AI in Retail: The Store of the Future

AI in retail can help with enhanced checkout systems, facial identification, and much more.

ai in retail

Artificial intelligence (AI) in retail is one emerging technology that is likely to reshape the future of shopping and commerce. It’s also poised to have a significant impact on loss prevention and retail security, say experts. AI in retail can help with enhanced checkout systems, facial identification, and supply-chain optimization. And those applications are just the tip of the iceberg.

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Contributing writer Garett Seivold examines both the potential benefits and the potential drawbacks of AI in retail in a feature article for the April 2018 issue of LPM Online. From the article:

Deep learning is at the heart of strides being made in myriad applications—face recognition, cancer cell detection, speech recognition—and so it will increasingly find it ways into a variety of retail applications. For example, in‑store displays, trained with AI, could be expected to answer plain‑language FAQs from shoppers. Improved facial recognition could facilitate more targeted marketing and personalized shopping experiences. And AI is predicted to result in tools that enhance retailers’ supply‑chain optimization. Grocers, for example, are expected to be able use insight into produce and perishables to dynamically adjust orders and cut waste. Overall, retailers plan to increase spending in the year ahead on AI by 7 percent, second only to increased spending on location‑based marketing, according to a survey in October 2017 by IHL Group and RIS News.

Naturally, the technology will filter into and improve LP tools, including predictive models for fraud prevention. For example, in testing with one major retailer, machine learning models created by Appriss Retail were able to identify employee deviance above and beyond what is identified by typical exception reporting, according to David Speights, PhD, chief data scientist for Appriss Retail, a leader in data and analytics solutions for retail organizations. “The economy of scale hasn’t always been there, hardware was more expensive, the cost to develop the models has been very high, and the ROI not clear enough,” he explained. But with costs having come down, he said it has become realistic to leverage AI to develop models for employee theft.

In the article, Seivold goes on to consider how the new technologies will mesh with traditional strategies—as well as what it might look like when we start using robots in retail applications. To read the full article, check out “Where Will We Go from Here?

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