Scaling a Responsible AI Framework for CQCC.

The challenge

CQCC, Canada's 2SLGBTQI+ Chamber of Commerce, approached Sam about developing a Responsible AI framework. We saw an opportunity to adapt our successful MOSAIC BC model for a more nimble organization.

With just 40 staff, CQCC needed a right-sized approach. Comprehensive enough to govern AI use across the chamber, but lean enough to leave resources for immediate value creation. They also had specific privacy considerations to address: 2SLGBTQI+ member data carries trust obligations that go beyond standard data privacy.

Our approach

Reuse what works, adapt what needs to fit

Building on the foundation we'd established with MOSAIC, we streamlined our process to match CQCC's scale while maintaining rigor. Rather than reinventing the wheel, we leveraged our existing framework templates and assessment tools from the MOSAIC engagement, accelerating development of CQCC's Responsible AI foundations.

Redirect efficiency into immediate value

The reuse efficiency let us dedicate more project resources toward direct value creation: hands-on training sessions, coaching for staff, and a dedicated Quick Wins Workshop to identify immediate AI implementation opportunities. By the end of the engagement, CQCC had both the governance structure and a queue of validated near-term AI moves.

What we delivered

• A right-sized Responsible AI Framework with AI principles grounded in CQCC's organizational values
• Streamlined risk assessment criteria appropriate to CQCC's scale
• Practical staff guidelines tailored to address privacy considerations for 2SLGBTQI+ member data
• Hands-on training sessions and coaching for staff
• A Quick Wins Workshop that surfaced immediate AI implementation opportunities

What's next for CQCC

• Implementing AI solutions to streamline membership processes and help reach their member targets
• Maximizing return on Microsoft Copilot through targeted training and use case development
• Empowering an AI Interest Group to lead knowledge sharing across the organization
• Applying the risk assessment framework to evaluate every new AI implementation opportunity

Why this approach worked

• Reuse without compromise. Adapting proven MOSAIC components let us deliver more value per dollar without cutting governance corners.
• Privacy as a first-class design constraint. The framework was built around CQCC's member data obligations from day one, not retrofitted.
• Governance plus immediate value. The Quick Wins Workshop ensured CQCC left the engagement with both a framework and a list of moves they could make in the first quarter.