Retailers today are faced with mounting pressure to maximize their bottom lines while maintaining cost expectations for consumers. But amid volatile market changes, ever-evolving customer expectations and increased competition online, this is increasingly challenging to realize. Many retailers are turning to AI to help boost productivity while keeping costs in check. Research finds the global market size for AI in retail is expected to grow drastically, from $4.84 billion in 2021 to $31.18 billion in 2028. This sharp increase is attributed to surging demand for AI-powered retail solutions as the industry continues to undergo digital transformation.

Over the last couple of years, the retail industry has been navigating against brutal headwinds: the worst pandemic in 100 years, global supply chain disruptions, and accelerating inflation -- all made worse by 1.1 million unfilled retail jobs. With a recession looming, retail businesses find themselves between a rock and a hard place. Still, every crisis brings about some positive changes. Retailers are discovering new strategies for customer service, supply chains, inventory management, pricing and promotion. They are preparing their brick-and-mortar stores for the digital age, reinventing legacy systems and beginning to tackle such advanced technologies as artificial intelligence (AI).

Competition for good workers is tight, and employees' expectations of their jobs have never been higher. They want an inspirational workplace where they feel motivated to be loyal, productive and engaged. Among many things, that means keeping up with technology. Giving retail teams access to leading-edge tech that uses AI and machine learning will provide them -- and you -- insights not previously available, increasing productivity and helping morale. For example, modern workforce management can empower employees with preferred scheduling options and flexible clocking.

Cashierless checkout technologies today are delivered in two iterations. The first takes the form of a typical mobile self-checkout -- a shopper scans products with a mobile device as they browse the aisles, then pays for the transaction via a mobile app without having to fumble through their wallet for cash. Consumers are warming up to the concept, with 48% of U.S. consumers expressing the belief that scan-and-go technology would make shopping easier, and 43% saying they would rather try scan-and-go than wait in a checkout line, according to data from GPShopper. The more recent iteration of cashierless has been heavily publicized with the openings of the first 10 Amazon Go stores. Most of the major players within this space have combined AI- and computer vision-based technology with in-store sensors.

In this new world, businesses need to translate massive volumes of complex data at unparalleled speed into omnichannel insight, with streaming data analysis, visual foresight and streamlined machine learning. As we see the growing importance of data across the omnichannel, a new mentality and foundation is required. This new approach must weave data into a unified strategy that consolidates customer experiences and translates them into out-of-the-box strategies for engagement that drive category leadership and competitive differentiation, from Inventory and Supply Chain Management, to Customer 360 for Omnichannel Experiences, to Next-best Offer/Next-best Action Personalization. To make the most of new types of data in order to understand your customer's buying process in an omnichannel world, you must be able to analyze new types of data. It's your job to quickly correlate social data with point-of-sale systems, and even weather forecasts with wearable devices, in order to build an accurate picture of your real-time customers -- and potential customers.

Chatbots deliver substantial value to organizations by answering basic questions and fielding simple requests for information. For example, an existing customer can ask a chatbot what a business's hours are or what their refund policy is. These questions, while elementary, are critical to customer satisfaction and would traditionally have required customers to connect with a human over the phone. Chatbots, however, can provide instant, personalized answers to customers while freeing up human reps to spend more time assisting customers with more complex challenges. And if it sounds like chatbots are impersonal, consumers clearly don't feel that way. In fact, messaging is the preferred method of contact when it comes to support for people 55 and under.

Repair failed transactions: Using machine learning to craft an optimal retry schedule tailored to each individual declined transaction, resolving credit and debit card payment issues more quickly. Minimize involuntary churn: When not managed appropriately, declined transactions can lead to involuntary subscriber churn, which can cause otherwise-satisfied subscribers to be lost. The engine can help subscription businesses minimize involuntary churn by resolving a greater number of declined transactions in a shorter amount of time. Streamline the customer experience: The Revenue Optimization Engine reduces the number of emails, texts or alerts asking subscribers to update billing info or resolve a problem with their payment. This allows subscription businesses to focus on subscriber satisfaction efforts rather than payment questions.

But that is all changing. Today's AI is more intelligent, contextual and can understand a customer's intent to help them get to where they need to be. Tools like chatbots and virtual assistants have quickly grown to become an integral part of many CX strategies across a variety of industries -- from retail to travel to financial services. And while there are still questions and reservations about the actual ROI of AI in customer service, more and more real-world use cases are revealing the true benefits of using AI in this capacity. So the question remains: Will AI be a friend to the customer experience or more of a foe?

Customers are more tech savvy than ever. Armed with smartphones, they have the entire Internet at their fingertips, which can be either a hazard or an opportunity for retailers. The last thing a brick-and-mortar store wants is to lose a sale because a customer scans a barcode and finds the item cheaper elsewhere. To combat this, retailers are using computing on the edge and Internet of Things (IoT)-connected devices to turn potential showrooming into sales. When a customer scans a pack of printer ink, connecting through the store's WiFi, the retailer can use this as an opportunity to serve a coupon or make personalized recommendations for complementary products such as printer paper or a computer mouse, upselling and cross-selling additional items.

In addition, in 2016 Etsy bought Blackbird Technologies to apply the firm's image-recognition and natural language processing (NLP) to its search function. UNIQLO, the global casual apparel retailer, has also taken a leadership position by offering its customers an expert, AI-powered assistant with deep knowledge about UNIQLO's product catalog, retail locations and more. "As one of the world's leading fashion apparel companies, UNIQLO is committed to delivering the industry's gold standard for customer service and support," said Makoto Hoketsu, Group Senior Vice President and CTO at Uniqlo's parent company Fast Retailing. The focus on natural language processing is dramatically growing as consumers are now accustomed to intelligent conversational assistance, fueled by the use and acceptance of consumer tools like Google Assistant, Siri, Microsoft Cortana and Amazon Alexa. When looking at voice/AI-enabled application tools, the market is flooded with options and growing more competitive with every passing day.