The latest Google devices include AI components Customers with questions about their online orders this holiday shopping season may think they’re asking a seasonal worker for help, but artificial intelligence (AI) is likely giving them the information they need.

SwissCognitiveTechnology of all kinds is being used on the back end of retail, to organize inventory and manage other operational functions. Whether it’s chatting with a bot or with Alexa, AI is also increasingly becoming a part of the consumer-facing shopping process in ways that, at least for now, are about improving customer service. According to the latest Accenture data, a majority of consumers are already using or would like to use a number of technologies that are powered by AI when shopping, including chatbots (“automated intelligent customer assistance,” 65%), virtually trying on clothing (65%), and voice commerce systems like Google Home GOOG, +1.67% (68%) and Amazon Alexa (71%).

Personalize everything

Personalization, ease and convenience are key to better customer service in retail, whether that’s through supply chain management and making sure items are in stock and can be sent to customers in a timely fashion, or through services designed to cater to the preferences of the individual shopper. “Customers are running into it every day and probably don’t know it,” said Pano Anthos, managing director of XRC Labs, an accelerator program that’s focused on innovation in retail and consumer goods. “You’re on a site and a chat window opens and says ‘hi,’ it’s probably natural language processing and that’s AI.”

Using the digital employee to its best capabilities

To that point, a recent survey from Narvar, a company focused on helping retailers provide exceptional customer experiences, found that 38% of consumers didn’t know whether a live chat or messenger app was a human being or AI. Only 10% knew it was not human. In part, that’s because there are specialists training computers to learn nuance and context that can make interactions seem more human-like. For example, a computer can understand that every face has a nose. But, in a world full of noses of different sizes and shapes, “deep learning” takes it to the next level, according to Anthos. “To strengthen the response, there has to be a comparison to many noses, a way for the machine to make inferences at a faster more complex level,” he said. […]

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