Running a retail company is tough in the current climate. Slowing market conditions and unplanned disruptions have affected many retail businesses, requiring them to be more agile and remain competitive in order to stay profitable. The challenges are real and so are the solutions. While companies toyed with the applicability of AI-enabled retail solutions, Generative AIhas emerged as a game-changer that will help businesses remain competitive and stay ahead of the curve.ChatGPT, an example of Generative AI, changed things with its conversational interface and ability to collate information in an intelligent manner. It even convinced decision makers who were still on the fence when it came to using deep learning algorithms and neural networks to create valuable information in retail.
The technology presents a huge opportunity for the retail industry as it has the potential to transform multiple aspects and features of the retail industry – from product design, development, e-commerce, sales and marketing to the supply chain and in-store operations. Business leaders in the retail sector have also been quick to recognize the potential of integrating Generative AI into their processes to enhance customer experiences, streamline processes, and drive growth.
The current enthusiasm over Generative AI got catalyzed by the significant advancement in content creation by the introduction of user-friendly interfaces like ChatGPT that have enabled users to effortlessly generate high-quality text, graphics, and videos in seconds.
The ability of Generative AI to produce human-like responses and the ability of developers to release improvements in natural language comprehension and generation have made it particularly suitable for the retail industry, which is an inherently data-rich environment. The challenge has been to convert large volumes of data into actionable insights at the speed of business and create personalized engaging customer experiences across different demographics.
In fact, even as business leaders embrace the power of Generative AI in their retail systems, there are several emerging trends that give us an indicator of how the tech-enabled retail landscape is evolving.
Hyper-personalized recommendations: One of the most prominent trends in Generative AI in retail is the use of a personalized recommendation engine. Retailers are leveraging AI algorithms to analyze customer behavior, purchase history, and preferences to provide tailored product suggestions. Generative AI will allow marketers to provide a more ‘personal’ shopping experience by presenting customers with products that align with their tastes—based on their searches, orders, browsing history, etc.—ultimately increasing conversion rates and revenue.
Recently, a multinational cosmetics brand partnered with a cloud service provider to develop Generative AI solutions to consolidate all its data to better understand customer intent and sentiment in order to personalize the experience.
Virtual trials: Trying products without having to visit the physical store has always been an area of research and basic implementations—like virtual 3D trials with the front camera and intelligent fitting mirrors—are being used to provide an immersive shopping experience. Generative AI has the potential to take the fashion and beauty segments of the retail industry to greater heights. This would apply to trying clothing, accessories and cosmetics before making a purchase.
Two global retailers in the cosmetics and eyewear space are already using augmented reality (AR) to let customers virtually try on their makeup and glasses, respectively. A Swedish fashion retailer is using Generative AI to allow its customers try select designs and receive suggestions based on ongoing trends. Even Google joined the bandwagon with its Generative AI try-on option in the US, which lets shoppers browse for apparel from select brands and see how it would look on real models of varying sizes. This enhances the online shopping experience and reduces returns, which can be costly for retailers.
Chatbots and virtual assistants: Chatbots and virtual assistants are not new features. However, programmatic behavior—like the menu-based systems—means they lack the ‘human’ element to cater to situations beyond the limited answer bank. Generative AI has allowed retailers to expand this and instill a semblance of human-like responses with advanced NLP and natural language generation (NLG). These AI-driven conversational agents provide real-time support and answer customer queries while guiding them through the shopping process.
A Canadian multi-brand fashion retailer has integrated ChatGPT Plus into its AI-driven chatbot to cater to the styling and shopping needs of its discerning customers. It employs five proprietary AI models to provide nuanced responses with accuracy and taste.
Demand forecasting: Generative AI has proven invaluable in optimizing inventory management and demand forecasting. Retailers can use AI algorithms to analyze historical sales data, seasonality, market trends, and even external factors like weather forecasts to predict demand accurately. This allows businesses to maintain optimal stock levels, reduce overstocking or under stocking issues, and minimize unnecessary costs.
A global supermarket retail store, for instance, has employed Generative AI for demand forecasting to improve its inventory management and optimize its buying, distribution and promotion strategy. This effectively leads to significant cost savings and enhanced customer satisfaction.
Content Generation and Marketing: Content creation and marketing are crucial aspects of the retail industry, and Generative AI tools like ChatGPT and Midjourney have helped simplify and even streamline some of the processes. Retailers are using Generative AI to create product descriptions, social media posts, and even video advertisements, saving time and resources while ensuring consistency in branding and messaging.
A global e-commerce company is using Generative AI to create a product summary based on customer feedback. Another online marketplace in the US has used Generative AI to help its sellers generate engaging product description with basic information like title, category, or features. This enhances the quality of product listings but also helps sellers reach a wider audience.
Dynamic pricing: The practice of adjusting product prices in real-time based on various factors such as demand, competition, and inventory levels has become more effective with Generative AI since it is a highly complex, data-driven process. Retailers can fine-tune their pricing strategies to maximize profitability while remaining competitive.
A British airline recently adopted a technology company’s Generative AI algorithm that would help it scan various market variables to predict ticket pricing. The engine scans the network every day to predict demand and inventory while making pricing recommendations to optimize its revenue stream.
Supply chain optimization: Supply chain management is another critical component of the retail industry where Generative AI is playing a pivotal role. AI-driven algorithms can analyze various factors such as transportation costs, lead times, weather and demand fluctuations to optimize the supply chain, reduce costs, and enhance efficiency.
Customer feedback analysis: Generative AI can also be trained to analyze customer feedback from various sources—including social media, reviews, and surveys—to extract valuable insights. This allows retailers to gain a deeper understanding of customer sentiment, identify issues, and quickly make data-driven improvements to their products and services to maintain a positive brand image.
In a slightly different use case for customer feedback, a UK retailer is using Generative AI to write responses to thousands of customer queries. This improves the customer support team’s productivity while ensuring prompt and consistent customer service.
Fraud detection and prevention: The retail industry is susceptible to fraud, especially in e-commerce. Generative AI can improve security by detecting fraudulent transactions in real time. AI algorithms analyze transaction patterns, customer behavior, and other data variables to identify and prevent fraudulent activity from the seller’s side as well as the buyer’s side.
Popular global e-commerce companies have integrated Generative AI into their processes to detect anomalies and fraud to protect their customers and sellers.
Environmental sustainability: Sustainability has become increasingly important, and Generative AI’s ability helps retailers in reducing their environmental footprint. AI can optimize supply chains to minimize waste, assist in designing eco-friendly products, and even suggest energy-efficient store operations.
A prime example here is about a global leader in the retail furniture space. It uses Generative AI to design products that are aesthetically pleasing and environmentally friendly. This aligns with their commitment to sustainability and resonates with eco-conscious consumers.
Retail companies should be aware of challenges as well
While Generative AI offers significant advantages to the retail industry, it also presents a few challenges and ethical considerations that businesses must be aware of and address.
The technology presents a huge opportunity for the retail industry as it has the potential to transform multiple aspects and features of the retail industry – from product design, development, e-commerce, sales and marketing to the supply chain and in-store operations. Business leaders in the retail sector have also been quick to recognize the potential of integrating Generative AI into their processes to enhance customer experiences, streamline processes, and drive growth.
The current enthusiasm over Generative AI got catalyzed by the significant advancement in content creation by the introduction of user-friendly interfaces like ChatGPT that have enabled users to effortlessly generate high-quality text, graphics, and videos in seconds.
The ability of Generative AI to produce human-like responses and the ability of developers to release improvements in natural language comprehension and generation have made it particularly suitable for the retail industry, which is an inherently data-rich environment. The challenge has been to convert large volumes of data into actionable insights at the speed of business and create personalized engaging customer experiences across different demographics.
In fact, even as business leaders embrace the power of Generative AI in their retail systems, there are several emerging trends that give us an indicator of how the tech-enabled retail landscape is evolving.
Hyper-personalized recommendations: One of the most prominent trends in Generative AI in retail is the use of a personalized recommendation engine. Retailers are leveraging AI algorithms to analyze customer behavior, purchase history, and preferences to provide tailored product suggestions. Generative AI will allow marketers to provide a more ‘personal’ shopping experience by presenting customers with products that align with their tastes—based on their searches, orders, browsing history, etc.—ultimately increasing conversion rates and revenue.
Recently, a multinational cosmetics brand partnered with a cloud service provider to develop Generative AI solutions to consolidate all its data to better understand customer intent and sentiment in order to personalize the experience.
Virtual trials: Trying products without having to visit the physical store has always been an area of research and basic implementations—like virtual 3D trials with the front camera and intelligent fitting mirrors—are being used to provide an immersive shopping experience. Generative AI has the potential to take the fashion and beauty segments of the retail industry to greater heights. This would apply to trying clothing, accessories and cosmetics before making a purchase.
Two global retailers in the cosmetics and eyewear space are already using augmented reality (AR) to let customers virtually try on their makeup and glasses, respectively. A Swedish fashion retailer is using Generative AI to allow its customers try select designs and receive suggestions based on ongoing trends. Even Google joined the bandwagon with its Generative AI try-on option in the US, which lets shoppers browse for apparel from select brands and see how it would look on real models of varying sizes. This enhances the online shopping experience and reduces returns, which can be costly for retailers.
Chatbots and virtual assistants: Chatbots and virtual assistants are not new features. However, programmatic behavior—like the menu-based systems—means they lack the ‘human’ element to cater to situations beyond the limited answer bank. Generative AI has allowed retailers to expand this and instill a semblance of human-like responses with advanced NLP and natural language generation (NLG). These AI-driven conversational agents provide real-time support and answer customer queries while guiding them through the shopping process.
A Canadian multi-brand fashion retailer has integrated ChatGPT Plus into its AI-driven chatbot to cater to the styling and shopping needs of its discerning customers. It employs five proprietary AI models to provide nuanced responses with accuracy and taste.
Demand forecasting: Generative AI has proven invaluable in optimizing inventory management and demand forecasting. Retailers can use AI algorithms to analyze historical sales data, seasonality, market trends, and even external factors like weather forecasts to predict demand accurately. This allows businesses to maintain optimal stock levels, reduce overstocking or under stocking issues, and minimize unnecessary costs.
A global supermarket retail store, for instance, has employed Generative AI for demand forecasting to improve its inventory management and optimize its buying, distribution and promotion strategy. This effectively leads to significant cost savings and enhanced customer satisfaction.
Content Generation and Marketing: Content creation and marketing are crucial aspects of the retail industry, and Generative AI tools like ChatGPT and Midjourney have helped simplify and even streamline some of the processes. Retailers are using Generative AI to create product descriptions, social media posts, and even video advertisements, saving time and resources while ensuring consistency in branding and messaging.
A global e-commerce company is using Generative AI to create a product summary based on customer feedback. Another online marketplace in the US has used Generative AI to help its sellers generate engaging product description with basic information like title, category, or features. This enhances the quality of product listings but also helps sellers reach a wider audience.
Dynamic pricing: The practice of adjusting product prices in real-time based on various factors such as demand, competition, and inventory levels has become more effective with Generative AI since it is a highly complex, data-driven process. Retailers can fine-tune their pricing strategies to maximize profitability while remaining competitive.
A British airline recently adopted a technology company’s Generative AI algorithm that would help it scan various market variables to predict ticket pricing. The engine scans the network every day to predict demand and inventory while making pricing recommendations to optimize its revenue stream.
Supply chain optimization: Supply chain management is another critical component of the retail industry where Generative AI is playing a pivotal role. AI-driven algorithms can analyze various factors such as transportation costs, lead times, weather and demand fluctuations to optimize the supply chain, reduce costs, and enhance efficiency.
Customer feedback analysis: Generative AI can also be trained to analyze customer feedback from various sources—including social media, reviews, and surveys—to extract valuable insights. This allows retailers to gain a deeper understanding of customer sentiment, identify issues, and quickly make data-driven improvements to their products and services to maintain a positive brand image.
In a slightly different use case for customer feedback, a UK retailer is using Generative AI to write responses to thousands of customer queries. This improves the customer support team’s productivity while ensuring prompt and consistent customer service.
Fraud detection and prevention: The retail industry is susceptible to fraud, especially in e-commerce. Generative AI can improve security by detecting fraudulent transactions in real time. AI algorithms analyze transaction patterns, customer behavior, and other data variables to identify and prevent fraudulent activity from the seller’s side as well as the buyer’s side.
Popular global e-commerce companies have integrated Generative AI into their processes to detect anomalies and fraud to protect their customers and sellers.
Environmental sustainability: Sustainability has become increasingly important, and Generative AI’s ability helps retailers in reducing their environmental footprint. AI can optimize supply chains to minimize waste, assist in designing eco-friendly products, and even suggest energy-efficient store operations.
A prime example here is about a global leader in the retail furniture space. It uses Generative AI to design products that are aesthetically pleasing and environmentally friendly. This aligns with their commitment to sustainability and resonates with eco-conscious consumers.
Retail companies should be aware of challenges as well
While Generative AI offers significant advantages to the retail industry, it also presents a few challenges and ethical considerations that businesses must be aware of and address.
- Data privacy: Retailers must handle customer data responsibly and ensure that AI-driven systems comply with the region’s data protection regulations. Unauthorized data breaches can damage a company’s reputation and result in legal consequences.
- Algorithm bias: AI algorithms can inadvertently perpetuate biases present in training data. Retailers must actively work to mitigate bias and ensure fairness in their AI systems to avoid discrimination.
- Job displacement: Automation of certain tasks through AI can lead to concerns about job displacement. Retailers should consider reskilling and upskilling their workforce to adapt to changing roles in an AI-driven environment.
- Customer trust: Maintaining customer confidence is essential when implementing AI technologies. Transparency in AI usage and data handling is crucial to reassuring customers about their privacy and security.
The potential uses and on-the-ground implementations listed here show that Generative AI is reshaping the retail industry. Businesses can now leverage these powerful tools to enhance customer experiences, streamline operations, and drive growth. From personalized product design, recommendations and virtual try-on experiences to supply chain optimization and dynamic pricing, the potential applications of Generative AI in retail are vast and continue to expand.
In the coming years, we can expect Generative AI to transform into versions that will offer even more sophisticated solutions to the retail industry’s evolving needs. As businesses strive to remain competitive and meet the demands of modern consumers, Generative AI will undoubtedly play a central role in shaping the future of retail. To succeed in this dynamic landscape, retailers must not only adopt Generative AI but also place priority on its ethical AI practices, data security policy, and innovation roadmap.
BY: Jasmeet Sraw, Senior Growth Partner, Course5 Intelligence
