Building an AI Governance Framework Using IBM OpenPages for Retail Conglomerate based out of Canada
With Integrated Support for Azure ML Ops, Azure ML, Databricks, and Google Vertex AI
Table of Contents
Table of Contents
1Executive Summary
A leading Canadian retail enterprise, launched a strategic initiative to operationalize AI governance across its enterprise functions. Leveraging IBM OpenPages as the governance backbone, the framework was extended to support model lifecycle management across Azure ML Ops, Azure ML, Databricks, and Google Vertex AI. This multi-platform integration enabled scalable, auditable, and adaptive governance aligned with emerging global standards, resulting in improved model transparency, reduced compliance risk, and enhanced stakeholder confidence.
2Background & Context
Organization
Retail Conglomerate based out of Canada
Industry
Retail, Financial Services, Automotive
Challenge
Fragmented AI development across cloud platforms without centralized governance posed risks in compliance, bias, and accountability.
Stakeholders
Data Governance Office, Risk & Compliance, IT, Business Units, External Auditors
Timeline
Phased rollout (Q3 2024–Q3 2025)
3Objectives
- Establish a centralized AI governance framework across hybrid cloud environments
- Ensure traceability, accountability, and compliance for all AI models
- Align with ISO/IEC 42001 and NIST AI RMF standards
- Integrate governance into existing enterprise risk systems and ML platforms
4Methodology
Approach
Hybrid waterfall-agile model with governance-first design
Tools Used
Governance Protocols
- Model registration and approval workflows
- Risk scoring based on use-case sensitivity
- Bias audit checkpoints
- Human-in-the-loop validation triggers
- Cross-platform model traceability and audit logs
5Implementation
| Phase | Activities | Outcomes |
|---|---|---|
| Phase 1: Discovery | Inventory of AI models across Azure, Databricks, Vertex | 63 models identified across 6 business units |
| Phase 2: Framework Design | Defined governance pillars: accountability, transparency, safety, fairness | Drafted governance playbook |
| Phase 3: Platform Integration | Configured IBM OpenPages modules for AI risk; integrated with Azure ML Ops, Databricks, Vertex AI | Unified model registry and risk dashboard |
| Phase 4: Pilot & Feedback | Ran governance workflows on 7 high-impact models across platforms | Reduced audit cycle time by 30% |
| Phase 5: Enterprise Rollout | Trained 120+ users, deployed dashboards | Achieved 100% model registration compliance |
6Results & Impact
Quantitative Outcomes
100%
AI model registration within 90 days
30%
Reduction in audit preparation time
25%
Improvement in model documentation completeness
Qualitative Insights
- Increased cross-functional collaboration
- Elevated trust in AI outputs among business leaders
- Enhanced readiness for external audits and regulatory reviews
Sample Visuals
7Lessons Learned
- Early stakeholder engagement is critical for adoption
- Bias audits require domain-specific expertise—generic tools fall short
- Embedding governance into existing workflows (e.g., Jira, ServiceNow) boosts compliance
- Multi-platform integration requires standardized metadata schemas
- Governance must evolve with model complexity and external regulations
8Strategic Implications
This retail conglomerate is now positioned to lead in responsible AI adoption across retail
The framework serves as a blueprint for other Canadian enterprises
Supports alignment with Universal AI Safety Framework initiatives
Enables scalable governance for future agentic AI systems
Demonstrates feasibility of cross-platform governance across Azure, Databricks, and Google Cloud