AI Procurement Checklists
Direct answer
A serious AI buying process should evaluate four things before the pilot expands: data risk, workflow fit, operational control, and measurable business value. If a vendor cannot support those conversations cleanly, the product may be impressive but not procurement-ready.
Who this is for
- procurement, security, legal, IT, and product leaders
- managers comparing AI vendors under real approval constraints
- teams trying to avoid buying a polished demo with unclear controls
The four-part checklist
1. Data and privacy
Ask:
- what data leaves our boundary?
- what is stored, for how long, and where?
- can the vendor use our data for training?
- what deletion and retention controls exist?
2. Workflow fit
Ask:
- what exact workflow improves?
- what happens on bad input or uncertain output?
- where does human review sit?
- what breaks first in production?
3. Governance and control
Ask:
- what permissions does the system need?
- what logging and audit trails exist?
- how do approvals, role access, and policy controls work?
- what is the incident path if the system behaves badly?
4. Measurable value
Ask:
- what baseline are we improving against?
- what metric changes if the pilot succeeds?
- how will we measure quality, not just speed?
What buyers should not confuse
Do not confuse:
- model brand with workflow fit
- admin controls with genuine governance
- a successful demo with repeatable production behavior
- usage with ROI
Red flags
- no clear answer on data retention
- vague responses about human review
- no explanation of failure handling
- no meaningful logging or auditability
- no baseline for business value
Pilot approval questions
Before approving a pilot, a team should be able to answer:
- what limited workflow are we testing?
- what data classes are in scope?
- what outcome would justify expansion?
- what would cause the pilot to stop?
FAQ
What should we ask before a pilot?
Scope, data exposure, workflow fit, and measurement plan.
What data terms matter most?
Retention, training use, deletion, residency, and admin controls.
How do we compare vendors fairly?
Use one workflow, one evaluation rubric, and one baseline instead of letting each vendor choose its favorite demo.
When should legal or security block the tool?
When data handling, permissions, logging, or contractual protections are too weak for the intended workflow.
Related AIReady guides
Sources
Refresh checklist
- review enterprise security and privacy terms across major vendors
- update the checklist if procurement review patterns change
- keep ROI and incident language aligned with related Learn pages
Last updated: March 18, 2026
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