Ninety-eight percent of retailers plan to invest in generative AI in the next 18 months, according to a new survey conducted by NVIDIA.
That makes retail one of the industries racing fastest to adopt generative AI to ramp up productivity, transform customer experiences and improve efficiency.
Early deployments in the retail industry include personalized shopping advisors and adaptive advertising, with retailers initially testing off-the-shelf models like GPT-4 from OpenAI.
But many are now realizing the value in developing custom models trained on their proprietary data to achieve brand-appropriate tone and personalized results in a scalable, cost-effective way.
Before building them, companies must first consider a variety of questions: whether to opt for an open-source, closed-source or enterprise model; how they plan to train and deploy the models; how to host them; and, most importantly, how to ensure future innovations and new products can be easily incorporated into them.
New offerings like NVIDIA AI Foundations, a curated collection of optimized, enterprise-grade foundation models from NVIDIA and leading open-source pretrained models, are giving retail companies the building blocks they need to construct their custom models. With NVIDIA NeMo, an end-to-end platform for large language model development, retailers can customize and deploy their models at scale using the latest state-of-the-art techniques.
Generative AI Use Cases
Multimodal models are leading the new frontier in the generative AI landscape. They’re capable of processing, understanding and generating content and images from multiple sources such as text, image, video and 3D rendered assets.
This allows retailers to create eye-catching images or videos for a brand’s marketing and advertising campaign using only a few lines of text prompts. Or they can be used to deliver personalized shopping experiences with in-situ and try-on product image results. Yet another use case is in product description generation, where generative AI can intelligently generate detailed e-commerce product descriptions that include product attributes, using meta-tags to greatly improve SEO.
Many retailers are testing the generative AI waters first with internal deployments. For example, some are boosting the productivity of their engineering teams with AI-powered computer code generators that can write optimized lines of code for indicated outcomes. Others are using custom models to generate marketing copy and promotions for various audience segments, increasing click-to-conversion rates. Meanwhile, chatbots and translators are helping employees accomplish their day-to-day tasks.
To enhance customer experiences, retailers are deploying generative AI-powered shopping advisors that can offer personalized product recommendations in customer-tailored conversation styles and display images of products being recommended. It can even display those products if shoppers want to see the recommended product, for example, in their home by uploading a picture of a room. Another use case is a customer service multilingual chatbot capable of answering simple customer inquiries and routing complex ones to human agents for improved, more efficient service.
NVIDIA at NRF
To learn more about how generative AI is shaping the future of retail, watch this speaking session from NRF 24 to hear from Azita Martin, NVIDIA’s vice president of AI for retail, consumer packaged goods and quick-service restaurants, and others on how Target and Canadian Tire are using generative AI to deliver personalized shopping experiences and drive revenue and productivity.
Download the State of AI in Retail and CPG: 2024 Trends report for in-depth results and insights.