Key Takeaways
- Distinguish real AI from automation marketed as AI
- Ask about data handling, training, and privacy
- Evaluate integration with your existing systems
- Understand the human oversight and control options
- Consider vendor stability and long-term viability
The AI Vendor Landscape
AI has become the most overused term in software marketing. Every tool claims AI capabilities. Some genuinely use machine learning to deliver value. Others slap "AI-powered" on basic features and hope nobody asks questions. Your job is to tell the difference.
Evaluating AI vendors requires different questions than traditional software evaluation. You need to understand not just what the tool does, but how it learns, what data it uses, and what oversight mechanisms exist. The questions that follow will help you cut through marketing to find solutions that actually work.
The Buzzword Problem
When everyone claims AI, the term loses meaning. Focus on capabilities and outcomes rather than labels. A tool that delivers measurable value through simple automation may be better than a "true AI" solution that doesn't solve your actual problem.
Technical Foundation Questions
Understanding the AI
- What type of AI/ML does your solution use? (Be specific)
- Is this built on foundation models (GPT, Claude, etc.) or proprietary models?
- How is the model trained, and on what data?
- How often is the model updated or retrained?
- What are the known limitations of your approach?
Performance Questions
- What accuracy/performance metrics do you track?
- How do you measure and report on AI performance?
- What happens when the AI makes mistakes?
- Can you share benchmark results or third-party evaluations?
| Red Flag Answer | Better Answer | What It Indicates |
|---|---|---|
| "Proprietary technology" | Specific model types and approaches | Transparency vs. obfuscation |
| "It just works" | Clear explanation with limitations | Engineering maturity |
| "100% accurate" | Realistic accuracy with confidence levels | Honesty about capabilities |
| "Set it and forget it" | Ongoing monitoring and improvement | Understanding of AI maintenance |
The Wrapper Problem
Data and Privacy Questions
Data Handling
- What data does your AI require access to?
- Where is our data stored and processed?
- Is our data used to train your models?
- Can we opt out of model training with our data?
- What third parties have access to our data?
Privacy and Security
- What security certifications do you hold? (SOC 2, ISO 27001, etc.)
- How do you handle sensitive/regulated data?
- What's your data retention policy?
- Can we delete our data completely upon termination?
- What happens to data sent to third-party AI providers?
Compliance
- How do you handle GDPR/CCPA requirements?
- What industry-specific compliance do you support?
- Can you sign our security questionnaire/BAA?
- Where can we review your privacy policy and DPA?
Data Residency
For some industries, data must stay in specific regions. Ask where data is processed—both by the vendor and by any AI providers they use. US data going through EU servers (or vice versa) can create compliance issues.
Training Data Usage
Some AI providers use customer data to improve their models. This might be fine, or it might be unacceptable for your use case. Understand the policy clearly and get it in writing. Opt-out options should be available.
Integration and Operations
Integration
- What integrations do you offer out of the box?
- Do you have APIs for custom integration?
- What's the typical implementation timeline?
- What support do you provide during implementation?
- Can we export our data and configurations?
Operations
- What's your uptime SLA?
- How do you handle outages and incidents?
- What monitoring and alerting do you provide?
- How do you communicate about planned maintenance?
Support
- What support tiers and response times do you offer?
- Is support included or additional cost?
- What self-service resources are available?
- Do you offer training for our team?
Control and Oversight
Human Oversight
- Can we review AI decisions before they're acted on?
- Can we configure confidence thresholds for automation vs. review?
- How do we audit what the AI has done?
- Can we override or correct AI decisions?
Customization
- Can we customize the AI's behavior for our use case?
- Can we provide our own training data or examples?
- Can we set guardrails on AI outputs?
- How do we give feedback to improve accuracy?
Explainability
- Can the AI explain its decisions/recommendations?
- What visibility do we have into how conclusions are reached?
- Can we trace back AI outputs to source data?
The Black Box Question
Business and Vendor Questions
Vendor Stability
- How long have you been in business?
- What's your funding situation and runway?
- How many customers do you have in our segment?
- What's your customer retention rate?
- What happens to our data if you're acquired or shut down?
Pricing
- What's the full pricing structure?
- Are there usage limits or overage charges?
- What's included vs. additional cost?
- How does pricing scale as we grow?
- What are the contract terms and commitments?
References
- Can we speak with customers similar to us?
- Do you have case studies with measurable outcomes?
- What do customers typically struggle with?
- Why have customers left, if any?
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Request a pilot or trial
Test with your actual data and use cases before committing.
-
Talk to real customers
References provided by the vendor will be positive—dig deeper.
-
Evaluate total cost
Include implementation, training, integration, and ongoing support.
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Check the exit path
Understand how to leave if it doesn't work out.
Evaluation Process
Before Vendor Conversations
- Define your actual problem and success criteria
- Document your technical requirements and constraints
- Identify your security and compliance requirements
- Establish budget parameters
During Evaluation
- Ask the same questions of all vendors
- Request written answers to key questions
- Get technical and business stakeholders involved
- Test with realistic scenarios
Decision Framework
- Weigh factors by importance to your situation
- Don't let impressive demos override practical concerns
- Consider long-term implications, not just immediate needs
- Factor in implementation and change management costs
Choose Wisely
AI vendor evaluation requires healthy skepticism. The technology is powerful, but the marketing often outpaces the reality. The right questions help you separate genuine capability from hype, find solutions that fit your specific needs, and avoid expensive mistakes.
Take your time. Test thoroughly. Get specifics in writing. The vendors that can answer these questions clearly and honestly are the ones worth working with. The ones that deflect or oversimplify probably have something to hide.
Frequently Asked Questions
How do I know if a vendor's "AI" is actually AI?
Should I require AI vendors to prove ROI before purchasing?
What about vendors using third-party AI like OpenAI?
How important is vendor stability for AI tools?
Need help evaluating AI solutions?
I help businesses cut through AI marketing hype to find solutions that deliver real value. Let's discuss your evaluation process.