Lessons From the Front Lines of Enterprise AI: Real World Impact, Infrastructure Realities, and What Engineers Must Understand
Santa Clara Ballroom
Tue Sep 29 | 10:55am
Abstract
Artificial Intelligence is often positioned as the next phase of hyperscale computing—but most Enterprises operate under a very different set of constraints. Unlike cloud native companies, Enterprises face a complex intersection of legacy systems, economic pressures, and real-world operational constraints—factors often invisible to engineers building the underlying technologies. This session focuses on the realities of deploying AI at scale across the hybrid cloud with environments that do not resemble hyperscale architecture.
In this session we will examine how three forces are reshaping Enterprise architecture. First, the economic model: inference, retrieval, and orchestration create ongoing non-linear costs making efficiency—not scale—the primary design principle. Second, the technical reality: retrieval-augmented generation (RAG) requires new compute with latency and security constraints, making hybrid cloud more essential to balance the tradeoffs of performance, cost, and control. Third, the operational considerations: legacy systems, regulatory requirements, and the inability to trust AI with sensitive data fundamentally shape what can be deployed into production.
This session will focus on lessons learned from managing AI workloads constrained by database bottlenecks, cloud capacity shortages, inconsistent supply of hardware components, and the operational challenges of model drift, quantization choices, and cloud-specific performance variability. It reveals why Enterprise buyers prioritize reliability, predictable supply, and cost efficiency over cutting edge performance—and how misaligned industry innovation can unintentionally disadvantage non-hyperscale customers.
Ultimately, this keynote aims to bridge the gap between AI system and component builders and AI operators—illustrating the practical consequences of design decisions, the constraints faced by real customers, and the opportunities for the industry to build more durable, accessible, and economically sustainable AI infrastructure.
Related Sessions
Keynote
SNIA Awards Presentation
Join us as SNIA presents annual member recognition honors, highlighting major technical contributions, task force advancements, and leadership.
Keynote
StorageAI - 1 Year of Progress
After only 1 year, SNIA's StorageAI has made incredible progress.
From workload performance to accelerator I/O to efficiency and sustainability to data movement to much more, the rapid pace of development has driven the industry towards greater open standardization. When the industry collaborate to solve foundational infrastructure problems, everyone scales faster - vendors, customers, end users, and entirely adjacent industries.
Join the Chair as we discuss the incredible work that's been done in a very short period of time and project the need for collaborative work as we move into the future.
Keynote
Divide, Then Conquer: A New Frontier for Storage Optimization
Hyperscale storage systems face extreme aggregate demands for throughput, IOPs, power, and endurance from concurrent workloads. Maximizing every metric simultaneously across all tenants is impossible due to conflicting design requirements. However, the scale and diversity of these platforms enable vertical integration and specialization—from high-level services down to the media—driving unmatched aggregate efficiency. This keynote will present counterintuitive examples and a novel case study.
Keynote
Lessons From the Front Lines of Enterprise AI: Real World Impact, Infrastructure Realities, and What Engineers Must Understand
Artificial Intelligence is often positioned as the next phase of hyperscale computing—but most Enterprises operate under a very different set of constraints. Unlike cloud native companies, Enterprises face a complex intersection of legacy systems, economic pressures, and real-world operational constraints—factors often invisible to engineers building the underlying technologies. This session focuses on the realities of deploying AI at scale across the hybrid cloud with environments that do not resemble hyperscale architecture.
In this session we will examine how three forces are reshaping Enterprise architecture. First, the economic model: inference, retrieval, and orchestration create ongoing non-linear costs making efficiency—not scale—the primary design principle. Second, the technical reality: retrieval-augmented generation (RAG) requires new compute with latency and security constraints, making hybrid cloud more essential to balance the tradeoffs of performance, cost, and control. Third, the operational considerations: legacy systems, regulatory requirements, and the inability to trust AI with sensitive data fundamentally shape what can be deployed into production.
This session will focus on lessons learned from managing AI workloads constrained by database bottlenecks, cloud capacity shortages, inconsistent supply of hardware components, and the operational challenges of model drift, quantization choices, and cloud-specific performance variability. It reveals why Enterprise buyers prioritize reliability, predictable supply, and cost efficiency over cutting edge performance—and how misaligned industry innovation can unintentionally disadvantage non-hyperscale customers.
Ultimately, this keynote aims to bridge the gap between AI system and component builders and AI operators—illustrating the practical consequences of design decisions, the constraints faced by real customers, and the opportunities for the industry to build more durable, accessible, and economically sustainable AI infrastructure.