How agentic AI is changing the way people shop

Agentic AI is transforming the entire customer journey

Agentic AI is changing how people shop, and it’s happening fast. These autonomous systems act for the user, analyzing options, comparing products, and in some cases, completing transactions without direct input. They integrate memory, reasoning, and task execution to manage nearly every stage of the shopping process, from product discovery to delivery. For retailers, this shift introduces both opportunity and existential risk.

Right now, AI agents are doing much of the heavy lifting in the purchase process. They recommend products, check inventory, find deals, and select substitutions when items are out of stock. They do this at a speed, scale, and accuracy far beyond human capability. When this technology is deployed correctly, it can drastically improve the customer experience, remove friction from transactions, and raise overall satisfaction levels. Yet the challenge remains, many consumers still aren’t ready to fully hand over decision control. About half of shoppers say they are uncomfortable with AI completing an entire purchase autonomously. That hesitation is temporary. Trust builds with use, and the more effective these systems become, the faster that comfort gap will close.

For executives, agentic AI marks the beginning of a new paradigm. Consumer expectations will evolve from “assisted shopping” to “delegated shopping.” Success will depend on how well companies integrate AI-driven decision-making into both their customer experience and back-end operations. Waiting to adopt this technology means giving ground to competitors that move faster and offer AI-assisted personalization at scale.

According to Bain’s Consumer Lab Generative AI Survey, 30% of U.S. consumers already use generative AI for product comparison and recommendations. Considering how new these tools are to the retail market, that level of penetration signals rapid mainstream adoption. Leaders should assume that AI’s role in shaping shopping decisions will soon become standard.

Generative and agentic AI are redefining shopping discovery and conversion

Shopping behavior is already shifting toward AI-led discovery. Consumers now start their searches in places that didn’t exist in the retail funnel a few years ago. About 70% begin their shopping journeys on Amazon, 21% use its AI assistant Rufus, more than 50% rely on Google’s AI-enhanced search, and 8% start with ChatGPT or another generative AI chatbot. This redistribution of discovery power changes how traffic flows across the internet and forces retailers to rethink their visibility strategies.

AI at the discovery layer means fewer open browser tabs and more direct answers. When a customer types or speaks a query, the AI engine surfaces a filtered, personalized shortlist. This gives consumers less to sift through but gives brands fewer opportunities to be noticed. For retailers, this dynamic compresses exposure and limits traditional search optimization efforts. Paid search, a fundamental driver of online sales, becomes harder to track and optimize when AI is the mediator between query and product listing. Advertisers can no longer depend solely on cost-per-click performance data to gauge outcomes because buyers are no longer clicking, they’re being handed results.

Retailers need to learn how to make their products visible to AI itself. That means ensuring accurate product metadata, clean descriptions, meaningful reviews, and transparent pricing. AI engines prioritize clarity and completeness in data. Executives who understand that principle can position their brands at the top of AI-driven recommendation lists, while others may fade from view.

Leaders should also see this as a chance to redefine marketing efficiency. Rather than paying for placement in chaotic search environments, companies that strategically adapt to AI-driven discovery will enjoy cleaner, higher-conversion traffic streams. The challenge is technical, but the strategic imperative is simple: if your products aren’t optimized for how AI sees the market, then they’re effectively invisible in the new world of retail.

Distinct AI agent models are reshaping retailer–consumer relationships

Retail is moving into a world where three main types of AI agents define how customers interact with brands: third-party objective agents, on-site retailer agents, and off-site retailer agents. Each model serves a different purpose, but all have one thing in common, they’re changing who owns the customer relationship.

Third-party agents such as ChatGPT, Perplexity, and Gemini collect data from many sources, offering users unbiased comparisons and recommendations. These platforms are gaining traction fast. Similarweb reports that ChatGPT shopping referrals more than doubled in the U.S., France, the U.K., and Germany over the past year. In some cases, AI referrals already drive up to 25% of retailer traffic. This is still early, but the direction is clear: third-party agents are becoming a dominant source of discovery and decision-making. For retailers, this presents both opportunity and loss. They can gain reach at lower costs, but risk losing direct relationships with their customers.

On-site retailer agents are the countermeasure. These AI systems live within retailer ecosystems and use proprietary data to improve conversion and build loyalty. Amazon’s Rufus is one strong example, it helped generate roughly $12 billion in extra annualized sales, with monthly active users up by 115%. Rufus works because it learns from Amazon’s deep dataset: purchase histories, return reasons, customer reviews, and fulfillment performance. Similarly, Magalu’s “Lu” in Brazil runs within WhatsApp, handling product recommendations, payments, and delivery optimization. When done right, these systems strengthen control over the buying experience and the underlying data structure that powers personalization.

Off-site retailer agents take a more expansive approach. They go beyond a single company’s inventory, helping customers shop across platforms. Amazon’s “Buy for Me” capability is an example, it can purchase from other brands’ sites while still keeping users engaged within Amazon’s environment. It’s a smart move to maintain loyalty even when customers want variety.

For executives, the immediate concern is choosing where to compete and where to collaborate. Third-party agents expand access but erode brand differentiation. On-site agents protect brand equity but require significant investment and technical expertise. A clear strategy that identifies where proprietary data provides competitive advantage will be critical. Customer trust is still higher for retailer-run agents, currently three times higher than for third-party ones, but this advantage will not last. Companies that fail to act quickly may see their customer relationships mediated entirely by external AI platforms.

Multi-vendor agent-assembled baskets could disrupt traditional retail ecosystems

A major shift on the horizon is the rise of agent-assembled baskets, AI systems putting together purchases from multiple vendors in a single order. These multi-vendor shopping carts offer a clear value to consumers: greater convenience and cost efficiency. A customer can request an entire trip’s worth of items, flights, hotel, dining, and transportation, and an AI agent can build it in seconds. The same logic applies to retail: a user could ask for “a week of groceries” or “a new office setup,” and the AI will compile items from multiple stores to meet that demand.

This innovation challenges the traditional retailer-owned basket. When AI agents decide which items come from which store, retailers lose control of the full purchase. It breaks up what has always been their strongest asset, the ability to curate, bundle, and upsell within a controlled environment. As agents gain the ability to optimize across brands and vendors, margins compress. Price transparency increases competition, and retailers that don’t offer distinctive value will be forced into price wars.

Grocery, home goods, and general merchandise sectors face the highest risk here. These categories involve multi-item purchases where consumers prioritize convenience and price over loyalty to a single retailer. Executives in these industries must act decisively to defend their brand ecosystems. Exclusive products, membership advantages, and proprietary fulfillment capabilities will become essential to retaining customer loyalty in an agent-driven marketplace.

The opportunity also extends to partnership models. Retailers could work with major AI providers to define how their products are represented within multi-vendor baskets, ensuring accurate pricing, stock visibility, and consistent post-purchase service. This will require strong data governance, flexible APIs, and direct relationships with AI engines to secure favorable placement in recommendations.

Agent-assembled baskets will redefine who dictates how products reach customers. Retailers that depend solely on traditional sales channels risk losing influence over the customer journey. Those who develop strategies for agent integration, while safeguarding data and brand control, will maintain leverage in the next evolution of retail commerce.

Retailers must tailor AI strategies to customer intent and purchase complexity

Not all shopping missions are equal, and AI affects each one differently. Retailers need to shape their AI strategies around the type of purchase their customers are making, directed, exploratory, or considered. Directed missions are task-focused, where a customer already knows what they want. Exploratory missions are open-ended, where shoppers seek discovery and inspiration. Considered missions, such as home improvement projects or travel planning, require deeper guidance and a higher degree of trust before a consumer is ready to let AI handle the full transaction.

Agentic AI changes how these missions unfold. For directed shopping, AI primarily threatens multibrand retailers by making product location and price comparison instantaneous. For exploratory missions, both brands and retailers are exposed because AI recommendations simplify the decision-making process, reducing the number of brands a consumer encounters. And in considered purchases, AI holds the most potential value because it saves time and improves decision accuracy, but consumer trust remains a barrier.

Leading players are already responding. Home Depot built “Magic Apron,” an AI companion that leverages its proprietary data and expert knowledge to guide customers through complex projects. Walmart, Target, and Etsy have partnered with OpenAI to bring their products into ChatGPT’s commerce integrations, and John Lewis announced plans for products to appear on AI shopping apps. Google is also aligning with major retailers such as Shopify, Wayfair, and Walmart through the Universal Commerce Protocol, a new open-source standard for connecting AI-driven commerce platforms.

For executives, the message is clear, AI strategy must connect to customer intent. High-consideration products benefit from specialized, retailer-owned AI systems that reflect deep category expertise. General merchandise goods should prioritize compatibility with major AI platforms to stay visible in multi-agent ecosystems. Consumer trust will determine adoption speed, but the companies that train their AI on proprietary data and emphasize quality user experience will lead this shift.

Success depends on clarity of focus. Executives should map their category mix, identify the dominant shopping missions their customers pursue, and deploy AI accordingly. A one-size-fits-all approach doesn’t work in an environment where every purchase decision is mediated by intelligent systems. Tailored AI strategies built on proprietary knowledge and targeted collaborations will separate winners from laggards as commerce becomes increasingly autonomous.

Immediate action is needed to protect customer relationships and retail media revenue

AI-driven platforms are eroding the foundations of retail media and customer loyalty. Retailers can no longer depend on steady on-site traffic or traditional advertising models. The competition is shifting to platforms that own AI-driven recommendations and customer intent data. To stay relevant, retailers must secure direct engagement through exclusive experiences, data-driven advertising models, and redefined value for both customers and brands.

Winning retailers are creating stronger moats around their ecosystems. They’re offering exclusive products, early access programs, and enhanced loyalty benefits to keep their most valuable customers inside their digital environments. Best Buy, for example, continues to display its products on external AI platforms but reserves protection services like Geek Squad for its own site, drawing customers back for full-service interactions. This approach doesn’t block innovation; it re-establishes the retailer as the reliable endpoint in an AI-mediated market.

Retail media models also need a structural update. Bain research shows that 65% of advertising spending in the U.S. and Europe currently happens on retail-owned online properties, mostly through sponsored product listings and paid search. If AI agents start driving significant traffic elsewhere, that revenue collapses. Retailers need to anticipate this by experimenting with new ad placements designed for AI environments: sponsored agent recommendations, agent-driven product suggestions, and adaptive metadata marketing. Amazon is already serving sponsored ads in Rufus chats; Google is introducing paid placements within its AI Overviews; and OpenAI is testing embedded retail partnerships for product promotion. These early experiments point to where the market is heading.

Leaders must also protect data integrity. Control over first-party data is the single biggest advantage a retailer can retain. That control supports high-value advertising, precise pricing strategies, and intelligent inventory management. Executives should focus on watermarking, access governance, and clear data ownership principles when partnering with AI platforms. Protecting purchase signals and customer behavior data is essential to sustaining long-term competitiveness.

Action cannot wait. As consumers grow comfortable with AI-mediated discovery, traffic patterns will shift quickly. Retailers that fail to evolve their engagement and monetization models risk losing both market visibility and brand relevance. Those that adapt fast, through differentiation, controlled partnerships, and redesigned revenue structures, will maintain influence as AI rewires the retail economy.

Data ownership, fulfillment control, and checkout integrity are critical for sustaining competitive advantage

Owning the customer relationship means owning the data and the transaction. As AI systems increasingly handle shopping and payments, this ownership is at risk. Retailers must ensure that their data remains protected, their fulfillment processes remain efficient, and their checkout experience remains recognizable and controlled. Losing direct access to these areas means losing influence over the customer journey and future decision-making power.

AI agents will often want to handle the full purchase process, from recommendation to payment. This can make transactions more efficient but also risks converting retailers into background services. To prevent this, companies should enforce data-sharing boundaries, use watermarking to authenticate digital content, and maintain tiered access to their AI systems. Where third-party agents complete sales, strong partnership agreements are vital to preserve transaction visibility. Retailers must still capture insights from these purchases: what was bought, what was returned, and at what price. This data remains essential for adjusting pricing, forecasting inventory, and maintaining customer lifetime value models.

Leaders should also defend ownership of last-mile fulfillment. This stage is often the last physical interaction customers have with a brand. By maintaining control over delivery and service quality, retailers uphold trust and brand credibility. Even when AI mediates discovery and recommendation, customer satisfaction still depends on reliable logistics. That’s an area where automation should enhance human oversight.

Control of checkout is another point of leverage. If retail brands allow agents full autonomy at checkout, they lose branding opportunities and customer data collection at the moment of transaction. Maintaining checkout transparency, clear branding, visible policies, and accurate pricing, ensures retailers stay identifiable and valuable in an AI-driven marketplace. Data precision in this phase also safeguards promotions and loyalty programs, reducing the risk of misrepresentation by external AI agents.

For executives, the priority is straightforward: secure complete oversight of data use, uphold brand integrity during every transaction stage, and retain physical control of delivery chains. In a retail landscape where AI intermediates consumer intent, autonomy without data access is not an advantage, it’s a liability. Strong digital infrastructure and active partnerships will determine which retailers retain control of their ecosystem.

Winning in agentic retail depends on brand distinctiveness, agility, and proactive rule-setting

The companies that succeed in the era of agentic AI will be those that define their value clearly, for both humans and AI systems. The shift toward autonomous shopping places performance, reliability, and differentiation at the core of competitiveness. Retailers must build systems that make their brands stand out in an environment where AI filters, ranks, and recommends on behalf of the consumer.

Brand distinctiveness now depends on how clearly a company can communicate its strengths through data. AI agents prioritize transparency, quality, and relevance. Retailers should ensure that their product data, customer reviews, and post-purchase support signals reflect those qualities. By maintaining detailed, consistent, and verified information across all channels, companies can strengthen their position within AI recommendation algorithms while deepening consumer trust.

Agility will determine survival. As AI evolves, the standards governing data access, attribution, and optimization will shift. Leaders must design their operations to adjust to new protocols quickly, both technically and commercially. When major AI platforms introduce new standards for displaying results or processing transactions, the fastest adopters will gain a decisive advantage. Strategic adaptability should therefore be a core competency across data management, pricing, and content development.

Proactive rule-setting is also essential. Retailers that participate early in shaping AI commerce standards can secure influence over how future digital interactions occur. Partnerships with major AI platforms, such as those led by Google’s Universal Commerce Protocol initiative, show how early collaboration defines the rules that everyone else follows later. Executives who want long-term control must ensure their companies are not just reacting to these standards but actively helping to build them.

For leaders, this moment requires confidence and precision. The focus should be on reinforcing what makes the brand essential, unique products, reliable service, and trust built through transparency. Companies that define their purpose and influence now will not only survive the AI transition, they’ll shape its trajectory. Acting early, investing in agility, and clearly communicating value to both humans and machines will determine who leads when agentic AI becomes standard practice in commerce.

Final thoughts

Retail is entering a new era where intelligent systems no longer assist the customer, they represent them. Agentic AI is rewriting how consumers make decisions, where loyalty rests, and how value is created. It’s not a question of whether this shift will happen but how quickly it will reshape your operating reality.

For business leaders, the path forward is simple but demanding. Secure control over your data and fulfillment. Build AI systems that amplify your brand’s strengths. Form partnerships that expand your reach without giving away what makes you unique. The companies that treat AI as an ecosystem, will set the precedent for everyone else.

This is a moment for decisive leadership. Every retailer with access to meaningful data and a differentiated customer experience has a chance to lead. Move early, move intelligently, and treat AI as core infrastructure. The organizations that act now will define the next phase of retail, and their competitors will adapt to the world they build.