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A5 Artificial Intelligence Strategy Summary

Core Content

The Independent Evaluation Group (IEG) has developed an Artificial Intelligence (AI) Strategy to guide the integration of AI into its evaluation and knowledge brokering practices. This strategy is part of a broader journey of discovery, aiming to leverage AI for more rigorous, efficient, and impactful evaluations. It outlines the vision, objectives, and enablers for AI adoption, while emphasizing the importance of maintaining ethical standards, transparency, and methodological integrity.

Main Objectives

The AI strategy is focused on two primary objectives:

1. Apply AI to evaluation and validation practice
2. Apply AI to leverage and broker IEG knowledge

These objectives aim to improve the quality, efficiency, and strategic value of evaluations, as well as to enhance the dissemination and use of evaluative knowledge.

Key Values and Guiding Principles

The strategy is guided by the following core values and principles:

• Quality and rigor first: AI is used to enhance the quality, rigor, and transparency of evaluations, with a focus on thorough testing for accuracy, recall, and bias.
• Efficiency: AI is used to accelerate data analysis and synthesis, ensuring efficiency gains without compromising quality.
• Leadership and engagement: IEG actively promotes the responsible use of AI across the World Bank Group and collaborates with external partners and the evaluation community.
• Independence: IEG aligns with institutional AI policies but also develops independent solutions when necessary to maintain methodological integrity and operational independence.
• Experimentation and adaptability: IEG encourages continuous experimentation and learning, adapting practices based on insights from AI applications.
• Responsible use and practical ethics: IEG adheres to the World Bank Group's guidelines on responsible AI use, ensuring that AI applications are safe, secure, and inclusive.

Supporting Building Blocks

To achieve the objectives, IEG has identified four key building blocks:

1. Governance

• Implement stronger standards and quality assurance processes.
• Ensure IEG representation in relevant Bank Group corporate bodies.
• Adopt a risk framework for responsible AI use.

2. Staffing, Capacity, and Resources

• Enhance AI literacy among leadership and task team leaders.
• Bridge capacity gaps across units.
• Foster adherence to good practices and digital fluency.
• Develop technical skills (e.g., software engineers, full-stack developers).

3. Data and Information Technology Ecosystems

• Strengthen the completeness and quality of IEG data systems.
• Develop effective data pipelines and ensure data usability.
• Build synergies with the Information and Technology Solutions (ITS) department.
• Enable access to compute resources for large-scale AI use.
• Develop AI functions for non-coding users.
• Establish quality control processes for code and outputs.

4. Partnerships and Strategic Engagements

• Strengthen strategic partnerships to leverage global public goods.
• Establish a clear link with the Bank Group Innovation Directorate.
• Ensure close coordination with the Global Evaluation Initiative (GEI) and Evaluation Cooperation Group (ECG) members.

Maturity Model

IEG has adopted a five-level maturity model to track progress in AI adoption across the evaluation cycle:

Current Status and Future Goals

As of FY2026, IEG is at Level 2 – Early adoption, with early gains in analytical depth and product clarity. The goal is to reach Level 3 – Emerging practice by FY2028 and Level 5 – Institutionalized use by the long-term horizon. The strategy emphasizes continuous experimentation, learning, and adaptation to ensure AI is used responsibly and effectively.

AI in Evaluation Practice

IEG's use of AI is focused on complementing and enhancing conventional evaluation methods rather than replacing them. AI tools are used in a controlled and targeted manner to support tasks such as:

• Portfolio identification using LLMs.
• Automated coding and synthesis of qualitative data.
• Literature reviews with AI assistance.
• Data extraction and text mining.
• Evidence visualization and synthesized summaries.

These tools aim to improve the consistency, efficiency, and strategic value of IEG's evaluations, while ensuring that ethical considerations and methodological rigor remain central to all activities.

Role Transformation

The strategy calls for a rethinking of evaluator roles and workflows, including:

• Transforming the evaluation cycle: AI is integrated across all stages, from design to dissemination.
• Transforming how we work: Evaluators must develop new skills and mindsets to work effectively with AI tools.
• Transforming evaluative products: AI enables more dynamic, user-centered, and interactive outputs.
• Transforming use of evidence: AI enhances the timeliness and responsiveness of evidence use, supporting better decision-making.

Conclusion

The IEG AI Strategy is a comprehensive roadmap to integrate AI into evaluation practice and knowledge brokering. It emphasizes responsible AI use, methodological integrity, and continuous learning. The strategy aims to position IEG as a global leader in AI-enabled evaluation and to enhance the impact of its work on development outcomes.