NRF’s 2020 Big Show brought many things we expected to see: large Sunday and Monday crowds, a myriad of solutions incorporating machine learning and artificial intelligence (AI and ML), along with a handful of new and promising technologies. The event also came with the unexpected: a determined focus on fundamental retail execution and managing change, as well as—perhaps most pleasing to all participants—warm January weather in NYC.

NRF Big Show 2020

The Expo floor was lively, and the retailer presence was energizing. Retailers we spoke with continue to explore emerging technologies, but with the recent years’ focus on the emergence of AI and ML, the big question now is, “How do I use it?” This query could be a sign that over the last year or two retailers have been in discovery mode with their legacy infrastructures (inclusive of people, process, and technology)—essentially trying to understand, “What must we replace vs. what we want, and can the legacy handle what we want?” Or it could mean they’ve been trying to adopt, but their current internal fundamental building blocks are not ready.

Many questions still remain on how to absorb the new tech, especially for those who are not in a position to incorporate a whole team of data scientists into their organization. The prequel question for many retailers is, “How do we stay relevant or just get started?” Let’s dive deeper into some of the key areas of focus we saw at the Big Show relative to AI and ML.

Many solution providers have realized that retailers cannot absorb a pure science platform without understanding how it fits with the exectution solutions, so they have started to mesh the concepts.

Solution Updates

There was buzz around new platforms and solutions that are now incorporating the science of AI and ML without the need for another stand-alone forecasting solution. Many solution providers have realized that retailers cannot absorb a pure science platform without understanding how it fits with the execution solutions, so they have started to mesh the concepts. This also brings a unique opportunity for retailers to manage more of the true science pieces on their own, without hiring a complete data science team. From a Parker Avery perspective, however, the jury is still out on this approach. As these solutions are implemented, it will be interesting to see their adoption and how today’s workforce engages with balancing the art and the science. Those implementations that result in clear business value will be significant in help driving AI and ML-infused solutions forward.

Flexible fulfillment is a good example here. Should it be automated replenishment? Allocation? The answer may be: both. Many software solutions we looked at are focusing on giving retailers choices on how they fulfill in one tool and having the ability to change modes based on needs or requirements. Also, the ability to infuse ML and AI into one solution with a single demand signal simplifies the applications and likely will make them easier to manage. This new approach may not only streamline and optimize inventory management but potentially a retailer’s organization as well.