Bain & Company Forecasts "Up to $500 Billion by 2030" for Agentic Commerce—Retailers Must Now Serve Both Human and AI Agent Customers

Bain Publishes Comprehensive Agentic Commerce Report with 2030 Market Projections

Agentic AI in Retail: How Autonomous Shopping Is Redefining the Customer Journey

As agentic AI transforms retail economics, industry leaders will compete for shoppers and agents alike.

Global consulting firm Bain & Company has published a comprehensive report analyzing the impact of agentic AI on the retail industry. The report forecasts the US market for agentic commerce—where AI agents autonomously handle everything from product discovery to purchase—at $300-500 billion by 2030, representing 15-25% of total e-commerce sales.

Already, 30-45% of US consumers use generative AI for product research and comparison. According to Salesforce data, AI agents influenced $3 billion in sales during Black Friday 2025. However, Bain also notes that approximately 50% of consumers still feel reluctant to delegate end-to-end transactions to AI.

Industry Trends

Market forecasts for agentic commerce have been released by multiple major research firms in rapid succession. Morgan Stanley projects that 10-20% of US e-commerce sales ($190-385 billion) will be agent-driven purchases by 2030. McKinsey estimates up to $1 trillion in the US alone, while BCG puts the affected spending at $1.3 trillion. Bain's forecast sits in the middle as a conservative estimate.

Currently, AI traffic has grown to account for up to 25% of referrals at some retail sites, though it remains below 1% of overall traffic. However, the growth rate is remarkable—according to Similarweb data, shopping referrals from ChatGPT have increased more than 7x year-over-year. The industry is in its early stages but entering a rapid expansion phase.

Bain's Three AI Agent Typologies and Major Player Strategies

A key contribution of Bain's report is its framework categorizing retail AI agents into three typologies. This classification serves as a useful framework for EC operators deciding where to invest.

Type 1: Third-party "objective" agents. General-purpose AI platforms like Perplexity, ChatGPT, and Gemini aggregate product information from multiple retail sites to compare and recommend. OpenAI has partnered with Walmart, Etsy, and Shopify to launch "Instant Checkout" within ChatGPT, enabling users to complete purchases directly from conversations.

Type 2: Retailer on-site agents. These include Amazon's Rufus, Brazilian giant Magalu's WhatsApp-based "Lu," and Home Depot's "Magic Apron"—AI agents that serve customers within retailers' own sites. According to Bain, consumers trust retailer-provided agents 3x more than third-party agents. Amazon expects Rufus to generate $10 billion in additional annual sales.

Type 3: Retailer off-site agents. Amazon's Buy for Me is the prime example. This feature enables purchases from competitor sites within Amazon's own app, powered by Amazon's Nova and Anthropic's Claude. By end of 2025, over 500,000 products were covered, though pushback from retailers who didn't opt in has also emerged.

A research team from Columbia University and others published a paper highlighting that AI agents cause "choice homogenization" by concentrating demand on specific products, and that model updates can cause significant market share volatility. Furthermore, agents tend to penalize sponsored tags while favoring platform recommendations, exposing contradictions with current advertising models.

Structural Transformation of Retail Media and Bain's Strategic Recommendations

Bain raises particular alarm about the impact on retail media—the advertising businesses operated by retailers. In the US and Europe, 65% of retail media spending is concentrated on on-site formats (search-linked ads on retail sites). In a world where AI agents handle product selection, consumers see search results less frequently, potentially overturning this model entirely.

In the report, Bain presents three strategic actions for retailers.

Strengthen direct customer relationships. Provide value-added services such as exclusive products, loyalty programs, and installation/protection services to give consumers reasons to visit directly rather than through AI agents. Best Buy's strategy of offering "Geek Squad" exclusively through its own channels exemplifies this approach.

Redefine retail media. A transition is needed from traditional sponsored search to new ad formats designed for AI agents, such as "sponsored agent recommendations" and "attribute-premium API pricing." How brands secure visibility when agents "discover" products is the next competitive frontier.

Maintain control over data and logistics. Recommended defensive measures include watermarking product data, implementing graduated access controls for critical systems, and retaining control over last-mile logistics and checkout processes.

Impact and Implications for EC Operators

The most important structural change highlighted in Bain's report is the shift in consumer loyalty from "loyalty to brands and retailers" to "loyalty to outcomes." AI agents transparently compare price, quality, and convenience, meaning undifferentiated products get drawn into price competition.

Prioritize your response by category. Bain's analysis indicates that "spec-driven" categories such as everyday goods and standardized products will be the first to shift to agentic commerce. Discretionary purchase categories like apparel and travel will shift gradually as consumer trust grows and AI's ability to understand preferences improves.

Optimize product data delivery to third-party AI platforms. According to HBR's analysis, there are four approaches to AI agents: "fully closed," "passive open," "partial partnership," and "full A2A integration." You need to decide early which strategy to adopt based on your scale and product characteristics.

Begin investing in SEO for AI agents (AEO). As Columbia University's research demonstrates, strategic adjustments to product descriptions alone can significantly influence AI agent selections. Beyond traditional search engine optimization, developing structured data that enables AI agents to accurately understand and recommend products becomes a new competitive advantage.

Summary

Bain & Company's comprehensive report demonstrates with data and concrete corporate case studies that agentic commerce is not merely a buzzword but a structural turning point for retail heading toward 2030. The fact that major institutions including Morgan Stanley, McKinsey, and BCG all project enormous market sizes suggests the irreversibility of this trend.

Particularly noteworthy is Bain's strong advocacy for redefining retail media. In a world where AI agents handle product selection, traditional sponsored search may cease to function, making the reconstruction of advertising revenue models an urgent priority for EC operators. Over the next 12 months, three areas will become strategic priorities for EC operators: deciding whether to build proprietary agents, establishing data-sharing policies with third-party platforms, and optimizing product information for AI agents.