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AI Readiness Assessment

Score your organization across 5 key dimensions to evaluate AI readiness.

5/10

Quality, availability, and governance of your data assets

5/10

Cloud, compute, MLOps, and deployment capabilities

5/10

AI/ML expertise, engineering capacity, and cross-functional readiness

5/10

Well-defined problems with measurable success criteria

5/10

Organizational appetite for AI uncertainty and iterative development

Data MaturityTechnical InfrastructureTeam SkillsUse Case ClarityRisk Tolerance
5.0/10
Developing

You have some capabilities in place. Target specific, well-scoped AI projects to build momentum and learn.

Recommendations by Dimension

Data Maturity5/10

Good foundation. Focus on building data pipelines and ensuring consistent data quality for your target AI use cases.

Technical Infrastructure5/10

Solid infrastructure base. Evaluate MLOps tools to streamline model deployment and monitoring.

Team Skills5/10

Good team foundation. Develop specialized skills in your target AI domains. Pair engineers with product for applied AI projects.

Use Case Clarity5/10

Promising use cases identified. Validate with quick prototypes before committing to full builds. Define clear success metrics.

Risk Tolerance5/10

Balanced risk approach. Implement guardrails and human-in-the-loop processes for customer-facing AI features.