Government AI Maturity Assessment Tool

Comprehensive evaluation framework based on international best practices

Instructions
Assessment
Results & Scoring
Export to Excel

How to Use This Assessment Tool

  1. Complete the Assessment: Navigate to the Assessment tab and answer all questions for each dimension. Select the maturity level that best describes your organization's current state.
  2. Review Low Maturity Insights: When you select levels 1 or 2, you'll see specific recommendations for improvement.
  3. View Results: The Results tab will automatically calculate your scores and display your overall maturity level.
  4. Export to Excel: Use the Export tab to download your results as an Excel file for further analysis.

Maturity Levels

  • Level 1 - Initial: Ad-hoc, unmanaged processes with minimal AI awareness
  • Level 2 - Developing: Basic awareness with initial process development
  • Level 3 - Established: Defined, documented, and repeatable processes
  • Level 4 - Advanced: Measured, monitored, evidence-based approaches
  • Level 5 - Optimized: Continuous improvement and innovation leadership

Assessment Dimensions

This assessment evaluates your organization across six core dimensions:

  • Governance and Strategy (Weight: 25%)
  • Technical Infrastructure (Weight: 20%)
  • Human Capital (Weight: 15%)
  • Data Management (Weight: 20%)
  • Risk Management (Weight: 10%)
  • Stakeholder Engagement (Weight: 10%)

Governance and Strategy

Evaluates leadership commitment, AI strategy, and policy integration

1. Does your organization have a formal AI strategy document?
Level 1 No AI strategy exists
Level 2 Informal or draft strategy
Level 3 Formal strategy approved
Level 4 Strategy with KPIs & metrics
Level 5 Adaptive strategy with continuous updates
Improvement Recommendation:
Start by forming a cross-functional AI task force to develop a formal AI strategy. Include representatives from IT, legal, operations, and key service departments. Begin with a current state assessment and develop a 3-year roadmap aligned with organizational goals.
2. What is the level of executive sponsorship for AI initiatives?
Level 1 No executive involvement
Level 2 Occasional executive interest
Level 3 Designated executive sponsor
Level 4 C-suite champion with budget
Level 5 Board-level AI committee
Improvement Recommendation:
Schedule executive briefings on AI potential and risks. Present case studies from peer governments. Propose a pilot project with clear ROI to demonstrate value. Consider appointing a Chief AI Officer or similar role to champion initiatives.
3. How well are AI policies integrated with existing frameworks?
Level 1 No AI policies exist
Level 2 Standalone AI policies
Level 3 Partially integrated policies
Level 4 Fully integrated governance
Level 5 Adaptive policy framework
Improvement Recommendation:
Review existing IT, data, and security policies to identify AI touchpoints. Develop an AI ethics framework aligned with international standards (OECD, UNESCO). Create policy templates for common AI use cases in government services.

Technical Infrastructure

Assesses computing resources, systems integration, and technical capabilities

4. What computing infrastructure is available for AI workloads?
Level 1 Standard desktop computers only
Level 2 Limited server capacity
Level 3 Dedicated AI servers/cloud
Level 4 Scalable cloud with GPU
Level 5 Hybrid cloud with edge computing
Improvement Recommendation:
Start with cloud-based AI services (Azure AI, AWS SageMaker, Google Cloud AI) to avoid large upfront investments. Pilot projects can run on standard cloud instances. Plan for GPU-enabled computing as you scale. Consider government cloud frameworks for compliance.
5. How mature are your MLOps/AIOps capabilities?
Level 1 No MLOps processes
Level 2 Manual model deployment
Level 3 Basic automation & monitoring
Level 4 Full CI/CD for ML
Level 5 Advanced MLOps with AutoML
Improvement Recommendation:
Begin with version control for models and data (Git, DVC). Implement basic model monitoring for drift detection. Use containerization (Docker) for deployment consistency. Consider MLflow or Kubeflow for workflow management as you mature.

Human Capital

Evaluates skills, training programs, and organizational readiness

6. What percentage of IT staff have AI/ML skills?
Level 1 Less than 5%
Level 2 5-15%
Level 3 15-30%
Level 4 30-50%
Level 5 Over 50%
Improvement Recommendation:
Launch an AI literacy program with online courses (Coursera, edX). Partner with universities for specialized training. Create internal communities of practice. Start with Python and basic ML concepts. Incentivize certifications (AWS ML, Google Cloud ML).
7. Do you have formal AI training programs?
Level 1 No training programs
Level 2 Ad-hoc external training
Level 3 Structured training plan
Level 4 Comprehensive curriculum
Level 5 AI academy with career paths
Improvement Recommendation:
Develop a tiered training approach: AI awareness for all staff, technical skills for IT, and specialized training for AI teams. Use a mix of online platforms, workshops, and hands-on projects. Track completion rates and skill assessments.

Data Management

Assesses data quality, governance, and accessibility for AI

8. What is the state of your data quality for AI?
Level 1 Unknown data quality
Level 2 Basic quality checks
Level 3 Systematic quality management
Level 4 Automated quality monitoring
Level 5 Predictive quality optimization
Improvement Recommendation:
Start with a data quality assessment of key datasets. Implement data profiling tools to understand completeness, accuracy, and consistency. Create data quality dashboards. Establish data stewards for critical datasets. Use tools like Great Expectations or Deequ.
9. How accessible is data for AI projects?
Level 1 Siloed, manual access
Level 2 Basic data warehouse
Level 3 Integrated data platform
Level 4 Self-service data access
Level 5 Real-time data mesh
Improvement Recommendation:
Create a data catalog documenting available datasets. Implement API-based access to common data sources. Start with a pilot data lake for unstructured data. Ensure proper access controls and audit trails. Consider cloud-based solutions for scalability.

Risk Management

Evaluates AI risk identification, mitigation, and compliance

10. Do you have an AI risk assessment framework?
Level 1 No risk assessment
Level 2 Informal risk identification
Level 3 Documented risk framework
Level 4 Quantitative risk metrics
Level 5 Predictive risk management
Improvement Recommendation:
Adopt an AI risk taxonomy covering bias, privacy, security, and operational risks. Use impact/probability matrices for each AI project. Implement the NIST AI Risk Management Framework. Create risk registers and mitigation plans for high-risk applications.
11. How do you ensure AI ethics compliance?
Level 1 No ethics guidelines
Level 2 Basic ethical principles
Level 3 Ethics review process
Level 4 Ethics board & audits
Level 5 Continuous ethics monitoring
Improvement Recommendation:
Establish an AI ethics committee with diverse stakeholders. Create ethics checklists for AI projects covering fairness, transparency, and accountability. Implement bias testing protocols. Document decision-making processes for high-stakes AI applications.

Stakeholder Engagement

Assesses public consultation, transparency, and feedback mechanisms

12. How do you engage citizens on AI initiatives?
Level 1 No public engagement
Level 2 Basic information sharing
Level 3 Regular consultations
Level 4 Co-creation processes
Level 5 Continuous dialogue platform
Improvement Recommendation:
Start with public AI awareness campaigns. Host town halls on AI in government services. Create citizen advisory panels. Use online platforms for feedback collection. Publish regular updates on AI projects and their impacts on services.
13. What is your AI transparency level?
Level 1 No transparency measures
Level 2 Basic project disclosure
Level 3 AI use case registry
Level 4 Algorithmic impact assessments
Level 5 Real-time transparency dashboard
Improvement Recommendation:
Create a public AI registry listing all government AI applications. Publish plain-language explanations of how AI affects citizen services. Implement "right to explanation" policies. Consider open-sourcing non-sensitive AI models for public scrutiny.

Assessment Results

0.0
Not Assessed
Governance & Strategy
0.0
Technical Infrastructure
0.0
Human Capital
0.0
Data Management
0.0
Risk Management
0.0
Stakeholder Engagement
0.0

Export to Excel Format

Click the button below to export your assessment results to a CSV file that can be opened in Excel. The export includes:

  • All assessment questions and your responses
  • Dimension scores and weights
  • Overall maturity calculation
  • Improvement recommendations for low-scoring areas
  • Benchmark comparison data

Excel Implementation Guide

To implement this assessment in Excel:

  1. Sheet 1 - Questions: Create columns for Question ID, Dimension, Question Text, Response (1-5), Weight
  2. Sheet 2 - Scoring: Use SUMPRODUCT to calculate weighted scores by dimension
  3. Sheet 3 - Dashboard: Create charts showing dimension scores and overall maturity
  4. Sheet 4 - Recommendations: Use VLOOKUP to display recommendations based on scores

Key Excel Formulas:

  • Dimension Score: =AVERAGEIF(Dimension_Column, "Governance", Response_Column)
  • Weighted Score: =SUMPRODUCT(Dimension_Scores, Dimension_Weights)
  • Maturity Level: =IF(Overall_Score<1.5,"Initial",IF(Overall_Score<2.5,"Developing",IF(Overall_Score<3.5,"Established",IF(Overall_Score<4.5,"Advanced","Optimized"))))