AI Procurement

AI procurement adapts traditional technology purchasing processes for the unique characteristics of AI solutions. Unlike conventional software that performs deterministically, AI systems have probabilistic outputs, require ongoing data and model management, and raise specific ethical and governance considerations. Key evaluation criteria include technical capability (model performance on your specific tasks), data handling (how the vendor processes, stores, and protects your data), scalability (ability to handle growing usage), integration (compatibility with existing systems), cost structure (pricing model, token costs, hidden fees), vendor viability (financial stability, market position, product roadmap), and compliance (regulatory adherence, certifications, audit support). AI procurement should also assess vendor lock-in risk, data portability, model customisation options, support and training, and the vendor's approach to responsible AI. These factors significantly impact long-term value and flexibility.

Poor AI procurement decisions can lock organisations into unsuitable solutions, create data sovereignty risks, generate unexpected costs, and undermine AI programme success. The rapidly evolving AI market makes informed procurement particularly challenging — and particularly important. A structured procurement process reduces risk and ensures that the selected solution meets both current needs and future requirements. Proof-of-concept evaluations (testing vendor solutions on your actual data and use cases) are essential and far more valuable than relying on vendor demos and benchmark claims. Organisations should involve technical, legal, security, and business stakeholders in AI procurement decisions. Technical teams evaluate capability, legal teams review data handling and contracts, security teams assess risk, and business teams ensure alignment with strategic objectives.