15 May 2019

Artificial Intelligence, or AI, is big news across all customer-facing industries. In retail alone, Juniper Research predicts that global spending on AI solutions will more than triple between now and 2023, reaching $12 billion.

Closely associated with automation and machine-led customer interactions, AI has been pioneered in ecommerce and digital business in general. From chatbots to data analytics, the use of AI in digital commerce has largely served two purposes – giving customers a more versatile and efficient range of touch points, and helping operators develop smarter, more intelligent services.

While Amazon, Google and other big-name digital players have been instrumental in developing first-generation commercial AI solutions, the technology is emerging in-store as well. With the majority of retail and hospitality businesses now having a presence online, it is natural that AI-based technologies are starting to appear across other operational areas in retail.

Point of sale will play a key role in this migration of AI capabilities. As a key hub which connects front-of-house to back office systems, applying AI solutions at point of sale will help to deliver insights into customer behavior patterns, service levels, operational efficiency and more with greater depth and speed than ever previously possible. In addition, as self-service POS endpoints become increasingly popular, AI will ensure the digital experience customers favor online is boosting convenience and choice in the store as well.


One of the key ways the advanced analytics will impact POS is by increasing the opportunities to personalize service. Amazon has demonstrated how effective machine learning is in automating everything from product selection to fulfilment preferences based on online customer usage data. In store, AI can be applied in similar ways at point of sale to make cross-sell and up-sell recommendations, target special offers or recommend follow-up services. These might be based on specific data about past customer behavior, for loyalty program members, or based on pattern analysis of the products and offers most commonly upsold with particular items.

At a broader level, AI can also be used to analyze customer expectations to create better experiences. Through behavioral and sentiment analysis, businesses can gain a better understanding of what motivates customers and, crucially, what drives sales. In retail, for example, this might be achieved by matching sales patterns to merchandising and store layout, providing intelligence into what does and does not work in terms of influencing a customer to make a purchase. In hospitality, it might be a case of revealing which services have the biggest impact on customer satisfaction, and focusing resources accordingly. In restaurants and catering, it might be used to make recommendations for recipe changes, based on customer requests and dietary requirements fed into the POS.


One of the key learnings that all retailers, restauranteurs and leisure operators can glean from digital commerce is that consumers value self-service options. Whether it’s looking up information or making a purchase without a sales assistant, the internet has inspired a do-it-yourself culture among consumers that businesses cannot afford to overlook.

AI bridges the gap between complete customer autonomy and relying on staff to deliver all aspects of service. An online chatbot, for example, helps a customer find answers to their queries quickly and efficiently without having to bother making a phone call or waiting for a reply to an email. It preserves their independence while ensuring a faster, more satisfactory resolution than if they were left entirely to their own devices.

In retail outlets, restaurants, leisure destinations, hotels and more, kiosks and customer information terminals with AI-powered software can play a similar role. A touchscreen that allows customers to order a meal or look up product alternatives, for example, can be loaded with a chatbot application to guide customers through the process. When used for self-service transactions, AI can also be set up to make up- and cross-sell recommendations – with the advantage that a bot will do this consistently, every time.

Further down the line, we are likely to see AI combined with other technologies to make this aspect of guided self-service even more intuitive and convenient. Augmented Reality applications are, for example, already being run on ‘virtual mirrors’ in fashion retail to enable shoppers to view a wide variety of garments onscreen without actually trying them on. The virtual assistant interacts with the customer to identify preferred colors and styles, then makes recommendations accordingly.

And we may even get to the stage where AI assistance in store is decoupled from screens. Shoppers of the future may not have to head to a self-help terminal to have their queries answered – they may simply be able to ask the smart assistant embedded in every shelf.