In a retail landscape transformed by economic shifts and evolving consumer habits, the role of artificial intelligence has moved from a novel curiosity to a cornerstone of modern ecommerce. To understand the currents shaping 2026, we sat down with Zainab Hussain, an e-commerce strategist with deep expertise in customer engagement and operations. Our conversation explored the rapid advancement of agentic commerce, the strategic pivot away from traditional search engine reliance, and how retailers are tailoring AI to meet the specific demands of a new generation of shoppers. We also delved into the profound impact of AI on conversion rates, the evolving dynamics of major sales events like Prime Day, and the future of personalized payment options.
The article highlights 2025 as a “breakout year” for agentic commerce. Beyond simple checkout tasks, how can retailers realistically advance these AI agents to interact with third-party platforms in 2026? Could you walk us through a specific use case for this more complex functionality?
Absolutely. We saw 2025 as the year retailers proved the concept internally, using AI agents for tasks like building curated shopping lists or handling checkout within their own digital walls. It felt very controlled. The next real leap, which I expect to see gaining traction in 2026, is allowing these agents to operate across different ecosystems. Imagine a customer planning a weekend camping trip. They could give their favorite outdoor retailer’s AI agent a single prompt: “Get me set up for a two-night trip to Yosemite next month, budget $500.” The agent would not only populate a cart with a tent and sleeping bag from the retailer’s own inventory but could also be empowered to interact with third-party platforms to book the campsite reservation, check the weather forecast to recommend appropriate clothing, and maybe even find a top-rated local guide service. This moves the AI from a simple shopping assistant to a true, autonomous trip planner, creating immense value and loyalty for the brand that orchestrates it.
With younger shoppers using social media and LLMs for discovery, retailers are preparing for a potential “Google Zero” event. What actionable steps should a mid-sized retailer take now to diversify its traffic sources and prepare for a future with far less organic search traffic?
The threat of a “Google Zero” event feels very real for many businesses, and waiting is not an option. For a mid-sized retailer, the key is to go where the customers are, and increasingly, that’s not a Google search bar. First, they need to double down on their social commerce strategy. This isn’t just about posting ads; it’s about creating authentic, engaging content on platforms where young cohorts spend their time and making those experiences shoppable. Second, they must start thinking about optimizing for LLMs. This means shifting from rigid, keyword-focused SEO to creating rich, descriptive, and conversational product information that an AI like Gemini or ChatGPT can understand and recommend confidently. Finally, building a strong community and first-party data through newsletters and loyalty programs becomes paramount. That direct line to the customer is the ultimate defense against any single traffic source disappearing overnight.
Given that Gen Z shows a strong preference for AI assistants that save them time and money, how should an ecommerce site design its AI tools to meet these specific priorities? Please share some key metrics you would use to measure if these tools are truly succeeding.
That’s the critical point—the fanciest tech is useless if it doesn’t solve a real problem for the user. For Gen Z, the core problems are often time and budget. So, an effective AI tool should be designed with ruthless efficiency in mind. Instead of a generic chatbot, imagine an AI assistant that, upon you landing on a product page, instantly shows you three similar, lower-priced alternatives from the same site, or automatically finds and applies the best possible coupon code at checkout without you having to hunt for it. To measure success, I’d look beyond simple engagement. I’d track the “time-to-purchase” for users who engage the AI versus those who don’t—is it genuinely faster? I’d monitor the “rate of discount utilization” to see if it’s effectively saving them money. And most importantly, I’d measure the repeat usage rate of the AI feature itself. If they come back and use it again, you know you’ve built something that truly adds value to their shopping experience.
The text cites a massive 3,300% year-over-year jump in AI-sourced traffic on Prime Day 2025. What does this tell us about evolving discovery habits, and how can smaller retailers leverage this trend during major sales events without Amazon’s vast resources?
That 3,300% figure from Adobe Analytics is staggering; it’s a flashing red light indicating a fundamental shift in how people find products. It tells us that using AI for shopping research isn’t a niche activity anymore—it’s mainstream, especially during high-stakes sales events where finding the best deal quickly is crucial. A smaller retailer can’t build Amazon’s infrastructure, but they can ride the wave. They should ensure their product feeds are flawlessly optimized for ingestion by major LLMs, so their deals surface in AI-powered recommendations. They can also use AI-driven advertising tools to run hyper-targeted campaigns that mirror the “tentpole” timing of events like Prime Day and Cyber Monday. It’s not about outspending Amazon; it’s about being present and discoverable within these new AI-driven discovery channels that customers are clearly embracing.
Conversion rates reportedly stabilized in 2025 due to AI and personalization. Looking ahead, how can AI tools like Gemini and ChatGPT move beyond just research to more directly influence purchasing decisions and reduce cart abandonment? Please describe a specific customer journey.
Stabilization was the first step; now it’s about growth. Moving AI from a research librarian to a persuasive sales associate is the next frontier. Imagine a customer, Sarah, who is researching high-end headphones. She uses an LLM to compare features and reads reviews, then clicks through to a retailer’s site. She adds a pair to her cart but hesitates on the price and closes the tab. An hour later, the retailer’s AI, integrated with her journey, sends a personalized email. It doesn’t just say, “You left something in your cart.” It says, “Hi Sarah, we saw you were looking at the audiophile-grade headphones. Did you know they come with a free subscription to a high-res music streaming service for six months? Also, we can offer a flexible payment plan to make it more manageable.” This proactive, context-aware engagement directly addresses the likely point of friction—cost—and adds value, making it far more likely she’ll complete the purchase.
During the 2025 Cyber 5, there were key shifts in spending and the use of Buy Now, Pay Later (BNPL). How do you see BNPL services evolving with AI personalization in 2026, and what potential pitfalls should retailers watch out for when promoting these payment options?
The Cyber 5 results showed us that BNPL is a fixture, but its application can be more intelligent. In 2026, I anticipate AI will transform the generic “Pay in 4” option into a truly personalized offer. Based on a customer’s purchase history, loyalty status, and the value of their cart, an AI could dynamically generate a unique payment plan—maybe it’s six payments for a large furniture purchase or an interest-free period for a loyal customer. The goal is to make a purchase feel more attainable. However, the major pitfall is the ethical boundary. Retailers must be incredibly careful not to use this personalization to encourage irresponsible spending or prey on vulnerable shoppers. Transparency is everything. The terms must be crystal clear, and the promotion of these services should feel like a helpful option, not a high-pressure tactic to push consumers into debt they can’t handle.
What is your forecast for the evolution of AI’s role in ecommerce, moving from a helpful tool to an indispensable partner for both retailers and shoppers by the end of the decade?
My forecast is that the distinction between “using AI” and “doing ecommerce” will essentially vanish. For shoppers, AI will evolve from a tool they actively use into an autonomous partner that works for them in the background—a personal shopping agent that understands their style, ethics, and budget, constantly scanning for the best products and deals across the entire web. For retailers, AI will become the central nervous system of their operation. It won’t just be a feature for the website; it will be an indispensable partner making critical decisions in real-time, from dynamically adjusting supply chains and pricing based on unforeseen events to crafting one-to-one marketing messages at a scale no human team could ever manage. It’s a future where commerce becomes more efficient, more personalized, and deeply interwoven with intelligent systems on both sides of the transaction.
