Responsible AI in practice: lessons from the field

Most organisations now have an AI principles document. Far fewer have translated those principles into checkpoints that engineers and data scientists actually encounter in their daily workflow.

We have found the most effective approach is to embed responsible AI checks directly into the model development lifecycle, at the same points as security and quality reviews.

This requires close collaboration between technical teams, legal, and risk functions from the earliest stages of a project, not a review appended at the end.