AI Procurement

AI procurement differs from conventional software procurement in ways that most enterprise procurement teams are not yet equipped to handle. Traditional RFP processes focus on features, integrations, support tiers, and price. AI procurement must additionally evaluate model behavioral properties that cannot be fully specified in advance — how the model handles edge cases, whether its outputs are consistent across semantically equivalent inputs, and how its performance changes as the model is updated by the vendor. These properties are difficult to assess without hands-on evaluation, which means AI procurement processes must include structured technical pilots with real enterprise data, not just vendor-provided demos.

Contract terms for AI services require clauses that are rarely found in standard software agreements. Data usage provisions should explicitly prohibit training on enterprise inputs and outputs, a protection that is increasingly expected but not always offered by default. Model behavioral continuity clauses should require vendors to notify customers a defined period before making model changes that could affect output distribution, with options to pin to prior model versions for a defined transition period. Audit rights should grant enterprises the ability to independently test model outputs for bias, accuracy, and safety on a periodic basis. Liability clauses should address the enterprise's exposure when AI outputs cause harm — a question that remains legally unsettled in many jurisdictions but is increasingly addressed through indemnification terms.

Procurement governance for AI should involve cross-functional review boards that include representatives from legal, privacy, security, risk, and the AI CoE, rather than treating AI purchases as standard software acquisitions. This ensures that the novel risks of AI — model drift, hallucination, bias, and data leakage — are assessed before deployment rather than discovered in production.