AB-731: AI Transformation Leader

Anonymizing the source data to strip out Personally Identifiable Information before it is processed by the AI solution directly reduces privacy risk at its source, since the system can no longer expose or misuse identifiable customer data while still allowing the underlying feedback content to be analyzed for product design insights. This aligns with core privacy-by-design and data minimization principles (e.g., GDPR data minimization requirements and Microsoft's Responsible AI "privacy and security" pillar), which call for reducing exposure of personal data as early as possible in a system's lifecycle rather than relying on later remediation such as deletion after the fact, keeping data attributable (which increases rather than decreases exposure), or deferring privacy reviews to the end of the project.