The Reality of AI in Retail: Executives Break Down Opportunities, Challenges at NRF

Som Yu

The rumor that every NRF speaker was contractually obligated to mention artificial intelligence (AI) at least once per presentation is definitely untrue, but you’d be forgiven for thinking that was the case because AI worked its way into almost every conversation and presentation at the 2024 NRF Big Show.

The ubiquitousness of the topic alone says something, and the themes and commentary that arose throughout the show say even more. The technology, particularly generative AI, already is powering use cases throughout the retail enterprise, and it’s still on a strong growth trajectory, even — or perhaps especially — because the technology itself is still developing.

Here are some of the key themes and commentary around AI collected by the Retail TouchPoints editorial staff from sessions at the 2024 NRF Big Show:

1. AI can be a powerful customer engagement and personalization tool.
Dave Kimbell, CEO, Ulta Beauty: “Ultimately every effort we have in delivering innovation starts with the idea of human connections; that’s so important in the beauty category. First and foremost, it has to start with how we can complement, elevate and highlight the human experiences that happen in the Ulta Beauty environment. We’re experimenting with a virtual consumer engagement tool where you can ask questions in a really human way to discover [products]. We also see AI giving us some really interesting opportunities within our guest services areas, to bring in more data and insights when customers contact us. And [we’re also exploring use cases] in content creation to allow us to develop content that’s more personalized. So I’m really excited about the technology and what it’s bringing to our business, [but it’s all being done through the lens of] how does this help us deliver human experiences even better?


Marc Metrick, CEO, Saks: “We’re using it right now for efficiency, but not just cost savings, that’s not what I’m talking about. [I’m talking about] from a customer standpoint — we want to personalize everything, [so we are looking at the] use of artificial intelligence to write copy for product, to create editorial, photography and images. It is an enabler of the personalized experience that we want to create and it’s allowing us to scale that. So we’re testing things around that, while being very careful and mindful about putting it in front of the customer in a very intentional way.”

2. AI requires training and accurate data for peak performance.
Jill Klein, Head of Emerging Technology and IoT, CDW: “AI and Formula One race cars have a lot in common: speed, performance optimization, fuel dependency — all those things are critical to make both of those models work. In a Formula One car, you’re speeding down the straightaway, you’re hitting the brakes, turning the corner, looking for that apex as you come around to speed out of that corner. The speed in your AI model is the same thing — that speed insight is critical to your business; it helps you reduce costs and create better scenarios for revenue generation. Same thing with process optimization: that AI model typically doesn’t work out of the box. There’s a lot of training that has to be done, it has to be optimized [just the same as] cars. And then, fuel dependency — data is the fuel to power your AI model. The efficacy of your AI is dependent on that fuel.”

3. AI can give associates more time to interact with customers.
Murali Sundararajan, CIO, Victoria’s Secret: “We have to understand the use-case scenarios that make the most sense. [One of the ones we are focused on] is the associate [experience], because when you go to the store there are multiple situations where gen AI can have an impact on the associates, and for retail, the store labor is always the premium. [We are looking at ways to] reduce their work behind the curtain and get them in the front of the store, working with the customer to make their processes easier, because that’s also going to improve the customer experience.”

4. AI can speed up marketing, merchandising and product development.
Jessyn Katchera, Executive Director and Head of Ecommerce, Carrefour: “The crux of the issue for us is how we can reinvent, end-to-end, big chunks of function that we have in our business. [That led to what] we call the Marketing Studio. The goal there is to think about how to use gen AI to be smarter and faster at generating assets for marketing campaigns, whether it’s audio, text or visual. For us, it’s really about accelerating agility and the time to market. So instead of waiting for weeks to get a customer-ready product from a marketing standpoint, we can develop that in a matter of hours or days, which means you can now expand and tailor the content you have to start to deploy local nuances. You can start to really extend the reach and the personalization of some marketing campaigns.”

Chandhu Nair, SVP of Data and AI, Customer and Marketing Technology, Lowe’s: “Merchandising is definitely one of the areas that we’ve focused on. We sell everything from appliances to lumber to paint, and for our merchant, [those things have to be handled] very, very differently. But we noticed a growing problem with product onboarding, such as onboarding a SKU or a vendor; it was a laborious, manual process [and it led to poor data quality]. It’s not that we never tried to solve this problem before, but now with generative AI we have seen an almost 60% reduction in the amount of manual labor that is needed to upload those initial product descriptions.”

5. Gen Z is increasingly comfortable with AI.
Ann Piper, Head of North American Ad Sales, Spotify: “One of the new functionalities we introduced was the AI DJ, and we’re seeing 83% of Gen Z interact with it. It’s a new way to interact along with the ability [for Spotify users] to blend their playlists with an FC Barcelona player or [the McDonald’s character] Grimace. It’s introducing me to someone I enjoy and spend time with, a new way of discovery. [We’re always asking ourselves], where can we spark joy, and at the end of the day, support earnings?”

6. AI could be as big a change for retail as the internet and mobile phones.
Amy Eschliman, Managing Director, Strategic Consumer Industries, Retail, Google Cloud: “These massive transformations are nothing new to retail — the internet is a great example, it changed the way we shop; mobile phones, same thing. Now it’s generative AI, which has the capability of transforming everything from the customer experience to the associate experience. It’s got tremendous potential, and it’s a really exciting time for retail because of this technology. It’s about the ability to synthesize and analyze information that we had did not have before. The possibilities are really endless.”

7. AI is still evolving, so constant testing and experimentation is needed.
Jessyn Katchera, Carrefour: “You need to be very realistic about gen AI, because it changes every day. It’s not true that today gen AI can do anything. It can do a lot of things, but can it solve every problem to the same level of skill and effectiveness [as a human] from a cost-savings perspective? No. So you need to be very particular about [how you prioritize]. Be pragmatic and humble [and don’t allow yourself to become] paralyzed because you don’t know everything. Gen AI is an area where you need to test fast so you can fail fast, and that’s okay.”

Adrian Mitchell, CFO and COO, Macy’s:“We’ve been investing and learning; AI is not just a capability but a culture opportunity. We’ve [already] been on this journey for about three years and have made a lot of progress over the last few years in pricing science. We’re still experimenting with personalization and how you [can] use this tech to automate processes and interact with customers in a humane way. Also inventory allocation, getting the right products in the right market, down to size, color and style. AI definitely makes the business better, but you have to lean in. You have to invest to learn; it’s worth learning about and investigating.”

Yang Lu, VP of Technology, Tapestry: “We built an AI/ML model to help our planning processes, harnessing a massive data set to help with assortment planning and buying decisions and what store to allocate to, because having product at the right store is so critical to complete that unified experience. The key is persistent, relentless experimentation — [you need to] build a culture of test-and-learn. We’re passionate about agile delivery and quick iterations — iteration is the new perfection.”

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