AI governance is often framed as a matter of compliance, internal policy, or innovation strategy. But in practice, it is both a procurement issue and a vendor management issue. This is increasingly true as many organizations are not just acquiring AI through stand-alone AI products; instead, AI capabilities are now embedded in a wide range of software and services. As a result, an organization may introduce AI into its environment through an ordinary sourcing decision, sometimes without fully appreciating how the technology works, what data it uses, or what authority or autonomy it has within business processes.
From a procurement perspective, the key challenge is identification and escalation. Organizations need a practical way to determine when a proposed purchase, renewal, or scope expansion involves AI-enabled functionality and when that functionality requires heightened review. Relevant questions may include whether the tool merely assists users or can take autonomous or semi-autonomous action; whether customer or company data will be input into or ingested by the tool; whether the vendor may change models or enable new AI features over time; and whether the tool will be used in sensitive, regulated, or customer-facing workflows.
But procurement is only part of the picture. AI governance also depends on what happens after contract signature. In many technology and services relationships, the vendor’s use of AI evolves over time. New features may be introduced, online/hyperlinked terms may be updated (see our prior articles on this topic in Part 1 and Part 2), subprocessors may change, and organizational or customer data may be used in ways that differ from the organization’s original expectations. A tool that initially had limited functionality may later include agentic capabilities, automated decision support, or expanded integrations with internal systems. If the organization is not actively monitoring those developments, it may find that its vendor environment has changed materially without any formal reassessment of risk.
For that reason, AI governance is also a vendor management issue. Organizations should consider whether they have a process to monitor strategic vendors for changes in AI functionality, data practices, model updates, security posture, and subcontracting arrangements. They should also consider whether internal owners are tracking performance issues, incidents, policy changes, and renewal events in a way that allows the business to reassess whether the vendor still fits the organization’s legal, operational, and governance requirements. In many cases, the challenge is not the absence of a contract right, but the absence of a practical internal process to detect when the vendor environment has changed.
AI governance is not a one-time exercise completed when a policy is written or a contract is signed. It begins at intake and sourcing, continues through diligence and contracting, and depends on active oversight throughout the life of the vendor relationship.
