As agentic AI moves from a futuristic concept to a practical tool, retailers are in a race to integrate these intelligent systems into their core operations. To unpack this transformation, we sat down with Zainab Hussain, an e-commerce strategist specializing in customer engagement and operations. Our conversation explored the burgeoning partnership between humans and AI “digital teammates,” the rise of autonomous decision-making in merchandising, and how brands like Balenciaga and Guess are leveraging this technology for hyper-personalization and supply chain efficiency. We also delved into the critical shift in employee skills required to thrive in this new, AI-augmented landscape.
Balenciaga describes its AI agents as “digital teammates” that help shift strategy from reactive to proactive. Can you walk me through how this human-AI collaboration works day-to-day in merchandising, and what metrics are used to measure the success of these real-time adjustments?
It’s a fascinating and powerful shift in mindset. Think of the AI agent not as a tool you command, but as a colleague who is incredibly fast at sifting through mountains of data. Day-to-day, the agent is constantly analyzing global inventory, sales figures, and market signals. It might autonomously flag an emerging trend in a specific region and suggest a merchandising adjustment. The human team then takes that insight and applies their strategic and creative judgment. Instead of spending their days buried in spreadsheets trying to spot these patterns, they are now focused on high-level strategy. Success isn’t just about sales uplift; it’s measured by the speed of decision-making and the ability to get ahead of demand signals before they become missed opportunities. The core metric is the transition from reacting to last week’s sales report to proactively shaping next week’s.
We’re seeing agentic AI autonomously identify and resolve merchandising challenges with minimal human input. Could you provide a step-by-step example of how an agent might respond to a local demand signal, and what kind of oversight or guardrails are necessary to manage this autonomy?
Absolutely. Imagine an AI agent detects a sudden spike in social media mentions and online searches for a particular Balenciaga sneaker in a specific city. The agent would first cross-reference this with real-time inventory levels in that city’s stores and warehouse, identifying a potential stockout. Autonomously, it could then initiate a stock transfer from a nearby store with a surplus to meet the anticipated demand, all with minimal human intervention. However, this autonomy requires robust guardrails. You’d set rules so the agent requires human approval for large-scale inventory shifts or anything exceeding a certain financial threshold. You also need a transparent log of every decision the agent makes. The goal is to empower the AI to handle routine, data-driven decisions at an accelerated pace, freeing up the human team to manage the exceptions and the bigger picture.
With nearly 90% of executives planning to boost AI budgets, many are focused on product data. How is using AI to create structured insights for product information, as Guess is doing, directly improving hyper-personalization and consumer recommendations? Please share some specifics on the process.
This is fundamental to the future of e-commerce. Historically, product data has been messy—a mix of creative descriptions, technical specs, and varied image tags. AI is changing the game by turning this chaos into clean, structured, and templated information. For a brand like Guess, an AI can analyze a jacket and systematically tag it with attributes like “denim,” “light wash,” “slim fit,” and “distressed detailing.” This structured data becomes the fuel for hyper-personalization. When a customer shows interest in light-wash denim, the recommendation engine can now surface not just similar jeans but also that specific jacket, creating a far more relevant and cohesive shopping experience. It’s about moving from basic keyword matching to a deep, attribute-level understanding of both the product and the consumer’s intent.
Agentic AI is set to transform the supply chain by continuously learning from real-time data to complete tasks. What is a practical scenario of this in action, and how does this ability to adapt to evolving conditions differ from traditional supply chain automation?
The difference is like comparing a programmed robot to a thinking one. Traditional automation follows a rigid script: “If X happens, do Y.” An agentic AI is far more dynamic. Consider a container ship of goods en route. The agent is continuously learning from real-time data—weather patterns, port congestion, geopolitical events. If it detects a storm that will delay arrival at the intended port, it won’t just send an alert. It will autonomously analyze alternative ports, calculate new ground transportation costs and routes, and potentially reroute the entire shipment to minimize disruption, all without needing a predefined outcome for that specific scenario. It adapts to complex, evolving conditions on the fly, a capability that goes far beyond the scope of traditional, rule-based systems.
As AI automates routine tasks, there is a growing emphasis on nurturing human cognitive capacity. In a retail environment, what new skills will be most critical for employees, and how should companies adapt their training programs to prepare for this new reality?
This is perhaps the most crucial point for leaders to understand. As AI takes over the “what” and the “how” of routine tasks, the value of the human workforce shifts entirely to the “why.” The most critical skills will be creative direction, strategic thinking, and interpreting the insights that AI provides. Employees will need to be collaborators and strategists, not just operators. Training programs must evolve accordingly. Instead of teaching someone how to run a report, you teach them how to question the data in the report and develop a creative merchandising strategy from it. Companies should invest in developing cognitive capacity through workshops on strategic planning and data storytelling, preparing their teams to guide their new “digital teammates” rather than just perform repetitive tasks.
What is your forecast for agentic AI in the retail sector over the next five years?
Over the next five years, I forecast that agentic AI will move from a competitive advantage to a foundational element of retail operations. We’ll see its application deepen, with agents not only managing inventory but also autonomously executing localized marketing campaigns and optimizing pricing in real time. The conversation will shift away from whether to adopt AI and toward how to build the most effective human-AI collaborative teams. The most successful retailers will be those who not only invest in the technology but also invest heavily in nurturing the cognitive and creative skills of their human workforce, creating a truly symbiotic relationship that drives innovation at a pace we’ve never seen before.
