AI Readiness Assessment

Evaluate your organization's readiness for AI projects based on five critical foundations

About This Assessment

This assessment is based on the framework outlined in "Is Your Organization Ready to Start an AI Project?" by Kathleen Walch and Ron Schmelzer. It evaluates five essential foundations that must be in place before launching AI projects:

Strong data governance framework
Working, well-understood data pipeline
Proactive AI data management
Measurable AI data quality standards
Clear-eyed AI maturity model

Assessment based on 5 critical foundations for AI project success

4.1/5.0

Good

Overall Readiness82%

✅ Your organization is MOSTLY READY. Address identified gaps before scaling.

Data Governance Framework

Strong data governance with clear ownership, policies, and compliance

5/5

Excellent

Data Pipeline

Working, repeatable data pipeline for AI use

4/5

Good

AI Data Management

Proactive data management with version control and monitoring

4.5/5

Excellent

Data Quality Standards

Measurable quality standards with automated tracking

4/5

Good

AI Maturity Model

Clear-eyed assessment of AI maturity and roadmap

3/5

Needs Improvement

Recommendations

Priority actions to improve AI readiness

High Priority: Create AI Maturity Model

Define 5 maturity levels, create self-assessment tool, and map current capabilities to maturity levels.

Medium Priority: Enhance Quality Monitoring

Implement real-time quality alerts, add bias detection algorithms, and improve quality dashboards.

Readiness Checklist

Signs you're ready for AI projects

Data lives in connected systems (not silos)

Data lifecycle ownership is clear

Solving real business problems (not just following trends)

AI outputs are monitored and governed

Full Assessment Report

Detailed evaluation with evidence, gaps, and recommendations

Data Governance Framework

ADPA's comprehensive data governance structure

RAG Integration

Semantic search-powered context retrieval

Template Analytics

Quality metrics and version control system

Trustworthy AI Framework

Five-layer framework assessment: Ethics, Responsibility, Transparency, Governance, Explainability

AI Project Manager Skills

Seven essential skills assessment: Data literacy, Critical thinking, Trustworthy AI, Communication, Agile delivery, AI lifecycle, Tool proficiency

AI Readiness Assessment

Evaluate your organization's readiness for AI projects based on five critical foundations

About This Assessment

Strong data governance framework
Working, well-understood data pipeline
Proactive AI data management
Measurable AI data quality standards
Clear-eyed AI maturity model

AI Readiness Assessment

Assessment based on 5 critical foundations for AI project success

4.1/5.0

Good

Overall Readiness82%

✅ Your organization is MOSTLY READY. Address identified gaps before scaling.

Data Governance Framework

Strong data governance with clear ownership, policies, and compliance

5/5

Excellent

Data Pipeline

Working, repeatable data pipeline for AI use

4/5

Good

AI Data Management

Proactive data management with version control and monitoring

4.5/5

Excellent

Data Quality Standards

Measurable quality standards with automated tracking

4/5

Good

AI Maturity Model

Clear-eyed assessment of AI maturity and roadmap

3/5

Needs Improvement

Recommendations

Priority actions to improve AI readiness

High Priority: Create AI Maturity Model

Define 5 maturity levels, create self-assessment tool, and map current capabilities to maturity levels.

Medium Priority: Enhance Quality Monitoring

Implement real-time quality alerts, add bias detection algorithms, and improve quality dashboards.

Readiness Checklist

Signs you're ready for AI projects

Data lives in connected systems (not silos)

Data lifecycle ownership is clear

Solving real business problems (not just following trends)

AI outputs are monitored and governed