Organizations are under pressure to accelerate AI initiatives, yet many are running on server and storage environments that were never designed for GPU-intensive workloads or high-throughput AI data pipelines. As refresh cycles approach, leaders face a pivotal choice: extend existing infrastructure incrementally or modernize in a way that supports long-term AI scalability.

This executive discussion will explore how modern server and storage strategies, including GPU-rich systems, high-performance storage, and hybrid architectures, are enabling scalable enterprise AI. We will examine how edge and core environments can operate cohesively, how full-stack AI platforms reduce deployment complexity, and how infrastructure refresh decisions can be aligned with measurable AI performance and business impact.

BWG Connect, Insight Enterprises, Lenovo & NVIDIA invite you to join an interactive event with industry peers to review & discuss practical insights across the digital space.

As always, there will be no sales pitch and all attendees are welcome to listen and learn.

• Many organizations are being asked to accelerate AI initiatives while still operating on infrastructure that was never designed for GPU-intensive workloads.
  • How leaders are aligning server refresh cycles with the performance demands of generative AI, RAG, and inference workloads
  • Why GPU-rich architectures, including platforms built on NVIDIA accelerated computing, are becoming central to long-term AI infrastructure planning
• Storage architecture is increasingly becoming the constraint in AI scalability and performance.
  • Where legacy storage environments create bottlenecks for high-throughput AI data pipelines
  • How modern enterprise storage platforms are being evaluated to support the data intensity of training and inference workloads
• Hybrid AI strategies require infrastructure that operates consistently across data center, cloud, and edge environments.
  • How pre-validated, full-stack approaches such as Lenovo Hybrid AI Advantage are reducing deployment risk
  • What leaders are considering when balancing flexibility, scalability, and operational simplicity in hybrid AI environments
• Edge AI is gaining relevance as organizations push intelligence closer to operational environments.
  • Where enterprise-grade edge systems such as ThinkEdge SE450 are supporting ruggedized, high-performance AI use cases
  • How organizations are architecting connectivity between edge and core AI environments to avoid silos
• Infrastructure investments are increasingly evaluated through the lens of measurable AI outcomes and ROI.
  • Real-world examples of hybrid AI platforms improving operational efficiency and decision velocity
  • What it takes to build scalable AI foundations without overextending capital budgets