What It Takes to Scale AI

Scaling AI is more than a question of models and compute. As deployments grow, organisations need to consider how data moves, where workloads run, how systems connect, and how they maintain performance, security, resilience, compliance and control.

In this “The Situation Room: What it takes to scale AI” guide, we examines three enterprise scenarios across financial services, manufacturing and technology service to show how different AI use cases would create different infrastructure demands.

Download the guide to learn:

• How infrastructure challenges emerge in real-world AI deployments, from meeting data residency requirements across markets to managing growing volumes of production data and multi-cloud complexity.
• What it takes to scale different AI use cases, through scenarios spanning financial crime compliance, quality inspection and a multi-agent AI platform.
• How agentic AI changes infrastructure demands, as increasing interactions across AI models, data and applications put greater pressure on network performance.