Online shopping has become part of our daily routine, but how we navigate the purchasing journey is noticeably evolving. Previously, shoppers would enter keywords into search bars, open multiple tabs, and manually compare information across pages. Today, however, a new pattern is rapidly emerging: users describe their needs in a single conversation window, and AI organizes and presents the relevant information.
This shift represents more than just added convenience—it marks a fundamental transition from search-based shopping to conversation-based shopping. Users can express their preferences more naturally, while AI interprets context and suggests specific directions. The shopping journey that once required repeated searches and tab-switching is now being restructured into a seamless flow of exploration, comparison, and decision-making within a single conversation.
For brands, this evolution goes beyond simple technological change. Consumer choices will increasingly be influenced not by platform layouts or ad placements, but by how AI understands and explains product information. We're approaching the beginning of this transformation, and commerce operations are facing new strategic questions.
"When AI takes the lead in recommendations, is our brand prepared to be included in that list?"
AI commerce carries deeper implications than simply having AI recommend products. The core shift is that the fundamental unit of shopping is moving from keyword-based searches to conversation-based understanding.
Traditional shopping required combining multiple keyword fragments to find desired information. With conversational interfaces, however, users simply describe their situation, preferences, and goals naturally, and the search begins. AI doesn't just process these conversations as simple sentences—it interprets context, organizes conditions, and proposes appropriate directions.
GPT's shopping research feature best demonstrates this evolution. When a user says "recommend a mid-size humidifier around $150," AI reads relevant information and reviews, compares pros and cons, and presents several curated options.
Users don't need to open multiple product pages throughout this process. Research, comparison, and refined recommendations all happen within a single conversational flow. While traditional shopping required users to manually gather information, AI commerce shifts toward a structure where AI proactively organizes user intent and presents a "direction for decision-making."
This change poses an important question for brands and sellers: When AI interprets consumer needs, organizes product differences, and narrows down final candidates, how clearly and accurately is our brand's information being communicated throughout that judgment process?
AI-powered shopping is now expanding beyond exploration and comparison to seamlessly include the checkout stage. OpenAI recently officially launched the Instant Checkout feature within ChatGPT in the United States. This functionality is designed so users can complete the entire flow—from product recommendations to ordering, payment, and delivery—without leaving the conversation window.
Initially rolled out for Etsy sellers, the feature will soon expand to over one million Shopify merchants, including well-known brands like Glossier, Skims, and Spanx. When users ask questions like "recommend hiking boots under $200," ChatGPT suggests products that match their criteria, and users can complete purchases by simply clicking a "Buy" button on the same screen.
OpenAI emphasized that this recommendation process excludes advertising and applies only relevance-based sorting, adopting a model that takes a small commission per sale. For brands, this creates an environment where securing a place in the single "recommendation slot" that AI presents becomes more critical than competing for search rankings, banner positions, or ad slots.
Meanwhile, the core technology behind Instant Checkout—the Agentic Commerce Protocol—has been released as open source, enabling any agent (ChatGPT, Claude, Gemini, TikTok AI, etc.) to implement the same checkout flow. While traditional commerce structures centered around platforms, the future is likely to be reorganized around AI interfaces serving as both the starting and ending points of shopping.
From a brand perspective, how to be included in the single purchase pathway that AI provides is emerging as a new strategic challenge.
In an environment where AI serves as the starting point for recommendations, how clearly brand information is structured and how well it connects to trustworthy evidence becomes increasingly critical. As consumers begin their shopping in conversation windows rather than search bars, how AI understands brands is also changing. The following three areas should be prioritized for brand review:
For AI to recommend products, text, images, and specification information must be logically organized. Not just basic information like size, model number, materials, and intended use, but also package contents, precautions, after-service standards, and other factors that influence purchase decisions—the more consistently these items are provided, the more accurately AI can classify and explain products.
This isn't about "how many keywords were included" from the SEO era, but rather about whether information is cleanly structured enough for AI to explain products without losing context. Ultimately, becoming "product information that AI can read" naturally increases the likelihood of being included in recommendation lists.
In AI-powered shopping, experience-based data such as reviews, ratings, and Q&A play a much larger role. AI doesn't simply look at star ratings—it interprets which customers were satisfied for what reasons, and what discomforts arose in which situations.
Therefore, brands should manage recurring issues and encourage reviews that clearly showcase product strengths. Additionally, through clear management of customer question responses, it's important to accumulate "data where quality is clearly visible."
Pre- and post-purchase inquiries, return reasons, and exchange reasons may also become evidence for how AI judges "which customers a product is suitable for." For brands, this becomes not just customer service management, but a process of creating the standards by which AI makes judgments.
AI tends to prioritize suggesting choices that users won't regret. Therefore, brands must clearly present evidence and transparency that confirm quality. The more objective evidence a brand can provide to customers, the higher the likelihood that AI will evaluate that brand as a trustworthy option.
From a consumer perspective, the changes AI brings are quite simple. Describe the desired conditions, receive organized product information and comparison results, and complete purchases on the same screen if satisfied. No need to open multiple tabs or manually review countless product descriptions.
However, the story is different for brands. AI must read all product information and customer experience data to suggest the most suitable choices for user questions. In this process, the structure of information that brands leave behind, the context of reviews, and records that prove quality will carry more weight than before.
Ultimately, competitiveness in the AI commerce era is moving closer to the question: not simply how products are displayed, but "on what evidence can AI explain our brand?"
As we enter an era where conversational shopping becomes standard, the questions brands need to prepare are also becoming clear. When AI explains our brand on behalf of consumers, it's time to consider what information and what evidence will make our brand a "selectable answer."
