Walmart

Philip Stanley

Applied Technology Authority

Philip is Director of Site Applied AI and Tools at Walmart, where he leads the development of AI-driven systems that support how teams shape the company’s digital storefront. With a background across banking, finance and technology, he brings a practical, operatorfocused lens to applied AI in e-commerce.

In this feature, he reflects on the evolving nature of customer behavior alongside the practical realities of AI adoption within largescale systems. He also examines how speed, data quality and associate experience are shaping the next phase of e-commerce, while underscoring the need to balance innovation with practical usability.

An Operator’s Lens on Applied Ai

My path into applied AI at one of the world’s largest retailers did not begin in a server room. It began behind a bank counter on the East Coast, where I worked with customers, managed branches and served as a commercial loan officer. Those formative years taught me how decisions ripple through complex systems and still define how I approach technology.

Tools Only Matter When they Improve how People Work.

After my MBA, I joined Walmart through its finance organization, moving across functions before transitioning into technology roles focused on forecasting platforms and early machine learning tools. About a year and a half ago, I pivoted into e-commerce, where I now lead the Site Applied AI and tools team.

My work sits at the intersection of associate experience and customer experience. When you notice a seasonal banner for Mother’s Day or a carefully curated product layout on Walmart.com, there is a dedicated team deciding which items to feature, how the page should look and what message to convey. I approach this as an operator, building the systems that support those decisions, simplify their work and integrate AI into that process.

The 40 Percent Problem

When the conversation turns to scaling applied AI in e-commerce, most discussions focus on data quality or model deployment. I see it differently. Two distinct challenges operate simultaneously, and both demand attention.

Customer behavior is evolving in unpredictable ways. Generative AI tools like ChatGPT are only recently part of everyday use, and customers are still learning how to shop with them, what to expect, and how much to trust them. With only months of meaningful AI shopping behavior to learn from, that uncertainty makes it difficult to design a fixed experience. Our responsibility is to remain agile, monitor behavioral data closely and adapt to constant changes.

On the associate side, the barrier is more psychological. When an AI tool handles 60 percent of a task and leaves the rest behind, it can feel more burdensome to do the work manually. At that point, AI can feel less like assistance and more like rework. That perception slows adoption more than any technical limitation.

For AI to be effective, it has to remove work, not redistribute it. People need to understand why a model reaches its output. Transparency is a prerequisite for adoption. Explainability is not an added feature but the link between performance and real trust. As that understanding deepens, adoption follows.

From Static Pages to Generated Experiences

Today, most e-commerce platforms are largely static, with limited personalization layered on top. They are functional, but not moment-responsive. We are actively building realtime, generated experiences where the entire page is tailored for each customer as they arrive. The upside of that shift is still largely unknown.

This shift introduces two tensions that require careful balance. The first is balancing deep personalization with broader business objectives, serving customers with what they already prefer while also introducing what the business seeks to promote. The second is data integrity. These systems rely entirely on the signals they receive, and even a single weak input can distort outcomes.

“AI only works when it truly reduces effort. If it leaves meaningful work behind, people will struggle to trust and adopt it.”

At Walmart, that challenge becomes tangible in product discovery. With one of the largest SKU counts in retail and a third-party marketplace that expands the assortment even further, identifying what to feature has traditionally been a manual, time-intensive effort.

We are introducing AI tools that surface relevant products faster and with greater precision. Associates save significant time, while customers benefit from better, more personalized recommendations, with fewer friction points between discovery and purchase. It also ensures that products are accurately represented and available in the customer’s local store.

Working Backward from the Associate Experience

Looking ahead, the next shift in e-commerce is not about smarter models, but faster execution. The constraint is not model intelligence but executing insight at an individual scale in milliseconds, where capacity, bandwidth and speed still set the limits.

Once a system can understand intent the moment a customer arrives and respond instantly, the experience changes in a fundamental way. It will feel less like navigating a site and more like interacting with a system that already understands what you need. As those constraints ease, the possibilities expand quickly.

My leadership approach is less about managing projects and more about maintaining clarity under constant change. I define a clear picture of what the associate experience should look like three or four years out, then work backward to identify problems standing between today’s reality and that vision.

With teams aligned around the problems that need to be solved and why they matter, alignment across product, engineering, and business teams becomes far easier. The real challenge shifts to prioritization. There is always more to solve than time allows, so the focus stays on what creates the greatest impact without losing sight of long-term goals.

For those building careers in this space, my counsel is straightforward. Technical knowledge helps, but lasting progress comes from keeping people at the center. The ability to adapt, work across teams and build strong relationships matters more, as that is what ultimately turns the promise of AI into real, everyday value. Those who succeed will be the ones who keep learning, adapt quickly and stay grounded in the human problems the technology is meant to solve.