An AI evaluation checklist we actually use

Choosing the right AI vendor is less about shiny demos and more about disciplined validation. Here is the 30-minute checklist we use internally before recommending any product in the catalog.

1) Define the decision gate

Set a clear bar before testing anything. We start with:

• Which teams will own the integration?
• What data leaves our boundary?
• What would make this an instant “no”?

2) Run the quick safety pass

Look for the basics first. If a vendor cannot answer these, we stop evaluating:

1. Data retention defaults and deletion timelines
2. Where models are hosted and whether they are fine-tuned on customer data
3. Clear DPIAs or security overview docs

3) Validate outcomes, not outputs

Spin up a controlled experiment that mirrors production. For example:

• Compare latency and cost against your baseline
• Measure hallucination rates with a small labeled set
• Check UX fit: does it reduce steps for the end user?

4) Decide with confidence

When the numbers are in, use a simple scorecard:

Impact:     OK / Maybe / No
Safety:     OK / Maybe / No
Operational fit: OK / Maybe / No
Cost to switch: OK / Maybe / No

Anything below “two greens and one amber” goes back to the backlog. Anything above moves to a small rollout with monitoring hooks.

Keep it lightweight

The goal isn’t bureaucracy—it’s clarity. A consistent checklist lets teams move faster because the standard is known. Copy, adapt, and use this with your own organization.