Member benefits are deeply personal, so when AI enters the picture, privacy and trust can't be afterthoughts—they must be the foundation.
Here's a scenario most benefits administrators face: A member questions why their dependent's claim was denied. Your team needs answers. The member deserves transparency. And if you're asked to review the process, you will need documentation.
Without proper governance, AI becomes a black box. You're left saying "the system denied it" with no clear explanation. That erodes trust and frustrates everyone involved.
Strong governance frameworks solve this by creating decision trails. When AI processes a claim, it needs to provide clear reasoning with citations. Your benefits team can stand behind those decisions, and members understand the reasoning through a complete trail of what data was accessed, what rules were applied, and who reviewed edge cases.
That's the practical value of frameworks like the Hiroshima AI Process—an international standard for responsible AI governance that TELUS has adopted to bring this rigor to every AI-powered benefits tool we deploy.
Consider this common risk: A benefits chatbot could accidentally surface one member's different plan documents when another member asks a similar question, or it could store sensitive data in ways that create vulnerability points.
Privacy-first AI prevents this at the architectural level—before the AI ever processes a query. Data isolation ensures each interaction is completely separate. Encryption protects information in transit and at rest. Access controls limit what the AI can retrieve based on who's asking.
The result: reduced breach risk, simplified Health Insurance Portability and Accountability Act (HIPAA) compliance, and protection against headline-making incidents that damage member trust and triggers possible regulatory issues. TELUS recently became the first organization globally to receive ISO 31700-1 Privacy by Design certification for our generative AI customer support tool—validation that privacy controls are embedded in the system architecture and tested against international standards.
For benefits administrators, that means having answers when members, plan sponsors, or regulators ask: How do you protect sensitive health data? Can you explain how AI reached this decision? What happens when the system makes a mistake?
The frameworks we've discussed—governance standards like the Hiroshima AI Process and privacy-first architecture validated by ISO certification—help provide those answers. They turn AI from a compliance risk into a transformative tool that can augment human capabilities and deliver positive outcomes.