Responsible AI governance programs that live in policy teams produce documents. Programs that live in the product and engineering organization produce controls. The difference is whether the guidance is written for the team that will be audited or the team that will be reading and following it.
The AI governance work I did at Atlassian on the Rovo integration touched model selection, output auditing, and data handling. None of those were policy questions. They were engineering questions that required policy inputs. The governance program's job was to translate the policy into requirements engineering could implement, not to review the policy with engineering and call that compliance.
The practical structure that works is a review gate at the design phase, not the deployment phase. When an AI feature passes through design review, the governance questions get asked: what data is the model trained on, what is the output distribution, what are the failure modes, and what is the monitoring plan. Asking these at design changes the design. Asking them at deployment changes the launch date.
The regulatory landscape for AI is moving faster than most product organizations can track. The TPM's job is not to know all the regulations but to know which ones apply to which products and to have a relationship with the legal and policy teams who do know them. That relationship is what gets regulations translated into actionable engineering requirements before they become compliance findings.
Responsible AI done well is competitive advantage. Products that pass external audits without surprises and that have documented evidence of governance controls are more deployable in enterprise accounts than products that do not. Frame it that way and the engineering investment becomes easier to justify.