Compare AI vendors with the same evidence-based scorecard and stop conditions.
An AI vendor evaluation should begin with your workflow, population, data, decisions, and risk. A compelling demonstration proves only that one configured example worked; it does not establish production fit, control, accessibility, resilience, or total cost.
Write requirements before watching demos
Define allowed tasks, prohibited decisions, user groups, channels, languages, knowledge sources, data classes, authentication, actions, escalation, quality thresholds, integrations, response targets, retention, regions, and exit requirements. Ask vendors to demonstrate the same scenarios, including failure and no-answer cases.
Request evidence across the lifecycle
- Product architecture, model and subprocessor roles, and material-change notice
- Data collection, training use, retention, deletion, location, access, and export
- Security controls, incident history process, testing, and customer responsibilities
- Accuracy evidence by relevant task, plus uncertainty and human review
- Accessibility, language, abuse, outage, escalation, and manual fallback
- Pricing units, limits, support, service levels, contract terms, portability, and deletion at exit
Score claims by evidence strength
| Evidence | What it can show | Question |
|---|---|---|
| Marketing statement | Intended positioning | Where is the substantiation? |
| Controlled benchmark | Performance under stated conditions | Does it match our task and population? |
| Customer pilot | Behavior in our configured workflow | Were exceptions and harms measured? |
Run a controlled procurement process
- Issue one requirements and data-flow questionnaire.
- Shortlist only products that satisfy hard constraints.
- Test identical normal, edge, abuse, and outage cases.
- Review legal, security, privacy, accessibility, and operations.
- Pilot with bounded data and authority.
- Record decision, exceptions, controls, and exit plan.
Notice hidden operating dependencies
- Undisclosed downstream models or changing behavior
- Pricing tied to units the team cannot forecast
- Customer responsible for controls unavailable in the product
- No practical export, deletion evidence, or fallback channel
Keep the evaluation alive after purchase
Track committed capabilities, material assumptions, contract protections, exceptions, review dates, model or provider changes, incidents, support performance, renewal terms, and exit readiness. Re-run critical tests after material configuration or vendor changes.
Continue with the next decision
Sources and further reading
Primary and contextual sources used to verify definitions or give readers a relevant next resource.
- NIST AI Risk Management Framework Primary lifecycle framework for governing and managing AI risks.
- FTC advertising substantiation guidance Primary regulatory guidance that objective product and performance claims require an adequate evidentiary basis.