Enterprise AI Readiness Checklist

Data inventory completed — you know what data you have and where it lives

Data quality assessment done — accuracy, completeness, and freshness measured

Data governance policies in place — ownership, access controls, retention rules

Data pipelines operational — ETL/ELT processes reliable and monitored

Unstructured data strategy defined — documents, images, emails catalogued

Executive sponsor identified with budget authority and staying power

AI champion(s) designated within business units

Current team skills assessed against AI capability requirements

Training plan created for all three tiers (executive, management, practitioner)

Recruitment plan for missing AI/ML engineering roles

AI governance framework established — policies, review boards, approval processes

Responsible AI principles documented — fairness, transparency, accountability

Regulatory compliance requirements mapped — GDPR, HIPAA, SOC2, industry-specific

Bias testing and fairness metrics defined for AI outputs

Incident response plan for AI failures or harmful outputs

Cloud platform selected and configured — Azure, AWS, or GCP

GPU/compute resources available or budgeted for training and inference

API management platform in place for model endpoints

MLOps pipeline established — version control, CI/CD, model registry

Monitoring and observability tools configured for AI workloads

AI use cases identified and prioritized by business impact and feasibility

Success criteria and KPIs defined before starting any AI project

ROI model built for top 3 use cases with realistic assumptions

Change management plan for AI-augmented workflows

Budget allocated for initial pilot AND production scale-up