Agentic Commerce Is No Longer Theoretical

The age of AI-powered shopping isn’t on the horizon – it’s already unfolding. With Shopify’s release of native checkout inside AI interfaces like ChatGPT (via Universal Checkout Protocol), agentic commerce has entered live environments where AI agents not only assist shoppers but actively complete transactions on their behalf. We are at an inflection point in AI adoption, as these technologies transition from assisted to autonomous systems, marking a pivotal change in the industry. Shopify merchants are among those benefiting from these new AI-powered shopping features and integrations. AI Mode is emerging as a new interface paradigm for shopping, expanding capabilities beyond traditional browsing to include autonomous checkout and purchase confirmation.

Unlike past AI applications limited to search or recommendations, agentic commerce introduces AI agents that move beyond suggestion. They execute. This shift is transforming online shopping, as global retailers are exploring and implementing agentic commerce to stay competitive in the evolving online shopping landscape. Widespread adoption of AI-enabled conversational interfaces and agentic commerce is rapidly transforming business models, customer engagement, and market dynamics across industries. That distinction reshapes not only how discovery happens, but how retailers are selected – and which are excluded. This represents a paradigm shift in commerce, fundamentally changing how businesses and consumers interact in the digital ecosystem. More than half of consumers anticipate using AI assistants for shopping by the end of 2025, indicating a significant shift in consumer behavior and underscoring the need for retailers to adapt rapidly. Traffic to US retail sites from GenAI browsers and chat services increased 4,700% year-over-year in July 2025, showing rapid adoption of AI-driven shopping.

In the near future, AI-driven shopping platforms will extend current browsing and comparison functions to include features like price tracking, purchase confirmation, and fully autonomous checkout, further accelerating the transformation of commerce.

What Is Agentic Commerce?

Agentic commerce refers to a shopping model where autonomous agents-AI-driven systems-manage the entire buying journey: from discovery to evaluation to checkout. These agents are not passive helpers; they act on behalf of the shopper. To do that, they must interpret product data, validate transaction logic, and ensure fulfillment promises can be honored. Agentic AI is the underlying technology enabling these autonomous, goal-driven systems, allowing them to initiate, learn from, and complete complex, multi-step tasks independently. AI agents act as digital proxies, interpreting needs, goals, and constraints for consumers or businesses.

Agentic shopping is transforming online retail by automating and personalizing the process, fundamentally changing consumer behavior and purchasing decisions. Traditional consumer journeys are being redefined as digital proxies and AI-powered agents now navigate and influence the entire shopping process, requiring a fundamental rethinking of engagement strategies. Consumer purchasing decisions are increasingly shaped by AI agents, shifting the focus from traditional marketing to AI-driven decision-making processes that proactively respond to consumer intent. For example, 61 percent of Gen Z consumers now start their product research with AI tools rather than traditional search engines. Half of all consumers now use AI when searching the internet, reflecting a significant shift in how consumers interact with digital platforms.

This evolution reframes ecommerce infrastructure. Retailers are no longer building experiences only for human eyes. The focus is shifting from designing for human shoppers to designing for machines, as AI agents become the primary audience for product data and digital experiences. They must expose structured truth that machines can read, verify, and act upon. Generative AI is a key enabler of agentic commerce, automating tasks, creating content, and enhancing customer interactions to improve efficiency and user experience.

AI shopping agents could drive roughly a quarter of all e-commerce, amounting to around $10 to $12 trillion in annual online sales by 2030.

The Agentic Ecosystem

The agentic ecosystem is rapidly emerging as the backbone of next-generation online shopping, connecting AI agents, AI platforms, payment providers, and retailers in a seamless digital network. At the heart of this ecosystem lies the Agentic Commerce Protocol (ACP), a universal commerce protocol that establishes a common language for secure, transparent, and efficient AI commerce.

AI shopping agents, empowered by the ACP, can autonomously navigate the entire shopping journey-from product discovery and evaluation to instant checkout-across multiple retailers and platforms. These shopping agents interact directly with commerce protocols, accessing real-time inventory, pricing, and fulfillment data to make informed purchasing decisions on behalf of consumers, all with minimal human intervention.

AI platforms and payment providers play a crucial role in this ecosystem, ensuring that agentic transactions are not only fast and frictionless but also secure and compliant with industry standards. By leveraging the universal commerce protocol, these stakeholders enable shopping agents to complete purchases, process payments, and manage sensitive payment credentials without exposing consumers to unnecessary risk.

For retailers, participating in the agentic ecosystem means making their product data, policies, and inventory accessible and verifiable by AI agents. This shift allows businesses to reach consumers through new AI-powered channels, while also benefiting from streamlined operations and enhanced fraud detection.

As the agentic ecosystem continues to evolve, it is redefining the way people shop online-ushering in a new era of digital commerce where AI agents, supported by robust protocols and infrastructure, deliver personalized, efficient, and trustworthy shopping experiences from start to finish.

The Funnel Collapses into a Single AI Shopping Agents Conversation

Traditional ecommerce unfolds over multiple touchpoints: search, comparison, cart, checkout. But AI collapses that funnel into a single moment. In a conversation like “Find me a 48-inch desk that ships by Friday and is returnable for free,” the agent must: AI powered search enables agents to instantly process and act on shopper requests, leveraging real time insights and a deep understanding of preferences and product data. Natural language interfaces allow shoppers to interact with agents seamlessly, making the shopping experience more conversational and personalized.

• Search eligible inventory
• Validate fulfillment timelines
• Confirm return terms
• Check price and payment methods
• Complete the transaction
• Earn extra revenue by fulfilling eCommerce orders for other merchants

AI agents can scan several platforms, filter results against individual preferences, compare features and prices, and make context-aware recommendations. These agents can also interact and collaborate with other agents to fulfill complex requests.

All of this happens mid-conversation, not across five browser tabs. Peak intent is no longer nudged down the funnel – it either converts instantly or disappears.

Execution of Agentic Transactions Has Become a Selection Filter

In agentic commerce, execution quality is not a post-purchase variable. It’s a selection filter upstream in the buying decision.

AI agents require structured inputs to verify fulfillment feasibility. If a retailer’s shipping time is ambiguous, returns unclear, or inventory inaccurate, the agent cannot confidently recommend or transact with them. To enable this, agentic commerce requires retailers to update their technology stack and existing systems to ensure data is structured and accessible for AI agents. The Model Context Protocol (MCP) is emerging as a standard for secure and seamless AI integration, acting as a universal adaptor for interactions between AI agents and back-end systems, and enabling interoperability and scalable deployment. As a result, the seller is skipped – not out of malice, but out of logic.

This means things that previously fell under “ops” – like accurate stock, timely delivery, and policy transparency – now determine visibility and eligibility in AI-led shopping environments. Agentic commerce automates tasks in marketing, inventory, and customer service, boosting operational efficiency.

Businesses can implement the Agentic Commerce Protocol (ACP) to transact with any AI agent or payment processor.

Brand Storytelling Doesn’t Offset Fulfillment Failure

Brand still matters in agentic commerce. A brand signals trust, identity, and aspiration. But the days of brand storytelling papering over operational shortfalls are ending. As AI agents increasingly influence purchasing decisions, brand loyalty and customer relationships are being redefined-AI agents now prioritize operational truth and real-time data over traditional marketing, shifting the focus from emotional connection to utility and trust built through AI interactions. Ethical considerations in AI governance are critical here, as responsible AI practices, regulatory compliance, and the integration of ethical standards into daily operations ensure trustworthy and fair AI deployment.

AI agents do not forgive missed promises. If a brand’s delivery estimate fails or the return process contradicts what was structured in its protocol, the agent will learn – and avoid the merchant in future queries. In this paradigm, operational honesty becomes the brand. This shift also transforms customer engagement, as retailers must leverage AI-driven personalization and seamless, autonomous shopping experiences to maintain relevance and loyalty.

Retailers that used to rely on slick marketing while tolerating backend chaos will find themselves deprioritized. Not because they’re disliked – but because they’re unreliable in structured logic.

Additionally, the emergence of agentic commerce threatens traditional revenue streams, particularly from advertising, as consumers shift towards AI-driven experiences. To remain competitive, businesses must ensure discoverability by enhancing earned visibility and capitalizing on emerging paid advertising opportunities.

What Breaks for Sellers Who Overpromise?

Overpromising introduces ambiguity that agents cannot resolve. Specific breaking points include:

• Late delivery: If fulfillment timelines are untrustworthy, agents cannot offer the product for date-sensitive requests.
• Unclear returns: Ambiguity around return fees or timeframes results in agents skipping the listing altogether.
• Inaccurate inventory: If availability can’t be guaranteed, agents avoid the risk of transaction failure.
• Hidden costs: Surprise fees (e.g., handling charges) are incompatible with agentic transparency, and are therefore filtered out.

Agentic commerce introduces new risks such as Bot Takeovers (BTOs), where authorized shopping agents can be compromised, making advanced fraud detection essential. The rise of agentic payments-autonomous payment methods executed by AI agents-brings new risks and accountability challenges, as these systems must ensure secure, verifiable transactions. Traditional fraud prevention tools must evolve to verify agent identities and establish protocol-level trust, ensuring secure, autonomous payments. Payment networks are rapidly evolving to support agentic payments, implementing delegated-auth tokens, dispute artifacts, and standard protocols to facilitate secure, autonomous transactions in this AI-driven environment. Additionally, concerns regarding data privacy and data ownership are heightened, as vast user data influences agent decisions and compliance with local regulations becomes critical. Businesses need to build the capabilities to differentiate between benign agents and malicious bots. Trust in AI agents is a significant challenge, since consumers may hesitate to share sensitive information with them. The ambiguity of accountability in agentic commerce complicates determining who is responsible for errors made by AI agents. Systemic risk also arises from the interconnectedness of AI agents, where a single error can have widespread consequences across multiple systems. The emergence of agentic payments is supported by collaborative standards like AP2, which involve players across North America, Europe, and Asia Pacific.

Importantly, agents don’t negotiate or rationalize – they calculate. Retailers who haven’t structured their policies in machine-readable formats (like UCP) will be invisible in these conversations, no matter how persuasive their branding.

Universal Checkout Protocol: A Glimpse of Agentic Commerce in Action

Google and Shopify’s Universal Checkout Protocol offers a clear glimpse into how this system works. It allows AI interfaces like ChatGPT to access product catalogs, confirm shipping and return policies, and execute purchases without redirecting users to traditional ecommerce pages. Shopify’s announcement framed this as “AI commerce at scale”. Platforms like Google Pay are also being integrated to facilitate seamless, in-platform agent-led transactions.

This model demonstrates how discovery, evaluation, and transaction are converging. It’s not just conversational UI – it’s protocol-enforced integrity. Agent-led transactions require new trust, accountability, and governance frameworks to ensure secure and verifiable payments. The existing payments infrastructure will encounter significant structural challenges as commerce transitions from direct user interactions to agent-initiated transactions.

Infrastructure and Security in Agentic Commerce

As agentic commerce becomes the new standard, the importance of robust infrastructure and airtight security cannot be overstated. The Agentic Commerce Protocol (ACP) is at the heart of this transformation, providing a common language that enables AI agents and businesses to interact seamlessly and securely throughout the entire shopping journey.

With AI shopping agents now responsible for everything from product discovery to instant checkout, retailers must ensure their systems can handle secure, real-time exchanges of payment credentials and transaction data. The ACP standardizes these interactions, allowing shopping agents to verify details, process payments, and complete purchases with minimal human input-while maintaining the highest levels of trust and data protection.

For retailers, this means investing in scalable, resilient infrastructure that can support agentic transactions at scale. As more consumers rely on AI shopping agents to navigate the digital world, only those businesses that prioritize security and interoperability will stay ahead in the next era of commerce. Adopting a universal commerce protocol isn’t just about compliance-it’s about enabling agents to deliver a seamless, secure customer experience from start to finish.

Fulfillment Accuracy and Fraud Detection Become Ranking Constraints

In agentic environments, fulfillment truth is not optional. It is part of the ranking algorithm that determines whether a product is even presented.

Agents pre-filter based on:

• In-stock status
• Delivery windows
• Return rules
• Total cost (including shipping and taxes)
• Overcoming Amazon’s inventory limits and order fulfillment alternatives

Actionable insights from fulfillment data enable agents to dynamically adapt and make better recommendations. By enabling agents to autonomously process and act on these insights, businesses can streamline operations and enhance personalization. If those values are undefined or misleading, the agent cannot include the product in results. Success for businesses in agentic commerce depends on data quality; messy product data leads to missed offers. This creates a new standard: operational execution becomes table stakes for being surfaced at all.

What Merchants Still Control – And What Agents Take Over

In this emerging architecture, merchants retain control over:

• Pricing
• Inventory availability
• Shipping policies and speed
• Returns terms
• Product content and taxonomy
• Merchants can also develop and utilize their own agents to enhance automation and customer interaction.

What shifts to the agent includes:

• Selection logic (based on shopper intent)
• Feasibility checks (can this product be delivered as promised?)
• Purchase execution (payment, confirmation)

Agents often operate across multiple systems, which introduces the need for careful management of risk and accountability. While agents function with minimal human intervention, users delegate authority by setting parameters within which the agents execute tasks.

Merchants don’t lose ownership of customers – but they do lose the ability to fudge details during the funnel. The agent sees and verifies everything upfront.

The Operational Shift Ahead

Agentic commerce doesn’t punish bad actors. It excludes unreliable ones – mechanically, quietly, and without appeal.

For retailers, this isn’t a marketing challenge. It’s an execution mandate. Business development, new business models, and changes to the operating model are essential for success in agentic commerce. Upgrading technology infrastructure and focusing on faster time to market are key for retail businesses to stay competitive. Industry leaders are actively shaping standards and best practices for agentic commerce, influencing the direction of payment solutions and interoperability. Fulfillment precision, delivery truth, and policy clarity are no longer operations problems. They’re discoverability problems. In the new AI shopping paradigm, the most honest brands win – not because of narrative, but because of math.

Companies need to rethink their existing business models to adapt to the emerging reality of agentic commerce. Retailers must make their platforms discoverable by agents to avoid becoming invisible in agentic commerce. Businesses must optimize product directories for agent readability to thrive in the agentic commerce era. Retailers must invest in AI technologies to reclaim relevance and assert their presence within AI ecosystems. Businesses should focus on building an efficient, intuitive API infrastructure tailored to agentic needs. Companies that move first to adapt to agentic commerce will help shape the future of consumer engagement.

Frequently Asked Questions

What Is the Universal Commerce Protocol (UCP)?

The Universal Commerce Protocol (UCP) is an open standard co-developed by Google in collaboration with industry leaders including Shopify, Etsy, Wayfair, Target, and Walmart, and is co-developed and endorsed by more than 20 partners across the ecosystem.

Is this live or still in development?

It’s live. Shopify, Google, and others have begun implementing UCP-enabled agentic commerce through tools like Copilot Checkout. This is no longer hypothetical.

Do merchants lose access to customers?

No. Orders are still routed through merchant systems. However, visibility is increasingly mediated by agents, not search engines.

Does this mean websites go away?

Not at all. Websites remain important, especially for brand and merchandising. But transactions will increasingly happen off-site via embedded AI interfaces.

Do I need to be on Shopify to participate?

No. While Shopify is a leading UCP contributor, the protocol is designed to be open. Any platform can adopt it to support agentic commerce.

The Universal Commerce Protocol (UCP) is an open standard co-developed by Google in collaboration with industry leaders including Shopify, Etsy, Wayfair, Target, and Walmart, and is co-developed and endorsed by more than 20 partners across the ecosystem. UCP is an open-source project that invites developers, businesses, and platform architects to contribute. In plain terms, UCP lets an AI “agent” treat any store like a programmable service rather than a website. The protocol defines how agents can discover products, understand checkout requirements, and complete purchases on behalf of a shopper in a structured way.

Merchants expose a machine-readable manifest (via APIs) of their catalog and checkout capabilities, and AI agents query that manifest to drive the sale. UCP was built to solve fragmented commerce journeys that lead to abandoned carts and frustrated shoppers. In practice, UCP allows an agent to:

• Discover products: AI assistants use UCP to query a merchant’s inventory and retrieve up-to-date product details, prices, availability, variants, images and descriptions via standard API calls.
• Handle checkout logic: UCP provides the agent with all the rules and inputs needed for checkout. This includes shipping methods, taxes, return policies, discount or promo codes, loyalty points, recurring subscription terms, etc. These are delivered in a structured data format, so the agent knows exactly what options to present or apply. For example, loyalty rewards or a special “guest checkout” rule are encoded in the protocol rather than hidden in a page UI. UCP uses reverse-domain naming for extensions, allowing merchants and agents to define their capabilities without needing approval.
• Complete transactions: UCP lets the agent assemble a cart and submit the order. It negotiates payment via the user’s preferred method (credit card, digital wallet, etc.) in a standard way. The protocol is payment-agnostic (it can work with any processor) and preserves the merchant’s checkout flow. UCP supports complex cart logic, dynamic pricing, tax calculations, and more across millions of businesses through unified checkout sessions. UCP features a modular payment architecture that separates payment instruments from payment handlers, promoting interoperability and payment method choice. Payment handlers are published by providers and selected during transactions, enabling flexible and dynamic payments. UCP uses OAuth 2.0 for secure account linking and AP2 for secure payment processing, and it uses tokenized payments, verifiable credentials, and cryptographic proof of user consent for every transaction to protect sensitive user information. UCP creates a transparent accountability trail between merchants, credential providers, and payment services, helping to ensure each transaction is secure. In short, the AI can finalize the purchase without manual page browsing, because it follows the machine-readable steps defined by the merchant.

These core capabilities – product discovery, checkout negotiation, and transaction completion – are what make UCP a “universal language” for e-commerce. The protocol is not a marketplace or app; it’s an industry standard supported by major partners such as American Express, Best Buy, Home Depot, Mastercard, and Stripe, demonstrating broad support across the ecosystem. It acts like an abstraction layer that translates between different store systems and AI interfaces. The result is that agents (whether built by Google, Microsoft, or others) can plug into any UCP-enabled store with minimal custom integration. UCP allows merchants to define their own bespoke functionality and capabilities, while maintaining security through proven standards for account linking, payment processing, and protecting customer data.

Introduction to Universal Commerce

The Universal Commerce Protocol (UCP) is ushering in a new era of digital shopping by redefining how businesses and consumers connect across the online ecosystem. As a groundbreaking commerce protocol, UCP is designed to create a seamless, unified shopping journey for everyone – no matter where they shop or which device they use. Developed as an open standard through the collaboration of industry leaders like Google, Shopify, and major retailers, UCP establishes a universal language for commerce that works across platforms, including Google’s AI Mode and the Gemini app.

By adopting the Universal Commerce Protocol, merchants can tap into the full potential of agentic commerce, where AI-powered agents handle everything from product discovery to checkout. This means shoppers enjoy a more intuitive, personalized experience, while retailers can reach consumers wherever they are – whether in search, chat, or voice interfaces. UCP is designed to break down barriers between different commerce systems, making it easier for businesses to participate in universal commerce and for shoppers to get what they need, when and where they want it. As the protocol gains traction, it’s set to become the new standard for digital commerce, benefiting both industry and consumers alike.

Technical Overview of UCP

UCP is built with flexibility and scalability at its core, enabling it to support a diverse range of commerce capabilities and extensions. The protocol’s architecture is designed to facilitate smooth, secure interactions between consumer-facing surfaces, businesses, and payment providers, ensuring a frictionless shopping experience from discovery to checkout. Thanks to its layered protocol design, UCP can be easily integrated with existing commerce infrastructure, allowing merchants to adopt the protocol without overhauling their current systems.

One of UCP’s standout features is its support for customizable capabilities and extensions. Merchants can define their own business logic, add new features, and tailor the protocol to fit their unique needs – whether that means supporting subscriptions, loyalty programs, or special promotions. UCP also accommodates a wide variety of payment methods, including Google Pay, PayPal, and other popular providers, making it easy for shoppers to pay however they prefer. This extensibility ensures that as new commerce trends and technologies emerge, UCP can evolve to support them, keeping the shopping experience fresh and relevant across all surfaces and platforms.

Agentic Commerce: Action-Taking AI Agents

UCP is the backbone of agentic commerce – a new mode of shopping where AI does the heavy lifting of the transaction. Unlike a simple chatbot or recommendation engine, an agentic shopping assistant acts. It can autonomously plan and carry out a purchase under user guidance. For example, imagine telling an AI: “Find me a lightweight suitcase under $200 and buy it.” An agentic commerce system could search multiple stores, compare options, handle any questions (“Should it be black or grey?”), and complete the checkout – all within the same flow. This is different from a traditional AI assistant that only suggests products or answers questions. AI platforms provide the foundational technology that enables streamlined business onboarding, integration with APIs, and enhanced user experiences through compatible frameworks.

In the agentic commerce model, the AI agent behaves like a diligent digital personal shopper. It follows the user’s instructions (and even proactively asks clarifying questions), then executes the purchase when the customer is ready. For instance, Microsoft’s Copilot example illustrates this: a shopper asks for a dress recommendation, the AI compares options, answers follow-ups, and the user decides – all in one conversation. Copilot Checkout can then finalize the order without the customer leaving the chat. The agent handles the multi-step process from intent to purchase seamlessly. These AI-powered tools and standards are designed to help retailers and consumers by simplifying connections, improving discovery, and enabling smarter shopping experiences. In short, agentic commerce goes “beyond chat” by converting conversation into action.

How UCP Checkout Differs from a Traditional Checkout

UCP-enabled checkouts are fundamentally different from the web pages shoppers see today. The key difference is machine-readable logic instead of visual UI. A traditional checkout page is designed for humans, often requiring form-filling and clicking through dialogs. UCP, by contrast, encodes those steps in a standardized data format. This means:

• No scraping or browser simulation. AI agents don’t need to interpret HTML or navigate webpages. Instead, they query UCP endpoints directly. A merchant’s server publishes a UCP manifest (at a well-known URL) that tells the agent what actions are supported (product search, add-to-cart, apply-discount, etc.) and how to call them. This removes the fragile, one-off integrations that come with screen-scraping or custom bots.
• Structured inputs and negotiation. Information like shipping options, tax rules, return window, subscription details, and available discounts are all included as structured data. For example, UCP can represent a merchant’s entire loyalty program or subscription terms in JSON, so an agent can automatically apply earned points or set up recurring orders. This ensures the agent respects all business rules: “Your discount codes, shipping rules, taxes, and loyalty settings still apply – even if the purchase happens through an AI interface”. In other words, nothing “disappears” just because the agent is handling the sale. When the agent negotiates payment, payment handlers are published by providers, selected during transactions, and integrated into profiles to facilitate seamless and dynamic payment negotiations between merchants and consumers.
• Embedded commerce flows. With UCP, the checkout is often embedded in the AI interface rather than redirecting to a website. When a customer goes to buy, the agent will push all required data (address, payment, items) through UCP, and the order is recorded on the merchant’s side just as if the customer filled a cart on the site. The shopping experience stays within the conversation window, giving a seamless feel without sacrificing merchant control.

In summary, protocol-based checkout means the AI and merchant talk the same “language,” so the exchange is transparent and reliable for machines. This is unlike brittle scripts that try to click through a generic checkout page – UCP provides a clear, versioned protocol that can evolve with new commerce features (like loyalty or subscriptions) without breaking agents.

AI Commerce Today: Where It’s Happening

Agents are already starting to sell. UCP-powered shopping is rolling out on several platforms and surfaces:

• AI Search & Smart Assistants: Google is launching UCP-powered shopping in its new AI search mode and Gemini app. Soon, when you search for a product in Google AI Mode, you can buy directly in the chat window. (For example, Target announced that shoppers will soon be able to browse and buy Target products right inside the Google Gemini app and Search AI Mode.) Google’s AI integrates with the Universal Commerce Protocol to enable seamless, agentic commerce actions across Google’s AI surfaces and shopping platforms, facilitating direct purchases and post-purchase support within AI-enhanced search environments. Similarly, Microsoft Copilot (in Bing and Windows) has enabled Copilot Checkout, an in-chat purchase feature for select retailers.
• Conversational Surfaces: Any app or device that can chat can also become a storefront. For instance, Google’s Business Agent lets users ask questions in Search and buy from a brand’s inventory without leaving the results page. The same could happen in messaging apps, voice assistants (like Alexa/Siri with shopping features), social media chatbots, or even productivity tools with AI assistants. The broad idea is that every place you can converse with an AI might one day handle commerce.
• Embedded E-commerce Tools: Companies are integrating shopping into tools people already use. Shopify’s “Agentic Storefront” concept means a brand can use Shopify’s backend to sell on AI channels even if it doesn’t have a Shopify website. That way, a retailer’s products and checkout live in Shopify but can be accessed by agents anywhere. Other commerce platforms (and payment partners like Stripe, PayPal, etc.) are also building UCP support so that AI agents have lots of stores to connect to.

In practice, this means AI commerce isn’t limited to one app. We’ll see it in search engines, voice assistants, chat apps, social media feeds – essentially any interface where people are asking questions or browsing interactively. For example, Google mentions “discovering and buying to post-purchase support” on any channel (search, shopping graph, etc.) and partners like Walmart, Etsy, Wayfair and Visa are involved. Major retailers such as Best Buy and Home Depot are also supporting or endorsing UCP, further expanding the protocol’s reach. Additionally, leading payment providers including American Express, Mastercard, and Stripe are collaborating with UCP to enable secure and efficient agentic commerce solutions across platforms and retail ecosystems. The key point: UCP is already being used by major players (Google, Shopify, Microsoft, retailers) to turn AI UIs into shopping surfaces.

How Agentic Checkout Looks in Practice

One concrete example of UCP in action is Microsoft’s Copilot Checkout. This feature lets customers buy products directly inside the Copilot chat (e.g. in Bing or Windows) without redirecting to the store’s site. Under the hood, Copilot Checkout uses a protocol like UCP to communicate with the retailer’s system. When you choose to pay, the agent calls the merchant’s checkout endpoint (via UCP) to submit the order. Importantly, this happens in an embedded frame in Copilot, so the entire experience stays in the conversation window.

From the merchant’s perspective, nothing magical happens: the sale is processed by their own checkout logic, and they remain the merchant of record. Microsoft states that “you stay the merchant of record. You own the transaction, the customer data, and the relationship” with customers who buy via Copilot. This means the merchant still sets the price, payment acceptance, shipping rules, and so on – the AI is simply filling in and submitting the order. For example, Urban Outfitters and Ashley Furniture announced they will use Copilot Checkout to sell in Bing. Shopify says: “If you’re on Shopify, you’ll automatically be able to sell in Copilot Checkout – no integration needed”.

Another example is coming from Google: Shopify merchants will soon be able to sell directly in Google Search’s AI Mode and the Gemini app using UCP. This “direct shopping” feature will use Google Pay behind the scenes, and again retailers remain the seller of record. (Customers will tap or click to buy in the Google interface, but the order will be owned by the original retailer.)

The pattern is consistent: the AI interface invokes the merchant’s checkout without losing any store-specific logic. The agent acts like a friendly front-end, but fulfillment, inventory, pricing, and post-sale support stay under the merchant’s control. This model – embedded checkout with UCP – is the opposite of third-party marketplaces. The brand does not hand off its customers to a new platform; it simply enables the agent to carry out its own checkout flow as if it were another channel.

Merchant Control vs. Agent Actions

UCP explicitly preserves merchant control over core business rules. In an agentic purchase:

• Merchants keep control of products and policies. The merchant decides what to sell, at what price, and under what conditions. All product data (images, descriptions, variants, pricing) still comes from the merchant. Likewise, shipping options, return policies, tax calculations, loyalty programs, subscriptions and discount codes are defined by the merchant’s backend. These rules are passed to the agent in UCP messages, but the merchant authored them. For example, “your discount codes, shipping rules, taxes, and loyalty settings still apply – even if the purchase happens through an AI interface”.
  
• Merchants remain merchant of record. The agent never replaces the checkout host. The retailer still processes the payment (via their payment gateway) and delivers the product. As noted earlier, with Copilot Checkout the retailer “owns the transaction, the customer data, and the relationship”. This also means the retailer is responsible for packing, shipping, and support. The AI agent simply initiates the order; fulfillment happens on the merchant’s side just like any normal order.
  
• Agents control selection and timing. The AI agent’s job is to find the right products and execute the purchase when the customer wants. The agent chooses the items (based on the conversation), decides when to hit “checkout,” and can even submit multiple payment attempts with the user’s saved methods if needed. However, the agent cannot override merchant constraints. It cannot, for example, promise a faster ship date than the merchant allows, or apply a discount that is not valid. It simply reads those constraints from UCP data and respects them. If a step requires human input – say the store requires the customer to pick a delivery date or upload a custom print file – the protocol includes a “continue” URL. The agent hands control back to the customer at exactly the right step in the merchant’s original interface. The customer finishes those steps, and UCP is designed so the agent can rejoin or complete the order afterward.
  

In short, UCP lets agents do the shopping work, but merchants keep the business logic. Pricing, inventory, branding, shipping options and after-sale service all stay with the merchant. The agent handles searching, decision support, and pushing through the checkout in the background, under the merchant’s pre-set terms.

The Universal Commerce Protocol is a new way for AI and stores to work together. It turns AI assistants into active shopping agents while keeping merchants fully in control of their business. As this standard rolls out, expect to see AI-powered checkout in many places – but always with the merchant managing pricing, shipping, and fulfillment on the backend.

UCP Roadmap and Future Development

The Universal Commerce Protocol is not a static solution – it’s a living, evolving standard designed to keep pace with the rapidly changing world of commerce. The UCP roadmap is packed with innovative features aimed at enhancing both the merchant and consumer experience. Upcoming developments include support for multi-item carts, seamless account linking for loyalty and rewards programs, and advanced post-purchase support, all of which will make the shopping journey even more streamlined and personalized.

As UCP continues to grow, its open-source foundation and collaborative development process ensure that it remains responsive to the needs of the entire commerce ecosystem. Industry leaders, merchants, and technology partners are all contributing to the protocol’s evolution, helping to shape the next generation of universal commerce. By joining the UCP ecosystem, businesses can future-proof their operations, offer cutting-edge shopping experiences, and ensure they’re never left behind as the industry moves forward. For consumers, this means more choice, convenience, and support at every stage of the purchase journey – heralding a new era in the future of commerce.

Frequently Asked Questions

Is this live yet? 

UCP itself was announced in January 2026 and the first agentic shopping features are just rolling out. Google has said UCP will power “native shopping on Google Search and Gemini” soon. Microsoft’s Copilot Checkout is in limited US rollout for Shopify merchants. In practice, expect pilot programs and staged launches in 2026. It’s not fully widespread yet, but it’s already real – major players are integrating it now.

Do merchants lose customer ownership? 

Not at all. UCP is designed so that the merchant stays the seller. In every agentic purchase, the retailer remains the merchant of record. This means the store gets the money, owns the order data, and keeps the customer for marketing/loyalty. The AI is only a guided interface, not a third-party middleman. Shopify’s announcement explicitly notes that merchants “retain sovereignty” and continue to own the checkout experience.

Does this replace my website or store? 

No. Agentic commerce is an additional channel, not a substitute for your site. Customers will still visit the merchant’s website for trust, content, and richer experience. UCP is more like SEO or advertising: it helps your store appear in new places (AI chats, searches) but it doesn’t eliminate your own store. The Shopify team emphasizes that websites will remain important for branding and customer education. Think of UCP as letting more doors open to your store via AI, but you still need your storefront.

Is Shopify required to participate?

No. UCP is an open industry standard. Any merchant or platform can adopt it. Shopify is one founder and provides tools (even letting any brand use Shopify’s backend to get agentic exposure), but it’s not mandatory. In fact, by design “UCP isn’t locked to Shopify” – the whole e-commerce ecosystem can adopt it. Google, Mastercard, Visa, Stripe and others are already on board too. You don’t have to use Shopify; you just need your platform or an app/plugin to speak the UCP.

Introduction

Black Friday Cyber Monday (BFCM) is no longer simply a seasonal sales spike—it has become a stress test for whether an ecommerce business is built for real growth or sustainable scale. Many ecommerce brands drove unprecedented sales through paid acquisition and promotional volume in 2020–2024, only to discover that scaling demand without scalable fulfillment, inventory, and shipping infrastructure produces customer friction, operational chaos, and margin destruction.

BFCM 2025 is expected to amplify this divide. Some brands will win not because they sell more, but because they can fulfill more profitably, reliably, and without breaking. Others will chase top-line growth only to experience out-of-stocks, carrier failures, late deliveries, refund requests, and return waves that erase their gains.

This article explains the fundamental difference between growth and scale in ecommerce, and how BFCM exposes which companies have truly built a scalable operation. We’ll break down common failure modes, key scaling metrics, and the operational strategies that allow brands to win the biggest shopping weekend of the year—without sacrificing customer experience or margins.

Growth vs. Scale: What’s the Difference in Ecommerce?

In ecommerce, growth means increasing demand—more orders, more customers, more revenue. Growth is typically fueled by marketing: paid ads, promotions, affiliate traffic, influencer campaigns, email blasts, and marketplace expansion. Growth is a top-line outcome.

Scale is different. Scale means your operation can handle more volume without a proportional increase in cost, complexity, or risk. Scaling is an operational outcome: it depends on fulfillment processes, inventory positioning, shipping strategy, systems integration, warehouse capacity, and return handling. Scale is the ability to grow profitably and consistently.

Many brands confuse the two. They assume that revenue growth equals business maturity. But BFCM reveals the truth: growth is easy to buy; scale must be built.

A simple way to think about it:

• Growth = more demand
• Scale = more volume with fewer problems

A business that grows without scaling becomes fragile. BFCM is when fragility turns into failure.

Why BFCM Exposes the Difference

BFCM creates a convergence of pressure points:

• Order volume spikes in 72 hours
• Carrier networks become congested
• Inventory accuracy matters more than ever
• Customer expectations for fast shipping increase
• Returns volume accelerates immediately after delivery

These conditions do not simply test marketing. They test the entire business system. If fulfillment is underbuilt, BFCM will overwhelm it. If inventory is mis-positioned, shipping becomes expensive and slow. If carrier strategy is weak, delivery promises collapse. If returns workflows are immature, the post-BFCM return wave becomes operational debt that drags into Q1.

Brands that are built for scale experience BFCM differently. They still feel the pressure, but they have designed systems to absorb it. Their operations do not break when demand spikes. They ship reliably. They protect margins. They deliver a customer experience that strengthens loyalty instead of damaging trust.

The Growth Trap: What Happens When Volume Outpaces Operations

Many ecommerce brands enter BFCM with a “growth-first” mindset. They focus heavily on driving demand and assume fulfillment will “figure it out.” This often produces predictable failure modes:

1. Stockouts and Inventory Inaccuracy

High-velocity demand exposes weak inventory controls. If your inventory system is not real-time and accurate, BFCM will cause:

• Overselling products that are not actually available
• Cancellations that harm marketplace performance and customer trust
• Backorders that create support tickets and refund requests

Brands built for scale use distributed inventory, tight sync, and demand forecasting. Brands built for growth alone often rely on a single node or manual inventory updates that fail under pressure.

2. Fulfillment Backlogs and Late Shipments

BFCM exposes whether your warehouse operations can handle surge throughput. Growth-first brands often face:

• Picking bottlenecks and packing shortages
• Staffing gaps and overtime cost explosions
• Orders that ship days late, missing marketplace SLAs

Late fulfillment does not just cost money—it destroys customer experience during the most visible moment of the year.

3. Margin Erosion from Panic Shipping

When orders are late or inventory is mis-positioned, brands often respond by upgrading shipping services to “save” delivery dates. This results in:

• Expedited shipping costs that wipe out promotional margins
• Zone 7/8 shipments from a single warehouse that drive cost inflation
• High surcharge exposure during peak carrier pricing windows

Brands that scale intentionally design fulfillment networks to avoid panic shipping. They route orders dynamically and position inventory closer to demand.

4. Customer Support Overload

Late shipments, stockouts, and unclear delivery promises generate customer contact volume. Growth-first brands often underestimate how fast support costs rise when operations break. The result is:

• Escalating ticket volume and response delays
• Negative reviews that permanently impact conversion
• Refund requests and chargebacks that compound margin loss

During BFCM, customer expectations are high. Failure is amplified, and damage lasts beyond the weekend.

What Scalable Ecommerce Operations Look Like During BFCM

Brands built for scale do not rely on heroics. They rely on systems. During BFCM, scalable operations show up in predictable ways:

1. Distributed Inventory and Smart Order Routing

Scalable brands avoid single-node fulfillment. They position inventory across multiple locations and use intelligent routing to ship from the best node based on:

• Customer location
• Inventory availability
• Carrier cost and performance
• Delivery promise requirements

This reduces shipping zones, lowers cost, and increases delivery speed without upgrading services.

2. Throughput-Ready Warehouse Processes

Scalable brands engineer fulfillment workflows so that doubling volume does not double complexity. They invest in:

• Batch picking and wave planning
• Pre-built kits and standardized packaging
• Labor planning and surge staffing readiness
• Automation where it matters (shipping, labeling, routing)

They do not wait until BFCM to discover bottlenecks.

3. Carrier Strategy Built for Peak Season

Scalable brands plan for peak pricing and congestion. They diversify carriers, monitor surcharge exposure, and avoid last-minute upgrades. Their shipping strategy includes:

• Multi-carrier rate shopping
• Fallback services when one network slows down
• Clear customer delivery promises that match reality

Scale means shipping remains predictable even when carrier networks are not.

The Metrics That Reveal Whether You’re Built for Scale

BFCM is when ecommerce metrics stop being theoretical and become real. The brands that scale are the ones that can maintain performance under pressure. Key indicators include:

• On-time shipment rate (did orders ship within promised windows?)
• On-time delivery rate (did customers receive orders when promised?)
• Cost per order shipped (did shipping costs spike under pressure?)
• Out-of-stock rate (did inventory accuracy survive demand spikes?)
• Customer contact rate (did support load stay stable?)
• Return processing time (did reverse logistics create post-BFCM operational debt?)

Brands built for growth alone often see these metrics collapse during BFCM. Brands built for scale stabilize them, even under high volume.

How to Prepare for BFCM 2025 Without Breaking Your Business

Preparing for BFCM is not just about launching a promotion. It is about ensuring the business system can survive the demand you create. Key preparation strategies include:

1. Forecast Demand and Stress Test Capacity

Forecast volume based on last year’s performance, growth rate, and planned marketing spend. Then compare forecast demand to:

• Warehouse throughput capacity
• Carrier pickup and transit capacity
• Inventory availability and replenishment lead times

If forecast demand exceeds capacity, growth will produce failure. Adjust accordingly.

2. Strengthen Inventory Positioning

Inventory that is positioned poorly becomes expensive and slow to ship. Prepare by:

• Splitting inventory closer to demand regions
• Using networked fulfillment to avoid zone inflation
• Improving inventory accuracy and real-time sync

BFCM is not the time to discover your inventory counts are wrong.

3. Build a Carrier Playbook

Carrier performance and peak surcharges shift quickly during BFCM. Build a playbook that includes:

• Primary and backup carriers by service level
• Surcharge exposure monitoring
• Rate shopping and dynamic carrier selection
• Customer messaging when networks slow down

Scale requires redundancy. Growth-only operations often have none.

Conclusion: BFCM 2025 Will Reward Scale, Not Just Growth

BFCM is not just a revenue event. It is an operational truth test. Brands that chase growth without scaling will generate volume they cannot fulfill profitably. Brands that have built scalable systems will win not only with revenue, but with customer loyalty, stronger margins, and repeat demand into Q1.

The difference is not marketing. It is operational maturity. BFCM 2025 will amplify this divide between ecommerce businesses built for growth and those built for scale—and the brands that invest in scalable fulfillment, inventory positioning, and shipping strategy will be the ones that emerge stronger.

Frequently Asked Questions

What is the difference between growth and scale in ecommerce?

Growth is increasing demand and revenue, often through marketing and promotions. Scale is the ability to handle increased volume without proportional increases in cost, complexity, or operational risk.

Why does BFCM expose operational weakness?

BFCM concentrates high volume, tight delivery expectations, carrier congestion, and inventory volatility into a short time window. Weak fulfillment, inventory, and shipping systems break under that pressure, leading to late shipments, margin loss, and customer dissatisfaction.

What metrics should ecommerce brands track during BFCM?

Key metrics include on-time shipment rate, on-time delivery rate, cost per order shipped, out-of-stock rate, customer contact rate, and return processing time.

How can ecommerce brands prepare for BFCM without destroying margins?

Brands can prepare by forecasting demand, stress testing fulfillment capacity, distributing inventory closer to demand, improving inventory accuracy, building a multi-carrier shipping strategy, and developing an operational playbook for surge conditions.

What sources were leveraged for BFCM 2025 metrics?

The Black Friday Cyber Monday 2025 metrics referenced in this article were sourced from publicly available Shopify disclosures, including Shopify’s official Newsroom recap and Shopify’s Investor Relations press release. A syndicated version of the same release distributed via Nasdaq was used for cross-verification.

• Shopify Newsroom BFCM 2025 recap: https://www.shopify.com/news/bfcm-data-2025
• Shopify Investor Relations press release: https://shopifyinvestors.com/media-center/news-details/2025/Shopify-Merchants-Achieve-Record-Breaking-14-6-Billion-in-Black-Friday-Cyber-Monday-Sales/default.aspx
• Nasdaq syndicated press release: https://www.nasdaq.com/press-release/shopify-merchants-achieve-record-breaking-146-billion-black-friday-cyber-monday-sales