Vendor demos are polished performances designed to impress. Your job is to cut through the presentation and understand what the product actually does, where it struggles, and how it fits your needs. These 30 questions help you do exactly that.
Executive Summary
- Demo questions should probe beyond the prepared script into real capabilities and limitations
- Focus on your specific use cases, not generic features
- Ask about failures and limitations—vendors who can't discuss them honestly are concerning
- Include questions for technical, security, and business stakeholders
- Watch for evasive answers, especially around security, pricing, and performance
- Demo is evaluation opportunity, not sales pitch reception—be an active participant
- Take notes on answers and compare across vendors
- Follow up on anything unclear—vague answers deserve clarification
Why This Matters Now
In a crowded AI market, vendors invest heavily in impressive demos. Features are polished. Edge cases are avoided. The demo environment is optimized.
Your procurement reality will be different: your data, your integrations, your edge cases, your scale. Demo questions should bridge this gap by exploring how the product performs in less-than-ideal conditions.
Definitions and Scope
Product Demo: Vendor presentation of product capabilities, typically using prepared scenarios and sample data.
Discovery Demo: Initial demo focused on fit assessment.
Deep-Dive Demo: Detailed demo following initial qualification, often customized to your requirements.
Scope of this guide: Questions for both discovery and deep-dive demos focused on AI products/platforms.
Before the Demo
Preparation checklist:
- Reviewed vendor materials
- Identified your key use cases
- Listed your must-have requirements
- Assigned question topics to team members
- Prepared to take detailed notes
Demo request:
- Ask for demo of your specific use cases
- Request to see error handling and exceptions
- Ask if you can provide sample data beforehand
- Request recording permission if helpful
30 Questions Organized by Category
Technical Capability (Questions 1-10)
1. Can you walk through how your AI handles [our specific use case] step by step? Why it matters: Generic demos hide implementation reality. Specific use cases reveal actual fit.
2. What happens when your AI encounters data it hasn't seen before or can't process? Why it matters: All AI has limits. Understanding exception handling is crucial.
3. What accuracy/performance levels do you typically see for this use case? What's the range across customers? Why it matters: Average performance hides variation. Range tells you what to expect.
4. Can you show me an example where your AI got something wrong and how it was corrected? Why it matters: Honest vendors acknowledge limitations. Evasiveness is a red flag.
5. How does your AI explain or provide confidence scores for its outputs? Why it matters: Explainability matters for trust, debugging, and compliance.
6. What data does your AI need for training and ongoing operation? Why it matters: Data requirements shape implementation effort and ongoing operations.
7. How does performance change as data volume scales? Why it matters: Demo performance may not reflect production scale.
8. How do you handle multiple languages/formats/variations? Why it matters: If relevant to you, this significantly affects accuracy.
9. What's on your product roadmap for the next 12-18 months? Why it matters: Current capabilities matter, but so does future direction.
10. Can you show the admin/configuration interface, not just the end-user experience? Why it matters: You'll spend significant time configuring and managing. UI matters.
Security & Compliance (Questions 11-18)
11. Where is our data stored and processed? Can we specify geography/jurisdiction? Why it matters: Data residency affects regulatory compliance.
12. Is our data used to train your AI models? How can we control this? Why it matters: Data usage for model training raises privacy and competitive concerns.
13. What security certifications do you hold? SOC2 Type II? ISO27001? Why it matters: Certifications provide independent validation of security practices.
14. How do you handle data subject requests (access, deletion) under PDPA? Why it matters: Regulatory compliance is your responsibility; you need vendor support.
15. Describe your incident response process. What's the notification timeline? Why it matters: When things go wrong, process matters.
16. Can we see a summary of your most recent penetration test? Why it matters: Regular security testing indicates mature security practice.
17. How are encryption keys managed? Can we bring our own keys? Why it matters: Key management affects data security and control.
18. What happens to our data if we terminate the contract? Why it matters: Data portability and destruction are critical for exit planning.
Integration & Implementation (Questions 19-23)
19. What APIs/connectors exist for [our key systems]? Are they pre-built or custom? Why it matters: Integration complexity significantly affects implementation time and cost.
20. What does a typical implementation timeline look like for organizations like ours? Why it matters: Vendor perspective on implementation helps set expectations.
21. What resources will we need to provide during implementation? Why it matters: Internal resource requirements affect feasibility and planning.
22. Can you show how changes are made to business rules/configuration without coding? Why it matters: Ongoing maintenance shouldn't require developers.
23. What does the data migration/onboarding process look like? Why it matters: Getting started is often the hardest part.
Support & Partnership (Questions 24-27)
24. What's included in support vs. what costs extra? Why it matters: Support tiers and boundaries affect total cost and experience.
25. What are your SLAs for response time and resolution? Why it matters: When things break, SLAs define what you can expect.
26. Who will be our ongoing contact after implementation? Why it matters: Relationship continuity matters for long-term success.
27. How do you handle feature requests and customer feedback? Why it matters: Your ability to influence product direction affects long-term fit.
Commercial (Questions 28-30)
28. How is pricing structured? What drives cost increases as we scale? Why it matters: Understanding the pricing model prevents surprises.
29. What's not included in the standard pricing that customers typically need? Why it matters: Honest vendors disclose add-ons; others surprise you later.
30. Are there minimum commitments or can we start small and expand? Why it matters: Starting with pilot reduces risk.
Red Flag Answers
| Question Topic | Red Flag Response |
|---|---|
| Security | "We can't share that information" |
| Accuracy | "We guarantee 99% accuracy" (unrealistic for most AI) |
| Data use | Evasion or unclear answers |
| Limitations | "We don't really have limitations" |
| Integration | "It's easy, just a simple API call" (oversimplification) |
| Pricing | Pressure to decide without clear pricing |
| References | Inability to provide relevant references |
| Roadmap | No roadmap or unwillingness to share |
Good Answer Indicators
| Question Topic | Good Answer Indicators |
|---|---|
| Accuracy | Specific ranges, acknowledgment of variability |
| Limitations | Honest discussion of where product struggles |
| Security | Detailed, confident answers with evidence |
| Implementation | Realistic timelines with caveats |
| Support | Clear escalation paths and SLAs |
| Pricing | Transparent model with scaling examples |
Demo Evaluation Checklist
During Demo:
- Asked prepared questions
- Probed unclear answers
- Saw relevant use cases
- Observed error handling
- Noted technical debt/limitations
- Captured pricing indicators
After Demo:
- Documented key answers
- Noted red flags and concerns
- Compared to requirements
- Identified follow-up questions
- Discussed with evaluation team
- Updated vendor comparison
FAQ
Q: How many questions can we realistically ask in one demo? A: Typically 10-15 depending on demo length. Prioritize based on your biggest concerns and uncertainties.
Q: Should we share our questions in advance? A: For deep-dive demos, sharing topics (not specific questions) helps vendors prepare relevant content. For discovery demos, cold questions reveal more.
Q: What if the vendor can't answer a question during the demo? A: Reasonable for complex questions. Note for follow-up. Concerning if they can't answer basic questions.
Q: How do we handle multiple stakeholders wanting to ask questions? A: Coordinate before demo. Assign question categories. Designate a lead to manage flow.
Q: Should we record demos? A: Ask permission; most vendors allow it. Recordings help compare vendors and refresh memory.
Next Steps
Demos are your opportunity to evaluate, not just observe. Come prepared with specific questions, probe beyond the script, and document what you learn. Better questions lead to better vendor understanding and better decisions.
Need help evaluating AI vendors?
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References
- Gartner: "Key Questions to Ask AI Vendors"
- Forrester: "AI Vendor Assessment Framework"
- Procurement Leaders: "Effective Supplier Evaluation"
- IAPP: "Questions to Ask Your AI Vendor"
Frequently Asked Questions
Ask about real customer results, how they handle your specific use cases, training data sources, integration approach, security practices, and what happens when things go wrong.
Look beyond polished demos to real-world scenarios. Ask to see error handling, edge cases, and actual customer deployments. Request references from similar organizations.
Be cautious of vendors who cannot answer specific questions, show only best-case scenarios, resist discussing limitations, or pressure you to commit before evaluation.
References
- Key Questions to Ask AI Vendors. Gartner
- AI Vendor Assessment Framework. Forrester
- Effective Supplier Evaluation. Procurement Leaders
- Questions to Ask Your AI Vendor. IAPP
