Imagine your organization has adopted an AI policy. It sets out which AI applications employees may use, what data may be processed, and when approval is required. The rules have been defined and published.
Now an employee in customer service wants to use an already approved AI tool to handle customer inquiries more efficiently. To do so, information from the internal customer system would be entered into the application.
So the question is: Is that allowed?
The policy may address this. But that still does not answer who assesses the conditions in this specific case and who is authorized to approve the use. The decision also has to work in practice.
Defining a rule is one thing. Being able to act on it is another.
An AI policy can describe what should apply within an organization. It can also define responsibilities, review procedures, and control requirements. Whether the organization can actually implement those provisions becomes visible in how it handles concrete use cases.
Suppose the employee receives approval to use the AI tool for certain customer inquiries. One condition is that no confidential customer data may be transferred to the tool.
A decision has been made, but the governance question is not yet closed.
What happens if the workflow later changes and confidential customer data is now transferred to the AI tool? The basis for the original approval may no longer hold. The organization needs to be able to recognize when a new assessment is required and who may restrict the use if necessary.
Not every organization needs permanent technical monitoring for this. What matters is that it has appropriate means to detect relevant changes and respond effectively.
The policy defines the rules. Operational governance is the organizational capability to apply those rules in concrete use situations, assess whether they continue to be met, and intervene when necessary.
The two belong together. A good policy can prepare and structure operational governance. But it does not create those capabilities simply by being adopted.
So if you want to understand how well your organization can actually govern its use of AI, it is not enough to ask:
Do we have an AI policy?
The more useful question is:
What can our organization actually decide, verify, and change when AI is being used?
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The customer service example shows the gap clearly: the approval was valid when it was given, but the workflow changed afterwards and nobody was positioned to notice that its basis no longer held. Your line "Defining a rule is one thing. Being able to act on it is another." captures why a policy document alone does not create the capability to govern use. What practical trigger would you recommend for deciding that an already approved use case needs a new assessment?