2 days Ago By Miller V
Share Tweet Share Share Email AI in shopping isn’t just about product recommendations anymore—it’s becoming the shopping companion itself. From voice assistants guiding your purchase journey to generative AI crafting real-time product responses, the retail experience is undergoing a seismic shift. At the center of this shift is Shiva Chandrashekhar , a Senior IEEE Member and seasoned product leader at Amazon.
With years of experience across AI, machine learning, and product innovation, Shiva has been at the forefront of developing intelligent, scalable, and human-centric product experiences that are transforming how consumers engage with brands. In this exclusive interview, he breaks down how AI is transforming retail interfaces and shares insights into the broader impact of generative AI and AI-driven advertising. Shiva, thanks for joining us.
Let’s begin with a big-picture question: why is interface design so critical in AI-driven products? Thanks for having me! When people interact with AI, what they’re really interacting with is the interface—the way information is presented, the feedback they receive, and the control they feel. Even the best models can fall short if the experience is confusing or opaque. Good interface design bridges the gap between powerful AI capabilities and real human needs.
It helps users understand what the system can do, what it’s doing now, and what it will do next. Many AI tools today are extremely capable—but they can also feel overwhelming or unpredictable. How do you build trust through interface design? Trust is foundational.
If users don’t understand or trust the AI, they won’t use it—no matter how accurate or powerful it is. I believe in building for progressive disclosure. Let the AI handle complexity under the hood, but show the user just enough to feel in control.
Expose decision logic when it helps, like showing why a recommendation was made or what data influenced an answer. When I led teams working on voice interfaces and intelligent systems, we constantly asked: “What would a human helper do here?” That lens often led us toward clearer, more empathetic design choices. You’ve spoken about reducing friction in multimodal AI experiences.
Can you explain what that looks like in practice? Sure—AI systems are now embedded in voice, touch, gesture, and even vision-based interfaces. The goal is to make them feel coherent across all of these. For example, a voice assistant shouldn’t require completely different phrasing than a chatbot to complete the same task.
And if the AI can’t do something, it should gracefully hand off to a human or suggest an alternative—not leave the user hanging. We design for recovery paths and “graceful failure,” which is just as important as successful task completion. You’ve also worked on scaling AI interfaces.
What are some common challenges there? Scaling an AI feature from prototype to millions of users is less about infrastructure and more about human behavior. What works in usability testing doesn’t always translate to real-world usage. So we invest heavily in user research, telemetry, and iterative design.
For instance, I once worked on an AI-driven assistant that provided contextual help. Early feedback showed that people loved the functionality but were confused by when and why the assistant appeared. We refined the triggers, added cues, and provided opt-in customization—resulting in higher engagement and satisfaction.
You’ve contributed to scholarly discussions as well, including your recent paper in the International Journal of Intelligent Systems and Applications in Engineering. Can you tell us more about that? Absolutely. That paper explored how GenAI-powered systems can be optimized for transparency and interpretability.
We focused on designing systems where AI explains its rationale without overwhelming the user—a topic I care deeply about. It’s critical that AI systems not only be accurate, but also legible and respectful of user cognition. These discussions are ongoing in academic and product circles, and I’m grateful to contribute from both angles.
AI will not replace human intuition but will amplify it. The goal is to relieve consumers of the burden of sifting through countless options and instead offer them tailored recommendations, so they can make confident, informed decisions. As the technology matures, we’ll see even more deeply integrated AI solutions, transforming not just online shopping but the entire consumer experience across physical and digital spaces.
As AI capabilities advance, so must our ability to make them approachable. With leaders like Shiva Chandrashekhar, a judge at the Globee awards for Artificial Intelligence, at the helm, the future of AI-driven products looks not just powerful, but truly intuitive—bringing technology closer to human understanding, one interface at a time. Related Items: IEEE , Shiva Chandrashekhar Share Tweet Share Share Email Recommended for you The Next Age of Data Engineering: AI, Cloud Computing, and Scalable Architectures Comments.
