Most enterprise AI projects fail before the hardware arrives. Here's the audit that changes that.

The companies that get AI infrastructure right don't have better hardware. They have better questions.

Before your team evaluates a single vendor, signs a single contract, or sends a single RFP, five questions need clear, written answers. Not estimates. Not "we'll figure it out." Actual numbers.

1. What is the model actually doing? Training and inference are different workloads with different hardware profiles. Conflating them leads to $2M of kit optimised for the wrong job.

2. What does your peak-to-average load ratio look like? Hardware sized for peak demand sits idle 90% of the time. That idle hardware still costs you power, cooling, and depreciation every single day.

3. Where does your data actually live? Data gravity decides your architecture before anything else does. Model it first.

4. What does this cost over 3 years — not just year one? The purchase price is the smallest number in the room.

5. What's the failure mode? Every vendor demo runs in ideal conditions. The question that separates good vendors from great ones is how they answer this one.