Article
AI Shopping Agents: Discovery Beyond Search
AI shopping agents (ChatGPT, Amazon Rufus, Perplexity, and a fragmented field of retailer-built tools) emerged as alternative discovery channels. Amazon reports Rufus users convert over 60% more than non-users. Strategic retailers are now testing agentic commerce.
- Published
- March 28, 2025
- Updated
- June 18, 2026
- Reading time
- 8 min
2026 updated analysis
What changed since the original article
This page keeps the original Transformidy article as the canonical record and leads with the current interpretation, source notes, and Revenue Unknown framing.
The AI Agent Emergence
Between 2024 and 2026, AI shopping agents became a parallel discovery channel to search engines. Amazon's Rufus assistant (marketed to shoppers as part of Alexa for Shopping) generated nearly $12 billion in incremental annualized sales during 2025, according to figures Amazon disclosed alongside its Q4 2025 financial results, with more than 300 million customers using the assistant over the year.
Coverage of the broader AI shopping landscape names a growing list of agents competing for discovery share: OpenAI's ChatGPT, Amazon's Rufus, Perplexity, and a wave of narrower tools such as Daydream, Phia, OneOff, and Swap, alongside retailer-built assistants like Walmart's Sparky and Ralph Lauren's AI styling bot. No single platform dominates; the field is fragmented and still forming.
Retailers initially treated agents as novelty. Then usage data arrived: Amazon reported that customers who use Rufus convert at rates exceeding 60% higher than those who do not, based on November 2025 data. The channel moved from experimental to strategic for at least one major retailer.
Higher conversion for shoppers who use Amazon's Rufus assistant vs. those who do not
Amazon's own November 2025 figure for its own product; not an independent, cross-retailer benchmark.
Why Agent-Sourced Shoppers Convert Higher
Agents ask clarifying questions. A customer says "I need blue running shoes for trail running." The agent can ask about typical weekly mileage, terrain, and budget, then recommend specific models and link to purchase. The customer arrives at the product page with clearer intent than a bare search query would produce.
Search does not work this way by default. A search query like "blue running shoes" returns many results the customer must evaluate, compare, and filter before deciding. That friction is one plausible reason Amazon's own data shows a conversion gap for Rufus users, though the company has not published the mechanism in detail and no independent study confirms this explanation.
Separately, industry survey data suggests the AI shift is also disrupting the search funnel itself: in a Modern Retail survey of retailers, 37% reported decreases in upper-funnel search traffic and 21% reported decreases in lower-funnel search traffic that they attribute to AI-driven discovery displacing traditional search.
Search Discovery vs. AI Agent Discovery
Two discovery flows. Different intent clarity and conversion rates.
Search: ambiguous query → thousands of results → customer filters and decides. Agent: clarifying conversation → specific recommendation → customer purchases.
A Fragmented, Fast-Moving Field
ChatGPT (OpenAI): The largest general-purpose conversational agent. An OpenAI research team and a Harvard economist estimated roughly 2% of ChatGPT queries are shopping-related, translating to an estimated 50 million shopping queries a day at ChatGPT's scale. Commerce integration is still largely link-based rather than native checkout.
Amazon Rufus: The deepest commerce integration of any agent, because it sits inside Amazon's own marketplace. Amazon captures roughly 40% of U.S. e-commerce spending, and about 90% of Amazon shoppers begin their search directly on Amazon.com—giving Rufus a uniquely large captive audience.
Perplexity: A discovery-focused answer engine gaining traction with research-first shoppers, named alongside Amazon and OpenAI as a serious competitor in trade coverage of the emerging agent landscape.
Retailer-built and narrow tools: Walmart's Sparky, Ralph Lauren's AI styling assistant, and a wave of smaller agents (Daydream, Phia, OneOff, Swap) show that the field is not settling into two or three winners—retailers are also building their own agents rather than ceding discovery entirely to third parties.
No single agent dominates the category yet. Retailers face a fragmented landscape and must decide where to invest discovery and product-data effort rather than betting on one platform.
Search returns choices. Agents make recommendations. Recommendations convert.
The Leadership Move
The strategic choice is whether to optimize for search engine discoverability (yesteryear's channel) or for AI agent discoverability (today's emerging channel). Most retailers are still trapped in search optimization. That window is closing.
- Ownership
Chief Marketing Officer owns discovery strategy. Chief Technology Officer owns product data infrastructure and API integrations. When CMO focuses only on search and social, and CTO is not planning for agent APIs and data feeds, the organization falls behind. Cross-functional ownership of emerging channels is essential.
- Tradeoff
Optimizing for AI agents requires richer product data, better data feeds, and API readiness. Initial investment is higher than search optimization alone. But the reported conversion advantage for agent-guided shopping, and the visible erosion in traditional search funnel traffic, suggest early adopters gain disproportionate advantage as agents mature.
- Human consequence
When discovery shifts from search to agents, marketing teams must learn new optimization techniques. Search marketers cannot simply transfer skill to agent optimization. Training and new hiring may be required. Org structure may need to reflect emerging channel ownership.
Next Move
If you have not optimized for AI agents: Start immediately. Audit your product data: are descriptions rich enough for agents to recommend? Are specifications detailed? Do you have data feeds ready for agent platforms? Work with ChatGPT plugin ecosystem, Alexa, and Perplexity to ensure your products are shoppable through agents. Measure conversion from each agent source.
If you are optimizing for search but seeing search ROI plateau: Diversify to agent discovery. Retailers surveyed by Modern Retail are already reporting search traffic declines they attribute to AI; build product data APIs, partner with emerging platforms, and allocate marketing budget to agent optimization alongside search. Treat agents as a distinct, still-forming channel worth testing rather than a proven one to bet everything on.
FAQ
What is an AI shopping agent?
A conversational AI (ChatGPT, Amazon's Rufus, Perplexity, and a growing set of retailer-built tools) that understands natural language product requests and recommends or links to purchases. Unlike search engines, agents can ask clarifying questions and guide users toward purchase decisions. They are discovery channels outside traditional search and social.
Do AI shopping agents convert better than search?
The clearest evidence is Amazon's own: the company reports Rufus users convert at rates exceeding 60% higher than non-users. That is a single retailer's self-reported figure, not an independent, cross-industry benchmark, but it is the strongest data point currently available. Separately, retailers surveyed by Modern Retail report declining traditional search traffic they attribute to AI displacing search behavior.
Which AI shopping agents matter most?
The field is fragmented and still forming. ChatGPT has the broadest general awareness; Amazon's Rufus has the deepest commerce integration because it sits inside Amazon's own marketplace; Perplexity is a growing discovery-focused competitor; and retailers including Walmart and Ralph Lauren are building their own agents rather than relying solely on third parties. No single agent dominates yet.
How do retailers optimize for AI shopping agents?
Provide rich product data: detailed descriptions, specifications, images, availability. Ensure product feeds are current. Enable direct purchase or cart links from agent recommendations. Schema markup matters for agents more than for search. Integration with agent APIs is critical for seamless checkout.
Sources & References
Original article archive
Original article published March 28, 2025: "Amazon Interests - New AI Shopping Technology". Preserved here for provenance, historical context, and citation continuity.
E-commerce continues to evolve. As a pioneer of online shopping, Amazon continues to push the boundaries of innovation, leveraging artificial intelligence to redefine the shopping experience. The retail giant's latest offering, Amazon Interests, reimagines how consumers discover and engage with products, potentially reshaping the entire e-commerce ecosystem. This Transformidy insight explores the groundbreaking technology, its customer experience, competitor analysis, and the future of AI-driven shopping.
Key Takeaways
- Amazon Interests uses AI to personalize product discovery based on user-defined preferences
- The feature continuously scans Amazon's store for relevant products, restocks, and deals
- Competitors must adapt their strategies to counter Amazon's AI-driven personalization
- AI is transforming various aspects of the shopping experience, from product recommendations to inventory management
What are Amazon Interests and How Does It Work?
Amazon Interests is an AI-powered feature designed to enhance the product discovery process for Amazon shoppers. Launched in beta to a select group of U.S. customers in March 2025, this innovative tool leverages large language models (LLMs) to interpret natural language inputs and translate them into actionable product recommendations. Fast Fact: Approximately 230 million Amazon customers are from the United States (2024).
The functionality of Amazon Interests is both simple and sophisticated. Users can create personalized shopping prompts tailored to their interests, price limits, and preferences using everyday language. For example, a user might input "Model building kits and accessories for hobbyist engineers and designers" or "The latest pickleball gear and accessories." Once a prompt is created, Amazon Interests continuously scans the vast Amazon marketplace, proactively notifying users about newly available products, restocks, and deals that align with their specified interests.
Steps:
1. Opening Interests: Open the Amazon Shopping app and tap the 'Me' tab, then tap the ‘Interests’ button.
2. Create new Interests: On the Interests page, tap the ‘Get Started’ button and enter a description of what you're looking for, such as "Clothes that is perfect for the Summer vacation in Ibiza.”
3. Let Interests do the work behind the scenes: Once your Interest Prompt is created it will auto-save, and you will see updates when new products are available.
4. Update your Interests prompt: On the Interests page, find the Interests prompt you would like and tap the ‘pencil’ icon to edit your prompt to better describe the products you are looking for.
What sets Amazon Interests apart is its ability to understand and interpret complex queries. For instance, if a user asks for "natural makeup products for summer glow from top brands," the AI utilizes its extensive knowledge base to translate "top brands" into specific, reputable makeup manufacturers, ensuring highly relevant recommendations.
This level of personalization goes beyond traditional recommendation systems, which typically rely on past purchase history or browsing behavior. Amazon Interests allows users to explicitly define their current interests and needs, potentially capturing shifts in preferences that might not be reflected in historical data.
Customer Experience Implications
75% of shoppers use Amazon to find new products and brands. The introduction of Amazon Interests represents a significant leap forward in personalizing the online shopping experience. By allowing customers to define their interests in natural language and receive tailored product suggestions, Amazon is addressing one of the key challenges in e-commerce: helping customers find exactly what they want among millions of products.
Let's examine the pros and cons of this technology on various aspects of the customer experience:
| Aspect | Pros | Cons |
|---|---|---|
| Product Discovery | - Easier to find niche products - Reduces search fatigue - Introduces customers to new items they might like | - May limit exposure to diverse products - Could create a "filter bubble" effect |
| Product Alignment | - Better matches between customer interests and product offerings - Potentially higher customer satisfaction | - Risk of over-personalization - May miss out on serendipitous discoveries |
| Inventory Management | - Improved stock predictions based on customer interests - Potential for more efficient inventory turnover | - Could lead to overstocking of trending items |
| Brand Management | - Opportunity for niche brands to reach interested customers - Increased visibility for products that match customer interests | - Potential for reduced visibility of established brands if they don't match interests |
The impact of Amazon Interests on customer experience extends beyond these aspects. By proactively notifying users about relevant products and deals, Amazon is creating a more engaging and dynamic shopping environment. This could lead to increased customer loyalty and higher frequency of purchases.
Moreover, the use of natural language processing in Amazon Interests makes the technology more accessible to a broader range of users. Customers who might struggle with traditional search filters or category navigation can now express their needs in their own words, potentially opening up e-commerce to new demographics.
Successful Implementation Takes Trust, Privacy and Quality Recommendations
For Amazon Interests to be successful, the company must focus on several key areas that balance personalization with privacy and trust. This innovative feature relies heavily on AI to provide tailored product recommendations, making it crucial for Amazon to address potential concerns while delivering value to customers.
Building Trust Through Transparent Recommendations
Amazon Interests' success hinges on its ability to build and maintain customer trust. As Leila Rouhi, Vice President of Trust and Privacy for Amazon's Devices & Services, emphasizes, "Privacy is foundational to everything we do. It's essential to customer trust. We know that if we break that trust, we're not going to get a second chance". To achieve this, Amazon must ensure that the AI-driven recommendations are not only accurate but also transparent in their origin.
The company should clearly communicate how Amazon Interests works, explaining that recommendations are based on user-defined preferences rather than invasive data collection. This transparency can help alleviate concerns about data privacy and foster a sense of control among users. In addition, the technology is meant to aid shopping decisions, users should also have the final choice and not be manipulated into bad options (e.g., recommending sponsored products that are not high-quality). As Rouhi states, "By thinking about privacy, you're building trust with the customer. When you build trust with the customer, they're much more likely to engage with your product"
Prioritizing Privacy in AI Implementation
Privacy considerations must be at the forefront of Amazon Interests' development and implementation. Amazon should adopt a "privacy by design" approach, incorporating privacy safeguards from the early stages of the system's development. This includes:
- Data minimization: Collecting only the data necessary for generating relevant recommendations.
- Consent and transparency: Obtaining explicit user consent for data usage and providing clear information about how data is processed.
- Access and control: Empowering users to access, correct, and delete their data, as well as opt out of the service if desired.
- Robust data security: Implementing strong encryption and access controls to protect user information.
By prioritizing these privacy measures, Amazon can demonstrate its commitment to protecting user data, which is essential for the success of Amazon Interests.
Delivering High-Quality Recommendations
The core value proposition of Amazon Interests lies in its ability to provide highly relevant and personalized product recommendations. To achieve this, Amazon must leverage its extensive experience with AI-driven recommendation systems, which have been a cornerstone of its e-commerce success.
Key factors for delivering quality recommendations include:
- Utilizing diverse data sources: Incorporating various data points such as purchase history, search behavior, and user-defined interests to create a comprehensive understanding of customer preferences.
- Employing advanced machine learning techniques: Using collaborative filtering, content-based filtering, and natural language processing to refine and improve recommendations continuously.
- Real-time updates: Ensuring that the system adapts quickly to changing user interests and behaviors.
- Balancing personalization with discovery: While tailoring recommendations to user interests, the system should also introduce users to new, relevant products they might not have discovered otherwise.
Competitor Implications and Management
The launch of Amazon Interests can pose challenges for both online and physical store competitors. Amazon's ability to leverage AI for personalized product discovery sets a new standard in the e-commerce industry that focuses on faster relevant choices.
For online competitors, the pressure to develop similar AI-driven personalization tools will be key. Companies like Walmart and Target, which have been working to catch up with Amazon's e-commerce experience, will need to accelerate their AI initiatives to balance shopper purchasing intent, recommendations, and activation. These retailers would need to invest in similar technological capabilities and train employees to offer comparable personalized features.
Physical store competitors face a different set of challenges. The convenience and personalization offered by Amazon Interests could further drive consumers towards online shopping, potentially reducing foot traffic in brick-and-mortar stores. To counter this, physical retailers might need to focus on creating unique in-store experiences that can't be replicated online, while also improving their own online and omni-channel offerings. Additionally, employees should be trained on understanding shopper intent and pairing it with relevant products.
To manage this development, competitors should consider the following strategies:
- Invest in AI and data analytics capabilities to enhance personalization
- Focus on unique product offerings that differentiate from Amazon's vast catalog (quality over quantity)
- Emphasize high-touch customer service and after-sales support
- Leverage physical store presence for omni-channel experiences (e.g., buy online, pick up in-store)
- Enhance loyalty programs that offer exclusive after sale benefits not available on Amazon
It is worth noting that while Amazon's scale gives it a significant advantage in AI development, smaller retailers can potentially partner with third-party AI providers to implement similar features. This could help level the playing field to some extent.
AI and Shopping Experiences
The introduction of Amazon Interests is just one example of how AI is transforming various aspects of the shopping experience. Let's explore some key areas where AI is making a significant impact:
Automation
AI is automating numerous processes in retail, from inventory management to customer service. Chatbots and virtual assistants, powered by natural language processing, are handling an increasing number of customer inquiries. This not only reduces costs for retailers but also provides 24/7 support for customers.
Relevant Product Recommendations
AI algorithms analyze vast amounts of data to provide increasingly accurate and relevant product recommendations. These systems go beyond simple "customers who bought this also bought" suggestions, taking into account factors like browsing history, purchase patterns, and now, with Amazon Interests, explicitly stated preferences.
Ad Network Management
AI is revolutionizing ad management by optimizing ad placements, targeting, and budgeting in real-time. This leads to more effective advertising campaigns and better returns on ad spend. For example, Amazon's own advertising platform uses AI to help sellers target the right customers at the right time.
Omni-channel Management
AI is crucial in creating seamless omnichannel experiences. It helps retailers maintain consistent inventory information across channels, personalize experiences based on channel preferences, and optimize fulfillment strategies. This is particularly important as the lines between online and offline shopping continue to blur.
Bundling and Dynamic Pricing
AI enables sophisticated bundling strategies, where complementary products are offered together based on customer preferences and purchase history. Additionally, AI-powered dynamic pricing adjusts prices in real-time based on factors like demand, competitor pricing, and inventory levels.
The impact of AI on shopping experiences extends to physical stores as well. Technologies like computer vision are being used for cashier-less checkout systems, while AI-powered robots are assisting with inventory management and customer service in some stores.
Transform for the Better
Transformidy explored the various facets of Amazon Interests and its implications for the e-commerce landscape, it is clear that AI is not just transforming shopping experiences – it's redefining them. The ability to understand and anticipate customer needs at an unprecedented level of granularity is opening up new possibilities for personalization and customer engagement.
However, as with any transformative technology, there are challenges to consider. Privacy concerns, the potential for AI bias, and the risk of creating echo chambers in product discovery are all issues that need to be addressed as these technologies evolve.
For retailers, the message is clear: embracing AI is no longer optional. Whether through developing in-house capabilities or partnering with AI providers, companies need to find ways to leverage AI to enhance their customer experiences and operational efficiencies.
As we look to the future, we can expect AI to play an even more central role in shaping our shopping experiences. From virtual shopping assistants that understand our preferences better than we do ourselves, to predictive inventory systems that ensure our desired products are always in stock, the possibilities are endless.
The launch of Amazon Interests is not just a new feature – it's a glimpse into the future of retail. As AI continues to evolve, we can look forward to shopping experiences that are more personalized, more efficient, and more enjoyable than ever before.rness the power of AI to transform customer experiences. Reach out today to start your journey toward smarter retail solutions!
Frequently Asked Questions - Amazon Interests
1. What is Amazon Interests?
Amazon Interests is an AI-powered feature that provides personalized product recommendations based on user prompts describing hobbies, preferences, or budgets.
2. How does Interests work?
It uses large language models (LLMs) to convert natural language into actionable queries that generate tailored product suggestions.
3. Who can access Interests?
Currently available to select U.S. customers via the Amazon app or mobile website; broader access is expected soon.
4. How does Interests improve customer experience?
It simplifies product discovery by proactively notifying users about relevant items, restocks, and deals aligned with their interests.
5. What other AI features does Amazon offer?
Amazon provides tools like voice-enabled shopping via Alexa, dynamic route planning for deliveries, and AI-generated review highlights.
6. How does Amazon use AI for operational efficiency?
AI helps optimize delivery routes in real-time based on traffic and weather conditions, reducing costs and delivery times.
7. What impact does Amazon’s AI have on competitors?
Amazon’s innovations force competitors like Walmart and Google to invest heavily in similar technologies to remain competitive.
8. How does personalization affect sales?
Personalized recommendations increase engagement and sales by aligning products with individual customer preferences.
9. Can small businesses benefit from similar tools?
Yes! Generative AI tools like those offered by Amazon can help small businesses streamline operations and enhance customer experiences.
10. What’s next for retail innovation?
AI will continue reshaping retail through advanced personalization, automation, and predictive analytics—setting new standards for customer engagement.
How Can Transformidy Help?
At Transformidy, we specialize in helping brands navigate the complex world of maximizing customer experience for improved engagement, satisfaction, and business growth. Our team of experts can assist you in assessing, tailoring, and implementing the right customer experience strategy for your company.
Contact us or set up a 30-minute complimentary consultation for more information on our services, insights, or showcases. We look forward to hearing from you.
FAQ
What is an AI shopping agent?
A conversational AI (ChatGPT, Alexa, Perplexity, Google, Microsoft) that understands natural language product requests and recommends or links to purchases. Unlike search engines, agents ask clarifying questions, understand context, and guide users toward purchase decisions. They are discovery channels outside traditional search and social.
Why do AI shopping agents convert higher than search?
Because agents understand intent and guide decisions. A search query is ambiguous: 'blue shoes' could mean athletic, casual, professional. An agent asks clarifying questions: What is the occasion? What is your budget? What brand do you prefer? This conversation increases purchase confidence and reduces decision friction.
Which AI shopping agents matter most?
The field is fragmented and still forming. ChatGPT has the broadest general awareness; Amazon's Rufus has the deepest commerce integration because it sits inside Amazon's own marketplace; Perplexity is a growing discovery-focused competitor; and retailers including Walmart and Ralph Lauren are building their own agents rather than relying solely on third parties. No single agent dominates yet.
How do retailers optimize for AI shopping agents?
Provide rich product data: detailed descriptions, specifications, images, availability. Ensure product feeds are current. Enable direct purchase or cart links from agent recommendations. Most importantly: be shoppable by agents, not just searchable by search engines. Schema markup matters for agents more than for search.
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