AIBest Practice


Jan 19 2024 ...

Retail AI in 2024: 3 key takeaways from NRF’s Big Show

One topic dominated all others at the 2024 Big Show: AI. With 20% of all sessions mentioning it in their titles alone, and many more discussing it at length, this year the topic drew huge crowds and generated major hype. While not new to the NRF agenda, renewed industry optimism, proven benefits of AI in retail, and the excitement around generative AI made one thing clear: this was the AI-est NRF Big Show ever. Will we be able to say the same about 2024?

Here are the 3 biggest retail AI takeaways from this year’s Big Show.

Learn how Cortefiel, Style Union and Hackett London are leveraging AI to achieve more full-price sell-through. 

Takeaway 1: Retail AI is on everyone’s mind and roadmap in 2024

There was a notable shift in tone at NRF this year. The last few editions of the Big Show have been marked by post-pandemic hand-wringing and an urgency to learn how to keep the lights on in retail. But this year, retailers were done talking about the past. Instead, they expressed confidence and renewed curiosity about what’s to come, especially in terms of AI and retail.

Underscoring this shift was the sheer number of speaker sessions centered around AI. The fact that so many day-one sessions covered the topic became something of a running joke by day two. The expo floor was also a testament to this energy, with countless booths listing “AI” front and center.

With Nvidia reporting that 98% of retailers plan to incorporate genAI and Salesforce finding that less than 3% of retailers have no plans to implement any AI in 2024, it’s clear that AI is being recognized as a valuable and proven retail ally or at least a hot topic.

What’s behind the AI frenzy? It could be the proven impact of AI capabilities like demand forecasting and decision automation that have helped their peers “roll with the punches” while navigating the uncertainty of the last few years. Others are attracted to the bright spotlight lit on this year’s AI goldenchild, genAI, and its accessible, flashy use cases (more on that next) within satellite applications.

Whatever the reason is, there has been a tangible paradigm shift that is paving the way for even more widespread AI adoption in the retail landscape.

Takeaway 2: GenAI has democratized AI for retailers

“Profound and immediate change”, “the elephant in the room”, “a revolution”, “an overnight shift”, “an upheaval”. Generative AI, or “genAI” was the undisputed star of the show, and retailers were eager, anxious and scrambling for seats to learn about its potential business implications .

While genAI is not actually new, the recent advances, best exemplified by OpenAI’s ChatGPT, have essentially democratized AI through its accessibility and practical use cases that have…ahem…generated excitement and plenty of new ideas over the last year.

Essentially, by taking Large Learning Models and getting them to do what Salesforce CEO Marc Benioff described on stage as “party tricks”, new genAI advances have cast a light on the possibilities of this tech and its implications for all areas of our lives, including the retail industry.

And with a potential $400-$660 billion a year to gain in retail according to a McKinsey report, it’s no wonder that genAI dominated the conversation on the stage and on the expo floor. In fact, 98% of retailers say they plan to invest in genAI in the next 18 months.

With major players like Microsoft, Salesforce and Google all launching genAI products and a number of new companies sprouting up to deliver new solutions and features, this technology is already being implemented to translate complex data into easily understood business insights, streamline marketing and customer service operations, and to enhance the customer experience.

Common early examples include auto-population and translation of product descriptions for new data onboarding, marketing content creation, enhanced intelligence of virtual assistants through machine learning, hyper-personalized customer recommendations and streamlined customer service.

However, genAI is primarily being discussed and implemented within satellite applications (e.g. chatbots, virtual showrooms, etc.) rather than in critical, core applications such as inventory planning and management since its strength does not lie in its calculating power.

As Jeff Courcelle, VP of UX and Chief Designer at Microstrategy stated, “Our interaction with [generative] AI is to leverage its cognitive capabilities to understand what is the best way to answer the question but not do the math. The AI is not very good at math.”

Takeaway 3: A strong foundation is a must for swift AI adoption

One key recommendation echoed across sessions was the importance of building a robust organizational and infrastructural foundation to ensure not just swift adoption of AI technology but also sustained success within retail businesses.

Organizationally, this means bringing AI use cases in where it makes sense and can best address unique challenges and opportunities. While the newest advances, namely genAI, might make it tempting to bring in as much AI at once and effectively bite off more than a retailer can chew.

Scott Lux, Global EVP of Technology and Innovation at Esprit said, “You have to step back and be introspective and think about the business case or customer experience we want to solve and that’s hard for retailers sometimes versus saying, ‘Okay, let’s do everything’”.

From a data perspective, since it is the lifeblood of AI, ensuring its quality, accuracy, and relevance is crucial. Before diving into AI implementations, speakers emphasized the need to not only aggregate and centralize data from various sources but also ensure its cleanliness and consistency.

Well-curated data sets empower AI algorithms to deliver more accurate insights and predictions, contributing significantly to the success of AI applications.

And as the volume and complexity of data grow, and AI evolves faster than ever, retailers need to invest in scalable infrastructure that can accommodate the increasing demands of AI algorithms. This includes cloud-based solutions, advanced computing resources, and a flexible IT architecture that can adapt to evolving AI requirements. Scalability ensures that AI initiatives can expand seamlessly and support the growing needs of the retail business.

Creating a strong foundation for AI in retail is not just a preliminary step but a strategic imperative for swift and successful adoption in 2024. The result is both the integration of cutting-edge technology and the creation of a resilient and future-ready retail landscape powered by artificial intelligence.

Discover the fashion retailers leveraging AI to automate and enhance their merchandising processes for more profit with less inventory and less effort.