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Source: Accenture – Accenture Consumer Research 2024<\/a>\u00a0<\/em><\/p>\n 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\u2019s ChatGPT<\/a>. 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.<\/p>\n 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\u2019s Jukebox<\/a> creates new music, for example, while GitHub Copilot<\/a> generates code.<\/p>\n 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<\/a> 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.<\/p>\n 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\u2019s architecture could provide a copilot that gives customers product information. Developing a shopping assistant is also possible with other types of AI for retail.<\/p>\n From predictive forecasting for retail inventory to GenAI assistants, Intellias has the solution to fit your needs.<\/p>\n 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.<\/p>\n Yet, the most common way to use GenAI in retail is to create personalized experiences. For example, a virtual assistant could use a customer\u2019s 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.<\/p>\n Use cases for GenAI for retail<\/strong><\/p>\n Other benefits of generative AI in the retail industry include:<\/p>\n Many companies already use generative AI and other types of AI in the retail industry for different tasks. The Amazon AI virtual assistant Rufus<\/a> 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.<\/p>\n Consumer likelihood of using conversational AI for advice and recommendations\u00a0<\/strong><\/p>\n Source: Accenture – Accenture Consumer Research 2024<\/a>\u00a0<\/em><\/p>\n 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, \u201cWhat do I need for a weekend camping trip?\u201d The list may include items based on the customer\u2019s preferences. Similarly, Rufus can provide product care information if a customer asks, \u201cIs this jacket machine washable?\u201d<\/p>\n Other examples of generative AI adoption:<\/p>\n 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<\/a>, 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%.<\/p>\n Benefits of AI for the retail industry include:<\/p>\n Microsoft Dynamics 365 for retail is all you need for unified commerce, personalized customer experience, demand forecasting, and more. Intellias can show you how.<\/p>\n 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.<\/p>\n Recent regulations governing GenAI in retail are another consideration. The EU\u2019s 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<\/a> 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<\/a> backlash in November 2024 when it released an AI-generated Christmas advertisement that replicated its iconic 1995 \u201cHolidays Are Coming\u201d campaign. TechRadar criticized<\/a> Coca-Cola for creating a \u201ccreepy dystopian nightmare\u201d that undercut artistic talent and lacked a human touch.<\/p>\n 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.<\/p>\n 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<\/a> 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.<\/p>\n In addition to data management policies, retailers must have the necessary infrastructure. Generative AI in retail requires a scalable, secure platform<\/a> 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.<\/p>\n Retailers need a structured approach to implementing generative AI. To be successful, a retailer should:<\/p>\n 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.<\/p>\n 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.<\/p>\n 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<\/a> 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<\/a> for companies globally.<\/p>\n Using a combination of open-source technologies and Microsoft Azure<\/a>, 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 \u201cWhere can I buy this product?\u201d and \u201cCan I order spare parts?\u201d The chatbot also features a test that can evaluate a sales rep\u2019s 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.<\/p>\n This chatbot is one example of many solutions we have developed using AI and ML. Generative AI services<\/a> at Intellias include consulting, model development, integration, AI governance<\/a>, and ongoing support for machine learning operations (MLOps)<\/a> \u2014 all within the context of responsible AI<\/a>. 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\u2019s journey into generative AI for retail, including with solutions for:<\/p>\n Once generative AI has proven to be a success, retailers can begin to expand their GenAI services<\/a>. 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n Contact our AI specialists<\/a> to see how a GenAI solution can improve your retail operations<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" 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\u2019s 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 […]<\/p>\n","protected":false},"author":15,"featured_media":86901,"template":"","class_list":["post-86898","blog","type-blog","status-publish","has-post-thumbnail","hentry","blog-category-machine-learning-ai","blog-category-retail"],"acf":[],"yoast_head":"\nHow does GenAI improve business?<\/h2>\n
Understanding generative AI for retail<\/h2>\n
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Generative AI retail examples<\/h2>\n
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Benefits of generative AI for retail<\/h2>\n
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Considerations in using GenAI for retailers<\/h2>\n
Implementation challenges, practical solutions<\/h2>\n
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Determining the value of generative AI for retail<\/h2>\n
Case study: A chatbot for retail<\/h2>\n
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Scaling the use of GenAI in retail<\/h2>\n
Reshaping the future of retail with generative AI<\/h2>\n
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