Forecasting AI-driven demand is no longer a theoretical exercise, it is a critical design input that directly shapes density planning, electrical distribution, cooling strategy, and capital allocation. As GPU platforms evolve rapidly and deployment behaviors diverge from early projections, design teams must balance ambition with realism to avoid overbuild or costly redesign. This workshop will explore how organizations are translating AI growth uncertainty into defensible planning assumptions that support scalable, highdensity data hall development while maintaining flexibility for future platform shifts.

• Comparing projected AI workloads against real deployment behavior to reduce misalignment between assumed and actual rack densities
• Stress-testing performance and capacity assumptions against multiple growth scenarios to improve resilience to rapid platform shifts
• Aligning end users, architects, engineers, and project partners on operating expectations to set realistic deployment timelines