With decades of experience navigating the complexities of the global supply chain, Rohit Laila has developed a unique perspective on the intersection of logistics, technology, and retail. He has witnessed firsthand how innovation can transform operations from the warehouse floor to the customer’s front door. Today, as retailers stand at the precipice of an AI-driven revolution, we delve into his insights on how this technology is reshaping the industry. This conversation explores the nuanced balance between data-driven efficiency and human creativity, the practical challenges of deploying personalized customer experiences, and the strategic decisions leaders face when choosing to use AI as either a cost-saver or a powerful engine for growth.
Retailers are using AI for precise tasks like inventory planning and measuring campaign performance. How do you balance these data-driven applications with the goal of empowering team creativity and speed? Please walk us through a scenario where both goals are achieved.
That’s the fundamental misconception about AI in retail—that it’s a choice between data and creativity. In reality, one fuels the other. Think about a merchandising team at a company like Target. They are expanding tools that help their teams spot trends earlier and plan inventory with much greater precision. This doesn’t replace the merchandiser’s intuition; it supercharges it. Instead of spending weeks buried in spreadsheets trying to forecast demand, the AI provides a highly accurate baseline. This frees up the team’s time and cognitive energy to focus on the truly creative work: discovering the next big trend, curating a unique assortment, and innovating. The AI handles the “what” and “how much,” so the human experts can focus on the “why” and “what’s next,” creating a space where creativity and speed can flourish together.
Some brands are developing highly personalized customer experiences, including website curation and real-time product visualization tools. What are the biggest technical and user-experience challenges in rolling out these AI tools at scale, and how do you ensure they feel intuitive rather than intrusive?
The ambition to create a completely unique website for every single visitor, as a retailer like Lowe’s is pursuing, is immense. Technically, the biggest hurdle is processing and acting on vast streams of data in real time. The website can’t lag; it has to feel instantaneous. But the user-experience challenge is far more delicate. The goal is to make the technology disappear. For example, a tool that lets you take a picture of your kitchen and reimagine it on your phone has to feel like magic, moving you seamlessly from inspiration to installation. If the interface is clunky, if the product suggestions feel generic, or if the visualization is inaccurate, you don’t just frustrate the customer—you break their trust. The key is to ensure the AI serves as a helpful, almost invisible guide. The customer shouldn’t think, “This website’s AI is really smart.” They should just feel, “This brand really gets me.”
Many digitally native brands use AI behind the scenes to reduce friction, such as analyzing store data or creating syndicated content for marketplaces. How do you prioritize which back-end processes to automate first, and what metrics prove these changes are genuinely improving the customer journey?
For any brand, especially a nimble startup, the guiding principle must be that customers want less friction, not more technology. So, you always prioritize automation based on the biggest points of friction in the customer journey. You start by looking at what your customers are telling you, directly or indirectly. Are you getting swamped with questions about order tracking? Are conversion rates low on a specific marketplace? That’s your starting point. A great example is using generative AI to create syndicated content for online marketplaces. This isn’t just an internal cost-saver on photoshoots; it directly impacts the customer. It ensures that when they’re shopping for your product on a partner site, they see high-quality, accurate images, which builds confidence and reduces hesitation. The proof isn’t just in reduced operational costs; it’s in higher conversion rates on those marketplaces, better product reviews, and a tangible decrease in customer service inquiries.
AI can be viewed as an incremental cost-saver or an exponential growth engine for acquiring higher-value customers. For leaders just starting their AI journey, what initial steps or investments offer the best path toward using AI for “smarter growth” from day one?
This is the most critical strategic choice a leader has to make. Viewing AI as just a cost-savings tool is table stakes; it will help you run a tighter business, but it won’t transform it. The real, exponential power of AI lies in using it as a growth engine. For a digitally native brand with rich data, the best first step is to invest in predictive modeling tools to better understand a customer’s potential value from day zero, not months down the line. This insight is incredibly powerful. It allows you to create smarter feedback loops directly into your advertising platforms, so you can intentionally acquire higher-quality customers more efficiently. The result is not just faster growth, but smarter growth. So, the initial investment shouldn’t be in a flashy, customer-facing chatbot. It should be in the data infrastructure and machine learning models that let you know who your best customers are before they even make their second purchase.
Establishing clear guardrails for AI is a growing priority, especially when distinguishing between utility-driven tasks and core brand creative. What are the most critical guidelines a company should set up, and what is your process for deciding where generative AI is and is not appropriate?
Setting up guardrails for AI cannot be done in a silo. It’s essential to create an internal task force with leaders from across the entire organization—finance, legal, brand, marketing, and operations. Everyone needs a seat at the table. The most critical guideline to establish is a clear distinction between utility and identity. We’ve found AI performs best when its use is focused on utility-driven applications. For instance, using AI for video editing is a fantastic application. Sifting through hours of footage is incredibly time-intensive, but AI can perform context indexing and semantic video analysis to quickly identify clips that speak to a specific consumer pain point. This is a powerful complement to a creative team. However, when it comes to the core advertising creative that defines the brand’s voice and emotional connection, that must remain human-led. The process for deciding comes down to a simple question: “Is this task about brand expression, or is it about operational execution?” That line in the sand is your most important guardrail.
What is your forecast for the evolution of AI in retail over the next five years?
Over the next five years, I believe AI will completely dissolve the remaining boundaries between digital and physical retail, creating a truly seamless, intelligent shopping journey. We’ll move beyond the current state of personalization into what you might call “agentic AI,” where technology anticipates our needs and helps us make decisions before we’ve even fully formed the thought. The back-end operations, from supply chain to inventory management, will become so efficient and predictive that they will feel almost invisible. But the most profound change will be in how brands compete. Success will no longer be solely defined by product or price. It will be defined by the intelligence of the ecosystem a brand builds around its customer, using AI to make every single interaction—from discovery to post-purchase support—feel effortless, intuitive, and deeply personal.
