Proposal Description

Motivation

Artificial intelligence (AI) is rapidly transforming data production and analysis across public institutions, including national statistical offices (NSOs). The global data ecosystem is rapidly evolving, presenting new opportunities and challenges for national statistical offices, international agencies, and other data producers by AI environment. As Artificial Intelligence(AI) becomes increasingly embedded in data analysis, policy-making, accessing information, and service delivery, the readiness of official data and statistics for AI consumption is a growing priority.

Objective of AI readiness for official Statistics and Data

The objective of AI-readiness of official data and statistics is to provide users who search for access data and statistics through AI with correct, timely, coherent, human-understandable, and contextually relevant official data and metadata. This active role requires to understand how AI models and tools works and to keep pace with their rapid evolution and applications.

Methodologies

Evaluating AI-readiness for official data statistics involves several key methodologies:
1. Assessment Framework :
2.Stakeholder Engagement :
3. Data Quality Evaluation :
4.Technology Audit :
5. Capability Assessment:
6. Pilot Projects :
- Initiate pilot projects using AI techniques on smaller datasets to test methodologies and tools.

On the base of Methodology there is a structured, high-level model for assessing AI readiness in an official statistics office are determined. It combines technical, organizational, and statistical dimensions.
1. Core Dimensions of the Model
The model evaluates readiness across 4 interdependent pillars:
(1)Infrastructure & Tools
(2)People & Skills
(3)Governance & Ethics
(4)Process & Culture.

2. Readiness Levels
Each pillar is scored from L1 (Ad-hoc) to L4 (Optimized).
Level Description AI capability
L1 – Initial No systematic AI use; manual processes dominate None / experimental only
L2 – Emerging Pilot projects exist; infrastructure siloed Basic ML (classification, imputation)
L3 – Operational AI integrated in selected statistical processes NLP, anomaly detection, synthetic data
L4 – Transformative AI across production; continuous learning Generative AI, causal ML, autonomous QA
3.Detailed Assessment Criteria
A. Data & Metadata Readiness
B. Infrastructure & Tools
C. People & Skills
D. Governance & Ethics
E. Process & Culture
4. Scoring & Visualization
A radar chart or heatmap shows gaps.
Data & Metadata ████████░░ L3
Infrastructure ████░░░░░░ L2
People & Skills ██░░░░░░░░ L1
Governance & Ethics ██████░░░░ L2
Process & Culture ███░░░░░░░ L1
5. Action Roadmap (Simplified)
Phase Focus Example actions
1 (0–6 months) Awareness & pilot Train all statisticians on AI basics;
2 (6–18 months) Infrastructure & data Set up secure ML sandbox; publish AIready metadata standards.
3 (18–36 months) Integration & scaling Embed AI in monthly production; implement MLOps; create synthetic data service.
4 (36+ months) Trust & transformation Public AI model registry; automated quality reports with AI uncertainty; federated learning across agencies.
6.Unique Considerations for Official Statistics
· Trust is non-negotiable .
· Long time series.
· Low-volume, high-impact data .
· Legal mandate
7.Recommended Assessment Method
1. Self-assessment workshop.
2. Use case mapping.
3. Gap analysis against the 5 pillars above.
4. Produce a readiness report with a priority score (e.g., 1–10) for investment.

Expected Findings:
The application of this framework Key findings are anticipated to include:
(1)Moderate but Uneven Readiness
(2)Policy Recommendations
(3) Maturity Model

Conclusion
This study proposes an AI readiness assessment framework tailored to official statistics ecosystems, focusing on infrastructure, human capacity, data governance, organizational strategy, and ethical preparedness.