AI vendor evaluation: a seven-step checklist
Choosing an AI vendor commits your data, workflows and budget. A structured evaluation reduces the risk of a tool that underperforms, leaks data or can't integrate.
Key Takeaways
- Choosing an AI vendor commits data, workflows, and budget. A structured evaluation reduces the risk of a tool that underperforms, leaks data, or cannot integrate.
- The seven steps on this page are: define needs and objectives, build a shortlist, test performance on the business's own data, review security, compliance and governance, check integration and deployment, review support and roadmap, and model cost at expected volume.
- A proof of concept should use the business's own sample data and measure accuracy, speed, and failure cases, not demo results.
- Weight each step by importance, score vendors 1–5, and document the rationale. The record helps with audits and future renewals.
- Compliance here means the vendor's data handling and contracts meet the business's legal and regulatory duties, such as HIPAA, privacy laws, and industry standards.
Why does vendor evaluation matter?
- Risk: data handling, retention and model training terms vary widely.
- Return: a tool that fits a real workflow pays back; one that doesn't becomes shelfware.
- Integration: tools that don't connect to your systems create new manual work.
What are the seven steps?
1. Define needs and objectives
Write down the problem, the users, the data involved and the measure of success.
2. Build a shortlist
Look at established vendors and specialists. Check references and how long each product has existed.
3. Test performance on your data
Run a proof of concept with your own sample data. Measure accuracy, speed and failure cases, not demo results.
4. Security, compliance and governance
- Where is data stored and processed? Is it retained or used for training?
- What certifications and attestations exist (SOC 2 report, ISO 27001, ISO 42001)? Request the reports themselves.
- Will they sign a BAA or DPA if required?
- How do they handle bias, explainability and incident notification?
5. Integration and deployment
APIs, SSO, existing connectors, data export and deployment options (cloud, private, self-hosted).
6. Support and roadmap
Support channels and terms in the contract, product roadmap, financial stability and exit terms.
7. Cost and pricing model
Licenses, usage-based fees, implementation, maintenance and switching cost. Model the cost at expected volume, not entry volume.
How should vendors be scored?
Weight each step by importance, score vendors 1–5, and document the rationale. The record helps with audits and future renewals.
FAQ
What is an AI vendor assessment?
A structured review of an AI provider's fit, performance, security, integration, support and cost before purchase.
What does compliance mean here?
That the vendor's data handling and contracts meet your legal and regulatory duties, such as HIPAA, privacy laws and industry standards.
Should a business run a pilot?
Yes. Test on your own data before committing.
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