Zainab Hussain is a seasoned e-commerce strategist who has spent years navigating the complex intersection of digital operations and consumer engagement. With a background rooted in streamlining large-scale retail ecosystems, she has a keen eye for how legacy brands can modernize without losing their soul. In this discussion, we explore Gap Inc.’s massive technological pivot, examining how the retail giant is leveraging a sophisticated AI-driven marketing overhaul to unify its portfolio of iconic brands like Old Navy and Athleta. We delve into the strategic implementation of agentic AI, the transformation of loyalty programs involving 40 million customers, and the move toward a predictive, real-time growth engine that aims to solve the industry’s most persistent friction points.
Managing a massive data foundation across different banners like Old Navy and Banana Republic requires breaking down deep-seated silos; how does this new AI-ready infrastructure change the way these brands interact with their customers?
The shift toward a unified, AI-powered platform is fundamentally about eliminating the friction that occurs when brands operate as isolated islands. By building this foundation with Google Cloud, Gap is moving away from fragmented data sets and toward a cohesive understanding of customer intent that spans its entire portfolio. When you have a “unified, AI-ready data foundation,” you stop looking at a customer as just a Banana Republic shopper or an Old Navy regular and start seeing their holistic behavior across all touchpoints. This integration allows the organization to move much faster, making decisions based on a real-time growth engine rather than lagging historical reports. It means that whether a customer is interacting via an app or an email, the experience is consistently relevant and grounded in the same deep intelligence layer.
There is often a fear that AI might stifle brand personality, but Gap suggests it will actually empower their creative teams. How do you see tools like Gemini and Veo enhancing the storytelling process for a retailer?
The goal here isn’t to replace the human element, but to liberate creative teams from the repetitive, manual tasks that often bog down marketing departments. By utilizing Google’s Gemini models for text and image generation alongside Veo for video content, the creative staff can pivot their energy toward high-level strategy and the kind of storytelling that builds lasting brand love. Imagine a designer using Nano Banana to edit photos or generate imagery in seconds, allowing them to test dozens of visual concepts that would have previously taken weeks to produce. This “agentic” capability means the AI handles the execution of content at scale, while the humans focus on the emotional resonance of the brand. Ultimately, this pairing allows a retailer to be more responsive to culture, showing up for customers in a way that feels both personal and incredibly fast.
Zeta Global’s Athena platform acts as an “intelligence layer” for this new stack; what does it look like when AI begins to predict customer behavior rather than just reacting to it?
Athena is designed to act as the brain of the operation, linking shopper data directly to marketing decisions and campaign execution in a way that feels almost intuitive. Instead of simply looking at what happened in the last quarter, this intelligence layer is built to predict what will happen next and determine the highest-value action to take for each individual customer. This transforms audience planning and creative production into a coordinated, real-time rollout where performance improvements are baked into the workflow. In practice, this means a campaign can be adjusted on the fly based on how users are behaving in that exact moment, making the entire marketing organization more responsive. By unifying these decisions across the stack, the retailer can deliver highly personalized, scaled experiences that feel tailor-made for the shopper’s current needs.
A major pain point in fashion e-commerce is fit and confidence; how is Gap leveraging partnerships like Bold Metrics and agentic AI to move technology from a novelty to a practical utility?
For Gap, the pursuit of AI is about solving real, tangible customer problems, particularly the uncertainty surrounding how a garment will actually fit. By integrating personalized size recommendations through Bold Metrics within their AI interfaces, they are directly addressing the primary reason many shoppers hesitate to click “buy.” This is further enhanced by “AI Mode” in Google Search and the Gemini app, where customers can discover and purchase products through a conversational interface. This uses the Universal Commerce Protocol, a standard developed with Shopify that allows AI systems to connect directly to checkout flows for seamless transactions. It is a sensory shift in shopping where a digital assistant on an e-commerce site provides curated, trend-based recommendations, making the virtual experience feel as guided as an in-store visit.
Despite these innovations, Gap saw a 2% dip in online sales recently, specifically within the Athleta brand. How can an AI-driven operating model address these specific quarterly fluctuations and category softness?
While Gap reported net sales of $3.50 billion—a 1% increase—the softness in online sales, particularly in categories like dresses at Old Navy, highlights the need for better product intelligence. An AI-driven model allows the company to improve how it designs, buys, allocates, and replenishes inventory, ensuring they aren’t over-indexed in categories that aren’t moving. By using enterprise AI to make the marketing model more measurable and responsive, they can pivot their digital spend to support brands like Athleta when they see a “digitally penetrated” brand starting to slip. The objective is to use data to understand why a specific category is underperforming and then use that insight to adjust the inventory lifecycle in real-time. This level of responsiveness is crucial for balancing the 3% growth seen in physical stores against the fluctuations of the online marketplace.
The transition of 40 million customers into the Encore loyalty program signals a shift toward engagement; how does AI help transform a simple transaction history into a meaningful brand experience?
The relaunch of the Encore loyalty program represents a massive migration of a 40-million-customer house file from a basic transaction-based system to a comprehensive engagement platform. AI plays a critical role here by taking those millions of data points and turning them into personalized access, exclusive content, and unique experiences that go far beyond traditional rewards. Instead of just earning points for a purchase, customers are now part of a broader ecosystem where the AI can suggest content or rewards based on their specific style preferences and past interactions. This move, managed through the formal Office of AI established in 2024, ensures that the loyalty communications are not just noise but are actually adding value to the customer’s life. It builds a deeper connection across all four banners, making the relationship between the brand and the shopper feel more like a continuous conversation than a series of one-off sales.
What is your forecast for the role of agentic AI in the future of retail?
I believe we are entering an era where the traditional “search bar” will become obsolete, replaced entirely by agentic AI that acts as a personal shopper with full transactional capabilities. We are already seeing the beginning of this with Gap’s use of the Universal Commerce Protocol, which allows a customer to go from discovery to checkout within a single conversational thread. As these AI agents become more sophisticated at understanding fit, style, and intent, they will move from being a “tool” on a website to being the primary interface through which we interact with brands. In the next few years, the retailers that thrive will be those that have successfully built a unified data foundation, allowing these AI agents to provide a frictionless, highly personal, and near-instantaneous shopping experience. The focus will shift entirely from driving traffic to a site to being present in the “AI-driven” moments where customers are already living their lives.
