Generative AI use cases for retail and consumer goods

Introduction

Ever tried to shop for a specific item in a sprawling department store without knowing how to navigate the different sections? You might have all the products at your fingertips, but you need the right guidance to save time and not leave empty-handed.

Trying to navigate the world of AI without a guide is similar. By now, we’re either all familiar with generative AI or are using it in our personal or work lives. But as a business leader, thinking broader and having a vision for organization-wide transformation is important.

Accelerating speed to market in retail and consumer goods with generative AI for mission-critical workflows

Leading retailers and brands are already using generative AI across their mission-critical workflows to achieve faster speed to market for new products, experiences, and campaigns. Don’t miss out on the opportunity to stay competitive and drive innovation in your own core business operations. ​​Embracing generative AI can help you stay ahead of the curve, grow the business, and drive efficiencies across the entire product lifecycle — from SKU to store.

“The purpose of generative AI isn’t intended to replace human talent but to augment and assist it. On average, 46% of working hours in retail across frontline and corporate roles could be enabled by this technology. Generative AI has internal and external applications spanning Strategy, Data & Analytics, Merchandising, Product Development, Supply Chain, Stores, Finance, and HR.”

How to get the most out of this guide

We organized this guide into modules by function, highlighting generative AI use cases for mission-critical workflows. From R&D in consumer goods to merchandising and store operations in retail — to marketing and customer support in both — we’ve got you covered with practical applications tailored to your industry.

We’ll also look at how one real-life retailer, Adore Me, uses the WRITER generative AI platform to create hundreds of product descriptions every month, follow environmental, social, and governance (ESG) guidelines, and free up resources to allow employees to work more strategically.

Generative AI is a broad term encompassing various technologies and techniques, such as deep learning and natural language processing (NLP). These tools can generate new images, sounds, text, or even entire websites.

In this guide, you’ll learn about use cases for generative AI in retail and consumer goods, which our full-stack enterprise platform, WRITER, supports. You’ll see different types of possible AI outputs and capabilities, as well as how to best implement those use cases using WRITER.

Note that WRITER isn’t just a ChatGPT or Microsoft Copilot alternative. While these tools are great for individual productivity and trained on general public data, our full-stack generative AI platform delivers enterprise impact and uses WRITER-built LLMs trained on business-curated data sets.

Get started with these generative AI use cases in retail and consumer goods

Research and development (R&D)

In consumer brands, R&D teams aim to protect the business and drive product innovation. But, divisional silos and an inefficient discovery process often lead to wasted resources on duplicate research. Generative AI can help fix this by breaking down those silos and giving every product team quick access to existing insights and research, making the R&D process more efficient.

Customer insights knowledge assistant

Customer insights come in from different places, such as social media, online or app store reviews, customer interviews, and support calls. With generative AI, brands can empower all team members to analyze vast amounts of customer feedback to derive valuable insights that can inform new products and improve the customer experience.

Defect discovery and resolution

Traditionally, addressing defects can take weeks to months. But with generative AI, R&D teams can automate the creation of detailed discovery documents that outline potential reasons for defects, proposed solutions, and supporting evidence, ultimately accelerating product improvements.

Merchandising

In the retail industry, generative AI provides instant insights and data-driven recommendations for merchandising, which inform product offerings and promotions aligned with customer preferences and market trends.

Competitive product insights

Merchandising teams can use generative AI to ensure the right products are always in stock by analyzing customer feedback and competitor data. For example, retailers can detect early signs that competitors are introducing new products, allowing them to respond quickly to market interest.

Store operations

For retailers with physical stores, store operations are critical for maintaining positive customer relations and driving sales.

Store associate onboarding

Using generative AI, retail businesses can create and update standard operating procedures tailored to the store’s specific needs, covering customer service scenarios and providing training simulations.

In-store associate enablement

An in-store associate knowledge assistant powered by a custom chatbot can ensure every store associate has all the right answers, pulling information from company documents and training materials.

Marketing

Generative AI helps marketing teams quickly apply valuable customer data and tailor content and campaigns at scale, resulting in faster outputs and improved quality.

Personalized PDP workflows

Marketers can use generative AI to improve product description creation and optimization, increasing organic traffic and ensuring brand consistency.

Unique campaign assets and test variants

With generative AI, marketers can ideate campaign themes, write creative briefs, and generate a variety of marketing assets tailored to multiple audience segments.

Customer support

Generative AI allows customer service teams to efficiently handle high call volumes and improve interaction quality.

Customer support agent knowledge assistant

A knowledge assistant helps customer support agents quickly answer questions, providing personalized and engaging interactions.

Knowledge or help articles

Generative AI can streamline the development of help articles, frequently asked questions, and product tutorials, making it easier to maintain content across multiple markets.

Evaluating generative AI solutions for your business

With the right generative AI tools, all of these business-transforming use cases are possible. Here are key capabilities a generative AI platform should have:

• Ability to create custom AI applications based on your business logic.
• Deliver fast, consistent, and reliable outputs.
• Securely connect to your structured and unstructured data.
• Assist with legal, regulatory, and compliance needs.
• Protect data integrity and security.

Case study

How Adore Me uses WRITER to speed up time to market from months to minutes

Adore Me is a digitally-native intimates brand that offers a wide range of lingerie and apparel. Ranjan Roy, Adore Me’s SVP of Strategy, found WRITER well-suited to solve real business problems.

Adore Me has deployed numerous custom AI apps through WRITER AI Studio that automate key processes. One use case was generating SEO-optimized product descriptions, leading to a 40% increase in non-branded search volume.

How a CPG company uses WRITER to scale international markets and save 2,000 hours

A leading global CPG brand needed to reinvent its content creation processes using generative AI. By centralizing product content and developing custom AI apps, they eliminated costly external agency reliance, improved workflow efficiency, and saved over $500,000 in operational costs.