What's Next for AI in Retail?

We've all heard that AI is "the next big thing" to simplify and improve everything from traveling the world to buying dog food. It's not just big tech brands pushing the AI agenda; retailers of all shapes and sizes are experimenting with this advanced technology to streamline operations and delight customers. But how are they actually using it? And how could they be using it more effectively?

What's Next For AI In Retail?

Judging by the number of times the words "artificial intelligence" get tossed around in sales pitches, presentations, technology descriptions, company product pages, etc., you would think that it is a mature capability, well on its way to being rolled out across every retail enterprise. The reality is different.

Gartner reports that a mere 2 percent of retailers have already invested and deployed AI, and 24 percent of retailers are "experimenting" with it.

So, what's next? We know some of the love for AI comes from shiny object syndrome – if AI is the next big thing, then it must be the thing that solves all the retailers' problems. However, retailers have to start by separating different kinds of AI from each other. There's activity within natural language processing – chatbots and AI's that can write product description copy, for example. There's activity going on with image processing – recognizing the difference between pants and shorts or assigning attributes to images that can be used in recommendations and other predictive customer-facing interactions. Either way, some of the most promising applications of prediction are in merchandise planning.

Delivering Increased Value With Prediction

Whether consumers are shopping online or in-store, the shopping experience seems to be the current playground for the AI players to call the shots. When AI meets the shopping experience, the goal is to predict what you buy – and when you will buy it. Prediction can drive everything from personalization to fulfillment optimization and all things in between. But the reality is, while AI can cut through the noise of an overwhelming amount of data and uncover the most critical factors that should be applied when calculating specific consumer behaviors, retailers are far too often assuming there's already a ton of usable data out there.

Good AI-driven prediction separates data that is noise from data that contributes to a better result, especially when it comes to personalized product recommendations. Basic algorithms can easily predict that black gloves will be nicely paired with a black jacket. But do you really need artificial intelligence to suggest to your consumer that two colors match? True thought-provoking, data-driven AI will predict what jacket is best for the consumer based on variables such as location, time of year and gender preference. And then it will throw in a pair of suggested gloves before you reach checkout. With personalization, predicting which items to show a shopper next can generate a lot of value even if it's right only 50 percent of the time.

For complex retailing scenarios, AI can equip retailers with the insights to stay ahead of the curve every season. For example, AI can help retailers compare what they would have done versus what the machine recommended, revealing where brands are succeeding and failing – and providing valuable insights for the next season.

AI is already adding value across the retail industry, and as retailers become more sophisticated in its adoption and learn to trust AI-driven recommendations, it will provide continued increase in ROI and major competitive advantages. Implementing the technology in alignment with unique business requirements will be key for improved customer experiences and brand differentiation.

About the Author - Chris Antonelli

As Solugenix VP of Customer Experience and Support Services, Chris Antonelli manages the delivery of customer support services and customer experience initiatives for clients in the retail and financial services industries. Chris leverages two decades of experience in leadership roles where she has guided best-in-class customer support service solutions to global brands like McDonald’s, BMW, Citibank, Sonic Drive-In, and Jiffy Lube. As an Executive Council member for Ellevate Network, Chris is dedicated to the professional development of women in the workforce and ardent supporter of remote workforce development.