Accelerate retail with AI.
Leading retailers are tapping into AI and generative AI to automate warehouse logistics, determine in-store promotions and real-time pricing, enable customer personalization and recommendations, deliver better shopping experiences, and more—both in stores and online.
Dive into the data compiled from a survey of over 400 professionals from around the world. Discover the trends, challenges, and opportunities that define the state of AI in retail and CPG in 2024.
Learn about the most important AI use cases in retail today across supply chain optimization, omnichannel management, and intelligent stores.
Building smart stores.
With AI, retailers are building intelligent stores that reduce shrinkage, eliminate stockout, and offer visibility into in-store customer behavior to optimize merchandising. Data from cameras and sensors provides valuable analytics that enable smart decision making, improve operations, and increase efficiency. Additionally, the same infrastructure can be used for a faster customer checkout experience, including fully automated checkout systems.
Delivering seamless customer experiences.
Retailers are moving to create a fully integrated shopping experience across multiple touchpoints, including brick-and-mortar, web, and mobile devices. Why? Four in five customers shopping in a physical store browse the internet before or during their purchase decision to compare products and offers. Reversely, customers who make online purchases visit a physical store to get a feel of the product and fit. In fact, 73 percent of customers use more than one channel during a single shopping journey. By harnessing the power of AI, retailers can offer an omnichannel experience that increases cart size and builds brand affinity.
Creating smart warehouses.
NVIDIA AI and simulation solutions are delivering better-than-ever efficiency and intelligence to the supply chain, ensuring retailers never miss a beat and can meet customers expectations. With intelligent video analytics, robotics, automation, and management, operations become more efficient, process throughput accelerates, and warehouse robots deliver end-to-end visibility, increasing the accuracy of orders picked, packed, and shipped.
Catch these recommended sessions on-demand.
Learn how to implement the Triton Inference Server to develop recommendations and personalized search systems. We'll demonstrate how you can serve multiple models at scale with Triton, A/B test models, serve ensemble models, and monitor progress in production. We'll also deep-dive in to the MLOps processes and the importance of a cross-functional approach from concept to deployment, and share performance results to illustrate the impact of this deployment.
A fulfillment center is a critical node in providing optimal customer service in a supply chain network and for e-commerce. Therefore, improving order fulfillment time is critical to world-class operations. A key process in order fulfillment is decanting and picking — activities that consume the most time when operating a fulfillment center in general, and fulfilling an order in particular. We'll analyze an actual system that stores products within an automated system and releases orders to a picking station. We'll demonstrate the use of two key platforms from NVIDIA — (1) Omniverse, to create digital twin 3D assets and an architecture enabling variations in simulation models to address different scenarios and strategies aimed at improving system performance, and (2) Metropolis, to enable highly scalable intelligent video analytics applications to provide high-quality perception data and operational situation awareness. We'll discuss specific key performance indicators to compare different strategies and scenarios, such as order fulfillment lead-time, picks per man-hour, average picking time per order, and average time to pack an order. These proposed solutions will provide insights that lead to improvements in order processing time, order fulfillment rate, and increased operator efficiencies.
In this session, we will showcase how developers can use new Retail Pre-trained Models and Metropolis SDKs and microservices to develop Retail applications for Loss Prevention. Developers will learn how to leverage the the pre-trained models as is as well as how to think about how to customize and fine-tune them for a variety of use cases. Additionally, developers will learn how to build and customize applications using Metropolis microservices.
Watch our on-demand Big Ideas sessions on generative AI, organized retail crime and digital twins that feature leading retailers and partners.
Generative AI became another tool in our AI toolkit for how we wanted to solve our problems.
– Melissa Ludack, Vice President of Data Science, Target
If you look at these coordinated teams of organized operators and theft, self-checkout is the land of opportunity. So we’ve got to stay one step ahead of them and we’re going to accomplish that through AI.
– Mike Lamb, Vice President, Asset Protection & Safety, Kroger
We take a human-centered approach to how we solve our problems, and we believe that AI is really the opportunity, whether it’s generative AI or traditional AI, to solve these problems in a different way.
– Cari Covent, Head of AI and Emerging Technology, Canadian Tire Corporation
We aim to provide our customers with the best shopping experience. Our catalog contains over 150,000 products with 100 items being added every week. To ensure we can offer personalized recommendations at scale, we leverage machine learning for high-quality recommendations throughout the customer journey.
– Rick Bruins, Machine Learning Engineer, ASOS
To have the collaboration between our two companies, to be able to come back with our feedback and say here's what we found, here’s what worked great, and here’s what needs improvement, and by combining all those things together, we’ve had a very successful partnership.
– Kurtis Van Horn, Co-founder and Senior Vice President of Logistics, Implementation, and Production, AWM
Metropolis microservices are brought to life with reference applications that understand the traffic flow, create heat maps, and more. With Metropolis microservices, it’s easy to build, test and scale deployments from edge to cloud with enhanced resilience.
– Ying Feng, Director of Data Science and Analytics, PepsiCo
NVIDIA is pushing the boundaries, and people like us partner with them to try to open doors to inspire people.
– Benoit Pagotto, Co-Founder at RTFKT, Brands & Partnerships, Nike
AiFi enables reliable, cost-effective, and entirely contactless autonomous shopping with AI-powered computer vision, providing an unrivaled shopper experience for retailers and consumers around the world.
NVIDIA Inception member RadiusAI uses AI to provide valuable, real-time, actionable data from existing vision-capturing devices in convenience stores. This lets retailers see real-time bottlenecks, as well as customers’ shopping habits.
Cooler Screens is reimagining the consumer shopping experience with digital signage and AI-based inventory checks that engage customers, reduce stockouts, and increase sales.
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Learn more about how NVIDIA AI is bringing leading-edge technology to our everyday retail experiences.
Sessions
Learn how to build and execute end-to-end, GPU-accelerated data science workflows that let you quickly explore, iterate, and move your work into production. In this self-paced lab, you’ll learn how to use RAPIDS™ accelerated data science libraries to perform data analysis at scale with a wide variety of GPU-accelerated algorithms.
In this lab, you’ll learn how to interact with the NVIDIA Riva speech server to process various conversational AI requests. You’ll learn how to send audio to an automatic speech recognition (ASR) model and receive back text, use natural language processing (NLP) models to transform and classify text, and send text to a text-to-speech (TTS) model and receive back audio.
Develop the skills you need to do your life’s work in AI, data science, and more. The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI, accelerated computing, and accelerated data science.
Listen to this Omni Talk Spotlight podcast about what generative AI is and how retailers can leverage it, particularly in ecommerce content generation, customer service responses, and employee productivity.
Connect with millions of like-minded developers and access hundreds of GPU-accelerated containers, models, and SDKs—all the tools necessary to successfully build apps with NVIDIA technology—through the NVIDIA Developer Program.
Evolve your startup with go-to-market support, technical expertise, training, and funding opportunities.
Learn about the AI hardware and software for Retail and CPG.
With NVIDIA Metropolis, retailers can improve customer satisfaction, in-store analytics, and business efficiencies. Discover how AI-enabled video analytics are helping build intelligent stores.
With NVIDIA RAPIDS™, processes that took days take minutes, making it easier and faster to build and deploy value-generating models. Retailers are using it to provide personalized recommendations with NVIDIA Merlin™ and improve demand forecasting, inventory management, and last-mile delivery.
Retailers are racing to adopt generative AI to create content and images for brand campaigns, hyper-personalized shopping advisors, ecommerce product descriptions, customer service responses, and more.
Our solutions for the retail industry go beyond products. Our partners are here to assist your organization at every level to build and execute transformative AI strategies, products, and services.
Our experts can help your business unlock potential and unleash innovation.
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