AI Procurement

AI Procurement is the structured process for selecting, evaluating, buying, and governing AI systems: models, agent platforms, data infrastructure, integrations, and ongoing operational services. Unlike traditional software procurement, AI procurement evaluates more than feature lists and license price. Teams must assess model quality, data flows, security boundaries, liability, vendor lock-in, auditability, usage-based cost, and the pace of model updates. Practical procurement criteria include hosting model, access to customer data, prompt and log retention, tool permissions, service levels, exit strategy, regulatory fit, and ownership of generated outputs. The term sits across purchasing, IT, security, legal, and business units: an AI system should move into production only when its value, risk, and operating model are measurable. Strong AI procurement reduces shadow AI, unreviewed SaaS contracts, and pilots that cannot scale. It gives organizations a repeatable decision framework for when to buy a model, self-host it, route across vendors, or build a custom AI solution. It also covers post-contract monitoring, because AI vendors can change models, prices, data policies, and integration capabilities faster than classic software suppliers.