Are you ready to run AI on infrastructure you can control and afford?
Fifteen questions on the infrastructure your AI runs on — GPU utilization, capacity delivery, cost predictability, data pipelines, governance, and production operations.
Where you stand
A readiness score out of 100 and a band — Exposed to Ready — across compute, cost, data, governance, and operations.
Your biggest gaps
Three insights pinpointing your weakest and strongest areas, from GPU orchestration and capacity delivery to governance and production monitoring.
What to fix first
A prioritized set of next steps matched to your score, so you know exactly where to start.
What this is based on
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emma
emma helps infrastructure leaders run AI workloads across cloud, on-prem, and edge from a single control plane.
This assessment shows infrastructure leaders where they stand on AI workloads — and what to fix first.
How the scoring works
The 2026 shift from virtualization to GPU-centric orchestration and agentic workload automation
The documented frustrations of infrastructure leaders running AI — GPU fragmentation, cost unpredictability, pipeline bottlenecks, model drift
Their stated 2026 goals — predictable baselines, unified control planes, self-healing workloads