Include AI-specific expectations in third-party procurement and vendor management. This practice ensures external providers meet your organisation’s standards for risk, transparency, documentation, and performance. Governance responsibilities don’t stop at the contract boundary and an organisation may still bear responsibility for AI-related impacts, even when systems are developed externally.
AI systems and capabilities are increasingly sourced through third-party products and services. These can include models embedded in SaaS platforms, custom-developed solutions, or APIs accessed via cloud marketplaces. Whether you are buying off-the-shelf or co-developing with vendors, it is critical to address how AI will be governed, tested, deployed, updated, and supported throughout its lifecycle.
This practice helps you structure procurement to reflect AI risks, clarify roles and expectations, and avoid governance gaps. It also helps build internal readiness, from contract clauses to evaluation processes, so you can move faster and more confidently when engaging the market.
Why it matters
Without clear procurement expectations, your organisation may lack visibility into how a system works, whether it was tested for fairness or explainability, or how updates will be handled.
This practice builds on earlier AI Ready efforts, including AI risk assessments and system inventory. You want to embed those principles into vendor processes, ensuring third-party systems are assessed, documented, and governed to the same standard as any internal projects.
Proactive vendor governance helps avoid surprises, such as sudden changes in model behaviour, missing audit trails, or unclear escalation pathways. Embedding governance into procurement helps stand up to internal and external scrutiny.
Implementation tips
- Update procurement templates to include AI-specific considerations (e.g. intended use, prohibited use, explainability, data rights, energy and carbon intensity, update responsibilities).
- Request basic documentation from vendors using model or system cards to improve visibility into key components and risks.
- Use standard contract clauses that reflect your AI governance needs.
- Develop a structured vendor evaluation process, including AI-specific criteria such as alignment with risk tier, system behaviour, model origin, and data center efficiency.
- Apply tiered due diligence: high-risk or high-impact systems may require additional documentation, independent assurance, or deeper review.
- Track third-party models and systems in your internal AI System Inventory or risk register.
- Coordinate between procurement, IT, legal, and risk teams to align responsibilities across the system lifecycle.
Support materials
AI Procurement Lab – Risk Management Framework for procuring AI solutions
Practical resources, templates, and case studies on embedding governance in AI procurement processes.
Center for Inclusive Change – AI Procurement: Essential Considerations in Contracting
Clause-level guidance covering transparency, explainability, data rights, audit and risk.
Australian Government – Artificial Intelligence (AI) model clauses
Model contract terms to embed governance requirements in supplier agreements.
FS-ISAC – Generative AI Vendor Risk Assessment Guide
Risk-focused vendor evaluation guide for generative AI and similar models.
Future of Privacy Forum – Privacy Tech Buyer Framework
This buyer framework supports evaluation of privacy and governance features during procurement.
VerityAI Blog post – Sustainable AI Procurement: Vendor Selection for Environmental Compliance
Strategic framework for procurement professionals to assess AI vendors’ carbon footprints, energy efficiency, and environmental commitments
