Forward-thinking retailers are already using AI to help them maintain and grow margins despite economic uncertainty, environmental pressures, and geopolitical instability. However, they will only realize the full potential of AI when they apply it 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 creation: revenue growth and cost reduction. However, the key is to establish the data base first.

Retailers should create data models that connect the entire business value chain, whether it's sourcing and buying goods or moving and selling goods. That, in turn, means integrating all of a company's data with information from partners and suppliers to create a unified data set that covers the entire company's operations.

The first step is not easy. While some companies decided to become data-driven companies years ago, they've been collecting data for years. A 2023 survey of U.S. chief data officers and chief data and analytics officers found that only 23.9 percent of companies describe themselves as data-driven and only 20.6 percent say they have a data culture in place within their organization.

To become data-driven, retailers must audit the quality of their data, address any shortfalls, and develop rules, practices, and structures for data governance. They also need to streamline processes, foster a data-first culture, and promote data accessibility, data-driven decision-making, and training and education programs. Of course, such change requires commitment from top management. Ultimately, retailers should strive to create a customer-oriented, intelligent business.

Today, fashion giant HUGO BOSS is a prime example. Years ago, it recognized the inseparable nature of AI and customer data. While investing heavily in AI capabilities, it has also built a robust data and analytics platform powered by SAP and Microsoft Azure. Today, it claims to have a number of AI engines in sales, pricing, marketing, product, and forecasting, and it launches new ones every month.

Use artificial intelligence to increase revenue

Beyond the need for a secure data base, there are two other key areas of experimentation for retailers looking to leverage AI: revenue growth and cost reduction. On the former, innovative retailers are already using AI to support dynamic pricing, personalization, and retail media optimization to increase revenue.

UK grocery chains Morrisons and ASDA are currently experimenting with dynamic pricing to respond more dynamically to changing market conditions. Morrisons experimented with dynamic pricing during 2023 by introducing electronic shelf labeling (ESL) in a small number of stores. ASDA has also completed ESL trials on 25,000 products. In addition to enabling these supermarkets to quickly adapt to fluctuating market conditions, dynamic pricing also helps reduce food waste. For example, by offering attractive discounts on fresh produce nearing its sell-by date, dynamic pricing can help the environment and retailers' bottom lines.

Other ways AI can help revenue growth include alleviating the complexity of adopting or scaling retail media operations. It can also help build profiles of predicted audiences, manage and optimize campaigns in real time, and create alternative versions of ideas based on feedback. According to a study by a leading digital transformation services and product engineering firm, AI-driven platforms can generate 40% operational efficiency and double the performance of retail media businesses.

Use artificial intelligence to reduce costs

Using AI to analyze data can save costs across the organization, enabling retailers to quickly respond to inefficiencies and identify potential areas for improvement. In marketing, for example, a true omnichannel approach using AI will enable marketers to understand the impact of changing budget allocations on overall results, resulting in increased efficiency or efficiency.

Ai solutions can also help facilitate a better customer experience, leading to higher satisfaction and lower returns. Fashion brand ASOS uses artificial intelligence and augmented reality (AR) in its app to help users determine if a particular color or style is right for them. AR filters superimpose products on customers, allowing them to experience them virtually.

In addition, retailers can use AI and customer data to skip expensive steps in the supply chain, improve visibility, and reduce losses by making goods more traceable. Like ASOS 'AI and augmented reality apps, Walmart uses similar technology in its Be Your Own Model feature. The solution, which was originally developed to display topographical features on maps, allows shoppers to view a highly realistic depiction of themselves in clothing. By allowing customers to virtually try out items, Walmart can minimize the number of items that need to be shipped to physical locations, eliminating an expensive step in its supply chain.

The ultimate goal - overall optimization

Key success factors for implementing data and AI in retail include a strong data infrastructure and investments in revenue growth and cost reduction. Add to this expertise in data science and artificial intelligence, and a culture that fosters innovation and experimentation.

Ultimately, well-implemented AI solutions will improve the efficiency of all major drivers of business value, from people and procurement to customer acquisition and pricing, ultimately achieving the ultimate goal of optimizing the intelligent enterprise as a whole rather than as a series of different parts.