AI Readiness
AI Portfolio & Value
Prioritization criteria, measurable outcomes, ownership and realized value.
Data & Knowledge Readiness
Required sources, fitness for use, permitted access and traceable outputs.
AI Engineering & Operations
Lifecycle, release thresholds, integration, monitoring and recovery.
AI Governance, Risk & Security
Inventory, risk classification, legal exposure and AI-specific testing.
Organization & Adoption
Responsibilities, capabilities, role-based training and real adoption.