Finding the Missing Links Between AI Analysis and Human Context: with Walmart Canada's Montserrat Padierna

*The views and opinions expressed are those of Montserrat Padierna and do not represent the official policy or position of any organization.

For all its promise, enterprise AI is revealing a gap between raw data and real meaning. While automation promises to turn mountains of customer feedback into clear business intelligence, most out-of-the-box models often miss the most critical element: human nuance. As the industry moves toward a hybrid future where human empathy remains a core component, this is creating a need for a new kind of role: the "weaver," an expert who serves as the connective link between automated systems and the customers they serve.

Montserrat Padierna has built her career on the strange art of teaching machines how to listen. Now leading Customer Intelligence and Experience at Walmart Canada, and formerly Chief of Staff to the Walmex CEO, she knows that real progress happens when technology and people learn from each other. She also lends her expertise as a board member at CX Network and as a mentor for Good Latinas for Good, reflecting her commitment to lifting voices across the community. For Padierna, understanding customers isn’t about making AI smarter—it’s about making it more human.

"When you run customer feedback through AI, it’s easy to think you’re getting the full picture, but sometimes meaning starts to slip away. The sentiment, the context, and the texture all get lost when the model isn’t tuned to how people actually speak. In Mexico, for example, we use a lot of sarcasm, and one word can mean something positive or negative depending on tone, so it took us months to refine the system before it could truly understand what customers were saying," says Padierna. Today, her work begins with a question that sits at the core of every AI-driven customer experience strategy: how to ensure that human voices aren’t lost in translation.

• Lost in translation: "How do I make sure that I correctly represent customer pain points, needs, tensions within the business? And how can we correctly connect them or reconcile them with the business?" she asks.

• Context creators: For Padierna, the answer lies in connective thinkers she calls "weavers," people who turn raw information into understanding. They sit close enough to the business to know how it really works and close enough to the customer to feel what the data can’t show. "Weavers are the people, mostly at the mid-level, who are connected to the business and know the processes. We need them more than ever to harness their expertise and feed these AI models so they learn to think with context, not just calculation."

But the weaver model isn't theoretical, Padierna explains. In fact, her own team operated on this very principle at Walmart de México y Centroamérica. "We collected over 30,000 comments a week across all regions in Mexico, and every week we needed to report back to the business on what was happening with our customers. The goal was to understand the complaints and to translate numbers into meaning. When we saw a drop in quality scores, the first instinct was always to blame the store, but that generally wasn’t the real issue. Finding the true insight meant we could finally fix the right problem," says Padierna. By digging past superficial themes, they could get the right feedback to the right teams.

• Viva Mamá Lucha: One of Padierna’s clearest examples of how weavers turn data into meaning came from Bodega Aurrerá, Walmart’s banner serving Mexico’s most price-sensitive shoppers. When her team used AI to analyze customer sentiment, it wasn’t the discounts or store layout that stood out, but the emotional connection to the brand’s mascot, Mamá Lucha. "We started seeing customers say things like 'she’s like one of us' and that 'everything she says is true,'" Padierna recalls. "That showed us that empathy was the real driver of trust. If you see the ads, she’s a regular housewife, but when she’s looking for the best price, she turns into a lucha libre. It’s funny and human, but it also carries a lot of responsibility, because customers see her as one of them."

Looking ahead, Padierna also highlights a critical talent management challenge that organizations must address to make their AI investments successful: the impending loss of institutional knowledge. The most effective weavers, she explains, are often tenured employees who hold the institutional knowledge needed to make AI smart.

As this generation nears retirement, organizations face a scenario where their AI models may fail to learn, as the human expertise required to "feed" them walks out the door. "These are the people who lived the shift from analog to digital, the ones who went from paper accounting books to computers to Excel sheets that now feed today’s AI models. They hold decades of practical knowledge that machines still depend on. And now they’re reaching retirement, while a new generation arrives with technical skill but little of that lived experience. We need to bridge that gap before the knowledge disappears and the models lose their teachers," Padierna explains.

Ultimately, the goal is not to choose between technology and humanity, she concludes, but to use one to elevate the other. "The priority must be to make sure that the humanness in this AI world is not lost, but rather enhanced. It can be done."