As the holiday shopping season approaches, technology companies are racing to introduce new artificial intelligence tools designed to enhance the online shopping experience. Amazon’s AI-powered Rufus shopping assistant now boasts new features aimed at making it a “faster, more useful, state-of-the-art shopping companion.” Meanwhile, Google’s agentic checkout feature promises to help users find the perfect item while staying within budget. On Monday, OpenAI unveiled a free ChatGPT tool that generates personalized gift-buying guides.
With US consumers projected to spend a record $253 billion online during the holidays, the demand for innovative shopping solutions has surged. A recent survey by Adobe Inc. revealed that more than one in three US consumers have utilized AI tools for online shopping, primarily for product research. Consulting firm McKinsey & Co. estimates that agentic commerce—automated agents assisting in purchases—could evolve into a $1 trillion industry in the US by 2030.
Despite this optimistic outlook, the agentic commerce landscape is still in a nascent stage, with companies grappling with technical challenges and the formation of partnerships. As part of a recent experiment, Bloomberg queried several AI bots, including Amazon’s Rufus, OpenAI’s ChatGPT, and Walmart Inc.’s Sparky, for gift suggestions for mothers. Recommendations ranged from a cozy bathrobe to a wooden photo frame, showcasing the bots’ varying capabilities.
Emily Pfeiffer, an analyst at Forrester Research Inc., noted, “There are a lot of really big bets being made right now that consumers want to shop differently and that chat is the way they want to start shopping.” However, she cautioned that significant changes to shopping habits may not materialize this holiday season. The appeal of AI-enhanced commerce is evident; navigating vast product selections across platforms such as Amazon and Walmart can be daunting. Chatbots can streamline this process, allowing users to ask for specific items and receive tailored recommendations.
Despite these advancements, the effectiveness of shopping bots remains limited. Amazon CEO Andy Jassy recently offered a mixed assessment of competitors’ technology, highlighting issues with personalizing shopping experiences and inaccuracies in pricing and delivery estimates. Established retail websites, designed for user navigation, have slowly integrated machine-readable interfaces but were not originally created for third-party purchasing.
Current shopping chatbots often resort to fetching product listings and directing users to retailer sites, a process not substantially different from traditional online searches. To resolve these limitations, bot developers are innovating. For instance, Anthropic PBC and Google have created protocols to enhance agent communication, while Microsoft has rolled out tools that assist retailers in optimizing their websites for bot interaction.
Data quality is crucial for the efficacy of AI shopping tools. Retailers have historically guarded customer information, which could enhance the shopping experience for bots. Amazon, which captures approximately 40 cents of every dollar spent online in the US, has not permitted autonomous shopping on its site and recently sued Perplexity, a startup attempting to facilitate purchases within its marketplace. This legal action may reflect concerns that permitting such features could undermine Amazon’s lucrative advertising business, projected to generate nearly $70 billion this year.
In contrast, Walmart has shown a greater willingness to collaborate with external companies. In October, the retailer announced that customers could make purchases directly on ChatGPT, although the feature is currently limited to single-item transactions. Partnerships with major retailers and payment processors will be essential for AI startups like OpenAI and Perplexity to gain traction in the e-commerce space. The goal is to allow users to browse and purchase directly within their platforms seamlessly.
OpenAI has also introduced a new shopping tool that asks clarifying questions to improve recommendations. Users can specify their needs and preferences, such as “find a small couch for a studio apartment,” and the tool will respond with a curated list of items. However, OpenAI has cautioned that users should verify product details on merchant sites, acknowledging that the tool may not always provide accurate information regarding prices and availability.
As AI shopping assistants evolve, they are becoming increasingly sophisticated. In Bloomberg’s gift-for-mom exercise, Rufus distinguished itself by asking detailed questions about a user’s preferences before providing suggestions. This approach highlights a potential direction for AI in shopping: deeper personalization. While challenges remain, the push towards more intuitive and effective AI-driven shopping experiences could reshape how consumers engage with e-commerce in the future.
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