Imagine a retail store that provides customers with recommendations that are so good they feel like advice from a trusted friend. At the same time, the retail company’s real-time inventory tracking system works behind the scenes to ensure that products are always available when a customer orders. Meanwhile, employees have more time to create memorable experiences for in-store customers because they are free from repetitive and manual tasks. This retail experience was unrealistic just a few years ago, but it is possible now with the help of generative AI.
The value of adopting generative AI and other artificial intelligence (AI) solutions is significant. According to McKinsey & Co., generative AI is expected to deliver between $400–600 billion in value for the retail industry. It can resolve billions of dollars in inefficiencies and provide more tailored customer services. Generative AI for retail can also reduce forecasting errors by up to 50%, helping retailers keep up with consumer trends. Furthermore, generative AI can lead to stronger customer loyalty by offering more personalized buying experiences.
Source: Accenture – Accenture Consumer Research 2024
How does GenAI improve business?
Generative AI is an innovation in machine learning (ML) and artificial intelligence (AI) that is useful for many applications, including business improvements. A common example of generative AI is Open AI’s ChatGPT. This service uses a large language model (LLM), a type of machine learning model, to detect patterns in data. When queried, the LLM compares patterns in the prompt with existing data to generate original text, images, and audio. It does this through natural language processing (NLP), so users can ask it to perform a task without knowing computer programming.
There are many types of generative AI models. They include generative adversarial networks (GANs), which create realistic images; variational autoencoders (VAEs), which are used to create images and music; and diffusion models that produce high-quality images. OpenAI’s Jukebox creates new music, for example, while GitHub Copilot generates code.
There are many applications for generative AI across industries. One practical use is analyzing unstructured data and providing insights. For example, marketers could use generative AI to rapidly produce content for email campaigns, social media posts, blogs, and ads for different market segments. Meanwhile, the product development team could calculate specific parameters for material use, aesthetics, or function to improve or design a product. Using generative AI, the team could reduce material waste, accelerate prototyping, or even create designs that had never been visualized.
Generative AI offers some of the best opportunities for business improvements. Companies that are interested in using generative AI do not need their own LLM agents. With LLM APIs, they can create applications that tap into existing models. For example, an LLM API connected to a website’s architecture could provide a copilot that gives customers product information. Developing a shopping assistant is also possible with other types of AI for retail.
Understanding generative AI for retail
Integrating generative AI in retail industry use cases is valuable for frontend and backend retail tasks. Retailers can use GenAI to create product descriptions in many languages, develop targeted promotions, predict customer churn, and improve store design, among other tasks.
Yet, the most common way to use GenAI in retail is to create personalized experiences. For example, a virtual assistant could use a customer’s purchase history to make recommendations. It could also let the customer choose a unique color combination or make other requests. This level of personalized service helps retailers retain customers and create lifetime loyalty.
Use cases for GenAI for retail
Other benefits of generative AI in the retail industry include:
- Predictive forecasting: Historical and real-time data helps retailers predict future trends, such as customer demand or market behavior.
- Risk management: Unusual transaction patterns provide insights that help retailers identify fraud, unauthorized access, or other criminal activity.
- Process automation: Chatbots and GenAI assistants answer customer inquiries, track orders, and solve many common problems.
Generative AI retail examples
Many companies already use generative AI and other types of AI in the retail industry for different tasks. The Amazon AI virtual assistant Rufus is one of many examples of GenAI in the retail industry. Rufus helps Amazon customers by answering product-related questions and comparing products. The AI assistant also makes personalized recommendations based on conversational context. Amazon trained Rufus using its product catalog, customer reviews, and other resources.
Consumer likelihood of using conversational AI for advice and recommendations
Source: Accenture – Accenture Consumer Research 2024
Rufus provides customers with the products they are seeking in a personalized way. For example, it can suggest a list of essential items when someone asks, “What do I need for a weekend camping trip?” The list may include items based on the customer’s preferences. Similarly, Rufus can provide product care information if a customer asks, “Is this jacket machine washable?”
Other examples of generative AI adoption:
- CarMax, a major used car retailer, uses generative AI to create detailed car comparisons with specifications, features, benefits, and customer reviews.
- Sainsbury’s, one of the largest grocery store chains in the UK, uses generative AI to offer location-specific specials. It also uses generative AI to improve online search results by analyzing user preferences.
- Sephora, a beauty products retailer, uses generative AI to provide personalized product recommendations. Based on information collected from a member’s profile, the company also offers makeup tutorials and skincare routines.
Benefits of generative AI for retail
The benefits of GenAI in retail go beyond personalized customer experiences. Retail companies spend billions on generative AI solutions to improve efficiency and profitability while fostering long-term growth. According to IHL Group, between 2023 and 2029, generative AI will increase retail sales by 51% and gross margins by 20% while reducing selling and administrative (S&A) costs by 29%.
Benefits of AI for the retail industry include:
- Cost reduction: AI-generated product descriptions save time by eliminating the need to type them manually.
- Revenue growth: Suggesting items based on a customer’s preferences increases sales and improves customer satisfaction.
- Waste reduction: Predictive models help retailers determine the size of their inventory. By reducing overstock, retailers also reduce waste and become better environmental stewards.
- Enhanced brand loyalty: Customized email campaigns based on individual preferences keep customers coming back.
- Increased workforce productivity: By automating routine tasks, retailers can focus on strategic, creative, or customer-facing roles that improve satisfaction of both customers and employees.
Considerations in using GenAI for retailers
Yet, generative AI in retail is not perfect. GenAI models can generate believable but inaccurate information. Retailers that make significant investments in GenAI technology must be realistic about its potential.
Recent regulations governing GenAI in retail are another consideration. The EU’s AI Act, China’s Interim Measures for the Management of Generative AI Services, and the proposed Generative AI Copyright Disclosure Act in the US are new regulations that businesses must navigate. Meanwhile, companies that adopt generative AI also must teach AI literacy, develop ethical policies, and be transparent about how they use it. They also need to consider the public perception of generative AI. For example, Coca-Cola experienced backlash in November 2024 when it released an AI-generated Christmas advertisement that replicated its iconic 1995 “Holidays Are Coming” campaign. TechRadar criticized Coca-Cola for creating a “creepy dystopian nightmare” that undercut artistic talent and lacked a human touch.
Data quality is another concern. If someone trains a generative AI model with incorrect or incomplete information, the output will be just as wrong. Bad or low-quality data can lead to unethical results, too. In fact, achieving desired results with generative AI can be challenging. The complexity of the models used for AI makes their predictions difficult to interpret. As a result, companies need to develop quality assurance policies to prevent employees from relying too heavily on generative AI.
Implementation challenges, practical solutions
Getting started with generative AI for retail also has its difficulties. Retailers have lots of data, but that data may not always be cohesive and of the highest quality, which is a must for implementing generative AI successfully. Sometimes, data is locked away in separate systems managed by different departments. Breaking down data silos to make high-quality data available is the first step to successfully implementing generative AI.
In addition to data management policies, retailers must have the necessary infrastructure. Generative AI in retail requires a scalable, secure platform capable of processing large amounts of data and complex calculations. Employees also must know how to use generative AI effectively to integrate it into their workflows.
Retailers need a structured approach to implementing generative AI. To be successful, a retailer should:
- Define objectives: Begin by aligning generative AI initiatives with other business goals. Identify the challenges you hope to address and the outcome you want to achieve.
- Select use cases: Focus on areas where generative AI can be the most beneficial, such as personalized marketing campaigns or inventory management.
- Collaborate with experts: Partner with experienced software engineering companies like Intellias that focus on AI/ML and data analytics to help with every step of your generative AI initiatives.
- Monitor and optimize: Continuously evaluate generative AI performance and refine models based on results.
Determining the value of generative AI for retail
How can you know how well your generative AI initiatives are performing? Measuring return on investment (ROI) requires analysis of revenue growth, cost savings, and customer retention.
Revenue growth is often an immediate indicator of the success of GenAI in retail. Personalized marketing campaigns and dynamic pricing strategies contribute to higher sales figures along with improved customer satisfaction and loyalty. As for cost savings, generative AI will cut expenses by automating many labor-intensive tasks. These savings can be substantial in areas like inventory management and demand forecasting. Generative AI for retail also strengthens the relationship between company and customer and helps retailers build lifetime value with benefits that grow over time.
Case study: A chatbot for retail
Even before the advent of generative AI, retailers began using advanced AI models for various services. One major retailer wanted to create an AI chatbot to train salespeople at stores and outlets on how to educate customers about new products. They chose Intellias as their partner because of our extensive experience creating AI and ML software solutions for companies globally.
Using a combination of open-source technologies and Microsoft Azure, we developed a secure model that employees could integrate into their social media accounts and access anywhere. The chatbot answers questions about a new product lineup even if different customers ask their questions differently. Users get answers to their questions, natural language, such as “Where can I buy this product?” and “Can I order spare parts?” The chatbot also features a test that can evaluate a sales rep’s learning progress and lets managers compare test scores among users. Finally, it creates reports that can be used to make future improvements. Using this chatbot, the retailer reduced operational costs, increased engagement with salespeople and customers, and got detailed analytics about customer interactions.
This chatbot is one example of many solutions we have developed using AI and ML. Generative AI services at Intellias include consulting, model development, integration, AI governance, and ongoing support for machine learning operations (MLOps) — all within the context of responsible AI. We use our framework to ensure that AI is a controlled, high-performance business asset that considers regulatory requirements and ethical questions. As a retail industry partner, Intellias accelerates a company’s journey into generative AI for retail, including with solutions for:
- Customer service automation
- Sentiment analysis in product reviews
- Voice and conversational commerce
- Product image sorting and enhancement
- Supply chain optimization
Scaling the use of GenAI in retail
Once generative AI has proven to be a success, retailers can begin to expand their GenAI services. Scaling generative AI in the retail industry requires planning and cooperation from everyone in the organization. The best approach is to start with small projects where generative AI appears to have a high impact. Retailers could then create even deeper levels of personalization if a service proves to be popular among customers.
Choosing successful use cases for pilot projects also creates early successes and helps retailers gain stakeholder trust. These early victories build momentum within the organization to continue scaling the generative AI initiative.
The generative AI models in retail also need regular updates as details in the data change. For example, a clothing retailer must update and retrain a generative AI model as fashion trends change. Providing opportunities for continuous learning also ensures that AI systems remain relevant and effective. Retailers who remain agile with system updates ensure they can respond quickly to market shifts to stay ahead of the competition.
Reshaping the future of retail with generative AI
Generative AI is transforming the retail industry. It is a cultural shift to embrace data and the insights it provides. By partnering with Intellias to capture the power of generative AI, retailers can provide personalized services with unprecedented growth. They can also enjoy customer satisfaction for many years to come.
Intellias has many years of experience creating software solutions. Let us help you create a scalable GenAI solution to meet the needs of your business.
Contact our AI specialists to see how a GenAI solution can improve your retail operations.
