AI governance is essential for helping enterprises adopt AI safely, consistently, and at scale. But as AI use expands across a business, mismatches can appear between the organization’s governance framework and how teams actually use AI. In this article, we explore common AI governance challenges and failure modes, and outline steps enterprises can take to address them. AI governance becomes more complex as adoption scales, particularly when AI use moves beyond controlled settings. For example, a tool initially approved for a low-risk internal task may end up being applied to higher-stakes customer-facing work; a vendor product may add AI features after the enterprise’s initial procurement or security review; or employees may start using publicly available AI tools before the organization has set clear rules for acceptable use. In these situations, the risk is not AI use itself. It is that the organization may lose visibility into where AI is being used, who is accountable for it, and whether the right controls are in place for each use case. Governance becomes a one-time approval step when a use case is reviewed before launch but not reassessed as its purpose, users, or risk profile changes. The original review may have been appropriate for the first version of the use case, but that does not mean the same controls remain suitable once the tool is used in new contexts. Ownership is unclear across teams when governance depends on input from business, technical, legal, compliance, security, and data teams, but no one owns a specific system or use case. Controls do not match use-case risk when governance requirements are either too permissive or too restrictive if they are not matched to the risk profile of each use case. Employee AI use becomes difficult to track when teams adopt AI tools or features faster than governance processes can account for them. Sensitive data is used without appropriate controls when AI systems are connected to internal data sources without appropriate controls for access, retention, retrieval, logging, or downstream use. *Source: [cohere](https://cohere.com/blog/ai-governance-challenges)*