AI Maturity

Governance is the foundation of scaling AI — without it, attempts to expand will collapse.

A governance pyramid includes: Foundational layer (brand voice, permissions, data usage rules, ethics), Systems and controls (templates, compliance, workflows), Loops and sequences (repeatable workflows, roles, responsibilities), and AI actions (the visible tools, sitting on top of everything else).

Four governance essentials: Access control — who uses which tools, and for what. Prompt review — brand, accuracy, and fact-checking standards. Approval loops — define roles and checkpoints before outputs go live. Logging and review — track who did what, with what results.

AI adoption moves through four maturity stages: Pilot (proof of concept, manual, overseen by leaders), Adoption (experimentation, trust-building, ROI visible), Workflows (orchestration, shared playbooks, early governance), and Culture (AI embedded in team DNA, cross-functional fluency, continuous refinement).

Scaling from pilots to culture requires both governance and orchestration to maintain speed without losing control.