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.

Sustainability has become an increasingly key issue in the retail industry over the last few years. While it's promising to hear retailers making the right noises, they won't be able to follow through on their good intentions if they don't use technology to help them to put their words into practice. This is where artificial intelligence (AI) and machine learning (ML) will become increasingly important, as they can drive the change, putting more sustainable behaviour within reach for many retailers. In a nutshell, AI enables businesses to measure their environmental and social impact, while ML helps take the next step by recommending tangible ways to adapt behaviour in line. As a result, AI and ML can help retailers make huge strides towards sustainability in their supply chains, through from transporting products to stores in the most intelligent way possible, to making sure they don't order too much stock.

Online retailers use A.I. applications to figure out what customers want and to get it to them quickly, all at a much lower cost than their brick-and-mortar competitors. The key innovation is using data to make better predictions. Amazon uses customers' browsing and buying histories to make personal recommendations. Purchasing patterns across places and over time are used to predict product needs and to stock warehouses accordingly. In traditional stores, the shelves are often full of items that no one wants and bereft of others that were hotter than anticipated.

Some of the biggest names in artificial intelligence, including two godfathers of the machine learning boom, are betting that clever algorithms are about to transform the abilities of industrial robots. Geoffrey Hinton and Yann LeCun, who shared this year's Turing Prize with Yoshua Bengio for their work on deep learning, are among the AI luminaries who have invested in Covariant.ai, The company, emerging from stealth Wednesday, announced the first commercial installations of its AI-equipped robots: picking boxes and bags of products for a German electronics retailer called Obeta. Picking up everyday boxes and plastic packages might sound trivial, and it is for most humans. Workers in factories and warehouses are frequently given new objects to handle, or a batch of different items mixed together, but it's deceptively difficult for a machine to quickly work out how to grab the next doodad.

Some of the biggest names in artificial intelligence, including two godfathers of the machine learning boom, are betting that clever algorithms are about to transform the abilities of industrial robots. Geoffrey Hinton and Yann LeCun, who shared this year's Turing Prize with Yoshua Bengio for their work on deep learning, are among the AI luminaries who have invested in Covariant.ai, The company, emerging from stealth Wednesday, announced the first commercial installations of its AI-equipped robots: picking boxes and bags of products for a German electronics retailer called Obeta. Picking up everyday boxes and plastic packages might sound trivial, and it is for most humans. Workers in factories and warehouses are frequently given new objects to handle, or a batch of different items mixed together, but it's deceptively difficult for a machine to quickly work out how to grab the next doodad.

For today's retailers, AI is fast becoming an invaluable tool to understand what shoppers want – and ensure their experience matches up with their expectations. From supermarket chains with thousands of retail outlets to online clothes brands, fast-evolving AI technologies are helping them to increase sales, reduce excess stock and overall, improve profit margins. "Retailers are under increasing pressure," says Kate Edwards, Senior Research Analyst at JLL. "AI is a growing part of their business model to use the data they have to better understand their customers, predict future trends and boost business, from improving the customer-facing experience, to optimising supply chain processes." The growing focus on sustainability is equally driving uptake. For example, AI can help identify the opportunities for unsold products, reduce the levels of returned products and decipher optimal fulfilment options, delivering both cost and environmental benefits.