• Define and maintain standards for AI readiness, adoption, stabilization, and sustained usage. • Establish clear criteria for what constitutes effective AI usage, trust, and operational fit. • Define behavioral and operational validation requirements tied to AI deployment. • Create reusable enablement assets including playbooks, validation checklists, supervisor guides, prompt standards, and reinforcement tools. • Define prompt and interaction standards that drive consistent generative AI performance across environments. • Ensure enablement assets are practical, field-tested, and directly usable by delivery and customer-facing teams. • Define how enablement is integrated into AI rollout, stabilization, and post-launch activities. • Partner with delivery and training teams to ensure enablement is executed as part of AI delivery, not as a parallel effort. • Define role-based enablement expectations for agents, supervisors, and operations leaders. • Establish internal and partner enablement standards to reduce variance and rework. • Ensure adoption and enablement standards are consistently applied across regions and delivery models. • Act as a point of alignment across product, delivery, training, and AI leadership. • Analyze adoption, usage, and enablement signals across customers to identify systemic gaps and patterns. • Translate insights into actionable recommendations for product, engineering, and delivery teams. • Support leadership with clear, data-backed views of adoption and enablement health.