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The Rise of Machines and AI in Retail and Logistics

Advances in artificial intelligence are destined to make our lives even better—but will machines eventually out-do humans?
By Uwe Hennig
Aug 23, 2017

There have been a number of buzzwords and defining technology trends throughout the last decade: from big data to the ubiquitous, omni-present cloud. Now, the Internet of Things (IoT) and artificial intelligence (AI) have seemingly become the latest crazes and talk of the town. Forrester expects investment in AI to triple this year. By 2020, 85 percent of customer interactions will be managed by AI, according to research by Gartner. It's clearly becoming big business across industries: AI is estimated to be worth $36.8 billion globally by 2025, predicts U.S. market intelligence firm Tractica.

With the proliferation and accumulation of so much data, the conundrum for many remains: there's just too much information to be able to make any meaningful sense out of it. And that's where artificial intelligence comes in. AI relies on a continual process of technological learning from experience and getting better and better at answering complex questions. Algorithms powered by AI can rapidly come up with alternative options which are otherwise much more time-consuming and laborious using conventional computer-powered A/B testing. Like the human brain, AI adapts to its environment and gets better the more you use it. But unlike humans, the capacity for improvement is unlimited. What's more, boring, repetitive tasks are never a problem.

AI is not necessarily a concept that's all that new. And with the tech industry's love of jargon, various names refer to more or less the same thing. Machine learning is used to steer self-driving cars. AI is proving instrumental in health care for identifying and diagnosing complicated ailments. In Fintech, all stock markets are now dominated by computer decision-making systems. Even everyday search engines like Google use AI to refine and improve the information they come up with the moment you tap in a few keywords.

Machine Learning
AI learns from past behavior, as well as from trial and error, to come up with more intelligent solutions. Old fashioned rules-based analytics will soon become a thing of the past.

This means making more informed product recommendations using predictive analytics. For example, whereas a retail sales assistant might, if you're lucky, recommend something that's evidently there on the shelves, an AI system would be better at identifying what would be the best items to offer, based on many more criteria. These would include fundamental credentials like real-time product availability and profitability, as well as other important considerations, like a consumer's browsing history, or what they've tried on before in the fitting room—thanks to smart RFID tags imbedded into garments.

Effective AI systems look for re-occurring patterns to help avoid out-of-stocks and unnecessary markdowns—for instance, by promoting underselling lines held in reserve that otherwise would later have to be discounted. Not only will such advanced technology know when shelves are empty, but more importantly, it will predict what will happen next.

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