Our retail landscape is shifting beneath our feet, and as we mark the tenth anniversary of the merger that created Ahold Delhaize, the role of artificial intelligence has moved from a futuristic concept to the very heartbeat of our operations. To understand this transformation, we are joined by Zainab Hussain, an e-commerce strategist who has spent years navigating the intersection of customer engagement and operational efficiency. Zainab brings a unique perspective on how one of the world’s largest retail groups is currently utilizing technology to empower tens of thousands of associates, streamline complex global supply chains, and make significant strides in the urgent fight against food waste. Today, she shares her insights on the delicate balance between rapid innovation and large-scale implementation, the surprising human elements found in machine learning, and the evolving relationship between store managers and the digital “agents” of the future.
The following discussion explores the strategic evolution of AI within a multi-brand retail environment, touching on the necessity of a dedicated product organization to bridge the gap between technical engineering and business needs. We delve into the practical challenges of associate adoption—including how different generations interact with technology—and look at the “invisible” AI that optimizes everything from bakery schedules to the way a truck is packed. Finally, we look ahead to a world where store managers transition from task-based management to orchestrators of both human and digital talent.
When you are moving a project from a successful pilot to a massive rollout across hundreds or thousands of stores, how do you handle the transition without losing the agility of a “fail fast” mentality?
The secret to a successful transition lies in starting small but always keeping the ultimate scale in your peripheral vision. We typically begin pilots in just one or two stores, which allows us to gather raw, unfiltered feedback before we even think about a wider release. As soon as a concept proves its worth in that controlled environment, we pivot immediately to scaling, but we do so with a very specific structural advantage: our Product Organization. This team acts as the essential architect, sitting right in the middle of our tech experts and our business leaders to ensure that the talented engineers are building exactly what the business actually needs to survive. We don’t just build tech for the sake of tech; we follow a “have problem, seek solution” philosophy, meaning we only pull the AI lever if it is the simplest and most effective way to solve a specific challenge for a customer or a colleague. If a pilot isn’t delivering measurable value to our P&L or improving the associate experience within that initial phase, we aren’t afraid to pull the plug quickly so we can reallocate those resources elsewhere.
With over 80,000 associates already using the new AI assistant and generating more than 50,000 conversations every week, what were the biggest organizational hurdles in achieving that level of engagement?
To get that level of adoption, we had to move beyond corporate assumptions and actually step into the shoes of our associates to see what their workday really looks like. Before we even wrote a single line of AI code, we launched a test app that used humans on the backend to answer questions, which revealed that colleagues were desperate for quick info on product locations, price checks, and promotions. We were seeing 250,000 requests every month during that phase, which proved the demand was massive if we could just get the interface right. One of our biggest learning curves was realizing that our younger associates—the core of our store workforce—actually found voice-activated tools “awkward” and preferred typing in short, one-word bursts. This led to some humorous but vital technical fixes, like when the AI misinterpreted the Dutch word for onion, “ui,” as “user interface,” or struggled to understand “Dubai” when a staff member was actually looking for the viral Dubai chocolate. By listening to these specific behavioral quirks and training the model to handle one-word queries and slang, we built a tool that genuinely empowers them rather than adding another layer of frustration to their shift.
How do you manage the creation of shared AI infrastructure across a global group while still protecting the local identity and unique needs of individual brands?
Our approach is built on a “share the engine, customize the car” philosophy where we prioritize getting the right people together from different brands to ensure we aren’t constantly reinventing the wheel. While our various brands might serve different cultures across nine countries, the core pillars of retail—managing products, stores, and people—remain remarkably similar. To support this, we’ve built an adaptive, LLM-agnostic platform that allows developers from any of our 17 brands to run applications on a unified infrastructure, which gives us a massive competitive edge without touching the local customer experience. Once the underlying platform is stable, we move into creating “domain visions,” where we collectively imagine the future of things like merchandising or logistics. This allows us to share a high-level strategic roadmap while giving each local brand the freedom to decide how those AI capabilities should feel to their specific customers on the ground.
Is there a specific AI application that has flown under the radar but has had a surprisingly large impact on how you serve a diverse customer base?
I am incredibly proud of a project that actually emerged from one of our 24-hour hackathons, where our tech teams literally lived and slept in the office to solve a complex problem. They developed an AI translation layer that sits between our internal systems and our customer-facing app, allowing us to automatically translate every single piece of content into multiple languages. In a country like the Netherlands, which has 1.2 million non-native speakers, being able to offer our app in Turkish, Polish, French, and English is a massive step toward true inclusivity. Traditionally, a manual translation project of this scale would have cost millions of dollars and taken months to execute, but our team built and deployed it for almost no cost in a single day. It really opened our eyes to how AI can be a tool for empathy and accessibility, allowing us to help a much broader range of customers navigate their daily shopping in their own language.
In an industry where experience is highly valued, how do you convince veteran store employees to trust an algorithm, especially when it comes to something as delicate as bakery replenishment?
Trust isn’t built with a memo; it’s built through transparent communication and a very strong feedback loop that respects the expertise of our veteran staff. When we launched the bakery app that tells associates exactly how many loaves of bread to bake and when, we knew we were asking people who had made these decisions by “feel” for decades to change their entire workflow. To bridge that gap, we held information nights at every single store and produced movies to explain exactly how the tool helps reduce waste and ensures we don’t have empty shelves during peak hours. We also had to show them the data behind “safety stocks”—proving that while they might think they need 15 types of white bread on the shelf to look “full,” the AI shows that customers see those products as similar enough that we can reduce the variety without hurting the experience. It took a few weeks of active listening and making adjustments based on their real-world feedback, but once they saw the reduction in physical waste and the improved availability, the skepticism turned into genuine appreciation.
Since fighting food waste is such a critical priority, could you walk us through the specific ways AI is currently intervening in the lifecycle of a product?
AI is our most powerful weapon against waste because it allows us to be surgical in how we stock and price our inventory. It starts with incredibly granular forecasting—our models generate over one billion forecasts every single day, looking 50 days ahead for 17,000 products across 1,200 different stores. But since no forecast is 100% perfect, we use AI to dynamically mark down items that are nearing their expiration date, with prices that automatically update on electronic shelf labels every 15 minutes. We are also rolling out new algorithms for assortment planning, ensuring that the specific product mix in each store matches what that local community actually wants to buy. By using simulation environments to test our replenishment algorithms before they ever touch a physical store, we can find the perfect “sweet spot” between having full shelves for our customers and minimizing the environmental impact of unsold food.
Why do you believe the “invisible” operational side of retail, like the supply chain, deserves just as much attention as the customer-facing AI?
The operational side is actually our most AI-intensive domain because it’s essentially a massive, high-stakes game of Tetris that runs 24 hours a day. We use optimization algorithms to figure out exactly how to stack articles on a load carrier in the distribution center so that they fit perfectly, and then we use another layer of AI to pack our delivery trucks with as little “empty air” as possible. Even within the distribution centers, AI designs the most efficient walking paths for our associates, saving them unnecessary steps and making the physical labor of the job much more manageable. These innovations are “invisible” to the customer standing in the aisle, but they save millions of dollars and significantly reduce our carbon footprint by making our entire logistics network run with extreme precision. When you operate at our scale, a 1% or 2% increase in packing efficiency translates into massive real-world benefits for both the business and the planet.
What is your forecast for the role of AI in retail as we move toward the end of this decade?
I believe we are entering the era of “agentic AI,” where the very definition of leadership in retail is going to be rewritten. We are likely the last generation of leaders who will only manage human teams; very soon, upcoming managers will be leading digital agents who have their own employee numbers and execute complex tasks around the clock. Instead of store managers spending hours in the back office hunched over a computer processing emails and reports, these AI agents will handle the data crunching and automatically distribute tasks to the floor staff. This shift will actually make our people “more human” by freeing store managers to spend their time coaching associates, interacting with customers, and responding to real-time feedback. Ultimately, the future of retail isn’t about machines replacing people, but about machines handling the repetitive tasks so that humans can focus on the empathy, creativity, and service that an algorithm simply cannot replicate.
