Aug 23, 2017There 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.
One of the biggest growth areas in which AI can make a significant difference to the bottom line is in intelligent forecasting systems. Previously, logistics teams were only able to predict roughly the quantities of products to order to keep shelves fully stocked using (often out-of-date) inventory levels and historical sales data (usually going back a few years, at best). These days, AI can develop a much more accurate picture of exactly what types of products, sizes and colors are likely to sell, by looking at multiple scenarios in real time (fashion trends, consumer behavior, the weather and so forth), and by drawing on data from the internet. This means forecasting is no longer so much stab-in-the-dark guess work.
Using AI, German online retailer Otto predicts, with 90 percent accuracy, what will be sold within the next thirty days and has reduced the amount of surplus stock it holds by a fifth, according to an article published at The Economist (see How Germany's Otto uses artificial intelligence). It has also reduced the number of returns by more than two million products a year. It claims to be so reliable, in fact, that it now uses an automated AI system to purchase 200,000 items a month from third-party suppliers with no human intervention. Humans simply wouldn't be able to keep up with the volume of color and style choices to be made.
Artificial Intelligence offers the potential for a considerable reduction in labor costs. For consumers, it means getting more reliable information and personalized offers, not to mention considerable time-savings for everyone.
Human Machines
A new report by PwC says that around 44 percent of jobs in the retail sector are at risk of automation by 2030. Some of the mid-level employee positions will disappear—particularly warehouse staff and employees in the back-office. AI technology is extremely good at repeated tasks and number-crunching, so a lot of manual processes will undoubtedly be performed by machines in the future. For instance, we're already seeing some retailers wanting to close off stock rooms and using robots to make automatic decisions about what needs to be replaced on the shelves, or managing the flow of goods for deliveries and onto the shop floor.
In the not-too-distant future, it will be common practice to pull out your phone and ask it a question as you enter a store, rather than seeking out a sales assistant or searching through the rails yourself. For instance, your smartphone can immediately respond that a desired article is available in your size, and that sales personnel can bring it to you. Voice-recognition systems and speaking to a computer or smartphone (like Apple's Siri) for answers are clearly the way forward. Talking interactive screens and self-checkouts in fitting rooms are something we're already engaged with.
It's still only the early stages of artificial intelligence. But with the promise of AI making forecasting and product selections even more accurate, it's sure to become a reality.
Uwe Hennig is the chief executive of retail tech company Detego. He has more than 20 years' leadership experience in the supply chain and mobility software market.
