AI Data Readiness Index Assessment

Assessment Tool: Interactive AI Readiness Evaluation Framework
Author: Oleh Ivchenko, PhD Candidate
Version: 1.1.0
DOI: 10.5281/zenodo.TBD (Registration pending)
Category: AI Economics & Digital Transformation

Overview

This interactive assessment helps businesses evaluate their readiness for AI adoption across five critical dimensions. Based on your responses, you’ll receive a comprehensive readiness score, personalized recommendations, and guidance on which AI technologies best match your organization’s capabilities.

What You’ll Learn
• Your overall AI Data Readiness Level (1-5 scale)
• Specific scores across 5 key dimensions
• Whether your organization is ready for Narrow AI (ANI) or General AI (AGI) applications
• Actionable recommendations based on your current state
• Real-world case studies matching your readiness level

Start Your Assessment

Answer the questions below honestly based on your organization’s current state. The assessment takes approximately 5-10 minutes to complete.

Progress: 0/25 questions answered

Data Quality & Availability

Evaluate the quality, completeness, and accessibility of your organizational data

1. How would you describe the quality of your organization’s data?
2. What percentage of your critical business data is digitized and accessible?
3. How standardized is your data across different departments and systems?
4. Do you have historical data available for analysis and pattern recognition?
5. How complete is your data (missing values, gaps)?

Data Infrastructure

Assess your technical infrastructure for storing, processing, and analyzing data

6. What type of data storage and management systems do you have?
7. Do you have the computational resources for AI workloads?
8. How well integrated are your data systems?
9. Do you have APIs or data pipelines for accessing and processing data?
10. What is your data processing capability?

Data Governance

Review your policies, security, and compliance practices around data

11. Do you have data governance policies and procedures in place?
12. How do you handle data privacy and security?
13. Are you compliant with relevant data regulations (GDPR, CCPA, etc.)?
14. Do you have clear data ownership and stewardship roles?
15. Do you have data lineage and audit capabilities?

Technical Capabilities

Evaluate your team’s AI/ML skills, tools, and development experience

16. Do you have data science or ML expertise in-house?
17. What ML/AI tools and platforms do you have access to?
18. Have you deployed any ML/AI models to production?
19. What is your MLOps maturity level?
20. How much technical debt affects your ability to innovate?

Organizational Readiness

Assess leadership support, strategy, and cultural readiness for AI transformation

21. Does leadership actively support AI initiatives?
22. Do you have a defined AI strategy or roadmap?
23. Is there budget allocated specifically for AI initiatives?
24. How strong is your change management capability?
25. What is the overall data literacy level in your organization?

Your AI Data Readiness Results

Understanding the Assessment

What is AI Data Readiness?

AI Data Readiness refers to an organization’s preparedness to successfully adopt and deploy artificial intelligence technologies. Our assessment evaluates five critical dimensions:

  • Data Quality & Availability: High-quality, accessible data is essential for training accurate models.
  • Data Infrastructure: Technical systems for storing, processing, and accessing data.
  • Data Governance: Policies, security, and compliance controls.
  • Technical Capabilities: Skills, tools, and experience for AI systems.
  • Organizational Readiness: Leadership support, strategy, and culture.

Narrow AI vs. General AI

🎯 Narrow AI (ANI)

Artificial Narrow Intelligence

Designed for specific, well-defined tasks. Examples: spam filters, recommendation engines, fraud detection.

Best for: Organizations at levels 1-4.

🌐 General AI (AGI)

Artificial General Intelligence

Can understand and apply knowledge across multiple domains. Examples: LLMs, multi-modal AI systems.

Best for: Organizations at level 5.

Interpreting Your Score

  • Level 1 (0-20%): Foundation Building
  • Level 2 (21-40%): Emerging Readiness
  • Level 3 (41-60%): Developing Capabilities
  • Level 4 (61-80%): Advanced Readiness
  • Level 5 (81-100%): AI-Ready Leader

Methodology and Research

Research Foundation
Based on: MIT Sloan AI Adoption Research, Gartner Data & Analytics Maturity Models, McKinsey AI Readiness Framework, and 200+ industry case studies. Validated through pilot testing with 50+ organizations.

Citation and DOI

Ivchenko, O. (2026). AI Data Readiness Index Assessment. Stabilarity Research Hub. DOI: 10.5281/zenodo.TBD
Version: 1.1.0  |  Last Updated: 2026-03-09  |  Dependencies: Chart.js 4.4.0  |  Related Research: Spec-Driven AI Development, AI Economics
Release Notes
v1.1.0 (2026-03-09)
• Added export results as text summary (.txt download)
• Added tool-meta footer with version and related research
• Design overhaul: removed gradients, rounded corners, colored fills
• Native/plain aesthetic per design standards

v1.0.0 (2026-02-23)
• Initial release with 25-question assessment
• 5 dimensions: Data Quality, Infrastructure, Governance, Technical, Organizational
• Radar chart visualization, recommendations, AI type guidance
• Case studies per readiness level