Technology & Data Leaders Luncheon: Scaling AI

Technology & Data Leaders Luncheon: Scaling AI

Scaling AI: Infrastructure, Data Sovereignty & Enterprise Platforms

Singapore

Hosted by Factor

Overview

The Infrastructure Reality After AI Works.

As AI shifts from experimentation to a core business capability, infrastructure is no longer a background concern. It becomes a defining factor in whether organisations can scale AI effectively or are constrained by it.

As use cases move into production, leaders are confronting the reality that not all AI workloads are equal. The requirements for training, fine-tuning, and inference differ significantly, and each places very different demands on infrastructure, cost models, and operating environments.

At the same time, data sovereignty, regulatory requirements, and the physical proximity between data and compute are becoming critical design considerations, directly impacting performance, latency, and cost efficiency.

This roundtable will explore how organisations are navigating these trade-offs in practice. Drawing on real-world experience across hyperscale cloud, sovereign and neo-cloud environments, colocation, and on-premise infrastructure, we will examine how leaders are making workload placement decisions as AI scales from proof-of-concept to production.

Key Discussion Points:

Where AI Workloads Actually Belong: How organisations are deciding which AI workloads should run on hyperscalers, sovereign cloud, colocation, or on-premise infrastructure — and why many are adopting hybrid approaches.

The Hidden Cost of AI at Scale: Beyond GPUs: how data movement, model inference, and infrastructure design are shaping the true cost of enterprise AI.

Data Sovereignty vs AI Innovation: How regulatory pressure, cross-border data restrictions, and governance requirements are influencing AI architecture decisions across Asia.

The AI Platform Question: Whether enterprises should build centralised AI platforms internally, rely on hyperscaler ecosystems, or adopt emerging sovereign and neo-cloud models.

What to expect

Agenda

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