Building an AI Policy Your Team Will Actually Follow

If you have read an AI policy recently, there is a good chance it made you feel better about having one without actually telling you what to do. This is the most common failure mode: a policy that is comprehensive, carefully worded, and entirely disconnected from the decisions people make when they are building or deploying AI systems.

The reason this happens is structural. AI policies are typically written by legal or compliance teams who understand regulatory language but not machine learning pipelines. They are reviewed by executives who want to demonstrate responsibility but not by engineers who need to implement the requirements. They are published with a communication plan but without any mechanism for enforcement or accountability.

The result is a document that checks a box. It exists, so if someone asks whether the organization has an AI policy, the answer is yes. But the engineer deciding whether to use a particular model, the data scientist choosing which training datasets to include, and the product manager signing off on a feature that uses AI are all making decisions without reference to the policy because the policy does not speak to their decisions in terms they can act on.

Good AI governance is not harder to write than bad AI governance. It requires a different starting point: beginning with the decisions that actually need to be made and working backward to the rules and principles that should guide them, rather than beginning with a set of principles and hoping they translate into decisions.