How to Evaluate an AI Vendor: A Practical Checklist
Choosing an AI tool is a business decision, not just a demo. These questions help you separate substance from sales pitch.
1. The Problem and the Proof
- What exact problem does it solve for us?
- Can they show results on our kind of data, not just a polished demo?
- Can we run a trial or pilot with clear success measures?
2. Data and Privacy
- Is our data used to train their models?
- Where is it stored and processed? For how long?
- Who can access it, including their staff and subcontractors?
- Can we delete our data on request?
- Which certifications and agreements do they provide?
3. Security
- Single sign-on, role-based access and audit logs?
- How do they handle prompt injection and misuse?
- What's their incident response and notification process?
4. Quality and Reliability
- How do they measure accuracy, and how often does it fail?
- Is there a human review option?
- What are the uptime and support commitments?
- How are model updates communicated? Will behaviour change without notice?
5. Fairness and Compliance
- How do they test for bias?
- Can decisions be explained and appealed?
- Does it meet rules relevant to our industry and region?
6. Cost and Lock-In
- Full pricing: usage, seats, overage, onboarding.
- Can we export our data and configurations?
- What happens if the vendor is acquired or shuts down? (Some AI services have closed.)
7. Fit
- Does it integrate with our existing tools?
- How much training and change management is needed?
- Who in our team owns it?
Scoring
Rate each section 1 to 5, weight the ones that matter most, and record your reasoning. Re-assess yearly.
Takeaway
Insist on evidence, understand where your data goes, and plan for change. The best vendor is the one that works reliably for your real use case.
