AI Vendor Selection: Questions to Ask Before Buying
Essential questions to ask AI vendors before making a purchase decision. Organized by evaluation phase with red flags to watch for and practical due diligence tips.
AI Vendor Selection: Questions to Ask Before Buying
Choosing an AI vendor is one of those decisions that looks straightforward until you’re six months into implementation and realize you asked the wrong questions upfront.
I’ve seen procurement teams focus entirely on features and pricing while missing critical questions about data handling, integration complexity, and exit strategies. The result? Projects that stall, unexpected costs, and painful vendor lock-in.
This guide gives you the questions you actually need to ask—organized by evaluation phase—so you can make informed decisions and avoid expensive surprises.
Why Vendor Selection Questions Matter
According to Forrester, 25% of planned AI spending in 2026 is being deferred to 2027 as enterprises grapple with disappointing results from hasty AI purchases. The pattern is predictable: companies get excited by demos, skip rigorous evaluation, and discover problems after contracts are signed.
The right questions upfront reveal:
- Whether the vendor can actually deliver for your specific situation
- Hidden costs that will appear later
- Integration challenges that could derail timelines
- The vendor’s long-term viability and roadmap
- How easy (or hard) it will be to leave if things don’t work out
Phase 1: Initial Discovery Questions
Before scheduling demos, get the basics right with these screening questions:
About the Company
1. How long have you been offering this specific AI solution?
Why it matters: AI is moving fast. A company that pivoted to AI six months ago has different maturity than one with years of experience.
Red flag: Vague answers about timeline or “we’ve always done AI in some form.”
2. Who are your largest customers in our industry?
Why it matters: Industry experience means faster implementation and fewer surprises.
Red flag: “We work across all industries” without specific references.
3. What’s your current funding situation and runway?
Why it matters: AI startups fail. Understanding financial stability protects your investment.
Red flag: Unwillingness to discuss or deflection about “continual investment.”
4. How many customers are currently using this product in production?
Why it matters: Pilot customers and production customers are very different. You want proven scale.
Red flag: Excitement about “early adopter” opportunity or total customer count that includes pilots.
About the Solution
5. What AI models power your solution, and can we choose different models?
Why it matters: Model flexibility protects against lock-in and allows optimization. Currently, leading models include GPT-5, Claude 4, and Gemini 3.
Red flag: Single model dependency with no flexibility roadmap. According to Gartner, model flexibility is increasingly important for enterprise AI.
6. Is this a platform we configure, or do you build custom solutions?
Why it matters: Platforms are typically faster and more predictable. Custom builds are riskier but potentially more tailored.
Red flag: Unclear answers that mix both approaches without clarity on your specific deal.
7. How do you handle updates when underlying AI models change?
Why it matters: AI models update frequently. You need to know how those changes flow to you.
Red flag: No clear update process or “we handle it behind the scenes” without specifics.
Phase 2: Demo and Deep-Dive Questions
Once you’re seeing demos, dig deeper with these questions:
Technical Capabilities
8. Can we see a demo with our actual data or scenarios?
Why it matters: Canned demos look great. Your data is messier and more revealing.
Red flag: Resistance to custom demos or excessive timeline requirements.
9. What are the known limitations of your solution?
Why it matters: Every AI solution has limits. Vendors who acknowledge them are more trustworthy.
Red flag: “There aren’t really limitations” or deflection to future roadmap.
10. How do you handle edge cases and errors?
Why it matters: AI makes mistakes. You need graceful failure modes.
Red flag: Overconfidence in accuracy or no clear error-handling strategy.
11. What’s your approach to AI hallucinations and accuracy?
Why it matters: All LLMs can hallucinate. Understanding mitigation strategies is critical.
Red flag: Dismissing hallucination concerns or claiming 100% accuracy.
Integration and Data
12. How does your solution integrate with [our specific systems]?
Why it matters: Integration is often the hardest part. List your actual systems.
Red flag: “We integrate with everything” or requiring significant middleware.
13. What data access do you need, and how is it secured?
Why it matters: AI needs data. Understand exactly what data flows where.
Red flag: Vague data requirements or inability to specify security controls.
14. Is our data used to train your models or improve your product?
Why it matters: Your proprietary data may be your competitive advantage.
Red flag: Burying this in terms of service or unclear data usage policies.
15. What are the data residency options?
Why it matters: Regulations may require data to stay in specific regions.
Red flag: Single-region deployment or unclear about compliance requirements.
Performance and Scale
16. What are your SLAs for uptime and response time?
Why it matters: Downtime has real business impact. Get specific numbers.
Red flag: No written SLAs or “industry standard” without specifics.
17. How does performance change as usage scales?
Why it matters: AI can get expensive and slow at scale. Understand the curve.
Red flag: Only discussing small-scale performance or no load testing data.
18. What are the concurrent user or request limits?
Why it matters: Hitting limits causes failures. Know them before you hit them.
Red flag: “Unlimited” claims without capacity planning discussion.
Phase 3: Commercial and Contract Questions
Before signing, get clarity on business terms:
Pricing and Costs
19. Walk me through the complete cost model including all usage charges.
Why it matters: AI pricing can be complex. API calls, tokens, storage, compute all add up.
Red flag: Inability to provide detailed pricing scenarios for your expected usage.
20. What happens to pricing if our usage grows 5x or 10x?
Why it matters: Success can become expensive. Understand volume discounts or escalations.
Red flag: No volume pricing or worse pricing at scale.
21. What implementation and professional services costs should we expect?
Why it matters: Licensing is often a fraction of total cost. Implementation and services add up.
Red flag: “It depends” without providing typical ranges.
22. Are there costs for data migration, integration development, or training?
Why it matters: These “extras” are often mandatory and significant.
Red flag: Bundled pricing that obscures these costs.
Use our AI ROI calculator to model total cost of ownership. For real-world examples, see our AI case studies to understand typical implementation costs.
Contract Terms
23. What’s the minimum contract commitment?
Why it matters: Long commitments are risky for new technology. Prefer shorter terms initially.
Red flag: Multi-year requirements for unproven solutions.
24. What are the termination clauses and exit costs?
Why it matters: If things don’t work, you need an exit path.
Red flag: Punitive termination fees or complex exit requirements.
25. How is data returned or deleted at contract end?
Why it matters: Your data needs to be portable and deletable.
Red flag: No data export process or lengthy deletion timelines.
26. Who owns models trained on our data?
Why it matters: Training on your data creates value. Understand who owns it.
Red flag: Vendor claims ownership of custom model improvements.
Support and Partnership
27. What’s included in standard support vs. premium support?
Why it matters: Support levels vary dramatically. Know what you’re getting.
Red flag: Minimal standard support pushing you to expensive premium tiers.
28. Who will be our primary contact during and after implementation?
Why it matters: Named contacts indicate investment in your success.
Red flag: “You’ll work with our team” without specific accountability.
29. What’s your product roadmap for the next 12-24 months?
Why it matters: You’re buying into a future, not just today’s capabilities.
Red flag: Vague roadmap or inability to share direction.
30. How do you incorporate customer feedback into product development?
Why it matters: Your needs should influence the product you’re paying for.
Red flag: No clear feedback mechanisms or influence paths.
Phase 4: Reference and Validation Questions
Before finalizing, validate with references:
Questions for Customer References
31. What was your implementation experience like? Did it match expectations?
Why it matters: Reference calls reveal the reality behind the sales pitch.
Listen for: Candid timeline and challenge discussions.
32. What surprised you—positively or negatively—after going live?
Why it matters: Surprises reveal hidden issues or unexpected benefits.
Listen for: Honest reflection, not scripted endorsements.
33. How responsive is support when issues arise?
Why it matters: Support quality only shows during problems.
Listen for: Specific examples of support interactions.
34. If you were starting over, would you choose this vendor again?
Why it matters: The most revealing question—listen carefully to the answer.
Listen for: Enthusiastic yes vs. qualified or hesitant response.
35. What do you wish you had asked during your evaluation?
Why it matters: Learn from their experience to improve your process.
Listen for: Specific gaps that applied to their situation.
Phase 5: Pilot and Proof of Concept Questions
For paid pilots (always prefer paid over free), establish clear criteria:
Pilot Scope
36. What specific success metrics will determine pilot success?
Why it matters: Vague success criteria lead to disputed outcomes.
Define: Specific, measurable outcomes before starting.
37. What data and access will the pilot require from us?
Why it matters: Pilot scope affects timeline and internal resources.
Clarify: Exact requirements before commitment.
38. What resources will the vendor provide during the pilot?
Why it matters: Under-resourced pilots often fail regardless of product quality.
Clarify: Named resources and availability commitments.
39. What happens if the pilot is successful? What if it’s not?
Why it matters: Understand the path forward in both scenarios.
Clarify: Contract options and any pilot-to-production discounts.
Pilot Evaluation
40. How will we measure accuracy and performance during the pilot?
Why it matters: Subjective assessments are unreliable. Define metrics upfront.
Define: Specific accuracy thresholds and measurement methodology.
41. What constitutes a fair test of the solution?
Why it matters: Both sides should agree on evaluation fairness.
Agree: Reasonable test scenarios that reflect real production needs.
Red Flags Quick Reference
Watch for these warning signs throughout your evaluation:
| Red Flag | What It Usually Means |
|---|---|
| Unwillingness to provide customer references | Limited production customers or unhappy ones |
| ”It works with everything” claims | Integration will be harder than promised |
| Vague or variable pricing | Cost surprises coming later |
| Pressure for long contracts upfront | Vendor uncertainty about retention |
| Can’t discuss limitations | Overselling capabilities |
| No specific SLAs | Accountability gaps |
| Executives only in sales, absent after | Post-sales support will be lacking |
| Rushing the evaluation timeline | Something to hide or desperation |
Creating Your Evaluation Scorecard
Use these questions to build a structured evaluation:
- Weight questions by importance to your specific situation
- Score responses on a consistent scale (1-5)
- Capture evidence for each score
- Compare vendors using the same criteria
- Involve multiple stakeholders in scoring
This structured approach makes vendor comparison more objective and defensible.
Frequently Asked Questions
How many vendors should we evaluate?
Evaluate 3-5 vendors for thorough comparison without evaluation fatigue. Start with a longer list for initial screening, then focus detailed evaluation on the shortlist.
How long should vendor evaluation take?
For enterprise AI platforms, expect 3-6 months for thorough evaluation including demos, references, and pilots. Rushing increases risk significantly.
Should we ask for discounts?
Yes, especially for multi-year commitments, volume usage, or being an early customer. Enterprise software typically has 20-40% negotiation room.
What if a vendor won’t answer our questions?
Consider it a red flag. Vendors confident in their offering answer tough questions directly. Evasiveness suggests problems.
Should we do paid or free pilots?
Paid pilots are better. Free pilots often get deprioritized by vendors and may use demo resources rather than production systems.
Next Steps
Armed with these questions, you’re ready for rigorous vendor evaluation. Remember:
- Customize these questions for your specific situation
- Document all responses for later comparison
- Verify claims through references and pilots
- Involve stakeholders across IT, business, and security
- Take time rather than rushing a decision you’ll live with for years
The right vendor becomes a partner in your AI success. The wrong one becomes an expensive lesson. These questions help you find the right one.
Evaluating enterprise AI platforms? Start with our Enterprise AI Platforms: Complete Buyer’s Guide for vendor comparison, then reference our AI Implementation Roadmap for deployment planning. For smaller organizations, check out our AI tools for small business guide.