Why AI Procurement Is Different
Procuring AI solutions demands a fundamentally different approach than buying traditional enterprise software. AI systems are probabilistic, not deterministic — they produce results that vary in accuracy and require ongoing monitoring. Their performance depends heavily on data quality, integration depth, and continuous refinement. Standard procurement checklists focused on features and uptime miss critical factors that determine AI success or failure.
Essential Evaluation Criteria
Organizations that apply traditional procurement processes to AI often end up with solutions that perform well in demos but disappoint in production, or that create hidden dependencies and costs that only surface months after deployment.
Procurement Best Practices
Beyond standard criteria, AI procurement should assess model transparency and explainability, data handling practices and privacy compliance, customization and fine-tuning capabilities, performance benchmarks on data similar to yours (not just general benchmarks), integration requirements and API maturity, vendor lock-in risks and data portability options, and the total cost of ownership including compute, training, and ongoing optimization.