Why do we govern data and AI models? Is our current governance approach working and is it sustainable? What is the sense of urgency in change? Reframe a governance approach that focuses on tangible business value | Developing business and stakeholder trust begins with demonstrating there is a game plan, roadmap and approach driven by business value that generates results | Focus on governance efforts that map to business priorities The business cares about their objectives, goals and KPIs | Learn the data and AI user challenges, inefficiencies and obstacles. What are their pain points? Understand the personas in the user stories. Each persona whether a business analyst, data scientist or compliance officer has distinct goals, pain points and success metrics | Prioritize the use cases based on their business value, technical feasibility and urgency Assess expected business outcomes, ROI, scalability and data readiness, alongside integration complexity and required skills | Governance builds Data Confidence™ which builds business confidence, resulting in a competitive advantage Business confident organizations can greenlight AI projects faster, articulate ROI of AI projects to accelerate funding, scale AI across business units and mitigate costly rework or penalties. Confidence is a tangible value that creates market growth |