It's watching, and knows a crime is about to take place before it happens. Vaak, a Japanese startup, has developed artificial intelligence software that hunts for potential shoplifters, using footage from security cameras for fidgeting, restlessness and other potentially suspicious body language. While AI is usually envisioned as a smart personal assistant or self-driving car, it turns out the technology is pretty good at spotting nefarious behavior. Like a scene out of the movie "Minority Report," algorithms analyze security camera footage and alert staff about potential thieves via a smartphone app. The goal is prevention: If the target is approached and asked if they need help, there's a good chance the theft never happens.

If you've been to a Sam's Club in the past two years, you might have used Scan & Go. The store's app lets you use your phone to scan the barcode of each item you put in your cart. When you're ready to check out, you can pay via the app and show your digital receipt to a store associate before leaving. Now, Sam's Club plans to make Scan & Go shopping even easier. This spring, the company will update its Scan & Go app at Sam's Club Now, its Dallas-based testing ground for shopping tech.

Adoption of AI-driven intelligent automation in the retail and consumer products industries is projected to leap from 40% of companies today to more than 80% in three years, according to a new study from the IBM Institute for Business Value study, developed in collaboration with the National Retail Federation. Intelligent automation represents a major technological breakthrough that has the potential to not just improve, but to transform the way companies do business. In intelligent automation, artificial intelligence (AI) is infused into automation, enabling machines to learn and generate recommendations and to make autonomous decisions and self-remediate over time. In retail, the highest growth is expected in supply-chain planning (85%). Retailers and brands are initially using intelligent automation to improve efficiency and reduce costs.

In a recent market analysis from UBS, retail was ranked among the top industries predicted to be most impacted by artificial intelligence (AI) in the years to come. In fact, analysts have estimated that global retailer spending on AI will reach $7.3 billion per year by 2022, up from just $2 billion in 2019. When it comes to where this money will be spent in the coming years, the majority of it is expected to be used to better the customer experience (CX), including everything from personalization to customer service. It's an essential area to invest in given one in three (30 percent) consumers would post a negative review online or to social media if they received poor customer support, proving the need for enhanced CX to maintain customer loyalty, and even gain new shoppers. Brands are still just beginning to untangle the many advantages of using AI in retail, so let's explore how AI can transform the customer experience.

Artificial intelligence and machine learning are an increasingly integral part of many industries, including marketing. But while we often talk about using or incorporating AI in marketing, what do we really mean by that? What does it look like in practice? Here are 15 examples of AI and machine learning in action in the marketing industry (P.S. remember to check out Econsultancy's Marketer's Guide to Machine Learning and AI). The practice of clustering customer behaviours to predict future behaviours began way back in 1998, with a report on'digital bookshelves' by Jussi Karlgren, a Swedish computational linguist at Columbia University. In the same year, Amazon began using "collaborative filtering" to enable recommendations for millions of customers. Fast forward to 2019, and some of the most successful digital companies have built their product offerings around the ability to provide highly relevant and personalised product or content recommendations – including Amazon, Netflix and Spotify.

Machine learning is one of those buzz words that gets thrown around as a synonym for AI (Artificial Intelligence). But this really is not accurate. Note that machine learning is a subset of AI. This field has also been around for quite some time, with the roots going back to the late 1950s. It was during this period that IBM's Arthur L. Samuel created the first machine learning application, which played chess. So how was this different from any other program?

There are many similarities between adoption challenges in price optimization and those facing AI.Bigstockphoto In order to understand the coming AI adoption problem in retail, you first need a little history. In the early 2000's a new technology hit the retail market, called price optimization. The first use-case to be adopted focused on markdown optimization, or pricing inventory near the end of its life to clear out as fast as possible at the greatest margin possible. Coming off of the Internet bubble bursting, retailers had big problems with too much inventory, and markdown optimization was their savior – helping them clear out overstocked items without taking an entire bath on margin in the process. Markdown optimization produced some counter-intuitive results, and was initially resisted, but as its value became proven, more and more retailers with short lifecycle products found themselves in a position of a market expectation that they would have markdown optimization to protect themselves from bad product purchase decisions. The counter-intuitive part came in two ways.

Considering we started talking about AI almost two decades ago, it's perhaps surprising that it is only just starting to make an impact on enterprises today. Certainly, this is the case with retail -- today's omnichannel shopping environment has placed a premium on efficient and relevant interactions with brands. Retailers recognise that AI, and specifically machine learning, has the ability to handle vast amounts of data and is able to use that data to identify patterns and to make decisions with minimal human intervention. In today's market conditions, this is an extremely appealing proposition; to be able to deliver more relevant shopping experiences whilst increasing operational efficiency at the same time. However, in many cases the anticipation of AI is still greater than its actual impact on day-to-day life for the vast majority of retailers.

The pushback against automation technologies in the workplace hinges on the use of robots to do humans' jobs. But enlisting robots to help humans perform their work is another matter -- one that enterprises are pursuing in myriad ways. Grocery store conglomerate Ahold Delhaize USA is deploying 500 robots to alert humans to food and beverage spills, the latest example of automation technology intended to augment, rather than replace, human jobs. Following a successful pilot phase, Ahold's services arm, Retail Business Services (RBS), is rolling out the "Marty" systems across its Giant/Martin's and Stop & Shop grocery chains, RBS CIO Paul Scorza tells CIO.com. The nearly five-foot-tall robots are manufactured by retail automation company Badger Technologies.

Machine learning is one of those buzz words that gets thrown around as a synonym for AI (Artificial Intelligence). But this really is not accurate. Note that machine learning is a subset of AI. This field has also been around for quite some time, with the roots going back to the late 1950s. It was during this period that IBM's Arthur L. Samuel created the first machine learning application, which played chess. So how was this different from any other program?