AB-731: AI Transformation Leader

Effective AI governance requires clear accountability that spans both business and technical stakeholders, since responsible AI decisions (data use, fairness, transparency, compliance) are not purely technical concerns — business leaders must also be accountable for outcomes. It also requires a formal, repeatable review process so that every AI initiative is checked against responsible AI principles (fairness, reliability, privacy, security, inclusiveness, transparency, accountability) before and during deployment, rather than relying on ad hoc or team-specific practices. Allowing each department to define its own governance process (A) leads to inconsistent standards across the organization, assigning ownership solely to engineering/data science teams (D) excludes necessary business and compliance oversight, and restricting governance only to systems handling regulated or sensitive data (E) leaves other AI systems ungoverned even though responsible AI principles should apply broadly across all AI initiatives.