Key Takeaways
- Define what problem you are solving and what success looks like before evaluating any AI vendors
- Evaluate accuracy on your use case with your data, not vendor benchmarks or pre-prepared demos
- Run a paid 2-4 week proof of concept with real data and users before committing to any vendor
- Contract negotiations should address data ownership, SLA commitments, pricing at scale, and exit terms
- Red flags include vendors who cannot demo with your data, claim 99% accuracy without qualification, or refuse a POC
Requirements Definition and Evaluation Criteria
The AI vendor market is flooded with solutions making impressive claims. Structured evaluation helps you separate genuine capabilities from marketing hype and select vendors that deliver real business value.
Before evaluating vendors, clearly define what problem you are solving, what success looks like, what data and integrations are required, and what budget and timeline constraints exist.
| Criterion | What to Evaluate | Weight |
|---|---|---|
| Accuracy | Performance on your use case with your data | High |
| Integration | Compatibility with your existing systems | High |
| Data handling | Privacy, security, and data ownership terms | High |
| Scalability | Pricing and performance as usage grows | Medium |
| Support | Implementation assistance and ongoing support | Medium |
| Vendor stability | Financial health and market position | Medium |
| Time to value | How quickly you see production results | Medium |
RFP Process and Proof of Concept
Include specific scenarios from your business in the RFP. Request demonstrations with your actual data, not pre-prepared demos. Evaluate responses based on specificity and honesty, not marketing polish.
Before committing, run a paid POC (2-4 weeks) with your real data and users. Define success criteria in advance. A vendor who refuses a POC may lack confidence in their solution.
Reference checks are critical. Speak with customers in your industry with similar use cases. Ask about implementation experience, actual vs promised accuracy, support quality, and hidden costs.
Contract Considerations and Red Flags
- Data ownership and handling terms (who owns your data, is it used for training?)
- SLA commitments for uptime, accuracy, and response time
- Pricing at scale as your usage grows (per-token, per-seat, or volume tiers?)
- Exit terms and data portability if you switch vendors
- Model update notifications so you know when the AI changes
- Liability for AI errors and their business impact
Vendors who cannot demonstrate with your data, claim 99% accuracy without qualification, lock you into long contracts without POC, or cannot explain pricing at scale should raise serious concerns.
If no vendor meets your requirements, consider building a custom solution. Building is more expensive initially but may be necessary for unique requirements or competitive differentiation.
Frequently Asked Questions
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