A Revolution Unfolding: AI Reshaping Consumer Shopping Habits

Signals on Direction of Travel from Recent Announcements and Implications for Consumer Behavior While buzz around AI has remained at a fever pitch since OpenAI’s November 2022 launch of ChatGPT, only recently has a clear picture of this transformative technology’s impact on commerce started to emerge. It is not a stretch to say that the foundation underpinning decades of consumer shopping behavior has forever shifted . And as would be expected of any emerging technology like this, capabilities continue to mature at a rapid pace and key ecosystem players — ranging from model and chatbot providers to leading retailers — have rolled out a dizzying array of AI-enabled shopping offerings. In our conversations with executives at leading retailers and consumer brands around the world, we consistently hear some variant of the same question: “What does this mean for my business and where should I be investing to enhance my data infrastructure, tech stack and marketing efforts?” This post — covering recent developments and the likely impact on consumer behavior — is the first in a series of three designed to answer that question. Subsequent posts will analyze the implications for retailers and consumer brands with a focus on providing actionable playbooks and an enablement framework that position organizations to thrive — not just survive — in this era of AI-fueled commerce. The Noise… In September, Google effectively fired the starting gun - albeit one unnoticed by the general public and many organizations — with the launch of Agent Payments Protocol . Dubbed AP2 and announced alongside more than 60 partner organizations including American Express and Mastercard, it is an open protocol designed to standardize how AI agents securely execute financial transactions on behalf of users. Architected with more complex, multi-agent transactions in mind, it is a useful peek into the agentic ecosystem Google expects to emerge over time. Less than two weeks later, OpenAI truly put AI-enabled commerce on the map with the launch of Instant Checkout . While many have long forecasted such a development, which enables consumers to buy products directly within the ChatGPT interface, the ‘weight’ of this release caught the market by surprise. What made this more than a standard press release was a combination of notable launch partners like Stripe and Shopify, an aggressive roadmap with plans for multi-item carts and expansion to new merchants and regions, and immediate GA availability for consumers to buy products from Etsy sellers. It was a message to the market that commerce would become a central feature within AI interfaces. Equally important, perhaps, was the decision to open-source the technology behind Instant Checkout at launch. Co-developed with Stripe, Agentic Commerce Protocol (ACP) was positioned as “an open standard for AI commerce that lets AI agents, people, and businesses work together to complete purchases.” Just as Instant Checkout itself was an early proof of concept for AI-enabled commerce, ACP — along with more recent feature launches like ‘Available from the Web’ within Amazon’s Rufus chatbot — makes it clear that interoperability and cross-ecosystem connective tissue will be the rule and not the exception as this form of commerce gains traction. While these announcements garnered the bulk of the early attention, there were a few developments months earlier that gave us a preview of the changes to come, notably: (i) OpenAI launched Operator Agent , a research preview of an agent that could interface with the web just as a human user would; (ii) Google released AI Mode , a rethink of the search-based shopping journey that blends Gemini’s conversational capabilities with the Google Shopping Graph and suggests relevant products, supports price tracking, and offers a seamless option to complete a purchase right from the chat interface; and (iii) Microsoft announced the Copilot Merchant Program which helps merchants integrate their shopping experience within the Copilot app and systematically share product details with Microsoft. In the months since, we have seen momentum build across five critical areas… Broadening out of retailer participation with existing offerings (e.g., transactions with Walmart and Sam’s Club supported within the ChatGPT interface using Instant Checkout). Entrance of other AI companies into the commerce space (e.g., Buy with Pro from Perplexity). New payment partnerships (e.g., PayPal integration with ChatGPT). More robust shopping features (including more insightful responses to shopping-related queries that resemble a personalized ‘buyer’s guide’). Communication protocols that will enable an efficient scaling of ecosystem partnerships (e.g., Perplexity Merchant Program which simplifies the process of sharing product specs with Perplexity). …Obscuring the Signal Taken together, these developments can represent one of two things for retail and consumer goods leaders: either a confusing cacophony of seemingly-unrelated noise or a mosaic revealing a picture of the commerce model of the future. In an effort to tip the scales in favor of the latter, we have identified four foundational trends that are likely to endure as this space continues to mature: The most critical unknown at the moment relates to a dynamic directly interwoven with each of these trends. In the next section we provide a point of view on this fundamental question: To what extent will AI change the nature of consumer shopping behavior and what specific shifts will we see in the path to purchase? Old Habits Die (Somewhat) Hard So in a world where the path to purchase is increasingly influenced by AI assistants and agentic commerce offerings, how does consumer behavior change? First it is important to identify the types of shopping missions that will have a higher propensity to be influenced by agentic or AI-enabled shopping tools. We can easily see a world emerge in which the role of AI in commerce and its impact on channel realignment neatly aligns with the type of shopping mission . Higher consideration purchases — think new winter parka or luggage for an upcoming trip to Italy — will be ripe for disruption with AI's deep research capabilities offering a step-change improvement in the efficiency and effectiveness of brand, product and retailer selection . The transaction itself will initially still take place away from AI on an ecommerce site offering sharp pricing and convenience or in a physical store where consumers can touch a product and satisfy a need for immediacy. Over time, however, transactions will begin to shift to the chatbot interface itself with a human shopper remaining 'in the loop' and clicking the 'Buy Now' button to make the purchase. Lower consideration purchases that are more sporadic (i.e., not consistently recurring) will remain, for the time being, largely untouched by AI. Most consumers will not see a need to consult AI on the purchase and will continue defaulting to their established brand, product and retailer preference. As an example, my family splurges every few weeks on special treats for our kids' school lunches. These are low-stakes and somewhat spur-of-the-moment decisions that are in part driven by our kids. The transaction happens in a physical store and the product choice is at the whim of their young minds. A structured, data-driven research journey ending in an agentic transaction has no role to play in these missions. On the other hand, recurring purchases of commoditized items will shift to automated and agentic offerings . This can be viewed as the logical evolution of existing in-market options like Amazon's Subscribe & Save. Unlike these types of solutions - which require a consumer to navigate to an individual product detail page before clicking 'Subscribe' - agentic commerce will enable consumers to simply define a need (e.g., "Take care of my weekly grocery shop to stock up on the 15 items I buy every Saturday") and provide guardrails (details on brand and product preference along with pricing limits in the previous example) after which the tool will proactively execute the shopping mission. Interestingly, as shopping journeys increasingly center around natural language conversations with a chatbot, the starting point will often be a specific need or want ( “I’m looking to buy all the things I need to run my first marathon” ) rather than a journey to buy a specific brand that has established salience (e.g., “I’m going to buy a pair of shoes from Brand ABC” ) or a proactive plan to visit a favored retailer ( “Let me check out the options at Retailer XYZ” ). With that said, as options are surfaced in a chat interface, brand does still matter . All else equal, consumers will still opt the majority of the time for a product brand or retailer where there is an established affinity underpinned by mental availability, distinctiveness and relevance. However, the wild card is how the chatbot positions each brand, product and retailer against one another. This directly gets to the changing nature of physical availability and consumer preference . In the past there was much more of an information asymmetry where shoppers operated with only minimal information on product quality and often defaulted to using ‘brand’ as a proxy for expected satisfaction with a product or retail experience. AI will fundamentally reshape that dynamic with consumer purchases increasingly driven by a nuanced analysis of extensive metadata and deep research of reviews and consumer sentiment. Put another way, gut feel decisioning will fade as dispassionate, data-driven analysis by AI gains prominence . On a related note, a key currency for both brands and retailers has long been ease of transaction (aka physical availability), namely door coverage and shelf space for brands, store location for brick and mortar retailers, and a smooth CX for ecommerce players. Yet an advanced shopping agent is uninterested in an elegant checkout flow on an ecommerce site and will never be swayed by visual merchandising on a store endcap. As we explore in subsequent articles, the implications for brands and retailers are profound.  In an AI-centric commerce landscape, how will consumers behave if they are predisposed to buy Brand A but the chatbot guides toward Brand B due to some combination of factors like online reviews and perceived functionality differences based on product metadata? And what if in a shopping-related chat with an AI assistant, an unknown merchant is priced slightly below a known retailer where a consumer already has a relationship? Finally, how will consumers, retailers and agents balance the differences between ‘human-in-the-loop’ transactions (user clicks ‘Buy’ in the chat interface or approves the transaction in another way) and ‘human not present’ transactions (user gives authorization and guardrails to an agent to make a future purchase)?  It is fair to speculate that over the longer term — measured in years, not months — we will see a truly fundamental reshaping of consumer behavior as AI dominates the full path to purchase . Discovery and research will almost entirely be enabled by AI with shoppers selectively engaging to ask follow-on questions, provide feedback and steer the human-AI collaboration toward the right product and retailer selection. The transaction itself will ultimately require only minimal human involvement with coordinated agentic interactions across shopper, brand, retailer and financial services provider.  Furthermore, entirely new shopping patterns will emerge from these capabilities, including: OOS monitoring  — Instead of consumers continually checking back on availability of an out-of-stock item, an agent will be given a set of guidelines ( “Buy a size small in light blue when it comes back in stock over the next three weeks” ) and authorization to transact when those conditions are met. Price checking  — Solving a common consumer frustration, agents will be able to monitor price fluctuations across retailers and transact only when the price drops below a pre-approved ceiling. ‘Can’t forget’ moments  — Agents will know key holidays and special dates for loved ones (like birthdays and anniversaries) and can use that information to suggest gifts proactively based on something like a holiday wish list built in advance. They will even be able to make purchases without a human in the loop ( “Every year I want to send a $75 gift certificate to my mother for her favorite restaurant in Sarasota” ). Queuing  — Agents will help consumers avoid the need to wait in a virtual queue or watch the clock for items (e.g., new shoe drop) that go on sale at a specific time. Recurring purchases  — Agents will reduce consumer time spent buying the same things at standard intervals. As an example, shoppers could provide standing instructions to automate weekly shopping grocery stock-up trips. It is worth mentioning, though, that consumer frustrations remain with mature offerings in the market like Amazon Subscribe & Save. ‘Super’ agents  — Retailer agents will increasingly coordinate with other agents and even complete a purchase with a competing retailer rather than lose the transaction. We already see this in the market with Amazon’s ‘Buy for Me’ feature in Rufus where users can buy a similar product from other retailers (‘Available from the Web’). With an appropriately heavy dose of pragmatism we can recognize that many of the most fantastical predictions for the role of AI in commerce will not come to pass for many years. Consumer behaviors are deeply ingrained and intensely sticky. Expectations for a path to purchase that is unrecognizable to today’s consumers is years off .  With that said, the standard shopping journey is rife with friction and inefficiencies. Where AI is able to uniquely solve those pain points, momentum will ramp more quickly than many pessimists assume.  Take the example of a consumer looking to buy a thoughtful gift for a spouse. Today she is forced to click on a dozen blue links, read multiple buying guides, visit several review sites, and price compare across a multitude of online offerings. The diligent consumer then makes a trip to one or more physical stores to see the product and check availability. Only after all of that work does she feel prepared to make a purchase. This example brings into sharp focus the near-term value of AI. Research and discovery will continuously shift from search engines to AI tools as the technology is both more efficient and more effective than how humans approach those tasks today.  In all likelihood, though, the transaction itself will take longer to be as intensely disrupted. Over the course of the next 12-18 months we will see a steady if unspectacular rise in the frequency of consumers transacting within AI chatbots. This will consist almost entirely of human-in-the-loop transactions where AI surfaces a ‘buy now’ option and the shopper makes the final decision. Uptake of this commerce offering will grow as options increase and the CX improves, including support for multi-item carts, a smoother checkout flow and integration with retailer loyalty offerings. That ‘push’ momentum from retailers and tech companies is unlikely to abate given the heavy investment in infrastructure and strategic importance of not ceding this competitively critical moment to other companies. The outlook is much muddier, however, for true agentic commerce where significant hurdles remain . Put simply, we as consumers are control freaks with a profound desire to retain a tight grip on our shopping decisions. While many consumers regularly engage in an ‘agentic-lite’ form of commerce through the use of outsourced online shoppers, it can’t be overlooked that ceding control over even the simple, low-stakes task of selecting the right avocado causes intense agita. Handing the reins to an agent with carte blanche to execute a shopping mission end-to-end, then, is unlikely to magically solve the consumer hesitations that have plagued grocery delivery for over a decade.  Projected Evolution of AI’s Role in Commerce But momentum is in fact building , behaviors are changing, and the outlook is bright. Roughly 42% of consumers have used AI tools for their holiday shopping this year and traffic from AI sources to retail sites increased 758% y/y in November 2025 (albeit from a low base). The optimistic case for ongoing acceleration centers around the fact that, put bluntly, the existing path-to-purchase remains inefficient.  At the end of the day, despite what they may say, consumers don’t want an overwhelming array of choices and ‘freedom’ to navigate the path to purchase how they see fit. They simply want the right item in their hands as soon as possible at a fair price. AI tools are purpose-built to reduce this friction, cut out entire sections of the buying journey and bring joy and satisfaction back to shopping. ------------------------------------------ As we will explore in the next article in this series, the implications for retailers are strategically critical, operationally tricky, and technically complex. To help navigate these challenges and opportunities we will share a 5-part playbook for succeeding in the era of AI commerce. Also be sure to sign up for our 2026 Marketing Predictions Webinar to learn how to set your marketing apart and how you should adapt as customer behavior evolves in the wake of AI reshaping the marketing landscape. ]]>

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- [Snowflake Blog](https://www.snowflake.com/content/snowflake-site/global/en/blog/AI-Reshaping-Consumer-Shopping-Habits)

- 작성자: Ryan Watson

- 발행일시: 2026. 1. 10. 오전 9:14:00