Why the Future of Retail is AI Everywhere

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Forward-thinking retailers are already harnessing artificial intelligence to help them sustain and grow their margins in the face of economic uncertainty, environmental pressures and geopolitical instability. Nevertheless, they will only realize AI’s full potential once they apply it holistically across their entire business. Retailers must develop a more strategic and integrated approach to their AI capabilities, focusing on two key levers of business value generation: revenue growth and cost reduction. However, the key is to build data foundations first.

Retailers should create data models that connect the entire business value chain, whether sourcing and buying goods or moving and selling them. In turn, that means drawing together all the company’s data with information from partners and suppliers to create a united data set that covers the whole company’s operations.

This first step is far from simple. While some companies decided many years ago to become data-driven, others have not been using it deliberately or intentionally, even though they’ve been collecting data for years. A 2023 survey of US Chief Data Officers and Chief Data and Analytics Officers found that just 23.9% of companies characterize themselves as data-driven, and only 20.6% say that they have developed a data culture within their organizations.

To become data-driven, retailers must audit the quality of their data and address any shortfalls, as well as set rules, practices and structures for data governance. They also need to streamline processes and foster a data-first culture that promotes data accessibility, data-driven decision-making, and training and education programs. Of course, such change will require commitment from the C-suite. Ultimately, retailers should strive to create a customer-driven intelligent enterprise.

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An archetypal example of such an enterprise today is fashion giant HUGO BOSS. Years ago, it recognized the inseparable nature of AI and customer data. As it invested heavily in AI capabilities, it simultaneously built strong data and analytics platforms powered by SAP and Microsoft Azure. Today, it claims to have many AI-powered engines in sales, pricing, marketing, product and forecasting, with new ones coming out at a monthly pace.

Using AI to Grow Revenue

Beyond the need for secure data foundations, there are two other key areas of experimentation for retailers looking to take advantage of AI: revenue growth and cost reduction. Concerning the former, innovative retailers are already using AI to support dynamic pricing, personalization and retail media optimization to grow revenues.

UK grocery chains Morrisons and ASDA are currently trialing dynamic pricing to respond more dynamically to changing market conditions. Morrisons experimented with dynamic pricing by introducing electronic shelf labels (ESLs) in a small number of stores during 2023. ASDA also completed an ESL trial on 25,000 products. In addition to empowering these supermarkets to adapt quickly to fluctuating market conditions, dynamic pricing would be instrumental in reducing food waste. For example, by providing attractive discounts on fresh produce approaching its sell-by date, dynamic pricing could help both the environment and the retailer’s bottom line.

Additional ways AI can aid in revenue growth include mitigating the complexities of adopting or scaling a retail media operation. It can also help build predicted audience profiles, manage and optimize campaigns in real-time, and create alternate versions of creative based on feedback. According to research from a leading digital transformation services and product engineering company, AI-driven platforms can generate 40% operational efficiencies and double the performance uplift of a retail media business.

Leveraging AI to Reduce Costs

Using AI to analyze data can result in cost savings throughout the organization, enabling retailers to act rapidly on inefficiencies and identify areas of potential improvement. In marketing, for example, a truly omnichannel approach using AI will allow marketers to understand the effect of changing their budget allocations on overall results, leading to increased effectiveness or greater efficiency.

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AI solutions can also help facilitate better customer experiences, resulting in higher satisfaction and lower returns. Fashion brand ASOS uses AI and augmented reality (AR) in its app to help users determine whether a particular color or style will suit them. The AR filter overlays the product on the customer, allowing them to try it virtually.

Furthermore, retailers can leverage AI and customer data to skip expensive steps in the supply chain, gain greater visibility and reduce losses by making items more traceable. Like ASOS’s AI and AR-powered app, Walmart utilizes similar technology in its Be Your Own Model feature. This solution (developed originally to demonstrate topographic features on maps) permits shoppers to view a highly realistic depiction of themselves wearing a clothing item. By allowing customers to try out items virtually, Walmart can minimize the number of items it needs to transport to its physical locations, eliminating a costly step in its supply chain.

The Ultimate Aim – Holistic Optimization

Critical success factors for implementing data and AI in retail include a robust data infrastructure and investment in revenue growth and cost reduction. Adding to this list is expertise in data science and AI, as well as a culture that fosters innovation and experimentation.

In the end, carefully implemented AI solutions will lead to efficiencies across all main drivers of business value, from personnel and procurement to customer acquisition and pricing, culminating in the ultimate goal of an intelligent enterprise optimized as a whole rather than as a series of disparate parts.

About the author:  Martin Ryan is the VP of retail at EPAM Systems, Inc. Ryan has more than 30 years of experience leading strategy consulting and digital transformation service providers. With a technical background, he delivers advisory services for retailers and brands on their technology strategies, software selection, and operating models, covering all aspects of retail, food service, eCommerce, and D2C business models and operations.

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