Institute of Internet Economics

E-commerce is undergoing one of its most profound transformations since the invention of online checkout. For years, the industry has leveraged artificial intelligence primarily as an assistive tool—chatbots to answer customer queries, recommendation engines to suggest related products, or personalized email campaigns. These systems supported human decision-making but rarely acted independently. Today, however, a new paradigm is emerging: agentic AI shopping systems capable of browsing stores, selecting items, and completing purchases on behalf of users without direct intervention. This shift marks a departure from assistive AI toward autonomous digital agents that blur the line between consumer choice and machine decision-making.

Salesforce, among other industry observers, notes that the emergence of AI shopping agents represents a turning point for retail. Unlike traditional algorithms, these agents are not limited to passive suggestions. Instead, they actively conduct transactions, integrating with payment systems and checkout flows. A striking example comes from OpenAI’s “Agent,” which can now handle end-to-end shopping tasks. From comparing product specifications across multiple merchants to finalizing checkout, the system allows purchases to occur without the consumer ever visiting a retailer’s website. For e-commerce giants and small merchants alike, this promises efficiency but also disruption, as the locus of control moves from brand-driven experiences to AI intermediaries.

The concept of agentic shopping is not entirely new. Early attempts appeared in the 2000s with price comparison engines and automated coupon finders. Yet those tools operated within narrow boundaries: they aggregated information, but they did not transact autonomously. The difference today is both technical and cultural. On the technical side, advances in large language models, reinforcement learning, and API integration allow agents to navigate websites, interact with dynamic content, and process secure payments. On the cultural side, consumers are increasingly comfortable delegating digital tasks, from scheduling meetings to managing investments. Shopping, long considered a personal experience, is now being reframed as a domain where trust in automation is growing.

Case studies highlight this transition. In 2024, a group of U.S. retailers partnered with Salesforce to pilot AI-driven shopping assistants for loyalty program members. These agents were instructed to monitor product availability, compare prices across partner stores, and automatically purchase replenishable goods such as household supplies when stock levels ran low. The results were dramatic: participating consumers reported a 30 percent reduction in time spent on routine shopping and a 15 percent increase in satisfaction due to the system’s ability to secure discounts and ensure availability. For retailers, the agents increased repeat purchases, but they also raised new challenges in terms of customer engagement. If the AI agent is doing the shopping, how does a brand maintain a relationship with the consumer?

The OpenAI “Agent” goes a step further by integrating directly with checkout flows. This development was reported by the Financial Times as a significant shift in e-commerce architecture. Merchants who previously relied on carefully curated websites and brand storytelling may find their influence diminished. Instead, the agent selects products according to user-defined preferences or learned behaviors, often reducing the role of marketing in final purchase decisions. For example, a consumer who sets parameters for “sustainable, budget-friendly athletic shoes” might receive a purchase confirmation for a pair of sneakers without ever browsing options or seeing promotional campaigns. This places enormous importance on how AI systems evaluate merchants, prioritize data, and define “value” in a marketplace.

The potential benefits of agentic shopping are undeniable. Time-strapped consumers can offload routine tasks, ensuring essentials are delivered without constant monitoring. For people managing complex needs—such as families with dietary restrictions—agents can enforce rules consistently, avoiding accidental errors. In B2B contexts, companies may delegate procurement of standard office supplies or raw materials to AI systems, freeing managers for strategic tasks. The efficiency gains mirror earlier shifts in supply chain automation, but now extend to the very last step of commerce: the purchase itself.

Consumer Perspectives: Trust Vs Benefits Of AI Shopping Agents

Yet with these benefits come significant challenges. One of the most pressing is consumer trust. If purchases are made without user review of individual transactions, transparency becomes essential. How does an AI explain why it selected one merchant over another? Bias in recommendation data could unintentionally favor certain brands, raising regulatory questions about competition and fairness. Furthermore, issues of liability remain unresolved. If an agent mistakenly orders an incorrect product, who is responsible—the user, the merchant, or the AI provider? Legal scholars are already debating whether consumer protection laws are sufficient to address autonomous digital transactions.

Another challenge is the disruption of brand-consumer relationships. Traditional e-commerce relies heavily on storytelling, visual design, and emotional appeal to differentiate products. If consumers never see a retailer’s website, those investments may lose value. Companies may instead be forced to compete on back-end integration—ensuring their catalogs, inventories, and pricing are optimized for AI agents to interpret. This dynamic could advantage larger platforms with strong technical infrastructure, while smaller merchants may struggle for visibility in an AI-mediated market.

Historical parallels can be found in the rise of search engines and online marketplaces. In the late 1990s, brands controlled access to their websites, but the emergence of Google shifted discovery to a centralized algorithm. Similarly, Amazon’s marketplace reduced brand visibility in favor of product-level comparisons. AI agents may represent the next evolution: the transfer of decision-making power from consumers and platforms to autonomous intermediaries. Just as SEO reshaped marketing budgets, “agent optimization” may become the next frontier for merchants.

Case examples from Asia illustrate the scale of this transition. In China, Alibaba and JD.com are experimenting with AI-driven procurement systems for corporate clients. These agents automatically negotiate with suppliers, ensuring competitive pricing and timely delivery. For consumers, AI shopping assistants embedded in platforms like WeChat are already influencing retail. By 2025, analysts predict that more than 10 percent of online purchases in China will involve some form of agentic decision-making. The cultural normalization of automation in Asian markets suggests that Western adoption may accelerate as consumers observe its convenience.

The economic implications are profound. If AI agents become mainstream, they could shift market power away from advertising-heavy models toward logistics, fulfillment, and data integration. Retailers may invest less in traditional marketing campaigns and more in ensuring they meet AI-driven criteria such as sustainability, price competitiveness, and delivery speed. For consumers, this could lead to lower prices and more consistent quality, but at the cost of reduced exposure to variety and discovery. The serendipity of browsing may give way to the efficiency of automation.

Policymakers will inevitably play a role. As agents handle financial transactions, regulators will need to clarify liability, data privacy, and consumer rights. The European Union’s Digital Services Act already addresses aspects of algorithmic transparency, but may require amendments to encompass fully autonomous commerce. In the U.S., the Federal Trade Commission has signaled interest in ensuring that AI-driven commerce does not harm competition. These frameworks will determine whether agentic shopping evolves smoothly or becomes mired in legal disputes.

What is clear is that the rise of AI shopping agents is not a passing trend. It represents a structural shift in how e-commerce operates, comparable to the arrival of mobile shopping apps or the launch of online marketplaces. For businesses, the imperative is to adapt to a world where consumer decisions may be made by algorithms. For consumers, the challenge is to balance convenience with oversight, ensuring they remain in control of their purchasing power even as they delegate tasks to machines.

Key Takeaways

E-commerce is transitioning from assistive AI to agentic AI, where autonomous systems browse, select, and purchase items for users.
Case studies such as Salesforce pilots and OpenAI’s “Agent” show agents integrating directly with checkout flows, bypassing merchant websites.
Benefits include efficiency, error reduction, and automated procurement, but risks involve trust, transparency, and liability.
Brand-consumer relationships may weaken as AI agents prioritize technical criteria over marketing.
Policymakers are beginning to address regulatory gaps around fairness, competition, and consumer protection.