Introduction
This free self-assessment tool helps you evaluate your current skills and knowledge across the AI landscape.
By completing this assessment, you will:
- Identify your strengths and gaps relative to your target persona (Managerial Leader, Organizational Orchestrator, or Technical Specialist).
- Discover which components of the Periodic Table of AI you should prioritize for learning.
- Create a personalized development plan based on the learning roadmaps.
Time to Complete: 5-15 minutes
How to Use: For each component listed, rate your current proficiency level. Then, compare your results against the target competency levels for your chosen persona.
Once you have completed the process, you will receive an anonymised code that shows how your skill level compares to the 3D benchmark in the Periodic Cube of AI.
Based on the Periodic Cube of AI Framework.
Proficiency Scale
Use this scale to rate yourself for each component:
| Level | Score | Description |
|---|---|---|
| No Knowledge | 0 | I have never heard of this or don’t know what it does. |
| Awareness | 1 | I know what this is and what it’s used for, but I have no hands-on experience. |
| Basic | 2 | I have some hands-on experience or have completed introductory training. I can perform basic tasks with guidance. |
| Intermediate | 3 | I can work independently with this component. I understand how it fits into the broader system. |
| Advanced | 4 | I am highly proficient. I can design solutions, troubleshoot complex issues, and teach others. |
| Expert | 5 | I am a recognized expert. I contribute to the field through innovation, thought leadership, or significant projects. |
Assessment by Functional Group
Functional Group 1: Data & Infrastructure
Rate your proficiency for each component:
| Component | Your Score (0-5) | Target for Managerial | Target for Orchestrator | Target for Technical |
|---|---|---|---|---|
| Data (Systems of Record, 3rd-Party Data Providers) | 1 | 2 | 4 | |
| Compute (GPU/Silicon, Cloud, Edge) | 1 | 2 | 4 | |
| Data Catalog & Lineage | 2 | 3 | 3 | |
| Enterprise DWH/Data Lake | 1 | 2 | 3 | |
| Connectors & Loaders | 0 | 1 | 4 | |
| Data Contracts & SLAs | 3 | 4 | 2 | |
| Supervised Data Providers | 1 | 2 | 3 | |
| Enterprise Knowledge Management | 2 | 3 | 2 |
Subtotal: _____ / 40 in Data & Infrastructure
Functional Group 2: Model Development
Rate your proficiency for each component:
| Component | Your Score (0-5) | Target for Managerial | Target for Orchestrator | Target for Technical |
|---|---|---|---|---|
| Models (Foundation Models, Classic Models) | 2 | 2 | 5 | |
| Refinery (Data Pipelines, Knowledge Index) | 1 | 2 | 4 | |
| Training Data Store | 1 | 2 | 4 | |
| Feature Store | 1 | 2 | 4 | |
| Model Hub & Inventory | 2 | 3 | 4 | |
| Deployment & Serving | 1 | 3 | 5 | |
| Serving Runtime | 0 | 1 | 4 | |
| Model Training Frameworks | 0 | 1 | 5 |
Subtotal: _____ / 40 in Model Development
Functional Group 3: Tooling & Integration
Rate your proficiency for each component:
| Component | Your Score (0-5) | Target for Managerial | Target for Orchestrator | Target for Technical |
|---|---|---|---|---|
| Developer Tools (SDKs/CLI, IDEs) | 0 | 2 | 5 | |
| Context (Context Catalog, Knowledge Cartridges) | 1 | 2 | 3 | |
| Test Harness | 1 | 2 | 4 | |
| Evaluation Harness | 1 | 3 | 4 | |
| Coding Agents & Vibe Coding Tools | 1 | 2 | 3 | |
| Data Labeling & Annotation Tools | 1 | 4 | 3 | |
| Feedback & RLHF Tools | 1 | 4 | 3 | |
| Synthetic Data Generators | 1 | 2 | 3 | |
| Version Control | 1 | 2 | 5 | |
| Low- & No-Code Platforms | 2 | 3 | 2 |
Subtotal: _____ / 50 in Tooling & Integration
Functional Group 4: Application & Orchestration
Rate your proficiency for each component:
| Component | Your Score (0-5) | Target for Managerial | Target for Orchestrator | Target for Technical |
|---|---|---|---|---|
| Applications (AI Apps, AI Work Interface) | 2 | 3 | 4 | |
| Agents (Universal/Enterprise Agents) | 2 | 3 | 4 | |
| Enterprise Workflows | 3 | 5 | 2 | |
| Orchestration, Planning, & Tool Execution | 2 | 4 | 4 | |
| Multi-Agent Interoperability (A2A) | 1 | 3 | 4 | |
| Assistants (Search & Retrieval) | 2 | 3 | 4 | |
| Response Generation & UX | 2 | 3 | 4 | |
| Human-in-the-Loop Supervision | 3 | 5 | 3 | |
| Semantic Ranking & Reranking | 1 | 2 | 3 | |
| Entitlement-Aware Grounding | 2 | 2 | 3 | |
| Agent Registry & Lifecycle | 2 | 4 | 3 | |
| Knowledge Elicitation & Management | 3 | 5 | 2 | |
| Model Context Protocol (MCP) Servers | 1 | 2 | 3 | |
| Tool & Action Schemas | 1 | 3 | 4 | |
| Memory Management | 1 | 2 | 4 |
Subtotal: _____ / 75 in Application & Orchestration
Functional Group 5: Governance & Operations (MLOps)
Rate your proficiency for each component:
| Component | Your Score (0-5) | Target for Managerial | Target for Orchestrator | Target for Technical |
|---|---|---|---|---|
| Ops & Control (Control Plane, Observability) | 2 | 3 | 4 | |
| Trust (Governance & Compliance) | 5 | 3 | 2 | |
| Identity & Access Management | 3 | 2 | 3 | |
| Control & Risk Catalog | 5 | 3 | 2 | |
| Regulations Tracker | 5 | 3 | 1 | |
| AI System Inventory | 4 | 3 | 2 | |
| Data/Model Lineage, DLP & Retention | 3 | 3 | 3 | |
| Risk Monitoring & Transparency | 4 | 3 | 3 | |
| KRIs Dashboard | 4 | 3 | 2 | |
| Drift Monitoring | 2 | 3 | 4 | |
| Safety & Robustness Evals | 3 | 3 | 4 | |
| Alerts & Reports | 2 | 3 | 4 | |
| System Cards | 3 | 2 | 2 | |
| Permissions & Entitlements Mgmt. | 3 | 2 | 3 | |
| Runtime Override & Escalation | 2 | 4 | 3 | |
| FinOps & Usage Management | 5 | 3 | 2 | |
| Adoption & Value Analytics | 5 | 4 | 2 | |
| Model SecOps | 3 | 2 | 3 | |
| Audit & Forensics | 4 | 2 | 2 |
Subtotal: _____ / 95 in Governance & Operations
Total Score Calculation
| Functional Group | Your Subtotal | Maximum Possible |
|---|---|---|
| 1. Data & Infrastructure | 40 | |
| 2. Model Development | 40 | |
| 3. Tooling & Integration | 50 | |
| 4. Application & Orchestration | 75 | |
| 5. Governance & Operations (MLOps) | 95 | |
| TOTAL | 300 |
Interpreting Your Results
Overall Proficiency Level
| Total Score | Proficiency Level | Description |
|---|---|---|
| 0-60 | Beginner | You are just starting your AI journey. Focus on Foundation-level learning. |
| 61-150 | Developing | You have some AI knowledge but significant gaps. Focus on Foundation and Intermediate learning. |
| 151-225 | Proficient | You have solid AI skills. Focus on Intermediate and Advanced learning to deepen expertise. |
| 226-300 | Advanced/Expert | You are highly skilled in AI. Focus on Advanced learning and thought leadership. |
Persona Alignment Analysis
Compare your scores against the target scores for your chosen persona. Calculate your alignment percentage for each functional group.
Alignment Formula: (Your Subtotal / Target Subtotal) × 100%
Example for Managerial Leader in Data & Infrastructure:
- Your Subtotal: 8
- Target Subtotal: (1+1+2+1+0+3+1+2) = 11
- Alignment: (8/11) × 100% = 73%
Calculate your alignment for each functional group:
| Functional Group | Your Alignment % | Interpretation |
|---|---|---|
| 1. Data & Infrastructure | ||
| 2. Model Development | ||
| 3. Tooling & Integration | ||
| 4. Application & Orchestration | ||
| 5. Governance & Operations (MLOps) |
Alignment Interpretation:
- 90-100%: Excellent alignment. You meet or exceed expectations for this functional group.
- 70-89%: Good alignment. Minor gaps to address.
- 50-69%: Moderate alignment. Significant learning needed.
- Below 50%: Low alignment. This is a priority area for development.
Gap Analysis
Priority 1: Critical Gaps (Your Score = 0 or 1, Target ≥ 3)
Here are the components where you have the biggest gaps relative to your target persona:
- [Component name] – Current: [score], Target: [score]
- [Component name] – Current: [score], Target: [score]
- [Component name] – Current: [score], Target: [score]
- [Component name] – Current: [score], Target: [score]
- [Component name] – Current: [score], Target: [score]
Action: These are your top priorities for learning. Focus on these first.
Priority 2: Moderate Gaps (Your Score < Target by 2 points)
In these components you are below target but not critically:
- [Component name] – Current: [score], Target: [score]
- [Component name] – Current: [score], Target: [score]
- [Component name] – Current: [score], Target: [score]
- [Component name] – Current: [score], Target: [score]
- [Component name] – Current: [score], Target: [score]
Action: Address these gaps after you’ve closed the critical ones.
Strengths: Areas Where You Exceed Target
With these components, your score exceeds the target for your persona:
- [Component name] – Current: [score], Target: [score]
- [Component name] – Current: [score], Target: [score]
- [Component name] – Current: [score], Target: [score]
Action: These are your strengths. Consider mentoring others or contributing thought leadership in these areas.
Your Personalized Learning Plan
Reassessment Schedule
Recommendation: Retake this assessment every 6 months to track your progress.
Next Assessment Date: July 13, 2026
Additional Resources
- Learning Roadmaps: See Learning Roadmaps for detailed guidance on what to learn at each level.
- Framework Data: See the page AI classification framework to explore the full framework and understand component relationships.
- Classification Dimensions: Review the 7 Periodic Cube Classification Dimensions to understand patterns across dimensions.
Document Revision: 1.7.0
Assessment Date: January 13, 2026
Target Persona: [Managerial Leader / Organizational Orchestrator / Technical Specialist]
Return to Periodic Cube of AI homepage.