In the mid nineties, designers did not know what online shopping was supposed to look like. So they borrowed from the physical world. Early retail websites featured isometric shopping carts gliding down digital aisles. Shelves were rendered in crude 3D. You clicked arrows to “walk” through a store. After a while, search bars replaced aisles, and recommendation engines became the new merchandising layer. Eventually, mobile screens collapsed the store into a feed. We are now at another such inflection point. Retailers are redesigning the storefront again. But this time, the shopper may not even be human.
Over the past eighteen months, the largest U.S. retailers have begun quantifying the impact of AI-powered shopping assistants. Walmart’s Sparky, embedded directly into its mobile app, is one of the clearest early case studies. On its most recent earnings call, Walmart disclosed that customers who engage with Sparky generate average order values approximately 35 percent higher than non-users, and that roughly half of U.S. app users have tried the assistant.
Lowe’s has reported similar traction. Its Mylow assistant answers nearly one million customer questions per month, and the company has stated that customer engagement with Mylow more than doubles conversion rates. In-store, the associate-facing Mylow Companion tool has been linked to a 200 basis point increase in customer satisfaction scores. The lesson is straightforward. When AI is embedded directly in high-intent surfaces and connected to fulfillment and inventory systems, it can drive measurable commercial outcomes.
Amazon, meanwhile, has positioned its AI assistant Rufus as a generative shopping guide capable of answering product questions, comparing items, and supporting research. But the more interesting move may be Amazon’s “Buy for Me” feature, which allows customers to purchase select items from third-party brand sites without leaving the Amazon app. That blurs the line between retailer and intermediary. Amazon becomes not just a marketplace, but a purchasing agent. According to Amazon CEO, Andy Jassy, Customers who used Rufus were about 60% more likely to complete their purchase.
The stakes are high. Retail media, the practice of selling sponsored placements within retailer ecosystems, has grown into a global market projected to approach $200 billion by 2026–2027. For companies such as Amazon, advertising has become one of the highest-margin segments of the business. If human eyeballs are replaced by machine queries, sponsored placements stop influencing people and must start influencing algorithms. Retail media becomes less about persuasion and more about protocol.
At the same time, AI is reshaping retail far beyond the front end. Amazon has reported that AI-driven forecasting has delivered a 10 percent improvement in long-term national forecasts for deal events and a 20 percent improvement in regional forecasts for millions of popular items. Walgreens has disclosed that its micro-fulfillment centers now fill approximately 16 million prescriptions per month, with shipped volumes up 24 percent year over year and roughly 40 percent of total prescription volume at serviced stores handled through these facilities. Best Buy has reported that AI-driven call summarization has reduced average engagement time in customer service interactions by nearly 5 percent.
These are early but tangible indicators of what might be called digital labor: AI systems that do not merely answer questions but execute tasks, compress cycle times, and reallocate human effort. The front-end assistant and the back-end automation are converging. Yet the most destabilizing force may not be retailer-owned assistants at all. It may be the rise of personal autonomous agents and AI-native browsers.
In 2025, OpenAI introduced Operator, a browser-based agent designed to handle repetitive tasks such as filling out forms or ordering groceries by interacting directly with web interfaces. Perplexity launched Comet, an AI-native browser explicitly marketed around delegating tasks such as shopping and applying promo codes. OpenAI later introduced Instant Checkout and an Agentic Commerce Protocol, allowing merchants to integrate directly so that users can complete purchases within ChatGPT with tokenized payments and explicit confirmation flows.
OpenAI has stated that ChatGPT now serves more than 800 million weekly users. Even if only a fraction of those users begin experimenting with agentic shopping, the distribution leverage is extraordinary.
Unlike Sparky or Rufus, these agents do not belong to a retailer. They operate across the open web. They can log into accounts, compare products across sites, and execute multi-step workflows. Some rely on formal protocols and APIs. Others use UI automation to mimic human browsing behavior. That distinction is not merely technical. It is strategic. Retailer agents optimize inside a walled garden. Personal agents optimize across the entire digital landscape.
The tension has already surfaced publicly. In late 2025, Amazon threatened legal action against Perplexity over its agentic shopping tool, alleging covert access to customer accounts and disguised automation. This is the first visible skirmish in what may become a broader contest over who controls the decision interface in commerce.
From the consumer’s perspective, the promise is appealing. Instead of browsing, filtering, and comparing manually, the user delegates intent: restock the pantry under a certain budget, plan a themed event, optimize purchases for sustainability. The agent executes. From the retailer’s perspective, however, the shift is existential. If the customer’s AI shops on their behalf, the traditional surfaces for merchandising, branding, and advertising are reduced or eliminated.
Adoption signals suggest consumers are becoming more comfortable acting on AI guidance, even if full delegation remains nascent. Adobe’s holiday retail reporting indicates that nearly half of consumers expressed trust in AI-driven shopping experiences in 2025, and that a majority of users who encounter AI-generated links click through on them. Traffic from AI chatbots to e-commerce sites has surged year over year, albeit from a relatively small base. The shift from answering to doing is underway.
Still, the security and governance challenges are nontrivial. There is growing research to demonstrate that large language model–integrated systems are vulnerable to indirect prompt injection, in which malicious instructions embedded in web content are treated as commands by an agent. In early 2026, a prompt injection vulnerability in an open-source agent toolchain was exploited to distribute OpenClaw, highlighting how quickly such systems can become vectors for abuse. When agents can execute transactions, prompt injection is no longer a hallucination problem. It is a financial risk.
These dynamics point to three plausible five-year scenarios.
In the first, retailer-centric assistants dominate. Sparky, Rufus, Mylow, and similar tools remain embedded in retailer-owned apps and sites, driving higher basket sizes and deeper loyalty. Retail media adapts to conversational interfaces but remains within the retailer’s walled garden. The retailer controls the intelligence layer and the economic capture.
In the second, platform-centric assistants become the primary gateway. Consumers initiate shopping journeys within ChatGPT, Gemini, or AI-native browsers. Retailers integrate via standardized commerce protocols, supplying product data and fulfillment capacity while ceding control of the initial interaction. Retail media migrates into new forms of sponsored recommendations within AI environments. The intelligence layer shifts upward.
In the third, personal agents gain traction. Consumers rely on persistent AI systems that maintain context across retailers and sessions. The website becomes less a destination and more an endpoint, a structured data feed and fulfillment engine optimized for machine legibility. Retailers compete on API quality, delivery speed, transparent pricing, and reliability rather than on visual merchandising.
Physical infrastructure remains decisive across all three scenarios. AI can mediate choice, but it cannot yet deliver a package. Forecasting improvements, warehouse automation investments and the fine-tuning of proprietary retail AI models are likely to remain the backbone of any agentic promise. If agents make purchasing instantaneous, fulfillment performance becomes even more visible.
The deeper shift is not from websites to chat interfaces. It is from human-driven browsing to delegated decision-making. The shopping journey is collapsing. What once required browsing, comparison, and deliberation is being compressed into a single delegated instruction. As AI assistants evolve from answering to acting, the central strategic question for retailers is no longer how to build a better interface. It is who sits between the customer and the transaction.
The next storefront may not be a store at all. It may be a negotiation between your AI and someone else’s.