From AI Ambition to AI Outcomes: Building the Infrastructure Foundation for Enterprise AI
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The bottleneck slowing enterprise AI is not the model or the compute. It is the memory and storage architecture feeding the compute. In this Six Five On The Road conversation at Dell Technologies World 2026, Alan Walker of Samsung Semiconductor and Ben Burgess of Dell Technologies join Matt Kimball to examine stranded GPU economics, co-engineered infrastructure, and what the shift to agentic AI demands from an enterprise stack that was built for a different operating model.
The biggest constraint on enterprise AI right now isn’t just the model or the amount of compute available. It’s the infrastructure feeding those systems. GPUs sitting idle while waiting on memory bandwidth, siloed data slowing real-time inference, and environments built for periodic training now being pushed to support always-on agentic workloads. That’s the real divide between AI pilots and AI systems that can operate reliably at scale in 2026.
At Dell Technologies World 2026 in Las Vegas, Matt Kimball speaks with Alan Walker, Senior Director of Sales at Samsung Semiconductor, Kris Williams, Senior Director of Customer Engineering at Samsung Semiconductor, and Ben Burgess, Chief Product Owner of PowerStore at Dell Technologies, about the infrastructure demands emerging underneath enterprise AI and why tighter co-engineering between storage, memory, and compute vendors is becoming critical for long-term performance.
The conversation digs into the storage and memory limitations many organizations still underestimate, how the Dell and Samsung relationship extends beyond a traditional partnership into deep product integration, and why the rise of agentic AI is forcing enterprises to rethink infrastructure originally designed around training cycles and batch inference.
Key Takeaways:
🔹 Stranded GPUs are one of the costliest problems in enterprise AI. When memory and storage can’t keep pace, utilization drops and expensive compute sits idle.
🔹 Data silos and infrastructure bottlenecks are what keep AI stuck in pilot mode. Enterprises that prioritized models before fixing data, storage, and networking are now hitting operational limits.
🔹 Dell & Samsung’s co-engineering goes far beyond marketing. They are validating memory, storage, and workload performance together at the architecture level before customers deploy the stack.
🔹 Agentic AI changes the infrastructure equation. Continuous agents demand real-time access to memory, storage, and dynamic data environments — not systems designed for batch workloads.
🔹 AI infrastructure decisions made today will shape enterprise capabilities for years. The organizations building for long-term scalability now will be the ones positioned to support agentic AI at production scale.
🔹 The gap between AI ambition and AI outcomes will come down to infrastructure. The winners will treat it as a strategic advantage, not a procurement exercise.
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