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Generative AI, in contrast to Narrow AI, represents a different form of AI that is capable of creating new content. It can be used to generate various modes of content, such as images, music, and text, by learning the underlying patterns and structures of data it has been exposed to during training, as well as the context given. This quality can be leveraged to improve the productivity of various business functions ranging from content creation to product design.

Building a generative AI solution requires looking at the business processes from content and data perspectives. There are a few ways to leverage the generative capabilities on content.

Text Generation: Text generation is a powerful tool that can be used to create content for various purposes, such as generating product descriptions, writing articles, and creating chatbot responses. At the backend, it can also be used to generate code, which can be used to automate the process of software development.

Image Generation: Image generation can be used to create and modify product images to design new products, create marketing materials, and generate visual content for various purposes.

Text Summarisation: Text summarisation helps to generate content from product manuals, detailed descriptions and other long-form content. This capability can also be utilized to create personalized content for marketing and customer engagement.

Content Translation: Translation can be used to translate content from one language to another, which can be used to localize content for different markets.

Logical Reasoning: Generative AI capabilities and built-in knowledge can be leveraged to assist in logical reasoning and decision-making process. For example, validating a customer query against the product catalog and finding the most likely products relevant to their needs can be a powerful use case with reasoning capabilities.

Here are a few use cases of Generative AI you may consider in retail industry.

Content Generation: Retailers can use generative AI to automatically generate product descriptions, reviews, and other content to improve search engine optimisation (SEO) and drive traffic to their online stores. These models can analyse product images and specifications to generate relevant and engaging content that resonates with customers.

Inventory Management: Generative AI models can analyze historical sales data, market trends, and other factors to generate forecasts and optimise inventory management. By predicting demand and identifying potential supply chain disruptions, retailers can minimise stock-outs, reduce excess inventory, and improve overall efficiency.

Customer Service Automation: Generative AI can be used to automate customer service interactions by generating responses to customer inquiries, providing personalised recommendations, and assisting with order tracking and returns. These models can help retailers improve customer satisfaction, reduce response times, and scale their customer support operations.

Visual Search: Generative AI can power visual search solutions that enable customers to search for products using images instead of text. By analysing product images and generating relevant search results, these models can improve the accuracy and convenience of product discovery, driving higher engagement and conversion rates.

Personalised Recommendations: Generative AI models can analyse customer data, including purchase history, browsing behaviour, and demographic information, to generate personalised product recommendations. These recommendations can help retailers improve customer engagement, increase sales, and enhance the overall shopping experience.

Supply Chain Optimisation: Generative AI can analyse supply chain data, including transportation routes, inventory levels, and demand forecasts, to generate recommendations for optimising supply chain operations.

Generative AI is creating new opportunities to build AI-Assisted Workforce. A good awareness of generative AI is required for responsible and effective use of AI in the workforce. These jobs are focused on creating, managing and using AI models, and they require a combination of technical skills, domain expertise, and business acumen.

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