Today we’re joined by Zainab Hussain, a seasoned e-commerce strategist with deep expertise in customer engagement and operations management. We’ll be diving into the seismic shift agentic commerce represents for brands, exploring how artificial intelligence is rewriting the rules of customer loyalty and product discovery. Our conversation will touch upon the critical need for new technical standards that allow AI to understand complex value propositions beyond just price, the strategic power of loyalty points in an automated shopping world, and the future roadmap for making every brand incentive “agent-ready.”
With agentic commerce projected to capture a significant market share by 2030, what specific challenges do brands face in making their promotions visible to AI? Please explain the practical steps a standardized format like UIP takes to help them overcome these hurdles and remain discoverable.
The fundamental challenge is a translation issue. Right now, an AI agent can easily scrape a price, but it can’t intuitively understand the nuanced value of a “buy one, get one” offer, or the long-term benefit of earning loyalty points. These complex incentives are essentially invisible, written in a language the machine doesn’t speak. With up to 20% of the market share at stake, invisibility is a death sentence. A standardized format like the Unified Incentives Protocol (UIP) acts as a universal translator. It structures the data about a promotion—the discount, the loyalty points, the tier unlock—into a clean, machine-readable format. This allows the AI agent to not just see the incentive, but to factor it into its core comparison logic, ensuring brands that compete on value, not just price, remain discoverable and relevant in this new landscape.
A key scenario described involves an AI agent recommending a brand based on loyalty points, not just price. Could you walk us through how an AI processes this incentive data via UIP and what makes this a more sustainable competitive advantage than simple price matching?
Imagine you ask your AI agent to find you a new pair of running shoes. Without a protocol like UIP, the agent would likely just return the cheapest option. With UIP, the agent’s process becomes far more sophisticated. It would see Brand A for $100 and Brand B for $95. But it would also ingest data via the protocol showing that purchasing Brand A earns you 200 loyalty points, which is enough to unlock “Gold tier” status and future perks. The agent’s decision-making logic now incorporates this long-term value. It can calculate that the immediate and future benefits of the loyalty points outweigh the $5 price difference. This is a game-changer because price is a race to the bottom—it’s fleeting and easily matched. Loyalty, on the other hand, builds a long-term, defensible relationship with the customer, making it a far more sustainable and profitable competitive advantage.
Your new loyalty and discount extensions for Google’s UCP provide visibility into point balances and tier status. How exactly does this expand on UCP’s native capabilities, and what new customer engagement strategies does this unlock for brands using AI agents for shopping?
Google’s Universal Commerce Protocol (UCP) is a fantastic foundation, but its native support for loyalty programs is currently quite limited. These new extensions essentially build a much-needed upper floor onto that foundation. They give an AI agent direct, clear visibility into a customer’s specific relationship with a brand—how many points they have, what loyalty tier they’re in, and exactly what they’ll earn or redeem in a given transaction. This unlocks a whole new level of proactive engagement. For instance, an AI agent could now tell a shopper, “You’re only 50 points away from your next reward with Brand X. If you add this item to your cart, you’ll get there.” It turns the agent from a passive order-taker into an active partner in maximizing the customer’s loyalty benefits, which is an incredibly powerful way to build brand affinity.
Many fear AI-driven commerce will devolve into a “race to the bottom” on price. How does UIP enable brands to communicate more complex value propositions, like loyalty tiers or card-based programs, and could you provide a real-world example of this in action?
That fear is completely valid if we fail to adapt. AI is brilliant at comparing numbers, and price is the easiest number to compare. The Unified Incentives Protocol is the essential tool to fight this commoditization. It allows a brand to communicate its entire value story in a way the AI understands. A perfect real-world example is a card-based loyalty program, which often operates without requiring a customer to log in. The new extensions for UCP can support this, allowing an agent to recognize the loyalty program linked to a payment card. The AI can then inform the user, “If you use your linked card, you’ll earn double points on this purchase,” even if the user wasn’t actively thinking about it. Suddenly, the value proposition isn’t just the sticker price; it’s the seamless, integrated reward system that enhances the entire shopping experience.
Looking beyond the initial UCP extensions, what are the biggest technical or strategic challenges in making all incentives “agent-ready”? Kindly describe the roadmap for ensuring every type of promotion, from simple discounts to personalized offers, can be understood across different agentic channels.
The initial extensions are a crucial first step, but the biggest challenge ahead is universality and complexity. Strategically, we have to convince a fragmented ecosystem of different agentic platforms to adopt these standards. Technically, the challenge is building a protocol robust enough to handle the immense variety of promotions brands create—from simple percentage discounts to hyper-personalized, “for-your-eyes-only” offers. The roadmap involves a phased expansion. First, we solidify the standards for common loyalty and discount structures. Next, we’ll tackle more complex promotional types, like bundled offers or tiered-spending rewards. The ultimate ambition is to develop the UIP to a point where any incentive created within a platform like Talon.One can be instantly and accurately understood and applied by any AI agent, on any channel, ensuring that brand creativity and long-term value, not just price, define the future of commerce.
What is your forecast for agentic commerce loyalty?
My forecast is that loyalty will become the single most important differentiator for brands in the age of agentic commerce. When AI agents can instantly compare every product on price and features, the only sustainable high ground will be the relationship a brand has with its customer. Loyalty programs, when made intelligible to AI, are the data-driven embodiment of that relationship. We’ll see AI agents proactively managing a customer’s entire loyalty portfolio, recommending purchases not just to save money today, but to maximize value and status over a lifetime. Brands that invest now in making their loyalty programs “agent-ready” will be the ones that thrive, while those who continue to compete solely on price will find themselves in an ever-accelerating race to the bottom.
