Artificial intelligence (AI) is transforming various industries, and retail is no exception. Generative AI can power lifelike chatbots and create digital models for trying on clothes, but it comes with risks that retailers should know. From soft drinks to blue jeans and cars, generative AI is radically changing the retail space. This article delves into the potential and the pitfalls of generative AI in retail, examining how it is reshaping customer experiences and operational efficiencies.
The Impact of AI on Retail: An Overview
AI is everywhere, but users may not have realized just how much it’s impacting shopping. Some stores are using the technology to accelerate supply chains or forecast demand, while others are using it to mimic human creativity and boost sales. Levi Strauss, for instance, announced a partnership last year with a studio that builds digital fashion models to augment the number of live humans paid to showcase its clothes. This allows customers to see body types similar to their own in the apparel maker’s products.
Coca-Cola, meanwhile, partnered with OpenAI and the consulting firm Bain on an AI platform that lets digital designers compete to develop artwork for the beverage maker’s billboards in New York’s Times Square and Piccadilly Circus in London. Auto dealer CarMax has deployed AI reporting to evaluate used cars for wholesale buyers. These examples illustrate how AI is being integrated into various aspects of retail.
Enhancing Customer Experience with Generative AI
Generative AI is helping with the most essential part of shopping: customer service. According to a 2024 consumer study by the IBM Institute for Business Value, only 9 percent of 20,000 customers in 26 countries report satisfaction with their in-store shopping experiences. For e-commerce shoppers, the number is only slightly higher, at 14 percent.
“Despite the supercomputers in their pockets, consumers must often spend hours searching for the right products, scouring reviews, comparing prices and assessing sustainability,” the report notes. AI is reducing that friction by giving “consumers the information they need to make better, faster decisions.”
Retailers can use AI to offer customers assistance from a chatbot that seems almost human. Holger Harreis, a McKinsey senior partner, explained in a podcast that AI “can suggest things that are based on what you’ve bought before.” It can also assist in product design, enabling smaller brands to generate numerous designs quickly, a task that would have required hundreds of junior designers in the past.
The Role of AI in Supply Chain and Inventory Management
AI is not only improving customer service but also revolutionizing supply chain and inventory management. Predictive analytics can forecast demand more accurately, reducing overstock and stockouts. For instance, Walmart uses AI to predict product demand and manage inventory efficiently. This technology analyzes vast amounts of data from various sources, including weather forecasts and social media trends, to anticipate customer needs.
Walmart’s AI-driven inventory management system has significantly reduced its inventory costs and improved product availability. By analyzing real-time data, the system can predict which products will be in demand and ensure they are stocked accordingly. This has resulted in a 10% increase in product availability and a 15% reduction in inventory costs.
AI in Personalized Shopping Experiences
Personalization is another area where AI is making significant strides. Retailers are using AI to analyze customer data and provide personalized recommendations. This enhances the shopping experience by making it more relevant to individual preferences.
Levi Strauss’s partnership to create digital fashion models is an example of AI-driven personalization. By using AI to showcase clothes on digital models that reflect diverse body types, Levi’s can offer a more personalized shopping experience. This approach not only attracts a wider audience but also improves customer satisfaction by helping shoppers visualize how products will look on them.
The Risks and Pitfalls of AI in Retail
Despite its potential, AI comes with risks that retailers need to be aware of. These risks include biased preferences, factual errors, and large language model (LLM) hallucinations.
Levi’s AI partnership drew backlash from critics who argued that the company should hire more diverse models. This highlights the risk of biased AI algorithms that can perpetuate existing inequalities.
The tech site CNET used AI to write articles, which led to widespread criticism from The Washington Post, Wired, Columbia Journalism Review, and CNN. They condemned the posts, calling it a “journalistic disaster” that spread misinformation. This underscores the importance of ensuring the accuracy of AI-generated content.
One of the pitfalls of current-state generative AI is that it can create outputs that seem entirely reasonable while also being entirely inaccurate. This phenomenon, known as LLM hallucination, occurs because AI engines are trained using publicly available data, not all of which is reliable. To mitigate these risks, organizations should implement a solid risk management framework encompassing both the generative AI algorithm and the data.
Mitigating the Risks of AI in Retail
To harness the benefits of AI while minimizing its risks, retailers need to adopt best practices and robust governance frameworks.
IT leaders can build a customized data set that is clean and normalized so that the generative AI model has good inputs to pull from. This involves regular audits of AI systems to ensure they are free from biases and inaccuracies.
Retailers must ensure that their use of AI complies with ethical standards and legal requirements. This includes obtaining customer consent for data collection and ensuring transparency in AI-driven decisions.
Ethical AI Implementation
A leading fashion retailer implemented a comprehensive risk management framework to govern its AI applications. This included regular audits, ethical guidelines, and a transparent data usage policy. As a result, the retailer was able to enhance customer trust and satisfaction while minimizing the risks associated with AI.
The National Retail Federation has formed a working group for its members to monitor AI-related concerns and issue policies. This collaborative approach will help retailers navigate the complex landscape of AI and ensure they use the technology responsibly.
As AI technology continues to evolve, retailers must stay informed about the latest developments and best practices. By adopting a proactive approach to AI governance and risk management, they can leverage the full potential of AI to transform the customer experience.
Conclusion
AI is poised to revolutionize the retail industry, offering unprecedented opportunities for enhancing customer experience and operational efficiency. However, to fully realize these benefits, retailers must address the associated risks through robust governance frameworks and ethical practices. By doing so, they can create a seamless, personalized, and trustworthy shopping experience that meets the evolving needs of today’s consumers.
Final Thoughts
The integration of AI in retail is not without its challenges, but the potential rewards are immense. By leveraging AI technologies responsibly, retailers can stay ahead of the competition and provide exceptional value to their customers. As we move forward, continuous innovation and collaboration will be key to navigating the AI-driven future of retail.
