The invisible hand of AI in retail experiences

Artificial intelligence is everywhere in today’s retail conversations, but the best uses of AI are often the least visible. Taco Bell is a prime example. “Taco Bell is actually applying AI on a human level every single day,” Phillip shares. Whether it’s customizing experiences at the drive-thru, streamlining back-end operations, or rewarding customers through its app, the real power of AI is in how seamlessly it fits into the everyday customer journey.

You don’t open an app thinking about algorithms, and you don’t eat a burrito because you’re excited about machine learning. You just want a great experience. “People don’t want technology for technology’s sake. What they want is a wonderful experience,” he reminds us.

It’s like a stage crew in theater: if they’re doing their job well, you don’t notice them at all—but you do enjoy a flawless performance.

Friction is not the enemy: the real story behind user experience

There’s a long-held belief that the best customer experience is always frictionless. Phillip challenges this idea, suggesting that friction and pain aren’t the same thing. “Friction has become a sort of curse word in our industry… but I think some friction is good,” he says.

Friction can prompt customers to slow down, consider their choices, and avoid costly mistakes. Remove too much friction, and you might end up with more returns, payment disputes, or buyer’s remorse. “Just like your tire needs a little bit of friction, some things have to have friction,” Phillip explains.

This insight is crucial for user testing, product design, and anyone invested in customer experience. Not all hurdles are bad—some are necessary checkpoints that help build trust.

Personalization: when AI in retail gets too personal

Personalization is another major topic in e-commerce trends, but the line between helpful and intrusive can be thin. Many brands have rushed to add customer names or basic recommendations to their websites, believing it’s the key to loyalty. But as Phillip puts it, “Surface-level personalization, like using a customer’s name, often misses the mark.”

He explains that true personalization is about intent and context, not just data points. If AI-powered recommendations feel generic or, worse, creepy, customers will disengage. “We are in a moment where a lot of brands depend on distribution from things like Meta and spend a tremendous amount of money driving traffic to ill-performing pages that do not reflect the promise made in the ad creative,” Phillip warns.

Personalization should serve the customer’s goals, not just the brand’s. That’s how you build real customer experience value.

Homogenization and pattern recognition: the sea of sameness

One of the unintended consequences of digital commerce is homogenization. Consumers are now experts at pattern recognition; they know exactly what to expect from a shopping site, whether it’s Target, Pottery Barn, or a luxury brand. In research Phillip’s team conducted, they found that when you strip away a brand’s logo and color palette, most sites are indistinguishable—and shoppers revert to price as the only meaningful differentiator.

This “sea of sameness” poses a major challenge for brand differentiation. Phillip notes, “Brand has never been more important, where we need to stick out and stand out in the sea of homogeneity, but also brand has never been less important because the only thing that the consumer cares about is a white screen with a text input box.”

If everything looks and feels the same, standing out will depend more on value, trust, and creativity than ever before.

Clean data is the real competitive advantage

When it comes to implementing AI in retail, most conversations focus on the latest features or big strategic hires. Phillip has a different take: “Clean data… I don’t think that you can power AI initiatives within the business with data that isn’t holistic and clean.”

He shares the story of Kith, a brand that launched a loyalty program by crediting customers for purchases made over 15 years. The only way this worked? Their purchase data was complete and accurate from day one.

For retailers, it’s not about the flashiest tool. Instead, success depends on a foundation of clean, reliable data, strong organizational memory, and the discipline to focus on high-value customer segments. That’s how you win with AI in retail and build lasting customer trust.

What shoppers really want: smarter search and real problem-solving

Despite all the buzz about innovation, the features that matter most to customers are practical:

• Smarter search
• Better product recommendations
• Easy product comparison
• Inventory visibility and availability
• Reliable post-purchase support

“Best investments are unsexy… it’s search, it’s discovery, it’s comparison… it’s definitely in inventory, it’s like availability. It’s post-purchase support. And those are the places where shoppers feel the most pain,” Phillip explains.

Brands that focus on these core needs are more likely to win loyalty and trust than those chasing novelty for novelty’s sake. AI-powered recommendations and search can collapse the customer journey, making it easier to move from discovery to purchase with less friction—when implemented thoughtfully.

Building for the future: trust, transparency, and experimentation

As AI and personalization become more sophisticated, the balance between automation and human connection becomes more important. Phillip suggests that brands must experiment, collect feedback, and continually adjust—not just deploy tools and hope for the best.

He also believes that the future of retail belongs to organizations that can navigate long-term change and adapt to new patterns of consumer behavior. “System and operational design is the moat,” he says. Investing in people, process, and clean data will outlast any single piece of technology.

For retailers and digital commerce leaders, that means:

• Prioritizing data quality and organizational memory
• Embracing thoughtful, human-centric design
• Continuously seeking and acting on customer feedback
• Avoiding the temptation to automate everything at the expense of experience

Final thoughts: where commerce, culture, and technology meet

Phillip’s candid perspective is a valuable reminder for anyone working at the intersection of digital commerce, customer experience, and retail innovation. Technology is only as powerful as the culture and people it serves.

As Phillip says, “Commerce is culture. Our culture is so obsessed with what we buy and how we buy it, that it is part of our fundamental identity. Commerce is worthy of unique examination.”

And as host Mike McDowell put it: “The basics are, you know, if you can’t get those right, you’re not going to get things right.”

In the end, the future of digital commerce will belong to brands that combine technological excellence with a deep, ongoing commitment to culture, creativity, and the human experience.

Episode links: 

• Phillip Jackson on LinkedIn
• Mike McDowell on LinkedIn
• Nathan Isaacs on LinkedIn
• Future Commerce website
• UserTesting’s 2026 State of AI in Retail Experiences Report
• Seeing is understanding: how early adopters are using AI to buy — This on-demand webinar explores how real shoppers use AI tools like ChatGPT during discovery and buying, and what it means for brands’ digital experience strategy.
• Designing AI-powered shopping experiences for the next generation of commerce — This guide offers actionable tips for retail and ecommerce leaders on crafting AI‑enabled shopping journeys that build trust, drive conversions, and prepare for future commerce models.
• The future of customer insights at UserTesting in 2026 — an Insights Unlocked episode about how AI and platform innovations are changing how teams scale human insights and enhance customer experiences.
• Retail’s 2026 moment is about trust over AI — discusses why trust should guide AI strategies in retail experiences and how brands can use AI without alienating customers.