Why AI Shopping is Stalling: An Interview with Rob Gonzalez
Rob Gonzalez, who co-founded Salsify and leads ecommerce technology development, possesses multiple years of experience studying how online retail operations use digital product information. Salsify, which introduced Product Experience Management (PXM) to the market in 2012, enables brands to keep their product information correct and discoverable while distributing it to their retail partners. Its clients include globally recognized names such as L’Oréal, Coca-Cola, and Nestlé.
In a recent conversation, Rob Gonzalez dissected the persistent shortcomings of AI shopping tools. He positioned the current state of AI shopping as a case where technological promise outpaces practical performance, largely due to fragmented product data and unrealistic expectations about AI’s role.
Why Product Data Matters More Than Ever
The shopping behaviors of consumers demonstrate a fundamental pattern that shows consumers prefer to make purchases across multiple shopping platforms, which include online marketplaces, physical stores, social media networks, and brand websites. The creation of artificial intelligence systems requires developers to build systems that users will adopt as their primary purchasing solution, but Gonzalez believes this expectation is too extreme.
He believes that AI will support existing shopping traditions which people use today but its application will differ according to product type such as electronics and groceries. He also highlights that AI, which completes purchases without user involvement, exists today as only a minor aspect of retail operations. The expectation that AI agents will dominate all commerce channels remains speculative and distant.
The New Data Demands of AI Shopping
Gonzalez states that traditional ecommerce platforms have relied on standardized product feeds and online storefronts, but AI systems that use the artificial intelligence (AI) systems require new data types that exceed the existing data requirements. The AI engines require contextual information, which includes the product’s intended use, its various applications, and the situations in which it should be used.
Brands need to enhance their data processing abilities through complete data development because it has become essential for them to achieve presence in AI-driven systems. Companies have to manage their existing e-commerce data systems while they begin developing contextual relevance attributes, which include vital information about product usage to help AI systems determine the appropriate time and manner of product application.
Preparing for Conversational Commerce
Gonzalez contends that brands need to change their data approaches because major technology companies now use conversational AI for product searching and discovery through their interfaces. The new adaptation process requires businesses to monitor how users mention products on social media platforms and third-party sites, such as Reddit, where consumers use informal language to explain product features and how they interact with products.
AI systems use unstructured context to analyze shopping behavior because this type of context is increasingly extracted from sources. He sees conversational AI as a new retail channel that needs businesses to provide comprehensive product details because it competes with established retail sales methods.
Visibility and Competitive Risk in the AI Era
The AI Era brings organizations two major challenges because they need to establish their online presence while facing threats from their competitors. The growth of e-commerce creates difficulty for brands because they must maintain their online visibility through both standard digital channels and AI-based platforms.
Companies that rely on artificial intelligence (AI) systems in their product discovery process will lose market presence to competitors who have developed effective data management strategies for both search engines and voice assistants. Rob Gonzalez believes that AI will not replace all current communication methods. AI will function as multiple navigational tools that work together to create a system that most users will navigate through different entry points.
