AI Governance & Risk Leadership
Managing Vendor Risk in AI Procurement
AI vendor landscapes shift rapidly. Leadership teams that select platforms based on demonstrations alone risk lock-in, data exposure, and stranded investment when vendors pivot or fail.
Evaluation dimensions beyond features
Data handling and privacy terms. Integration architecture and exit strategy. Financial stability and market position. Support model and SLA commitments. Total cost at projected scale—including inference, storage, and professional services.
Maintain vendor-neutral assessment discipline
Independent advisory assessments evaluate vendors against business requirements—not vendor sales incentives. This is particularly important when multiple stakeholders have relationships with competing platforms.
Key Takeaways
- Evaluate data handling, lock-in, and total cost—not features alone
- Define exit strategy before multi-year vendor commitments
- Use vendor-neutral assessment for significant investments
- Reassess vendor fit annually as market and needs evolve
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