Why Grocery Retailers Struggle so much with Adopting AI Technologies

Episode 1: Teaching the AI to train itself

This is the first part of a three part series looking at why retailers struggle to adopt AI technology, what can be done to help them and why it’s important for tech companies to focus on making the processes easier, while directing all their efforts to improving business outputs.

Why is there an adoption problem

NRF 2020 showed that there is an appetite to adopt AI into grocery retailers checkout systems, and with there being a vast number of companies offering possible solutions to retailers’ pain points such as preventing marginal loss at the checkout between similar items, identifying non-barcoded items with competent accuracy and also with Amazon Go making a massive push up the retail ladder, you’d expect most retailers would move with a bit more pace and already integrate some form of AI into their storefront. This just isn’t the case though.

Lack of internal expertise and fear of complexity

For AI to be used by anyone, somebody has to train an AI Model that knows how to do what the customer needs it to do. To keep in line with the simple AI use case we were discussing earlier (the one that has been the cornerstone of NRF 2020), we shall focus on an Item Recognition Model that needs to identify the items placed on the checkout counter or scale. The main problem lies in the fact that these AI models need to be constantly retrained once they have been integrated into a store. Retraining is an inherent part of AI as Models always lose their accuracy and performance over time (in scientific terms this is called “Data Drift”). Most grocery retailers feel they don’t have the expertise, time or perhaps budget to adequately do that and also, this retraining process is usually why the AI software doesn’t work very well to begin with. If there is one thing that grocery retailers can’t afford is a missed prediction because then the AI is practically begging the customer to lie. This concern is not without merit, as adding a new or different looking product to an existing AI model usually requires some heavy duty tech work.

The issue with the AI model

Before I can discuss how this issue can be solved, we have to understand what the status quo is.

Make the AI work for you

Edgify’s product recognition at the checkout explainer video

A foundational shift in the world of AI training. Deep Learning and Machine Learning training directly on edge devices.

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