Exploring the transformative potential of generative AI in retail
Generative AI is a subset of artificial intelligence that has the ability to create new and unique content, such as text, visuals, audio, and video, using the information it has been trained on.
Unlike most AI-based solutions that are designed for specific tasks (think recognizing characters in images and PDF files or detecting anomalous payment transactions), generative AI models perform multiple tasks and produce various outputs, as long as they are similar to the training datasets.
However, the noticeable differences between the two types of AI do not mean that they cannot coexist. On the contrary, the technologies help address each other’s shortcomings, empowering retail brands to make better-informed business decisions and revamp their digital strategies.
On a broad scale, the use of generative AI in retail falls to:
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Synthetic data generation. Traditional AI systems rely heavily on large datasets for training. However, collecting this data can be a time-consuming and costly process that also raises privacy concerns. And that’s where generative AI comes in handy. Thanks to its versatility in generating different types of data, this novel technology can assist in synthesizing information for traditional AI model training. Furthermore, it addresses the obstacles related to data privacy and security, allowing retailers to optimize AI model performance in a risk-free way.
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Advanced analytics. Traditional business intelligence (BI) systems are adept at processing and analyzing structured data, presenting insights in readable formats. AI-infused BI systems boast the ability to analyze structured, semi-structured, and unstructured data coming from various internal and external IT systems. Generative AI solutions for retail imitate the functionality of AI-powered data analytics tools. These solutions provide a user-friendly interface for employees without technical expertise and access to different types of data from various sources, such as customer reviews and social media mentions. Additionally, they can produce data similar to the information you already have to amplify your analytics efforts and simulate realistic scenarios reflecting current market trends and changes in customer behavior.
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Smarter content creation. Generative AI’s ability to create content is unparalleled. That’s why the world’s leading eCommerce companies turn to generative AI to write SEO-friendly blog posts, landing pages, and product descriptions. In brick-and-mortar retail, the content-related applications of generative AI might not have such a transformative impact. However, physical stores can still leverage the technology to craft contextually relevant content, from flyers and personalized marketing messages in shopping apps to product videos running on interactive displays.
Let’s see how these capabilities align with specific generative AI use cases.
Top 5 generative AI use cases in retail
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Providing personalized shopping guidance to customers. To personalize customer experience in brick-and-mortar stores, businesses can use foundation AI models to create digital shopping assistants trained on their corporate data. Living inside your brand app, such assistants may help shoppers find products in a store, arrange related products in bundles, create shopping lists, offer discounts based on past purchases and browsing data, and more. You can also harness the retail generative AI technology to develop dynamic, adaptive content for digital signage and kiosks. Some early examples of retail brands tapping into generative AI-driven personalization include Carrefour, a multinational retail and wholesale chain operating almost 14 thousand stores in 30 countries. Earlier this year, the company launched Hopla, a ChatGPT-powered chatbot that provides personalized shopping tips and even recipes to Carrefour customers taking into account their budget, past purchases, and dietary restrictions. Such chatbots can be a welcome addition to checkout-free shopping solutions, offering seamless assistance to tech-savvy customers.
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Enhancing display design in physical stores. With generative AI models, retailers can design more appealing, efficient, and effective store layouts and product displays, boosting customer experience and sales. As we mentioned in the previous section, artificial intelligence helps boil miscellaneous customer data down to meaningful insights, establishing correlations between store layouts and buyer behavior. An example of this could be heat maps highlighting high-traffic areas in your store, which can be used for optimal product placement. Forward-thinking retailers may also utilize AI to craft displays that cater to specific customer segments or individual preferences and stimulate customer interactions with the designs using interactive screens, augmented reality (AR) apps, and proximity marketing solutions relying on Bluetooth technology. While some of these ideas might seem a sci-fi concept at first glance, sometimes the generative AI’s advice in retail may be as simple as putting up a point-of-purchase (POP) display, which alone could increase sales by up to 32%.
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Assisting with inventory and supply chain management. Ever since the pandemic struck, the retail sector has been dealing with daunting supply chain challenges. These included closing borders and subsequent shipping delays, disrupted production caused by stringent lockdown rules in countries like China, and persistent overstocks and stockouts resulting from the massive changes in buying behavior. Tech-savvy businesses like H&M and Zara have long tapped into retail software development services to solve these problems with the help of integrated data ecosystems infused with AI capabilities. Zara, for instance, tracks all purchases using stock-keeping unit (SKU) numbers, analyzes sales trends for each of their physical shops, and adjusts manufacturing volumes based on actual demand. Similarly, H&M uses artificial intelligence to monitor sales in all of its 4,700 locations, anticipate sales volumes, and timely restock items. By using generative AI in retail supply chains, it is also possible to forecast demand, maintain optimum inventory levels, and optimize logistics operations. The question is, how does generative artificial intelligence compare to traditional AI — and what benefits does it bring to the table? Unlike traditional retail AI solutions, which rely on historical data to detect patterns in new information and deliver intelligent recommendations, generative AI retail systems can produce synthetic training data. Using this data, the smart algorithms simulate market conditions and scenarios and stress-test supply chain models. Such capabilities make generative AI a viable option for retailers lacking substantial amounts of sales and logistics data, empowering companies to take a more granular approach to inventory planning and optimize supply chain operations with complex variables.
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Developing competitive pricing strategies. Brick-and-mortar retailers can use generative artificial intelligence to develop dynamic pricing strategies. As a first step, they need to collect data on customer demographics, behavior, and purchasing history. Next, it is crucial to gather up-to-date information on competitors’ prices for specific product categories. You can enhance your datasets with information from external sources like market reports. Additionally, it is important to consider other factors that may influence customers’ buying patterns, such as seasons, holidays, and recurring events like Black Friday. Retail generative AI systems will absorb this data and acquire the necessary skills to interpret real-time information and make instant pricing decisions based on actual demand. The smart algorithms can also help develop personalized pricing strategies driven by a customer’s buying history.
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Eliminating fraud. Generative AI can be instrumental in detecting and preventing fraudulent behavior in brick-and-mortar retail stores through various means. For instance, you can task generative AI with creating realistic synthetic data to train machine learning models when actual data is scarce or sensitive. This data can be used for teaching computer vision-powered security systems to spot shoplifting and sweethearting events; for more information about these AI applications in retail, check out our recent blog post about the supermarkets of the future. Generative AI can also create authentic transaction data that aids in detecting fraudulent activities, such as phony returns and purchases. This not only increases customer trust but also improves your overall financial performance. There is even an option to combine blockchain-based smart contracts with generative AI retail solutions to detect unauthorized sellers and counterfeit products in traditional retail supply chains! Your company could use blockchain smart contracts that automatically execute when certain conditions are met, while generative AI will analyze blockchain data in real time, identifying patterns and trends that human operators might miss. Some practical use cases for this combination include verifying products using unique QR codes or serial numbers and tapping into generative AI to predict fraudulent patterns associated with the generation of these codes. Furthermore, it’s technically possible to implement AI algorithms to analyze vendor information and transactions on the blockchain to identify unauthorized or fake sellers.
Although retail generative AI is still in its early stages, as a visionary leader, you should consider adding the technology to your digital toolbox ASAP.
With customers becoming more reliant on their smartphones and apps while shopping in physical stores, you could leverage generative AI to personalize your message, fine-tune your upselling and cross-selling strategies, and gain deeper insights into consumer behavior.
However, there are certain obstacles your organization might need to overcome when implementing any type of AI in business.
To help you sail through your AI pilot project, the ITRex team has written several practical guides:
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An explanation of what an AI proof of concept (POC) is and why it is essential for your project’s success
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A rundown of AI implementation challenges
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The AI in Business handbook that provides step-by-step instructions for implementing artificial intelligence in your organization
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A summary of the factors affecting AI development costs (with ballpark estimates of AI projects from our portfolio)
A summary of the factors affecting the cost of AI solutions, as well as Gen AI pricing (with ballpark estimates of AI projects from our portfolio)
