Answer 13 questions - one per knowledge area of the AI-BOK. You will receive an instant score and radar chart showing your strengths and gaps.

1 / 13

Does your organization have a formal AI governance framework with clear roles, mandates and escalation paths?

No AI governance Informal agreements Basic governance structure Formal framework with roles and mandates Fully integrated into enterprise governance

2 / 13

Is there a multi-year AI strategy aligned with the organizational strategy?

No AI strategy Ad hoc AI initiatives AI vision document exists Multi-year strategy with portfolio AI strategy integrated into business strategy with KPIs

3 / 13

Are there architecture principles and reference architectures specifically for AI systems?

No, AI is built ad hoc Some guidelines Reference architecture in development Established AI architecture with principles Cognition plane explicitly positioned in enterprise architecture

4 / 13

Is the lifecycle of AI systems managed with quality gates?

No lifecycle management Informal process Basic phases defined Quality gates per phase with criteria Fully iterative with feedback loops and sprint governance

5 / 13

Are data quality, semantic precision and bias detection structurally ensured for AI systems?

No specific data governance for AI Basic data quality measured Terminology frameworks and quality profiles RAG pipelines, bias detection and data lineage Fully semantically grounded with knowledge graphs and key vault pattern

6 / 13

Are AI models centrally registered, versioned and documented with model cards?

No central overview of models Spreadsheet with models Model registry set up All models registered with cards and metrics Automated model management with fairness audits and retraining policy

7 / 13

Are there guidelines for AI interaction design, prompt quality and user experience?

No attention to AI UX Some prompt templates Prompt library and evaluation criteria Interaction design with trust calibration and source citation Iterative prompt improvement cycle with metrics and A/B testing

8 / 13

Are there MLOps/LLMOps pipelines for deployment, monitoring and incident handling?

Manual deployments, no monitoring Basic monitoring CI/CD pipeline for models Observability, drift detection and cost monitoring Fully automated pipelines with canary deployments and rollback

9 / 13

Are AI systems systematically assessed for risk, with red teaming and adversarial testing?

No AI risk assessment Occasional risk review Risk classification per system (EU AI Act) Structural assessments with red teaming Continuous risk monitoring with threat modeling and AI-BOM

10 / 13

Is there a compliance framework for the EU AI Act, including DPIA, registration and audit trails?

Not yet started Aware of EU AI Act, no action Classification started, first DPIAs Compliance framework with audit trails and registration Fully compliant with ISO 42001 certification

11 / 13

Is there an ethical framework with bias audits, explainability and human oversight?

No attention to AI ethics Ethical principles on paper Ethics committee and first bias audits Structural ethical reviews with fairness metrics Fully integrated ethical framework with value alignment and impact assessments

12 / 13

Are there trusted knowledge sources, RAG architecture and hallucination prevention in place?

AI systems run without knowledge grounding Some documents as source RAG pipeline with basic retrieval Trusted knowledge sources with authority levels and fact-checking Knowledge graphs, GraphRAG and layered hallucination prevention

13 / 13

Is AI literacy structurally embedded, with role-specific competency profiles and an Article 4 evidence file?

No attention to AI literacy Occasional training Basic curriculum for key roles Role-specific profiles with a literacy register Continuous capability development with drills and Article 4 audit readiness

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