This article explains how modern data governance requires a cybernetic approach, treating data as a self-regulating system that adapts through feedback and control mechanisms. It highlights the importance of continuous monitoring, reconciliation, and shared semantics in maintaining data quality and managing risk effectively.

Organizations face significant challenges in scaling AI proofs of concept (POCs) into production, with nearly 40% remaining stuck at the pilot stage. The FOREST framework outlines six dimensions of AI readiness—foundational architecture, operating model, data readiness, human-AI experiences, strategic alignment, and trustworthy AI—to help organizations overcome barriers and successfully implement AI initiatives.