Agentic Commerce Reshapes Retail: How Brands Must Adapt Loyalty and Promotions for AI-Driven Shopping

The landscape of digital commerce is undergoing a profound transformation as agentic commerce rapidly evolves from a theoretical concept into tangible, real-world applications. This pivotal shift sees autonomous AI systems stepping in to manage the entire shopping journey for consumers, from initial product discovery to final purchase and beyond. Major technology players like Google and OpenAI are at the forefront, actively integrating AI agents into their search, browsing, and checkout experiences, signaling a fundamental change in how consumers will interact with brands and products.

Understanding the Rise of Agentic Commerce

Agentic commerce, at its core, refers to a form of digital commerce where sophisticated AI systems operate autonomously on behalf of customers. These virtual personal shoppers are designed to discover products, meticulously evaluate options, apply relevant incentives, and complete transactions with minimal human intervention. Unlike earlier forms of automated shopping, which often required human approval at various stages, AI agents in agentic commerce are goal-oriented: they set a purchasing objective, assess various options against structured criteria, and execute the transaction independently.

Laurens Van Wiele, Chief Product Officer at incentives platform Talon.One, highlights the natural progression of this technology. "Many people are already using tools like Gemini, ChatGPT, or Claude for discovery," Van Wiele notes. "The next phases of agentic commerce will bring more aspects of the entire shopping flow directly into the AI assistant. It won’t just be about deciding which product you’re going to buy, but also completing the checkout within your assistant and seeing your order status." This integration means that the traditional multi-step process of browsing, comparing, and purchasing across different websites is consolidated into a single, seamless interaction within an AI assistant’s interface. "Everything happens on one screen," Van Wiele explains, emphasizing the efficiency and convenience this offers to the end-user.

The Impending Shift: Market Projections and Urgency

The implications of agentic commerce are far-reaching, with significant market impact projected in the coming years. By 2030, agentic AI is forecasted to account for a substantial 15%–25% of total U.S. e-commerce, according to a report by Bain & Company. This projection underscores that true omnichannel presence for businesses will soon necessitate the incorporation of agentic shopping experiences. Furthermore, Morgan Stanley predicts that up to $385 billion in U.S. e-commerce spending could be influenced by AI agents by 2030, illustrating the massive financial stakes involved.

This rapid growth and influence mean that purchase decisions will increasingly be mediated by AI agents that interpret brand information differently from human shoppers. Unlike humans, AI agents cannot "feel" a brand’s story, respond to emotional cues in marketing, or intuitively grasp nuanced value propositions presented on a homepage. Their decision-making is driven by structured data, making the way brands present their offers critically important. This necessitates an immediate focus on "agent-readiness" for marketers and commerce teams. The decisions brands make today, particularly regarding their loyalty and incentive programs, will profoundly shape their competitive standing for years to come. As Van Wiele advises, "With any new technology, early adopters are usually rewarded for having already built a reputation and built the trust that they can handle the technology and are good at it. It’s important for brands to stay on top of the agentic commerce evolution and ensure that they don’t miss the first train."

The Challenge for Brands: Differentiated Value vs. Price

In this emerging landscape, loyalty and incentive programs are poised to become the decisive factor in whether brands can compete on differentiated value or are forced into a race to the bottom on price. Currently, AI agents are optimized to surface the most straightforward discounts, often pulling unofficial or even expired discount codes found online. This mechanism largely overlooks critical outcomes that brands and retailers rely on, such as maintaining healthy margins, fostering customer retention, and building long-term customer value – all of which are traditionally achieved through robust loyalty programs.

The absence of structured, machine-readable data for loyalty benefits means that AI agents primarily evaluate products based on price and availability. This creates a significant risk of margin erosion for retailers. Christoph Gerber, CEO of Talon.One, highlights the growing concern of fraud in this context. "Coupon fraud is already a problem with people double-dipping loyalty bonuses and generic promotions, and AI will only make it easier to do that," Gerber states. Without a unified system to govern which discounts AI agents can access and when, brands risk uncontrolled proliferation of incentives, leading to significant financial losses.

Traditional loyalty mechanics, such as point balances, tiered benefits, exclusive product access, and member-only offers, are often embedded within a brand’s ecosystem in ways that are currently invisible to AI agents. These programs are designed to influence human shoppers at the point of purchase by introducing differentiated value beyond mere price reduction. However, because these benefits are not exposed as structured, machine-readable data, AI agents cannot factor them into their purchasing recommendations, defaulting instead to simple monetary discounts.

The Solution: Structured, Agent-Visible Loyalty & Promotions

To navigate this shift, brands do not necessarily need to reinvent their entire loyalty and promotions strategies, but rather refine them technically. The key lies in ensuring that loyalty and promotion data is centralized, governed, and made visible to AI agents in real time, complete with clear rules and customer identity attached. Van Wiele emphasizes this foundational requirement: "That’s the foundation that matters for everyone, ensuring your data is ready to be consumed. Agentic commerce is still being shaped, and there is still uncertainty around what the intricacies of agentic commerce experiences will look like once things standardize. But one thing is clear: a brand’s data has to be ready to be consumed."

When these foundational data structures are in place, connecting to emerging agentic commerce platforms becomes a straightforward integration rather than a costly and complex replatforming effort. This readiness ensures that when AI agents "crawl the internet," they are not just anonymous visitors seeing generic online offers. Instead, through proper surfacing, AI agents can leverage membership data to weigh a consumer’s brand preferences and loyalty perks alongside discounts, moving beyond a sole focus on the lowest price. "An AI agent won’t know that unless it can report to the merchant that this member is looking for an item and request all the offers available for them. Then it becomes much more personalized," Gerber explains. Agentic commerce will inherently favor brands that design their loyalty and promotions with these technical considerations in mind, ensuring that the full breadth of customer value is recognized and utilized by AI agents.

Google’s Unified Commerce Protocol (UCP): A Foundational Standard

A critical development in making loyalty and promotions visible to AI agents is Google’s Unified Commerce Protocol (UCP). UCP serves as a standardized framework designed to enable AI agents, commerce platforms, and merchant systems to seamlessly collaborate across the entire shopping journey, from initial search query to final purchase. It defines how core shopping activities – including product discovery, identity linking, checkout processes, and order execution – should occur between AI platforms and merchants’ backend operations.

Van Wiele elaborates on UCP’s function: "UCP is a standardized way for merchants to expose data and communicate with Gemini, and platforms other than Gemini. The UCP defines data structures in terms of, this is what a product looks like and this is where you enter a coupon. It’s standardizing the communication between an AI platform and merchants’ different backend systems to ensure that data can flow bi-directionally."

A cornerstone of UCP for loyalty programs is "identity linking." This feature allows an AI agent to securely associate a user with their merchant account, effectively enabling the agent to "sign in" on the customer’s behalf and access their brand membership program benefits. "Identity linking is the key to making agentic commerce personalized," Van Wiele asserts. "The status quo is searching for products and getting generic results. With identity linking, the AI assistant knows who that person is and can ask a brand for offers specifically for that person. Or, the assistant can tell the brand that this person is going to make a purchase and then see, for example, that the consumer is a gold-tier member. The consumer receives a more personalized experience from within the AI assistant window."

Promotions within UCP are implemented as an extension of the checkout capability. By enabling promotions, merchants can expose their discount data to AI agents, allowing these incentives to be discovered, displayed, and applied directly within agent-led shopping experiences. This prevents agents from resorting to exhaustive web crawling for discounts, ensuring controlled and intended promotion usage.

Talon.One’s Unified Incentives Protocol (UIP): Bridging the Gap

While protocols like UCP establish a crucial foundation for how agents interact with merchants, they may not always represent the full depth and specificity of every unique loyalty or promotions capability. Van Wiele acknowledges this, stating, "UCP will support loyalty soon, but the reality is that no matter how much base functionality is added, there will always be aspects that are unique to individual platforms." For instance, specific loyalty mechanics like the exact points needed to reach the next tier, or nuanced promotional structures like a "buy three, pay for two" offer, might not be immediately covered by a universal standard.

To address this, Talon.One has developed the Unified Incentives Protocol (UIP). UIP is designed to surface comprehensive loyalty and promotions mechanisms in a unified way across all AI agent-based shopping journeys. It grants AI agents access to the same rich set of incentives that customers would see in any other channel, including personalized promotions, discounts, bundles, loyalty points, perks, and program benefits. The critical advantage of UIP is its ability to enable AI agents to understand all incentivization methods in a standardized, machine-readable format.

Christoph Gerber articulates Talon.One’s mission: "Our mission is to enable our clients to be successful in agentic commerce, to ensure that all of the experiences and incentives they provide to shoppers on their websites or apps are also available in the new agentic shopping channels." The initial standard released under UIP is a loyalty extension. This extension ensures that when consumers shop via AI platforms like Gemini, they fully benefit from a brand’s complete loyalty offerings. This includes providing AI agents with visibility into a customer’s point balance and tier status, highlighting how many points a customer will earn or spend in a transaction, and even supporting card-based loyalty programs that operate without requiring explicit identity linking.

Gerber confirms, "This loyalty extension is the first building block of UIP, with more to follow. Our ambition is that every Talon.One incentives mechanism – personalized promotions, enterprise loyalty management, offer management and execution – will be fully leveraged in agentic channels through UIP." By establishing these robust foundations, standards like UCP and UIP become critical accelerators for brands navigating the complexities of agentic commerce. Talon.One’s long-term vision for UIP extends beyond UCP extensions, aiming for an open protocol that can support other emerging standards and models of extendability, ensuring clients remain competitive across the evolving AI landscape.

Navigating the Evolution: A Brand’s Roadmap to Agent-Readiness

Achieving agent-readiness isn’t about hastily adopting new protocols; it begins with solidifying fundamental data management practices. Brands that proactively take the following steps will be best positioned to compete and thrive in the agentic commerce era:

1. Centralize and Govern Incentive Data: Establish a single source of truth for all loyalty programs, promotions, and discounts. This centralization ensures consistency, prevents data silos, and forms the bedrock for machine-readable information. Implement robust governance rules to control the creation, distribution, and expiration of all incentives.
2. Ensure Real-Time Visibility and Machine-Readability: Transform complex loyalty benefits and promotional terms into structured, standardized data formats that AI agents can easily parse and understand. This means moving beyond human-readable marketing copy to machine-interpretable attributes for every offer, perk, and loyalty tier.
3. Integrate with Emerging Protocols: Actively plan for and integrate with foundational protocols like Google’s UCP for core commerce functions, and specialized extensions like Talon.One’s UIP for comprehensive loyalty and promotions management. These integrations will enable seamless data exchange between brand systems and AI agent platforms.
4. Prioritize Customer Identity Linking: Develop secure and efficient mechanisms for identity linking, allowing AI agents to recognize and authenticate customers, thereby accessing their personalized loyalty benefits and purchase history. This personalization is crucial for moving beyond generic discounts.
5. Focus on Long-Term Customer Value: Redesign promotions and loyalty programs to emphasize long-term customer relationships, retention, and lifetime value, rather than solely short-term price reductions. Ensure these value-driven incentives are clearly communicated and structured for AI agent comprehension.
6. Implement Robust Fraud Prevention: Centralize the management and execution of all discounts and loyalty offers to mitigate the risk of coupon fraud, which AI agents could inadvertently exacerbate. A controlled system prevents the unintended application of leaked or expired promotions.
7. Monitor and Adapt Continuously: The agentic commerce landscape is dynamic. Brands must establish processes for continuous monitoring of new technologies, evolving standards, and consumer behavior shifts, adapting their strategies and technical integrations accordingly.

The Future Landscape: Evolving Standards and Market Dynamics

Agentic commerce, while rapidly advancing, remains in its nascent stages. The journey towards fully enabled, end-to-end shopping on AI platforms and widespread adoption by brands and retailers is ongoing. The technology’s swift evolution is evident in recent developments, such as OpenAI’s initial rollout of its Agentic Commerce Protocol (ACP) in fall 2025, which aimed to facilitate instant checkout through ChatGPT with Etsy sellers and a planned expansion to Shopify merchants in early 2026. However, OpenAI later adjusted its approach, with Shopify merchants’ products still appearing in ChatGPT conversations, but purchases typically redirecting buyers to the merchant’s own online storefront. This pivot highlights the fluidity and experimental nature of the current landscape.

"There have been some checkout prototypes – Etsy within ChatGPT and Shopify," Van Wiele notes, acknowledging the rapid progress. "Everything is pointing in the direction that by the end of the year, there will be multiple brands whose products you can purchase directly from at least one of these AI solutions." Yet, Van Wiele cautions against definitive predictions, given the technology’s rapid evolution. "We’re talking about an ever-evolving world, with ever-evolving standards. A really standardized process, which is what Google’s UCP is trying to achieve and widespread adoption, will come next year. It will be an interesting journey to see how it evolves because so much has changed in just the last year."

Talon.One, as a leading incentives engine, is uniquely positioned to assist brands in this transition. By unifying loyalty, promotions, and gamification into a single, scalable platform, Talon.One empowers companies to build personalized and profitable incentive programs using any data. With over 300 global brands, including Adidas, Sephora, and Carlsberg, already leveraging Talon.One to deepen customer engagement and loyalty, the company is at the forefront of ensuring brands can effectively compete and win in the impending era of agentic commerce. The path forward demands vigilance, strategic investment in data infrastructure, and a proactive embrace of emerging standards to ensure brands remain relevant and competitive as AI agents increasingly mediate the consumer shopping experience.