7 Decisions Every CIO Should Make in 2026
1. Start With Workload Reality: Map training, inference, analytics and simulation requirements before choosing infrastructure.
2. Prioritise GPU Utilization: AI impact improves when expensive GPU resources are allocated and monitored intelligently.
3. Design for Data Movement: Storage and networking must keep AI pipelines moving without avoidable latency.
4. Separate Training and Inference: Both need different performance, governance and availability models.
5. Build for Sovereign Control: Sensitive data workloads need clear rules around location, access and auditability.
6. Measure Infrastructure ROI: Track time-to-provision, utilization, model cycle time and production conversion.
7. Scale With Business Outcomes: AI infrastructure should grow with measurable enterprise value, not isolated experiments.
From workload planning and GPU optimisation to AI storage, HPC, sovereign control, and infrastructure ROI, this video explores the key decisions that help enterprises move from AI experimentation to measurable AI impact.
Watch now to see how the right AI infrastructure strategy can accelerate enterprise AI success.
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