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Samstag, 14. Februar 2026

The Data Behind the Periodic Table of AI – a Multi-Dimensional Framework

Overview

This framework provides a comprehensive, multi-dimensional view of the AI technology landscape, organized as a «Periodic Table of AI.»

Each AI component is classified across seven strategic dimensions, enabling organizations to make informed decisions about technology adoption, resource allocation, and strategic planning.

The three-dimensional representation is available as the Periodic Cube of AI.

Periodic Cube of AI navigation items
3D Periodic Cube of AI navigation items

Framework Structure

Functional Groups (Columns)

The periodic table is organized into five functional groups representing the AI lifecycle:

  1. Data & Infrastructure – Foundation layer for data acquisition, storage, and compute resources
  2. Model Development – Core processes for creating, training, and deploying AI models
  3. Tooling & Integration – Developer tools, platforms, and frameworks that enable AI development
  4. Application & Orchestration – User-facing applications and workflow management systems
  5. Governance & Operations (MLOps) – Practices for monitoring, security, and compliance

Classification Dimensions

Each component is classified across seven dimensions, each providing unique strategic insights:

1. SFIA Activity Category

Purpose: Identifies the type of work and skills required based on the Skills Framework for the Information Age (SFIA).

Categories:

  • Process and Operations – Business process improvement, measurement, and operational management
  • People and Change – Job analysis, leadership, learning and development, workforce planning, change management
  • Data – Data management, engineering, science, machine learning, database administration
  • Technology – Solution architecture, infrastructure operations and design, software development, security operations
  • Strategy and Governance – Stakeholder relationship management, governance, AI ethics, compliance, portfolio management

Use Case: Workforce planning, skills gap analysis, training program development, organizational design

2. Build vs Buy vs Integrate

Purpose: Strategic sourcing approach for each component.

Categories:

  • Build – Develop internally with custom solutions
  • Buy – Purchase commercial off-the-shelf solutions
  • Integrate – Adopt open-source or third-party integrations
  • Hybrid – Combination of multiple approaches

Use Case: Technology strategy, vendor selection, investment planning, make-or-buy decisions

3. Technology Readiness Level (TRL)

Purpose: Assesses maturity and production-readiness of the technology.

Categories:

  • Emerging – Experimental, cutting-edge, rapidly evolving
  • Maturing – Proven but still evolving, increasing adoption
  • Established – Stable, widely adopted, industry standard
  • Foundational – Commodity technology, fully mature

Use Case: Risk assessment, innovation portfolio management, technology roadmap planning

4. Organizational Ownership

Purpose: Identifies the primary organizational team responsible for the component.

Categories:

  • Data/Platform Engineering – Infrastructure, data pipelines, platform services
  • ML/AI Engineering – Model development, training, evaluation, deployment
  • Application Development – User-facing applications, APIs, integrations
  • Security/Compliance – Governance, risk, compliance, security operations
  • Business/Product – Business process, product management, value realization

Use Case: Organizational design, responsibility assignment, budget allocation, hiring planning

5. Cost Structure

Purpose: Financial model and expenditure type for the component.

Categories:

  • Capital Expenditure (CapEx) – Upfront investment in assets
  • Operational Expenditure (OpEx) – Ongoing operational costs
  • Usage-Based – Scales with consumption (pay-as-you-go)
  • Mixed/Variable – Combination of cost models

Use Case: Financial planning, budget forecasting, cost optimization, ROI analysis

6. Criticality / Risk Level

Purpose: Impact level on AI system success and business outcomes.

Categories:

  • Mission-Critical – System fails without it, severe business impact
  • High Priority – Significantly impacts quality and performance
  • Enhancing – Improves outcomes but not essential
  • Optional – Nice-to-have, minimal impact if absent

Use Case: Risk management, prioritization, disaster recovery planning, SLA definition

7. Human-in-the-Loop Intensity

Purpose: Degree of human involvement required for the component.

Categories:

  • Fully Automated – No human intervention needed
  • Human-Supervised – Automated with human oversight
  • Human-Collaborative – Significant human-AI collaboration
  • Human-Driven – Primarily human-led with AI support

Use Case: Automation strategy, workforce planning, process design, efficiency optimization

Using the Framework

Strategic Planning

The multi-dimensional framework enables organizations to:

  • Identify gaps in their AI technology stack
  • Prioritize investments based on criticality and readiness
  • Plan workforce development aligned with SFIA skill requirements
  • Optimize costs by understanding expenditure patterns
  • Manage risks through criticality assessment
  • Design organizations based on ownership patterns

Decision-Making Scenarios

Scenario 1: Technology Investment Planning

Question: Where should we invest our limited AI budget?

Approach:

  1. Filter for Mission-Critical components (Criticality dimension)
  2. Identify Emerging or Maturing technologies (TRL dimension)
  3. Assess Build vs Buy options (Build/Buy/Integrate dimension)
  4. Evaluate Cost Structure implications (Cost Structure dimension)

Scenario 2: Skills Gap Analysis

Question: What skills do we need to hire or develop?

Approach:

  1. Review SFIA Activity Category distribution
  2. Cross-reference with Organizational Ownership
  3. Identify gaps in current team capabilities
  4. Prioritize based on Criticality and TRL

Scenario 3: Automation Roadmap

Question: Which processes should we automate first?

Approach:

  1. Filter by Human-in-the-Loop Intensity
  2. Prioritize Human-Driven and Human-Collaborative components
  3. Assess TRL to ensure technology readiness
  4. Consider Cost Structure for ROI calculation

Scenario 4: Vendor vs Internal Development

Question: Should we build or buy this capability?

Approach:

  1. Review Build vs Buy vs Integrate classification
  2. Assess TRL for market maturity
  3. Evaluate Criticality for strategic importance
  4. Consider Organizational Ownership for capability fit

Visualizations

Periodic Cube of AI navigation items
3D Periodic Cube of AI navigation items

Find the 3D model Periodic Cube of AI here.

Seven periodic table visualizations are provided, one for each classification dimension:

  1. periodic_table_ai_sfia_category.png – SFIA Activity Category view
  2. periodic_table_ai_build_buy_integrate.png – Build vs Buy vs Integrate view
  3. periodic_table_ai_trl.png – Technology Readiness Level view
  4. periodic_table_ai_org_ownership.png – Organizational Ownership view
  5. periodic_table_ai_cost_structure.png – Cost Structure view
  6. periodic_table_ai_criticality.png – Criticality / Risk Level view
  7. periodic_table_ai_human_intensity.png – Human-in-the-Loop Intensity view
Each visualization uses color-coding to highlight horizontal relationships across the functional groups, revealing patterns and insights specific to that dimension.

Methodology

Classification Approach

Each component was classified based on:

  • Industry best practices and common patterns
  • Technology maturity assessments from research and market analysis
  • SFIA framework alignment with AI-specific activities
  • Enterprise adoption patterns and organizational structures
  • Cost models prevalent in the market
  • Risk assessments based on system dependencies

Limitations

  • Classifications represent typical patterns and may vary by organization
  • Hybrid approaches are common and classifications may oversimplify
  • Technology evolution may shift TRL classifications over time
  • Organizational structures vary significantly across enterprises

Customization

Organizations should customize this framework based on:

  • Their specific technology stack and vendor choices
  • Organizational structure and team capabilities
  • Industry-specific requirements and regulations
  • Strategic priorities and risk tolerance
  • Budget constraints and financial models

Next Steps

For Strategic Planning

  1. Review each dimension’s visualization
  2. Identify patterns relevant to your organization
  3. Compare against your current state
  4. Develop gap closure and investment plans

For Workforce Development

  1. Focus on the SFIA Activity Category dimension
  2. Map current team skills to required categories
  3. Identify critical skill gaps
  4. Design training and hiring programs

For Technology Roadmap

  1. Use TRL dimension to assess technology maturity
  2. Combine with Criticality to prioritize initiatives
  3. Leverage Build/Buy/Integrate for sourcing strategy
  4. Plan phased adoption based on readiness

For Cost Optimization

  1. Analyze Cost Structure dimension
  2. Identify opportunities to shift CapEx to OpEx
  3. Evaluate usage-based models for variable workloads
  4. Optimize based on criticality and actual usage

References

  • SFIA Framework: Skills Framework for the Information Age (https://sfia-online.org/)
  • Technology Readiness Levels: NASA TRL scale adapted for AI/ML systems
  • AI Full-Stack Architecture: Original framework inspired by Swami.