The retail sector is the poster child for the use of artificial intelligence. Self-driving delivery robots, automated warehouses, intelligent chatbots, personalized recommendations, and deep supply chain analytics have been making significant impact on the bottom line -- if you're Amazon.com. Other retailers, however, are struggling to adapt. In fact, only 19 percent of large retailers in the U.S., UK, Canada and Europe have deployed AI and are using it in production, according to Gartner. Get the latest insights with our CIO Daily newsletter.

Amazon Comprehend is a fully managed natural language processing (NLP) service that enables text analytics to extract insights from the content of documents. Amazon Comprehend supports custom classification and enables you to build custom classifiers that are specific to your requirements, without the need for any ML expertise. Previously, custom classification supported multi-class classification, which is used to assign a single label to your documents from a list of mutually exclusive labels. Starting January 6, custom classification also supports multi-label classification. With multi-label classification you can train models and classify your documents with more than one label.

Two-thirds of retailers in France considered the use of AI indispensable for ecommerce, and a further 18.5% said it was very useful, per Octopeek. "Besides customer experience, some AI implementations operate at the back-end of retail operations," said Karin von Abrams, eMarketer principal analyst and author of our latest report, "Western Europe Ecommerce Trends 2020: Consumer-Facing AI, Social Commerce and Delivery/Returns on the Agenda." "For example, fashion store H&M uses AI to analyze purchases and returns at physical stores; these patterns help determine whether a specific store should promote or increase stock of certain items," she said. Stores can also analyze consumers' online search behaviors over time to identify products that regularly attract interest but could be marketed more effectively. Retailers typically see consumer-facing AI as a natural extension of traditional in-store activities.

At a warehouse on the outskirts of Berlin recently, a new addition to the warehouse, a robot, drew press attention. The New York Times called the component-sorting robot "a major advance in artificial intelligence and the ability of machines to perform human labor." A video demo of the robot in action revealed the robot placing various items, with different shapes, in different containers. "As millions of products move through warehouses run by Amazon, Walmart and other retailers, low-wage workers must comb through bin after bin of random stuff--from clothes and shoes to electronic equipment--so that each item can be packaged and sent on its way. Machines had not really been up to the task, until now," said The New York Times.

Artificial intelligence and augmented reality are still in the early stages of adoption in retail. The beauty care sector, however, has grabbed these bulls by the horns, which isn't surprising when you consider how personal the buying experience is. "Try more, (you) definitely buy more," observed Forbes contributor Laura Heller in introducing "Beauty Tech 360: AI and AR Personalized Solution" during the recent CES show at the Las Vegas Convention Center. The session, moderated by Adam Gam, U.S. chief marketing officer at Perfect Corp., addressed how brands and retailers are leveraging custom technology solutions across omnichannel touchpoints -- including interactive kiosks -- to better understand and meet customers' needs. Perfect Corp's AI-powered YouCam beauty app allows customers to "try on" beauty products and has launched in several stores.

Walmart's Intelligent Research Lab, or IRL, is both a technology testbed and a fully functioning store covering 50,000 square feet. The store is filled with sensors and cameras and can automatically alert store associates when a product is out of stock, shopping carts need collecting or more registers are necessary to quell long lines. There's enough cable in IRL to scale Mt. Everest five times, and the store has enough computing power to download 27,000 hours of music per second.

Consumers have always demanded innovation from the retail industry. Shopping habits and product demands are constantly evolving, and retailers invest a significant amount of capital to monitor trends and cater to fluctuating behaviors. Recently, advancing technology has quickened the pace of change and made it even harder to win consumer attention in an increasingly crowded marketplace. More than ever, success requires financial and managerial flexibility and adaptiveness--areas where private equity can play a vital role. Below, read my thoughts on three key areas where our industry is partnering with retailers to help them keep ahead in the fast-changing sector.

Incorporating innovation and technology that is both interactive and engaging has changed the dynamics of fashion retail. In recent years, technology has been drastically dabbling in the world of fashion. With the tilt from brick-and-mortar retail shopping, technology's impact has been difficult to ignore – especially with e-commerce in full force. Considering the technologies such as AR, VR, artificial intelligence and automation, the retail sector is burgeoning as well as the need for retailers to adapt to the new landscape and embrace the turning point of the fashion world. Artificial intelligence is reshaping the retail industry at a rapid rate.

Bundling, the practice of jointly selling two or more products at a discount, is a widely used strategy in industry and a well examined concept in academia. Historically, the focus has been on theoretical studies in the context of monopolistic firms and assumed product relationships, e.g., complementarity in usage. We develop a new machine-learning-driven methodology for designing bundles in a large-scale, cross-category retail setting. We leverage historical purchases and consideration sets created from clickstream data to generate dense continuous representations of products called embeddings. We then put minimal structure on these embeddings and develop heuristics for complementarity and substitutability among products. Subsequently, we use the heuristics to create multiple bundles for each product and test their performance using a field experiment with a large retailer. We combine the results from the experiment with product embeddings using a hierarchical model that maps bundle features to their purchase likelihood, as measured by the add-to-cart rate. We find that our embeddings-based heuristics are strong predictors of bundle success, robust across product categories, and generalize well to the retailer's entire assortment.