Author: Daniel Gabay / Source: The Next Web
The demise of Sears provides a perfect cautionary tale. By all accounts, the former retail giant — which was in a “death spiral for well over a decade” — is paying a steep price for its failure to innovate in new technology. Yet, the means for Sears to do just that was available.
Business technology has been advancing at a rapid pace over the past decade. Deep Learning and Artificial Intelligence (AI), along with an unprecedented availability of data, have made it possible to extract business insights like never before. Could an early adoption of AI have saved Sears?
I think so. Loyal Sears customers have described poorly stocked stores, and a lack of personalized marketing and service. Machine learning and predictive analytics would have enabled Sears to use its data more strategically by forecasting product needs across its many locations, and creating store-specific promotions.
To be fair, shoppers have changed. Sears isn’t the first big box to fail and it won’t be the last. In 2017, J.C. Penney closed 138 locations and there are signs the department store giant is still struggling. But, earlier and more confident adoption of retail technology might have given both brands more time to deal with changes in the retail sector.
What must be acknowledged is that despite so much hype, the retail industry overall has been slower to adopt AI than other sectors. A new report from Microsoft of UK retailers, for example, shows that an astonishing 56 percent of retail companies have still not applied AI tools into their operations. Why is that?
There have been a number of reasonable reasons why retail has been dragging its heels. For starters, there’s been a fear factor. Artificial intelligence poses a daunting learning curve for many of us. With new technology comes a need for new skill sets and a a level of data…
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