Today 48% of businesses use some form of AI to utilize big data effectively. Having a clear understanding of “how” the business applies AI to their operations and customers offerings directly impacts revenue and marketshare. Roll up your sleeves with Andrew Taylor as he leads the HPE / NVIDIA team discussion highlighting selected customer AI use cases applied across different industries. AI applications areas include: Computer Vision, Digital Twin, Generative AI with use cases in the Automotive, Energy, Hospital Healthcare, Manufacturing, Public, and Retail Industry verticals. Bring your own challenge and AI application questions to connect and learn with the group.

Data volumes are growing rapidly—building opportunities for more insights, and better business outcomes. The capability of conventional datacenter processors, and the four walls of the datacenters they sit in—meanwhile—is not.Scaling artificial intelligence can create a massive competitive advantage, and GPUs unlock those opportunities by delivering massive improvements in performance per watt.But what about the workloads that aren’t yet accelerated? Will you be able to unlock the capacity to grow your revenue and support the increasing performance your existing customers demand? Ian Finder, Principal Product Manager at NVIDIA Corporation shares his perspective on this dilemma exploring trends around CPU designs, Arm in the data center, and how architectures like NVIDIA’s Grace and Grace Hopper superchips are designed to unleash datacenter efficiency.How do you plan for this and what are the most important trade-offs you need to consider? Ian shares practical use case examples to open up the group discussion on this critical challenge.

Challenge:  Enterprise IT and the Line of Business counterparts have an equal stake in driving meaningful business outcomes from AI adoption. Many enterprises face severe challenges in moving AI projects from pilot to production. This is driven by the sense of urgency to accelerate time to production and because of siloed deployments that cannot be standardized for enterprise-wide adoption.Discussion:  Sri shares his experience with clients' overcoming this challenge sharing best practice to increase the probability of success AND leads the group discussion with other leaders working through this right now.

70% of companies report minimal or no impact from artificial intelligence implemented. Many companies don't have the objectives, quality data, talent, or budget to scale for success. Scaling artificial intelligence can create a massive competitive advantage, but how do you make it happen to have an impact? Joey Zwicker, VP of AI Portfolio and Sales Strategy at Hewlett Packard Enterprise, explores key indicators, practical lessons learned, and success factors to recognize and prioritize for your AI success. As Co-Founder and COO of recently acquired Pachyderm, Joey shares practical use case examples and a glimpse into the future of AI at HPE.

Adapting AI resources for continuously changing business requirements requires scalability, meterability, manageability, on-demandability... lots of flexibility. When faced with a decision to develop AI resources in the Cloud or on Premises, business owners struggle making a choice. On-premises compute gives you more control and is often the cheapest solution, but the Cloud gives you fast access and flexibility. Bill Mannel Chief Technologist, Americas HPC & AI Solution Sales explores what's at stake for the business and how these decisions are made.

AI and groundbreaking techniques like ChatGPT are not only revolutionary but controversial. Most agree we now live in the AI era where companies must adopt AI (and quickly) to survive... yet many AI projects fail to get off the ground. Dale Brown, AI Software Global Head of Sales with Hewlett Packard Enterprise, explores the key elements for successful AI projects and shares real customer examples.