AI Readiness Assessment

Is your core data clean, documented, and accessible for analytical use?

Are data quality rules defined and monitored for critical datasets?

Is data lineage documented for the pipelines that would feed AI models?

Are data ownership and stewardship roles clearly defined?

Is there a process for classifying sensitive data (PII, financial, health)?

Are data access controls enforced consistently across systems?

Is there a prioritized backlog of AI use cases with business sponsors?

Have you estimated the ROI or business value for your top AI use cases?

Is there a process to move AI use cases from ideation to production?

Do you have data engineers and scientists who can build and maintain AI models?

Are business users trained to interpret and act on AI-generated insights?

Is there a plan for upskilling the existing workforce on AI concepts?

Is there a policy for responsible AI use including bias detection?

Are AI decisions explainable to stakeholders and regulators?

Is there human oversight for AI-driven decisions that impact people?

Do you have the compute and storage infrastructure to train and serve AI models?

Are ML ops practices in place for model versioning, monitoring, and retraining?

Can your data platform handle the volume and velocity needed for AI workloads?