For buyers
A buyer should understand what the vendor’s AI system does, what data it uses, whether customer data trains models, what subprocessors are involved, how outputs are monitored, and what happens if the system fails or produces harmful results.
For sellers
A seller should prepare evidence before the questionnaire arrives. The strongest vendors can answer consistently without forcing sales, legal, security, and product teams to rebuild the same story for every buyer.
What good evidence looks like
Good vendor evidence is specific, current, and linked to the actual product or workflow being bought. It avoids vague claims and shows where responsibility sits.
Practical evidence checklist
- Describe the AI capability, intended purpose, and user group.
- Explain whether customer data, personal data, or confidential data is processed.
- Document model providers, APIs, hosting, subprocessors, and data retention.
- Provide security, privacy, DPIA, and vendor risk evidence where relevant.
- Explain human oversight, escalation, monitoring, and incident handling.
- Record whether outputs are advisory, automated, or used in consequential decisions.
- Keep clear customer-facing summaries and internal evidence links.
FAQ
What should buyers ask first?
Ask where AI is used in the product, what data it touches, whether customer data trains models, who the model or platform providers are, and what evidence supports the vendor’s risk position.
What should vendors prepare before enterprise sales?
Prepare an AI system inventory, data and vendor map, security alignment notes, role/risk view, oversight notes, and a reusable procurement response pack.
Does every AI vendor need the same level of review?
No. Review depth should reflect the use case, data sensitivity, sector, decision impact, and customer risk appetite.