How to Evaluate AI Vendors Without Getting Burned

The AI vendor landscape is a minefield. Every company claims to have "state-of-the-art AI" that will "transform your business." Most of them will waste your time and money.

After evaluating dozens of AI vendors for enterprise clients, I have developed a framework that separates signal from noise.

The Evaluation Framework

1. Problem-Solution Fit

Before evaluating any vendor, be crystal clear on:

What specific problem are you solving? Not "we need AI" but "we need to reduce customer support ticket resolution time by 40%."

What does success look like? Define measurable outcomes before you talk to vendors.

What is your baseline? You cannot measure improvement without knowing where you are starting.

Only evaluate vendors whose solutions directly address your specific problem.

2. Technical Depth

Ask these questions to assess technical substance:

What models are you using, and why?

• Good answer: Specific models with rationale for the choice
• Red flag: "Our proprietary AI" with no details

How do you handle edge cases and failures?

• Good answer: Specific examples of failure modes and mitigation strategies
• Red flag: "Our AI does not make mistakes"

What is your training data, and how is it maintained?

• Good answer: Clear data sources, refresh cycles, and quality processes
• Red flag: Vague or evasive answers

Can we see the model's performance on our data?

• Good answer: Yes, here is how we structure a proof of concept
• Red flag: "Trust our benchmarks"

3. Integration Complexity

What does integration look like?

• API-based integration: weeks
• Data pipeline integration: months
• Full platform replacement: quarters to years

What data do you need, and in what format?

• Well-defined data requirements: good
• "Send us everything and we will figure it out": red flag

What is your uptime SLA?

• 99.9% with financial penalties: serious vendor
• No SLA or vague commitments: walk away

What happens if you go down?

• Graceful degradation plan: good
• "That does not happen": red flag

4. Total Cost of Ownership

The license fee is the tip of the iceberg. Calculate:

Direct costs:

• License or subscription fees
• Per-transaction or usage-based pricing
• Implementation and onboarding fees
• Training and certification costs

Indirect costs:

• Internal engineering time for integration
• Ongoing maintenance and monitoring
• Data preparation and pipeline development
• Change management and training

Hidden costs:

• Vendor lock-in (what does it cost to switch?)
• Scale pricing (what happens when usage grows 10x?)
• Feature gates (what costs extra?)

5. Vendor Viability

How long have they been in business?

• Startups: higher risk, potentially better technology
• Established: lower risk, potentially slower innovation

What is their funding situation?

• Well-funded: runway to support you long-term
• Bootstrapped: may have different priorities
• Recently raised: good sign, but evaluate terms

Who are their reference customers?

• Ask for references in your industry and at your scale
• Call references and ask about implementation challenges
• Ask references what they would do differently

What is their support model?

• Dedicated support: critical for enterprise
• Community support only: fine for experimentation
• "Our AI is so good you will not need support": run away

Red Flags That Kill Deals

• Cannot provide a live demo on their environment
• Will not do a POC on your data
• Pricing requires "talking to sales" for basic information
• No clear documentation or API references
• Reference customers are all logos, no contacts
• Contract requires multi-year commitment before POC
• Cannot explain how the AI works in plain language

The Evaluation Process

Step 1: RFI (2 weeks)

Send a structured questionnaire to 5-8 vendors. Evaluate responses and narrow to 3-4.

Step 2: Demos (2 weeks)

Schedule demos with shortlisted vendors. Bring technical team members. Ask hard questions.

Step 3: POC (4-8 weeks)

Run a proof of concept with 1-2 finalists. Use your data. Measure against your success criteria.

Step 4: Reference Checks (1 week)

Talk to existing customers. Ask about implementation, support, and surprises.

Step 5: Negotiation (2-4 weeks)

Negotiate pricing, SLAs, and contract terms based on POC results.

Questions to Ask References

1. 1How long did implementation take vs what was promised?
2. 2What was the biggest challenge you did not expect?
3. 3How responsive is support when things break?
4. 4What would you do differently if starting over?
5. 5Would you choose them again?

The Bottom Line

AI vendor evaluation is not about finding the "best" AI. It is about finding the right fit for your specific problem, data, and organization. Take the time to do it right. The cost of a failed AI implementation far exceeds the cost of thorough evaluation.