As the global travel sector experiences a digital rebirth, the integration of artificial intelligence has moved from a speculative luxury to a core operational necessity. To navigate this shift, we sat down with Zainab Hussain, an e-commerce strategist and retail expert who has spent years optimizing customer engagement and operations within high-traffic environments. With a background that bridges the gap between digital efficiency and physical retail excellence, Zainab offers a grounded perspective on why the travel retail industry must treat AI as an operating-model transformation rather than just a shiny new technology project.
In this conversation, we explore the nuances of “invisible AI” and how it differs from the disruptive shifts seen in software industries. We dive into the practicalities of agentic AI—systems that can autonomously manage stock and pricing—and discuss the delicate balance of maintaining a human touch in high-pressure airport settings. Zainab also addresses the structural challenges of the industry, such as decade-long concession contracts and the inherent scarcity of traveler data, providing a roadmap for how retailers can leapfrog traditional development cycles to build a more fluid, data-driven future.
AI is often seen as disrupting software companies at their core. In physical retail environments, how do you distinguish between a fundamental shift in business models versus an evolution in operational efficiency? Please provide specific examples of where AI should remain invisible to the traveler to be most effective.
In the software-as-a-service sector, we have seen AI actually challenge the value of the product itself, such as when the AI work platform Monday.com saw its market capitalization drop by more than 30% in a single month following major AI model releases. For travel retail, however, the shift is transformative rather than revolutionary because our core business is anchored in physical assets, human relationships, and long-term credibility with airport landlords. The business model—securing a physical space to sell goods to travelers—remains intact, but the engine running that model is being completely overhauled. AI is most effective when it remains entirely invisible in areas like stock visibility, background forecasting, and logistics. When a traveler finds exactly the product they want on a shelf in a busy terminal, they shouldn’t be thinking about the complex algorithms that ensured that item was replenished just in time; they should only experience the seamless availability of the goods.
As agentic AI begins to execute complex tasks autonomously, how can these systems specifically manage stock replenishment and pricing without human intervention? What metrics would you use to measure success in these autonomous background operations, and how do store teams transition into oversight roles?
We are moving toward 2026 as a decisive year for agentic AI, where systems shift from simple conversation to breaking down complex problems and executing actions autonomously across different data environments. In stock replenishment and pricing, these systems can continuously learn from real-time sales data and external travel trends to adjust prices or trigger orders without a person having to manually update a spreadsheet. To measure the success of these autonomous operations, we look for a significant reduction in executional friction and improved accuracy in inventory allocation, ensuring that high-demand items are never out of stock during peak travel windows. This allows our store teams to move away from the “executional burden” of counting stock or checking price tags and instead step into oversight roles where they apply human judgment and coaching. The goal is to have the AI handle the data-heavy tasks so that the staff can focus on the relational aspects of the job that machines cannot replicate.
Some companies have struggled when replacing customer-facing roles with automation. How can staff use AI-driven product knowledge to build deeper trust with customers in a high-pressure airport environment? Could you share a step-by-step approach for integrating these tools without degrading the quality of personal service?
Recent history has shown us that pushing AI too far to the front line can backfire, which is why companies like Klarna have recognized the ongoing need for human-led interactions in complex service environments. In an airport, where travelers are often stressed or pressed for time, the human touch is the primary driver of trust and reputation. To integrate AI without losing that connection, we start by deploying AI as a “behind-the-scenes” assistant that provides staff with instant, deep product insights and personalized recommendations. The second step is to ensure these tools surface information naturally, allowing a salesperson to offer a traveler a specific skin-care solution or a rare whiskey based on data-driven insights without ever looking like they are reading from a script. Finally, we focus on training teams to use these insights to facilitate more meaningful conversations, ensuring the technology acts as a support system that empowers the employee rather than a barrier that replaces them.
Airport retail operates under long-term concession contracts and strict security constraints that often outlast technology cycles. How do you maintain a modern digital infrastructure when legal and physical environments are so rigid? What strategies help bridge the gap between rapid AI updates and decade-long business agreements?
The structural reality of travel retail is that while a tool like ChatGPT can revolutionize the tech world in just a few years, a typical airport concession agreement can last 10 years or more. This creates a disconnect between the rapid pace of AI updates and the slow, heavy cycle of airport operations, which are often weighted down by security requirements and regulatory constraints. To bridge this gap, we must focus on building robust, flexible data foundations that can evolve even if the physical store layout or the legal contract remains static. We approach innovation with a different rhythm, treating AI as an operating-model transformation that happens incrementally rather than a one-off technology project. By embedding flexible, AI-enabled data platforms into our daily operations, we can swap out specific algorithms or tools as they improve without needing to renegotiate the foundational terms of our long-term business agreements.
With travelers making infrequent purchases, customer data is often limited compared to daily e-commerce platforms. How can retailers and brands overcome this data scarcity to personalize the experience? What specific steps are needed to break down the silos between airlines, airports, and retailers for better data sharing?
Data scarcity is one of the most significant hurdles in travel retail because a customer might only walk through our doors once or twice a year, providing very few behavioral signals compared to a daily grocery app. To overcome this, we have to stop operating in silos where airports, airlines, and retailers each guard their own small piece of the traveler’s journey. The first step is to move away from fragmented collaboration—like the endless exchange of emails and manual spreadsheets—and toward shared, AI-enabled data platforms where insights can circulate fluidly. We need to create a “connected version” of the industry where a retailer can understand a traveler’s preferences based on broader travel patterns provided by airline or airport partners. When data moves seamlessly between these functions, we can provide a personalized experience that feels relevant even to an infrequent shopper, because the recommendation is based on a collective understanding of their journey rather than a single past purchase.
Traditional retail has the potential to leapfrog years of missed digital development by adopting AI now. What specific back-office functions, such as finance or HR, are ripe for this immediate acceleration? Please describe the process of moving from fragmented spreadsheets to a fluid, AI-enabled data platform.
Travel retail has historically lagged behind in digital adoption, but AI gives us a unique opportunity to skip several stages of traditional development in one move. In the back office, finance teams are ripe for this change; they can transition from spending hours reconciling data to using AI to surface instant analyses, allowing them to focus more on strategic decision-making and judgment. Similarly, HR departments can shift their energy from administrative tasks to talent development and coaching, using AI to answer routine employee questions and manage documentation. The process of moving away from fragmented spreadsheets involves centralizing data into a single, structured environment where intelligence is embedded into the daily workflow. This creates a fluid system where information isn’t trapped in a single file but is instead available to every department, removing friction and allowing the entire organization to operate with much greater consistency and speed.
What is your forecast for AI in travel retail?
My forecast is that AI will not redefine the fundamental “what” of travel retail, but it will dramatically improve the “how” by creating a far more fluid and connected ecosystem. Over the next few years, we will see the industry move away from chasing the most advanced, flashy gadgets and instead focus on strengthening the data foundations that allow AI to genuinely add value behind the scenes. We will see a shift where human teams are firmly “in the cockpit,” making the high-level emotional and strategic decisions, while AI acts as the engine, handling the complex execution and optimization tasks that currently slow us down. Ultimately, the winners in this space will be the organizations that treat AI as a cultural and operational evolution, investing in their people’s ability to work alongside these new tools to deliver a shopping experience that feels more personal, efficient, and human than ever before.
