Zainab Hussain is a seasoned e-commerce strategist who has spent years helping brands navigate the complexities of customer engagement and operational efficiency. In a retail world increasingly dominated by digital interactions, she has become a leading voice on why the “full automation” trend is often a trap for growing businesses. Our conversation explores the innovative AI-native support model that is currently serving over 200 merchants globally. We delve into how blending machine speed with human oversight can slash support costs by 40% while maintaining the high quality required in a $470 billion global market. Through the lens of scaling operations and managing millions of weekly inquiries, Zainab outlines why the future of retail support relies on a sophisticated operating system that prioritizes human judgment as much as artificial intelligence.
Many brands find that full AI automation fails to handle complex or brand-sensitive issues, so why is the human-in-the-loop approach becoming the new standard for excellence?
Many companies rushed into chatbots thinking they were a silver bullet, but they quickly realized that the customer experience felt hollow and disconnected. When a customer has a high-stakes issue, such as a missing shipment from a premium brand like Transformer Table, they do not want to be trapped in a loop of generic, robotic responses. Large Language Models are incredibly powerful tools for processing information, but they lack the accountability and nuanced human judgment required for sensitive interactions. By moving to an AI-native model, we ensure that while the AI does the heavy lifting for two million inquiries a week, vetted humans provide the quality control that prevents a brand’s reputation from souring. It is about creating a safety net where technology handles the speed and humans ensure the heart of the brand remains intact.
How does an AI-native operating system actually transform the day-to-day workflow for a merchant compared to traditional outsourced support?
Traditional outsourced support often feels like a black box where quality is inconsistent and costs are difficult to control, but an AI-native operating system changes the entire math of the operation. This system functions as a central hub where workflow memory and automated quality checks are baked into every single interaction. This allows merchants like TRIP Drinks or Kahawa 1893 to see tangible, measurable outcomes, such as a 40% reduction in support costs without any dip in service quality. We are not just throwing more people at a problem; we are making every individual workflow more intelligent and scalable. It allows a brand to modernize its support stack without the immense risk of trying to build and manage a complex technical operation entirely on its own.
With plans to reach $100 million in revenue by 2028, what specific hurdles must be cleared when scaling these hybrid operations across the competitive U.S. market?
Reaching a $100 million revenue goal requires more than just efficient software; it requires a massive expansion of localized human expertise to handle the diverse needs of the U.S. market. We are currently fueling our growth by tripling our workforce to over 100 employees, specifically targeting top-tier talent for leadership roles in sales, partnerships, and customer success. The biggest challenge is ensuring that as we scale, our deep workflow understanding remains as sharp as it was when we were a smaller team. This expansion is essential because AI transformation is not just a software problem—it is an operations problem that requires a strong human foundation to manage the complexity of global e-commerce. By building out our teams in both the U.S. and India, we can provide the around-the-clock reliability that modern Shopify merchants expect.
The recent sentiment report shows that nearly half of consumers want a blend of AI and human support, so how do you bridge that gap without losing the efficiency of automation?
Our data shows that nearly half of all consumers are now explicitly asking for a blend of AI and human support because they value both speed and empathy. They appreciate the instant response an AI agent provides for simple tracking questions, but they feel a palpable sense of relief when they know a human is overseeing the more complex parts of their journey. We bridge this gap by using AI to handle the initial data gathering and simple resolutions, while keeping a human “in the loop” to step in the moment a situation requires emotional intelligence. This hybrid model allows us to deliver the lightning-fast responses customers crave while maintaining the human oversight that ensures every resolution is accurate and helpful. It is the only way to modernize the support experience without making the customer feel like they are shouting into a digital void.
What is your forecast for the future of AI-native customer service over the next decade?
I believe the $470 billion global support market will shift entirely away from the old choice between “cheap and robotic” or “expensive and manual.” Over the next ten years, we will see a standard where AI-native service delivery is the baseline, and companies that do not integrate human judgment into their automated workflows will simply lose their competitive edge. By 2028, the most successful brands will be those that use AI not to replace their people, but to empower them to handle millions of inquiries with a level of precision that was previously impossible. We are building the operating system for this new era, where the goal is a seamless, measurable, and highly intelligent service that benefits both the brand’s bottom line and the customer’s overall experience.
