The Association of Faculties of Medicine of Canada (AFMC) is the national voice of Canadian academic medicine. AFMC manages the national repository of information on faculties of medicine across Canada and is mandated to lead and deliver a highly responsive system of medical education and research aligned with the health needs of all Canadians, driven by quality, excellence, and social accountability.

Given its role, partnerships, and access to data and statistics, AFMC is uniquely positioned to provide strategic insight on the priorities faced by its stakeholders, members, medical students, physicians, and other medical practitioners. AFMC already brings real strengths to that role: robust data partnerships with Canadian medical colleges and universities, basic best practices in data privacy, security, and governance, an established AFMC Data Dictionary of data elements and survey-specific definitions, and an approach to individual learner identifiers including the DDTID and MINC.

However, increasing data volumes and complexity, the emergence of AI and ChatGPT, and growing expectations around transparent decision-making meant AFMC needed an enterprise data strategy to take the next step. The strategy needed to enhance data quality, governance, and accessibility, support data-driven decision-making, ensure interoperability with data partners, build a data infrastructure aligned to AFMC’s mission, and provide a responsible approach to AI use.

Bronson was engaged to develop that enterprise data strategy and the 3-year implementation roadmap that would put it into action.

The Challenge

Building an enterprise data strategy for a national academic medicine body is not a routine data exercise. It requires balancing the realities of a mission-driven non-profit with the analytical ambitions of a sector that runs on evidence.

The main challenges Bronson tackled:

• Mission-aligned strategy. The data strategy had to align with AFMC’s mission, vision, and strategic goals, not just adopt off-the-shelf data management practices.
• Growing data volume and complexity. AFMC’s data landscape had grown alongside the organization, surfacing gaps in management, access, quality, and integration that the strategy had to address head-on.
• AI and ChatGPT governance. The emergence of generative AI created both opportunity and risk. The strategy needed to define how AI and ChatGPT should be used in decision support, with rules and guidelines that protect the integrity of AFMC’s evidence base.
• Data partner interoperability. AFMC works closely with Canadian medical colleges, universities, and other data partners. Strategy choices had to support interoperability and sharing across that ecosystem.
• Multi-stakeholder alignment. Stakeholders spanned executive leadership, business units, data partners, and cross-functional teams. The strategy had to be validated across all of them to be credible and operable.
• Sustainable data culture. AFMC needed a strategy that fostered a durable data-driven culture and governance practice, not a document that would sit on a shelf.
• Actionable 3-year roadmap. Strategy without sequencing rarely sticks. AFMC needed a phased roadmap with clear milestones, resource requirements, and success metrics to translate ambition into execution.
• AFMC needed an enterprise data strategy that was mission-aligned, AI-aware, partner-interoperable, and sequenced for delivery over a realistic 3-year horizon.

Our Solution and Impact

Bronson designed and delivered the engagement as a structured, multi-phase program that combined strategy development, AI governance, stakeholder engagement, and roadmap design. The work was organized into the following streams:

1. Discovery and Current State Analysis

Bronson began with a comprehensive review and assessment of AFMC’s current data landscape, including data assets, infrastructure, governance policies, and analytical capabilities. The discovery work identified gaps in data management, access, quality, and integration, and surfaced pain points and opportunities across business functions through stakeholder interviews and workshops.

2. Data Vision and Strategic Objectives

Bronson defined AFMC’s data vision, guiding principles, and strategic objectives for data, anchoring the strategy in how AFMC will use data to achieve its broader mission rather than treating data as an end in itself.

3. Data Governance Framework

Bronson developed a structured data governance framework establishing the policies, procedures, roles, and accountability structures needed to manage data across AFMC consistently. The framework was scoped to be operable within a mission-driven non-profit context, not borrowed from a heavier corporate model.

4. Strategic Pillars and Focus Areas

Bronson identified the strategic pillars that would underpin the data strategy, including data quality, analytics, data access, security, compliance, and data architecture. Each pillar was defined in enough depth to drive specific initiatives in the implementation roadmap.

5. AI and ChatGPT Governance

Bronson developed a structured approach to the responsible use of AI and ChatGPT at AFMC. The work defined intended uses, considerations, and the rules and guidelines AFMC will apply to ensure AI is leveraged for decision support without compromising data integrity, privacy, or trust.

6. Stakeholder Engagement and Validation

Stakeholder engagement was woven through the engagement rather than treated as a single phase. Bronson facilitated cross-functional round tables, business engagement sessions with operational units and data partners, and executive leadership briefings to validate and refine the strategy as it developed.

7. 3-Year Implementation Roadmap

Bronson produced a phased 3-year implementation roadmap. The roadmap details specific initiatives for improving data governance, data integration, analytics capabilities, and technology modernization, with key milestones, deliverables, timelines, and resource requirements defined for each phase. Performance metrics and success criteria were attached to track progress against AFMC’s strategic objectives.

8. Project Governance

A project steering committee comprising senior executives, project sponsors, and key stakeholders provided oversight and decision-making support throughout the engagement. Regular meetings and structured updates ensured alignment, addressed roadblocks, and managed risks across the project lifecycle.

Key Deliverables

• Current State Analysis – A documented assessment of AFMC’s current data landscape, including data assets, infrastructure, governance policies, and analytical capabilities, with identified gaps in data management, access, quality, and integration.
• Enterprise Data Strategy Document – The full enterprise data strategy, covering AFMC’s data vision, guiding principles, and strategic objectives, alongside recommendations for data management, integration, security, analytics capabilities, and data architecture.
• Data Governance Framework – A documented governance framework defining policies, procedures, roles, and accountability structures for managing data across AFMC.
• Strategic Pillars Definition – The defined set of strategic pillars underpinning the data strategy, including data quality, analytics, data access, security, compliance, and architecture.
• AI and ChatGPT Governance Guidance – Documented uses, considerations, rules, and guidelines for the responsible use of AI and ChatGPT in AFMC decision support, balancing opportunity with risk.
• 3-Year Implementation Roadmap – A phased roadmap for executing the data strategy over three years, with detailed timelines, milestones, resource requirements, key activities, performance metrics, and success criteria for each phase.
• Stakeholder Engagement Summary Reports – Documented summaries of discussions, feedback, and outcomes from each stakeholder engagement session, including round tables, business engagement sessions, and executive leadership briefings, with recommendations incorporated into the final strategy.
• Finalized Enterprise Data Strategy and Implementation Roadmap – The consolidated, stakeholder-validated final strategy and roadmap, ready for adoption by AFMC and rollout to its data partners.

The Impact

• A mission-aligned enterprise data strategy that connects AFMC’s data ambitions directly to its responsibility for Canadian academic medicine.
• A data governance framework that sets clear policies, roles, and accountability structures across the organization.
• A defined approach to the responsible use of AI and ChatGPT in AFMC decision support, with the rules and guidelines needed to leverage AI without compromising trust.
• A 3-year implementation roadmap that sequences data governance, integration, analytics, and technology modernization initiatives into a deliverable plan, with attached metrics for tracking progress.
• Documented stakeholder alignment across executive leadership, business units, and data partners, giving the strategy the buy-in it needs to actually be implemented.

The result is a comprehensive enterprise data strategy that positions AFMC to manage, govern, and leverage its data effectively across its business lines and with its strategic data partners, supporting its strategic objective of incorporating progressive digital and technological solutions to revitalize its programs, services, advocacy, community engagement, and communication efforts. AFMC can now build its data acumen and capacity with a clear direction, creating value for the Faculties of Medicine and the many communities it serves.