Is Your Data Actually Ready for AI?

Move AI from isolated pilots to scalable, ROI-driven business impact. The results will help you determine what to address first to ensure AI success.

AI Readiness Checklist

Data Quality & Stability

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For each statement, select Yes if it is true for your organization. Otherwise select No.

Data accuracy and completeness are regularly monitored and maintained
Data validation rules are established and enforced across systems
Data cleansing processes are in place to handle inconsistencies
Data schemas are standardized and well-documented
Historical data is stable and available for training models

Data Governance & Ownership

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0 / 5

For each statement, select Yes if it is true for your organization. Otherwise select No.

Clear data ownership and stewardship roles are defined
Data governance policies and procedures are documented
Data privacy and security measures comply with regulations
Data lineage and metadata management systems are in place
Data access controls and permissions are properly managed

Architecture & Infrastructure

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0 / 5

For each statement, select Yes if it is true for your organization. Otherwise select No.

Scalable cloud or on-premise infrastructure is available
Computing resources (CPU/GPU) can support AI workloads
Data storage solutions are optimized for AI/ML processing
Architecture supports real-time data processing when needed
CI/CD pipelines exist for model deployment and updates

Connected & Accessible Data

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0 / 5

For each statement, select Yes if it is true for your organization. Otherwise select No.

Data from different sources can be integrated and connected
APIs and data pipelines are established for data access
Data catalog or inventory is maintained and accessible
Cross-functional teams can access data they need (with proper permissions)
Data is available in formats suitable for AI/ML tools

Measurement, Monitoring & Controls

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0 / 5

For each statement, select Yes if it is true for your organization. Otherwise select No.

Key performance indicators (KPIs) for AI initiatives are defined
Model performance monitoring and alerting systems are in place
Data drift and model drift detection mechanisms exist
Audit trails for model decisions and predictions are maintained
Feedback loops are established to continuously improve models

People, Skills & Operating Model

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0 / 5

For each statement, select Yes if it is true for your organization. Otherwise select No.

Team has data science and AI/ML expertise (or access to it)
Training programs exist to upskill staff on AI technologies
Cross-functional collaboration between business and technical teams exists
Clear processes for AI project prioritization and resource allocation
Executive sponsorship and organizational buy-in for AI initiatives
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