Without measurability, AI remains random.

Ready for AI? Our 8-criteria scorecard evaluates strategy, data quality, infrastructure, organization, governance, security, processes and change management using specific KPIs .

Practical, measurable, groundbreaking. For CEOs, executives, and investors who value effectiveness over hype.

👉 Schedule a 30-minute Executive Initial Review now or take advantage of our comprehensive three-stage audit. For strategic foresight and sound decisions.

Our OAK AI 8-criteria scorecard:

1. Strategy and Leadership

• Criterion: The existence of a clear AI strategy, goals with measurable KPIs, and visible sponsorship by management.

• Measurable: Strategy document; 1-3 prioritized use cases; Executive sponsor named; KPIs defined.

• Why it's important : Without a strategic direction, AI projects remain sporadic and do not deliver sustainable added value.

  
  

2. Data quality and availability

• Criterion: Relevant data is available, accessible, clean, up-to-date, and well-documented.

• Measurable: Data catalog exists; data quality metrics (completeness, consistency, timeliness) > defined thresholds; access times & API availability checked.

• Why it's important: Data is the fuel for AI; bad data leads to faulty results and loss of trust .

  
  

3. Technical infrastructure

• Criterion: Scalable infrastructure (cloud/on-prem), computing capacity, MLOps/CI-CD pipelines, monitoring and logging.

• Measurable: Available GPU/cloud quotas; MLOps tooling for model deployment; automated tests and rollbacks.

• Why it's important: Only robust infrastructure allows for repeatability, scalability, and rapid iteration of AI solutions.

  
  

4. Talent and Organization

• Criterion: Availability of data engineers, data scientists, machine learning engineers, product owners and domain experts; clear roles and responsibilities.

• Measurable: Skills matrix; number of dedicated roles vs. required roles; training plan for existing employees.

• Why this is important: Interdisciplinary teams combine technical skills with domain knowledge and ensure feasibility.

  
  

5. Governance, Ethics and Compliance

• Criterion: Guidelines for responsible AI, usage rules, transparency requirements, vendor selection standards, deletion periods, data minimization.

• Measurable: Existence of AI guidelines; review process for providers (transparency, server location); decision matrix for permitted use cases.

• Why it's important: Protecting sensitive data, ensuring legal compliance, and avoiding unwanted risks are prerequisites for scaled use.

  
  

6. Security and data protection

• Criterion: Integration of AI systems into IT security, access restrictions, encryption, and data exfiltration checks.

• Measurable: Penetration/Red Team tests; Identity and Access Management policies; Audit logs; Data protection impact assessment for personal data.

• Why this is important: AI interfaces can encourage shadow IT and expose sensitive information; therefore, strict security precautions are necessary.

  
  

7. Processes and Integration

• Criterion: Processes for experimentation, validation, A/B testing, feedback loops and integration into existing business workflows.

• Measurable: Defined experiment lifecycle; SLA for model validation; number of integrated production pipelines.

• Why this is important: Operational benefits only arise when AI results are reliably fed into processes .

  
  

8. Culture and Change Management

• Criterion: Acceptance of data-driven decisions, willingness to learn, handling of mistakes, transparent reporting.

• Measurable: Training participation; survey on the acceptance of AI; number of documented lessons learned from pilots.

• Why it's important: Technological maturity without cultural readiness leads to mistrust and low adoption rates.