The AI Retail Assistant Manager: Extend AI and inference at the edge to empower associates

Summary: In this video, Mike Ensor, Tech Lead for Google Distributed Cloud, explores how retailers can leverage inference at the edge to enhance their operations and customer experiences. Learn how retailers can leverage the power of cloud infrastructure, AI, and edge hardware, powered by Intel Xeon processors, with Google Distributed Cloud to optimize operations and enhance the customer experience. Challenge: Retail associates face a significant challenge in accessing and utilizing information efficiently while in a retail setting, hindering their ability to make quick, informed decisions. Enabling use cases such as fixing a point of sale terminal, answering customer questions accurately, and expanding knowledge on new or bespoke products can be daunting. Navigating complex digitized manuals and relying on generic training materials often proves cumbersome, especially when immediate answers are needed for customer interactions or problem-solving. This lack of available, contextualized guidance and unreliable connectivity can slow productivity and lead to missed opportunities, Solution: The video presents three types of AI and ML applications that can be implemented using existing AI, cloud infrastructure and edge CPUs by Intel Xeon processors: 1. Google Cloud infrastructure extends to the edge: Build applications on the cloud leveraging the latest in open source and cloud software and extend them directly to your location without compromising on latency, connectivity, or data residency. 2. Inference at the edge: Run applications to solve use cases such as store analytics, transaction processing, and associate enablement directly from your store locations cost efficiently and quickly with CPUs by Intel Xeon processors. 3. AI Agents: Enable AI agents to provide employees with quick access to solve challenges, provide high bar customer experiences, and leverage company documents and procedures quickly and on-site. Results: Implementing these AI solu