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Amazon Rufus Search Bar

Will AI’s Next Search Battle Be Between Google and Amazon?

As we venture into the early stages of the New Year, Google, Walmart and Amazon have decisively marked the dawn of a new era in generative AI search technology, each launching pioneering initiatives that signal the advent of a more interactive, conversational and contextually relevant search paradigm.

Google has unveiled its Search Generative Experience (SGE), a cutting-edge feature powered by AI that promises to transform the way we interact with information online. SGE is designed to provide rapid, comprehensive overviews on a wide array of topics, thereby fostering a deeper exploration of the web through meticulously curated links. This AI-driven innovation streamlines the inquiry process by offering users the ability to pose follow-up questions or navigate new avenues of investigation seamlessly—without the need to revisit previously stated context.

Google AI Search

Especially for topics of a complex or evolving nature, the ability to ask follow-up questions through generative AI significantly enhances the user experience. By eliminating the redundancy of reestablishing context, SGE enables users to delve deeper with inquiries like “Why is this?”, and immediately receive AI-crafted summaries replete with links that facilitate effortless web exploration. As we have observed with ChatGPT, the more descriptive the prompt, the more robust the response will be. This approach brings the search experience closer to a real human conversation, fostering a continuous dialogue of questions and answers between users and the AI platform.

“This brings the search experience closer to a real human conversation, fostering a continuous dialogue of questions and answers between users and the AI platform.”

In domains such as shopping or local information, generative AI serves as a catalyst for enriched search experiences by delivering detailed insights across multiple dimensions. In the realm of e-commerce, for instance, SGE aids consumers by unveiling essential product details and considerations, thereby expediting the decision-making process with concise snapshots of key features, a variety of product choices, descriptions, reviews, ratings, prices and images. This wealth of up-to-the-minute information is sourced from Google’s Shopping Graph—an expansive, ever-evolving repository of data on products, sellers, brands, reviews and inventory.

Amazon’s Late but Bold Entrance into AI

In parallel, Amazon has introduced Rufus, a chatbot embedded within its mobile application that leverages AI to act as a personal shopping assistant. As highlighted in a recent New York Times article, “Customers can ask the tool, Rufus, product questions directly in the search bar of the company’s mobile app, Amazon said in a blog post. The AI will then provide answers in a conversational tone. The examples provided in the announcement included comparing different kinds of coffee makers, recommendations for gifts and a follow-up question about the durability of running shoes.”

Despite the fact that it is playing catch-up with other tech companies that released AI tools more than a year ago, Amazon’s foray into generative AI-driven search and shopping assistants underscore a strategic pivot towards more nuanced, dialogue-based interactions between users and technology. Rufus utilizes Amazon’s extensive product catalog and gathers information from across the web to respond to customer inquiries about shopping requirements and product details and comparisons that enable customers to:

Get Deeper Product Insights. Customers can get more in-depth product information by asking questions in order to help narrow their purchase search or to discover other products related to their query.

Shop by an Activity, Event or Purpose. For example, “What do I need for cold weather running?” or “I want to start baking sourdough bread.” Rufus suggests shoppable product categories—such as thermal hats, gloves and running tights, or sourdough bread starter kits, flour and proofing boxes—as well as asking related questions to help customers narrow down their search.

Compare Product Categories. Now customers can easily compare different product types by asking questions like, “What’s the difference between bread flour and all-purpose flour?” or “What’s the difference between these two brands of carry-on luggage?” This helps customers choose products that best meet their needs for more confident buying decisions.

Get Tailored Recommendations. Customers seeking personalized suggestions can query, “What are good Valentine’s Day gifts?” or “What are the best educational toys for a five-year-old?” and Rufus delivers custom results in order to streamline and refine the shopping experience.

Inquire About Specific Products. While browsing a product’s details page, customers can use Rufus to ask detailed questions about the item, such as “Are these golf clubs good for beginners?” “Can I machine wash this sweater?” “How many hours will this cordless screwdriver last?” and Rufus provides answers that leverage product details, customer feedback and community Q&As.

As one of the first movers in retail AI, Walmart announced at CES last month its own proprietary search tool for Apple iOS that will allow customers to search for products by use cases, instead of by product or brand name.

This was further explained here on TechCrunch: “For example, you could ask Walmart to return search results for things needed for a “football watch party,” instead of specifically typing in searches for chips, wings, drinks or a 90-inch TV. These enhanced search results will span categories, rivaling Google’s SGE, which can recommend products and show various factors to consider, along with reviews, prices, images and more.”

An AI Moat that Contains Users

“If I can find all the answers I need within Amazon, there’s no reason for me to look elsewhere, especially when all my research and shopping can be done seamlessly in one place.”

These advancements signify not only a leap forward in search engine functionality, but also herald a future where AI becomes an integral part of our daily decision-making processes, reshaping our approach to information discovery and consumption. Reshaping a user experience that removes need to filter and select search criteria based on size, color, type, or purpose. Amazon’s Rufus and Walmart’s AI shopping assistant have essentially created a moat around their respective shopping apps, making it more challenging for users to turn to Google for initial answers. Conversely, as Rufus is rolled out to a broader audience, users could potentially bypass Google altogether—i.e., if I can find all the answers I need within Amazon, there’s no reason for me to look elsewhere, especially when all my research and shopping can be done seamlessly in one place.

I can also see other sectors, such as healthcare, legal, financial and specialized retailers, building out similar AI platforms that pull answers from the extensive content and data sets that are specific to their industry. Think about the complex questions we face every day when it comes to comparing healthcare plans or financial planning. Imagine being given a response that is simplified and clear when comparing two plans and the AI platform continues to ask additional questions to help tailor the answer. Or what if someone was looking to understand the tax implications of withdrawing from an IRA and what their other options could be to offset or avoid the penalty? If this does become the norm, whereby companies are creating their own version of Rufus, will it potentially eat into Google’s search business by bypassing it entirely?

With advancements in AI, like Google’s SGE, Walmart’s shopping assistant and Amazon’s Rufus, there has been a shift towards conversational interfaces requiring consumers to refine how they query. This means learning to ask more precise, full-sentence questions that mirror natural speech. As the technology evolves to understand and interpret detailed inquiries, it’s crucial for users to adapt their approach to ensure they receive the most relevant responses. This adaptation involves moving from short keyword searches to engaging in more detailed, question-based interactions—a change that promises to make these AI interactions more intuitive and effective.

Questions? Please email me here. As always, thank you for reading.

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