AI Readiness
A practical checklist to evaluate organizational readiness for AI delivery
AI readiness is the ability to move from AI ideas to reliable business outcomes using trustworthy data, clear governance, and operational discipline.
Readiness Dimensions
- Strategy and outcomes: Business goals and decision workflows are clearly defined.
- Data foundation: Critical data is accessible, reliable, and governed.
- Platform and architecture: Core data and AI infrastructure is scalable and secure.
- Governance and risk: Privacy, security, and model-risk controls are in place.
- Operating model: Teams, ownership, and delivery processes are defined.
- MLOps and monitoring: Model deployment, observability, and retraining are operationalized.
Quick Self-Assessment
Use a 1 to 5 score for each dimension:
- 1: Ad hoc and inconsistent
- 3: Defined in key areas but not repeatable across teams
- 5: Standardized, measurable, and continuously improved
Common Gaps
- No clear owner for data quality and stewardship
- AI use cases selected without measurable business KPIs
- Models built in isolation without production monitoring
- Governance controls added late instead of by design
Next Step
Prioritize the two lowest-scoring dimensions and define a 90-day plan with clear owners, milestones, and success metrics.
CTA
Ready to move from strategy to measurable impact?