How AI is redefining the retail experience
Artificial intelligence (AI) has the potential to change the day-to-day work of retail professionals in a range of roles.
While the potential behind the technology is significant, AI is a polarising subject. Some believe it will transform our lives and be the key to the future of retail. The extreme counterpoint is that AI could take over the world in a matrix-style machine coup.
I’d like to argue that AI is a powerful but incremental step that follows very similar patterns and characteristics of the millennia of technological advances that preceded it. The advancement of technology typically allows us to do things more efficiently, to a higher quality, or even do something that wasn’t possible before.
Ultimately, AI technology is still in its infancy so understanding where and how AI can add value is key. This will ensure retail brands invest in, and then apply the technology in the most effective way to deliver a return on investment.
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The role AI can play in driving time and cost efficiencies
The scenario where AI can add the most value, especially from an eCommerce perspective, is through the processing of unreasonably large sets of data. It’s especially powerful when the data is messy and unstructured, as AI can be trained to navigate this and process it in a more efficient way than a person could at scale.
If the source data is large and the answer can be binary or score-based, AI is often a quick win. For example, AI could find all the v-neck t-shirts in a catalogue of 100,000 products using only the images by first finding if it is a t-shirt and secondly finding out if it’s a v-neck or not. Of course, a person could do this pretty quickly and cheaply, but the power of AI comes when you want to keep re-processing a large data set to find dozens of attributes, or the data set becomes millions of images.
AI can also be implemented into the product design process to forecast trends, enabling brands to design and create products that are more likely to sell. This can help reduce excess stock and increase sales margins.
Creating a personalised shopping experience
AI can be used to “understand” a large data set with multiple dimensions. For example, simultaneously understanding customers, products, and orders to provide a personalised shopping recommendation. In this instance AI would have had to look at things like a customer’s past purchasing habits, ‘liked’ items, and the time spent on a webpage. All of these would be overlayed against the available stock to seamlessly provide product suggestions the customer is likely to respond positively to. This nirvana of recommendations has been hard to achieve in reality, often because most customers only make a handful of purchases each year which hinders AI accuracy. AI is typically only as good as the data it is trained on.
Given the complexity of the decision-making process, it requires a lot of investment in time (and often money) to build and train the model, and even then, it can still make mistakes. For example, AI can recommend cream thinking it is milk, or flag an innocent transaction as fraudulent. A human touch is still needed to oversee the outputs and maintain a seamless shopping experience. The key is ensuring that the machine-generated recommendations truly fit the customer’s requirements. This is why roles like retail assistants will continue to be in demand as they will need to “mark the homework” of the AI recommendations.
Risky investments and potential distractions
Another example is product descriptions. AI, with enough training and feedback, can create good results. However, in some cases, the time spent using AI, manually correcting mistakes, and giving feedback could be just as much as the time taken to write the description manually so there would be little money saved.
For some businesses, this may be a worthwhile trade-off, for example, many products lacking descriptions and users aren’t fussy about complete accuracy in the description. Still, for other businesses, it’s a distraction.
We are still very early on in AI’s journey. Over the next decade, humans and AI working in tandem will probably become the norm in retail. That said, the overall integration and implementation of AI will be an evolutionary rather than a revolutionary process.