This article was published on February 24, 2019

Sears’ bankruptcy underscores the need for tech innovation in retail

Sears’ bankruptcy underscores the need for tech innovation in retail
Daniel Gabay
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Daniel Gabay

Daniel is CTO and co-founder of Trigo Vision, an advanced computer vision startup reshaping the retail experience. Aged 29, and a world clas Daniel is CTO and co-founder of Trigo Vision, an advanced computer vision startup reshaping the retail experience. Aged 29, and a world class expert in machine learning and computer vision algorithms, Daniel is a graduate of the prestigious Talpiot program, the most elite unit of the Israel Defense Forces (IDF), where he gained a B.Sc. in Math and Physics, and an M.Sc. in Physics, both with Magna Cum Laude, from The Hebrew University of Jerusalem. During his service program, Daniel led a team of researchers, and was recognized for both creativity and an ability to overcome new challenges in a range of scientific areas, especially algorithmics.

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 literacy that has been a challenge. And, as with all technological change, there has been apprehension about ROI on costs to innovate, and how AI might affect management roles and workplace security.

The good news is that all that has begun to change. A recent IBM survey says as many as 91 percent of retail executives with knowledge of cognitive computing believe it will play a disruptive role in the industry. Retail businesses seem to be now accepting both the value and inevitability of artificial intelligence and other deep learning tools if they are to maintain a competitive edge. The reality is that with millions of metrics being collected, AI systems can examine more data – and in real-time – than any human could possibly manage.

It’s hard not to talk about Walmart here, which has been at the forefront of this thinking. Walmart has shown a real appreciation for the fact that it’s not enough to simply adopt and deploy new technologies. The American retail giant has been investing in all sorts of AI and data-related projects seeking innovative and creative ways  to enhance customer experience, and improve operational efficiency.

By staying ahead of the curve, Walmart will both benefit from new technologies, and shape the conversation around their use. Sears, on the other hand, failed to adapt early on and, to make matters worse, missed every opportunity to catch up.

What does AI in retail look like?

When it comes to AI and retail, there’s been a lot of emphasis on marketing and more personalized consumer experiences. But, I’m happy to tell you, there’s much more to come. For retail operators, AI offers a means of cashing in on previously missed opportunities, seriously curbing shoplifting, increasing efficiency in inventory management, and the end of scheduling headaches.

For consumers, there’s a future where we can forget long lines, frustrated cashiers, incorrectly priced goods, and all the stress we currently experience in brick and mortar shopping.

This is why retail heavyweights such as Amazon and Target, like Walmart, are investing millions in machine learning. More importantly, smaller retail operations will also benefit from advances in AI and Deep Learning. In large part, this is because of a confluence of related technologies, especially IoT.

We’re already seeing it in the smart-home with devices like Nest and Alexa. This is extending to smart devices like connected fridges and even smart clothing. All of these technologies will interface with retailers of all sizes to both enhance the shopping experience and improve sales.

The fact is we’re just beginning to scratch the surface of the potential of artificial intelligence and deep learning. Smart retailers are investing now, even knowing some new technologies may lead to dead ends, because as we’ve seen with Sears, waiting to innovate is too risky.

If you represent a retail business that’s been reticent to apply AI solutions, I suggest you consider the lesson that Sears learned the hard way.

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