PwC on the Rise of Shopping Agents: 54% of Millennials Expect AI Product Comparison, Why Retailers Must Act Now

PwC Analyzes the Current State of Shopping Agents in Exclusive Interview

EXCLUSIVE Q&A: PwC explains the rise of shopping agents | Chain Store Age

Shopping agents are rapidly growing in usage among consumers, and retailers need to adapt.

In March 2026, U.S. retail media outlet Chain Store Age published an exclusive interview with Eric Shea, Principal of Commercial & Service Excellence at PwC US. The topic was "the rise of shopping agents." The interview presents specific insights, backed by PwC's research data, on how AI agents are transforming consumers' product discovery and purchasing behavior, and how retailers should adapt.

Background and Industry Trends

Shopping agents are software that uses generative AI to search, compare, and purchase products on behalf of consumers. Unlike traditional chatbots, they can act autonomously based on users' goals and preferences.

This space has undergone dramatic changes since late 2025. According to Adobe's analysis, generative AI tool referral traffic to retail sites surged 693% year-over-year during the 2025 holiday season, reaching 760% growth mid-season (November 1 to December 1). PwC's own analysis further reported a "1,200% surge" in GenAI referral traffic within just six months.

McKinsey estimates that agentic commerce could influence $3 to $5 trillion in global retail spending by 2030. This is no longer a future scenario but a platform shift happening right now.

Changing Consumer Behavior: AI Shopping Adoption by Generation

The most noteworthy aspect of PwC's interview is the generational consumer behavior data.

According to PwC's "Future of Consumer" survey, 54% of millennials expect to use AI for product search and comparison, and 46% expect to complete purchases through AI. Gen Z follows closely behind, and Gen Alpha is growing up as "AI natives." A separate PwC survey found that over 50% of high-income millennials are already using or planning to use AI shopping.

However, the research also reveals varying levels of acceptance depending on the task. PwC's "Customer Experience" survey shows that about half of consumers are open to AI-powered order tracking and delivery confirmation, while only 29% feel comfortable with AI-powered payments. This "convenience-trust gap" is the biggest challenge in shopping agent adoption.

Forrester's research also reports that only about one-third of respondents agree to make payments via answer engines, and 54% feel uneasy about sharing personal information with generative AI tools. The finding that 88% of consumers demand "clear source attribution" and 87% want "verified reviews" provides concrete guidance for building trust.

Structural Changes in Product Discovery and Brand Response Strategies

Shea emphasizes that "product discovery is being reconstructed." When consumers ask an AI agent, "Where can I get the best vintage white T-shirt under $30?", options are instantly presented based on social content and reviews. This is AI's solution to "choice overload" and "decision fatigue," suggesting that purchase decisions may be completed before consumers even reach a brand's website.

PwC outlines three pillars for brands to address. The first is "discovery optimization": making content machine-readable and practicing "Generative Engine Optimization (GEO)" through structured data and clean tagging. The second is "transaction readiness": building flexible architecture that supports AI-powered in-app purchases and API-based checkout. The third is "operational restructuring": integrating AI into merchandising and supply chains to enable collaboration with AI agents.

Shea explicitly states that brands should "build an integrated commerce engine, not a standalone chat tool." This means connecting product data, inventory, order management, payment, and marketing systems to deliver accurate recommendations and real-time fulfillment visibility.

Impact and Strategies for E-commerce Merchants

Shea's warning that retailers "should not wait" is clear. PwC's AI agent survey found that approximately 90% of senior executives plan to increase AI-related budgets, and 59% believe AI agents will become entities that acquire end consumers within five years.

Here are the actions e-commerce merchants should take immediately.

First, "agent-ready product data preparation." Implementing structured data (Schema.org, etc.) that AI agents can read is no longer optional but essential. Google's Universal Commerce Protocol (UCP) has attracted major players including Shopify, Etsy, Wayfair, Target, and Walmart, making compliance with this standard an urgent priority.

Second, "built-in trust design." Shea states that "trust is foundational" and that governance, transparency, and clear consent should be embedded from the very beginning of the experience. PwC's research also found that "64% of consumers want guardrails for AI transactions."

Third, "cross-category data strategy." Over the next 6 to 12 months, agents will begin connecting signals across categories such as food, beauty, wellness, and home goods. As the shift from category-specific missions to "outcome-driven ecosystems" progresses, product data must be designed within the context of "life scenarios."

Conclusion

The future Shea describes is the fourth platform shift in retail: from physical stores to e-commerce, from e-commerce to mobile, and from mobile to AI agents. His observation that "the fastest-growing customers may not be humans but agents" challenges e-commerce operators to rethink their fundamental assumptions.

The winners will not be brands that were fastest on every platform, but those that "built foundational capabilities early." Responding to agentic commerce should now be positioned as a strategic investment, not an experiment. Key developments to watch include the expected mainstream adoption of UCP in late 2026 and the progress of agent authentication infrastructure by payment networks.