Procurement Frameworks for AI

Traditional procurement frameworks need adaptation for AI because the technology has characteristics that standard purchasing processes weren't designed to handle. AI systems require ongoing investment in data, compute, and maintenance - they're not one-off purchases. Performance can degrade over time as the real world changes, which means contracts need to address not just initial delivery but ongoing quality. The procurement process itself needs technical input earlier than usual, because purchasing decisions about AI that are made purely on price or feature lists without engineering evaluation often go badly. Effective AI procurement frameworks include clear requirements that specify business outcomes rather than technical features, structured evaluation that includes hands-on testing, commercial terms that address data ownership and usage rights explicitly, and governance provisions for ongoing monitoring and exit. They also account for the pace of change in AI - contracts that lock you into a specific solution for five years may not serve you well when better alternatives emerge every six months.