Dimension 1: Data Infrastructure

AI models are only as good as the data they're trained on. Assess your data infrastructure across these areas:

  • Data quality: Accuracy, completeness, consistency
  • Data accessibility: Can you access data when you need it?
  • Data governance: Policies for data ownership and security
  • Data volume: Sufficient historical data for training

"80% of AI projects fail due to poor data quality, not poor algorithms."

Dimension 2: Use Case Clarity

Successful AI projects start with clear business problems, not technology. Evaluate your use case definition:

  • Business impact: Does this solve a real problem?
  • Feasibility: Do you have the data and talent?
  • ROI potential: Can you measure the return?
  • Scalability: Can this solution scale across the organization?
63%of companies lack a clear AI strategy (Deloitte, 2024)

Dimension 3: Talent & Skills

AI projects require specialized skills. Assess your team's capabilities:

  • Data science skills: In-house expertise for model development
  • Engineering skills: Ability to deploy and maintain models
  • Business acumen: Understanding of AI's business implications
  • Change management: Skills to drive adoption across the org

Dimension 4: Technology Stack

Your existing technology infrastructure impacts AI readiness:

  • Cloud infrastructure: Scalable compute resources
  • Integration capabilities: Connecting AI to existing systems
  • Security posture: Protecting sensitive data and models
  • Tool maturity: Modern data platforms vs. legacy systems

Dimension 5: Executive Alignment

AI initiatives require sustained investment and organizational change. Ensure executive support:

  • Strategic alignment: AI goals match business strategy
  • Budget commitment: Funding for multi-year initiatives
  • Risk tolerance: Willingness to experiment and learn
  • Cross-functional support: Collaboration across departments

Scoring Your Readiness

Rate each dimension on a scale of 1-5. A total score below 15 indicates significant gaps that should be addressed before launching AI projects. Focus on improving data infrastructure and use case clarity first.