surface key info fast
Specific: Don’t generalise — say exactly who it’s for, how to use it, and why it’s better
Conversational: Use real-world language, not sales jargon
Think of it like this: if your PDP doesn’t read like an answer, it won’t become one.
Tools to Make This Easier
Trying to update PDPs for five retailers and three languages manually? Not scalable. Not smart.
That’s where Genrise fits:
It automates content updates using voice-ready, AI-optimised formats
It ensures compliance and consistency across all retailers
And it lets you refresh hundreds of listings in days, not cycles
No more spreadsheet wrangling. No more copy-paste fatigue. Just sharper content — ready for how people shop now.
The brands that succeed in this AI-first landscape will be the ones who stop tweaking old systems — and start building for the new behaviour. Rufus isn’t waiting. Neither should you.
Step by Step Guide for Marketplace Content Optimization in an AI Assistant World
Step-1 : Define the Digital Shelf Optimisation Concepts
Before you even start writing titles or editing bullets, step back and define what you’re actually trying to show up for.
The AI assistant doesn’t think in products — it thinks in needs. That’s where Digital Shelf Optimisation starts: not by tweaking keywords, but by mapping the real-world use cases your customers care about.
You need to group your product content strategy around broad, relatable, shopping concepts — the kind that voice queries are already triggering.
Start with 3–5 core concepts per brand. These should reflect:
Common shopper needs and intent
Voice search behaviour
Seasonal or situational use cases
Customer personas (e.g. busy parents, health-conscious millennials, budget-focused families)
Let’s make this real.
For a snack brand, here’s how that might look:
By structuring your optimisation strategy around these concepts, you’re doing two things:
Speaking the language of intent-driven shoppers
Feeding AI assistants like Rufus the context they need to recommend your product confidently
This is no longer about getting found through search terms. It’s about being the answer to “What’s a good snack pack for back-to-school season?” — and that starts with the right concepts at the core of your content.
Genrise’s Ecommerce Content Agent proactively builds concepts tailored to your brand and category using real search trends and AI intent data — so you can define these content themes in minutes instead of spending weeks going back and forth with your agency.
Step-2: Group your products based on the optimization concept
Once you’ve defined the core concepts that match your shopper’s intent, the next step is mapping your product range to those buckets. This isn’t just about how you see your SKUs — it’s about how shoppers describe their needs.
Grouping your products around these concepts makes it easier to align titles, bullets, and content strategy to the right use-cases — which is exactly what voice-first AI assistants are optimizing for.
Here’s how you might approach it for a snack brand using the earlier concepts:
1. Kids Lunchbox Snack Variety Pack Product Fit:
| Why it works: This grouping hits a high-frequency, high-intent search zone. AI assistants are already surfacing products for “best school snacks” or “lunchbox treats for picky eaters.” Make sure these SKUs are optimised to answer those exact needs. |
2. Family Value Snack Pack Product Fit:
| Why it works: Shoppers looking for “bulk snacks” or “snacks for large families” are often price-sensitive but brand-loyal. Content should highlight value, quantity per pack, and reusability (resealable bags, etc.). AI will favour listings that clearly state cost-per-serving, serving count, or suitability for family sharing. |
3. Mini Treat Party Pack Product Fit:
| Why it works: These products align well with searches like “birthday party snacks” or “treats for kids’ events.” Make sure the content calls out festive appeal, portion size, and group-serving ease. Including a phrase like “perfect for goodie bags or snack tables” helps AI assistants position it correctly. |
4. Homestyle Variety Pack Product Fit:
| Why it works: Ideal for older demographics, family settings, or gifting. This is where voice queries like “snacks for grandparents” or “something soft and sweet for tea time” might land. Product content here should feel comforting, descriptive, and clear about texture and flavour balance. |
5. Seasonal Treat Packs Product Fit:
| Why it works: These SKUs should map to seasonal voice search patterns: “snacks for Halloween,” “best Christmas treats,” or “back-to-school snack packs.” Content needs to be nimble and updated per season. Rufus is likely to prioritise recency and context here, so fresh content and occasion-based keywords are critical. |
When your product groupings are aligned with shopper concepts and structured clearly in content, you’re not just organising inventory — you’re speaking the language of AI-powered discovery. And the AI listens to structure. Make it obvious. Make it consistent. That’s how you get chosen.
Step-3 : Optimize titles, bullets, and descriptions for AI parsing
When AI assistants like Rufus parse your product listing, they’re not scanning it like a human. They’re dissecting it — looking for specific, structured signals that match shopper intent. If your content isn’t clear, complete, and logically formatted, AI will skip it — even if your product is a perfect match.
Here’s how to craft titles, bullets, and descriptions that both AI and humans can understand — instantly.
Start with the Three Core Questions AI Tries to Answer
What problem does this product solve?
Think beyond features. Say what the product does for someone.
Example: “No-mess, individually wrapped snacks perfect for school bags” speaks to the parent’s problem of mess and prep time.
What occasion or context is it best for?
Every purchase is tied to a moment. Is it for lunchboxes? Holiday gifting? Hosting guests?
Build your bullets around those situations — that’s how voice queries are phrased.
What is the buyer really asking?
People don’t ask, “Show me a 12-count snack pack.” They say, “What’s a good snack to keep in my car?”
Your copy should reflect that language and intent.
The Formula: Be Clear. Be Complete. Be Helpful.
AI doesn’t infer. It looks for clean answers. So give it what it needs:
State features plainly
Don’t say “delicious and satisfying.” Say “made with real fruit, 100 calories per pack, nut-free.”
Skip buzzwords. Say facts.
Group benefits logically
Use bullets like mini-sections: nutrition, usage, packaging, occasions.
Don’t scatter benefits — keep each point focused and scannable.
Avoid fluff
If it doesn’t answer a shopper’s likely question, cut it.
“Family-favourite” means little without context. “Trusted by parents for over 10 years” means something.
Combine Keyword Relevance with Conversational Phrasing
Old SEO: “low sugar, protein bar, healthy snack bar.”AI-first SEO: “Looking for a snack that won’t spike your sugar levels? These low-sugar protein bars are perfect after workouts.”
You still use keywords — but you wrap them in real phrasing. That’s what gets picked up by AI parsing models.
Align Your Copy with Intent — Not Just Search Terms
Ask yourself:
Is this listing answering a real-world question?
Would someone using voice search hear this and say, “Yep, that’s what I need”?
Rework anything that doesn’t feel like a reply. That’s the shift: from listing content as information, to positioning it as an answer.
AI isn’t asking for poetry. It’s asking for clarity. When your content mirrors the way people search — and the way AI understands — your product becomes easier to recommend, easier to trust, and easier to buy.
Final Thoughts: Are You Ready for the Future of AI-Led Ecommerce?
AI shopping assistants aren’t an add-on — they’re the new front door to your brand.
Whether through voice, chat, or contextual prompts, AI is quietly becoming the first interaction most shoppers have with your products. It’s reshaping not just how people search, but how they decide. And unlike traditional search, which gave you a chance to compete on page 2 or 3, AI-driven results often surface just a few top picks.
That means your PDP in ecommerce isn’t just your shelf — it’s your entire pitch. And if your content isn’t ready for AI parsing, intent-driven queries, and voice-first discovery, it doesn’t matter how great your product is. You won’t be recommended. You won’t be seen.
Rufus (Amazon), Sparky (Walmart), and other AI engines are already making daily decisions about which products to surface — and which to ignore.
If your copy is outdated, overly technical, or keyword-stuffed, it simply won’t land. If your benefits are buried, if your use-cases are unclear, if your structure is messy — your brand will be skipped.
But here’s the opportunity: AI isn’t favouring big brands. It’s favouring clear ones. Relevant ones. Useful ones. The brands that show up with intent-driven content will win. Not because they spent the most — but because they spoke to the customer best.
So, ask yourself:
Are your listings structured for how people talk — or how search engines used to read?
Are you optimising for intent — or chasing old SEO tricks?
Are you giving AI the answers it needs — or hoping it fills in the blanks?
Because if your content isn’t built for Rufus, you’re not just behind the curve — you’re building for a shelf that no longer exists.
