Enterprise AI Procurement Guide: How to Evaluate and Buy AI Solutions
Enterprise AI Procurement Guide
How to evaluate, compare, and procure AI solutions: vendor assessment, TCO analysis, security review, and red flags.
By 2026, every enterprise is buying AI. But procurement teams were trained on SaaS contracts, not probabilistic systems that can hallucinate, leak data, or degrade silently. This guide gives you a framework for evaluating AI vendors that accounts for what makes AI different from traditional software.
What Makes AI Procurement Different
Traditional software procurement asks: Does it work? Is it secure? What’s the SLA? AI procurement has additional dimensions that can make or break your investment:
- Model behavior: The system’s outputs vary by input. You can’t just test a checklist — you need statistical evaluation.
- Data risk: Many AI vendors train on customer data. Where does your data go? Can it leak into other customers‘ outputs?
- Dependency risk: Your vendor depends on OpenAI/Anthropic/Google. What happens when API prices change or models are deprecated?
- Drift: An AI system that works at launch may degrade in 6 months. Who monitors? Who retrains? At what cost?
The 7-Point AI Vendor Assessment Framework
1. Model Performance & Benchmarks
Don’t accept demo results. Ask for:
- Independent benchmarks on YOUR type of data (not just MMLU scores)
- Performance disaggregated by relevant subgroups (language, domain, user type)
- A commitment to minimum performance SLAs in the contract
li>False positive/negative rates for your specific use case
2. Data Governance & Privacy
This is where most AI procurement fails. Get clear answers:
- Does the vendor use our data for training? (If yes, opt out.)
- Where is data processed? (EU data must stay in EU for GDPR.)
- Is data encrypted at rest and in transit?
- Can we get a DPA (Data Processing Agreement)?
li>What happens to our data if we terminate?
3. Total Cost of Ownership (TCO)
AI costs are not linear. Model the 3-year TCO:
| Cost Component | Year 1 | Year 2-3 |
|---|---|---|
| License / platform fee | $$$ | $$$ |
| API / token costs (variable) | $$ | $$$ (grows with usage) |
| Integration + setup | $$$ | $ |
| Monitoring + maintenance | $$ | $$ |
| Retraining / model updates | $ | $$ |
Red flag: Vendors who quote only platform fees without discussing variable API costs. Your token bill at scale may exceed the license fee.
4. Vendor Lock-in & Portability
- Can we export our data and fine-tuned models in standard formats?
- What’s the migration path if we switch vendors?
- Does the vendor use open standards (ONNX, HuggingFace formats) or proprietary formats?
- Is there an abstraction layer (or are we locked to one model provider)?
5. Explainability & Compliance
- Can the system explain its decisions? (Required for EU AI Act high-risk.)
- Can we audit the model’s behavior on our data?
- What bias testing has been performed?
li>Does the vendor provide model cards?
6. Reliability & Degradation Monitoring
- How does the vendor detect and alert on model degradation?
- What’s the process for retraining or updating models?
- Do they offer uptime SLAs? (Different from API availability — model quality matters too.)
- Can we run our own evaluation suite against their endpoint?
7. Support & Incident Response
- What’s the escalation path for AI-specific incidents (hallucination, bias, data leak)?
- Do they have a dedicated AI incident response team?
- What’s the SLA for critical model failures?
Red Flags: Walk Away If…
- Vendor can’t explain what data the model was trained on
- No opt-out from data being used for training
- Benchmarks only on public datasets, not on your data type
- Contract has no performance SLAs (only uptime)
- Vendor is a thin wrapper around a single API with no abstraction layer
- Won’t commit to data deletion on termination
- No model cards or bias documentation
Need help evaluating a specific AI vendor? Our AI Architecture Decision Tree can help you determine whether to build, buy, or hybrid.
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