1. What Is the IEG Artificial Intelligence Strategy?

The World Bank Independent Evaluation Group (IEG) Artificial Intelligence Strategy is a landmark document that sets out a five-year vision for embedding AI into evaluation practice, validation work, and evaluative knowledge brokering. Adopted in 2025, the strategy builds on years of experimentation with both discriminative and generative AI tools dating back to 2018.

The strategy is guided by a clear vision: “By thoughtfully embedding AI at the core of its evaluation practice, IEG will set a global benchmark for producing and brokering high-quality, AI-enabled evaluative evidence.”

The scope of the strategy is limited to the use of AI in evaluation, validation, and knowledge brokering — not day-to-day productivity tasks like composing emails or managing calendars. It covers both generative and discriminative AI, including large language models (LLMs), geospatial AI, computer vision, and machine learning classification models.

2. Key Values and Guiding Principles

The strategy is anchored in six core values that guide all AI-related decisions and activities:

3. How AI Is Being Used in Evaluation Practice

The strategy identifies four categories of AI application across the evaluation cycle: data capture and collection, data analytics, visualization and interpretation, and communication and reporting.

3.1 Automating Routine Evaluation Tasks

AI helps reduce manual and repetitive work that traditionally consumes large amounts of evaluator time. Examples from the strategy include:

• Interview transcription
• Text extraction from reports
• Portfolio identification (using the IEG-developed tool called “irr”)
• Automated document summarization
• Coding qualitative data
• Literature review synthesis
• Drafting sections of validation reports

The strategy emphasizes that AI is being used to semiautomate tasks — not fully automate them. Human oversight remains essential.

3.2 AI-Assisted Data Collection and Capture

AI is increasingly used to collect and process large volumes of structured and unstructured data from diverse sources. The strategy highlights:

Specific examples from IEG evaluations include:

• The evaluation on epidemic and pandemic preparedness used RAG to extract core information from a wide portfolio of interventions
• Geospatial AI was used to augment geospatial datasets
• Evaluations on biodiversity and Bank Group guarantees used AI to extract and summarize key information from Country Partnership Framework documents

3.3 Advanced Qualitative and Quantitative Analysis

The strategy documents extensive use of AI for analytical tasks:

• The COVID-19 evaluation used decision tree analysis to identify drivers of successful implementation
• The gender strategy evaluation used classification models to track gender gaps over time
• The jobs and labor market reform evaluation used LLMs to classify outcome indicators
• The procurement evaluation applied cluster analysis to categorize country contexts

3.4 Geospatial and Remote Sensing Applications

AI-powered geospatial analysis is becoming increasingly important in development evaluation. The strategy cites:

• The urban spatial growth evaluation used image segmentation to assess spatial and economic impacts
• The blue economy evaluation used remote sensing to assess coastal ecosystem health
• The Tanzania Country Program Evaluation visualized flood modeling data to understand transport effectiveness
• The biodiversity evaluation used geospatial analysis to answer key evaluation questions and generate powerful visualizations

3.5 Visualization and Dashboards

AI improves how evaluation findings are presented and interpreted. Examples include:

• The undernutrition evaluation offers an interactive dashboard for evidence visualization
• The water resource management evaluation used AI to create visualizations for its conceptual framework
• Heat maps, dashboards, and geospatial analysis ease interpretation of complex findings

4. AI in Validation and Quality Assurance

Validation is a core part of IEG’s work — the group validates on average about 500 microproducts per year, including Implementation Completion and Results Report Reviews, Expanded Project Supervision Reports, and Completion and Learning Review Validations.

The strategy envisions piloting AI capabilities to enhance the consistency, efficiency, and quality of validations. Current and planned AI applications include:

• Summarization of project documents to serve as background for validators
• Structured draft sections tailored to specific report types
• Chain-of-thought summaries showing AI reasoning for validation steps
• Identification of gaps or inconsistencies with IEG validation methodologies
• Industry or sector performance benchmarking

The strategy notes that early experimentation has already seen staff using AI for scoping, summarizing meetings and documents, editing text, and drafting sections of validation reports.

5. AI for Knowledge Management and Evidence Brokering

The strategy identifies three audiences for IEG knowledge: Bank Group operational staff, IEG evaluation staff, and the broader evaluation community. AI is expected to transform how evaluative evidence is accessed, synthesized, and used.

Key initiatives include:

• Ensuring IEG documents are properly accessible to Bank Group and IEG-developed AI tools
• Aligning with Bank Group initiatives like the Lessons Explorer, which classifies and synthesizes lessons content from IEG validations
• Leveraging ECG member AI-enabled repositories of evaluations for collaborative knowledge sharing
• Using AI to deliver synthesized knowledge to specific audiences at key points of need (for example, curated knowledge emails for new Bank Group leaders)
• Incorporating AI bots into IEG’s public website and the Global Evaluation Initiative’s website, including BetterEvaluation content

6. The Maturity Model: A Phased Journey

The strategy adopts a five-level maturity framework to guide and assess progress:

7. New Roles for Evaluators: The Transformation of the Profession

The strategy explicitly states that AI will transform the identity of evaluators themselves. As AI automates routine tasks, evaluators will take on new strategic roles:

Future M&E professionals will need new competencies including AI literacy, prompt engineering, data governance knowledge, ability to validate AI-generated outputs, and ethical AI risk assessment skills. The strategy emphasizes that AI literacy among leadership and task team leaders is currently low and must be addressed.

8. Governance and Risk Management

The strategy introduces two novel governance mechanisms:

The AI Review Board (AIRB)

A cross-functional peer body composed of data scientists, evaluation team leaders, managers, methods advisors, and knowledge staff. Responsibilities include:

• Developing quality standards and a code of conduct for AI use
• Creating a tiered framework to identify low- to high-risk use cases
• Establishing a list of prohibited use cases
• Running an AI innovation fund
• Developing safe feedback and escalation channels
• Reviewing ethics reports and issues

The AI Community of Excellence

A group of IEG staff with specialized technical skills who lead continuous experimentation and scale-up of AI use. Membership is voluntary but requires a commitment of three staff weeks per fiscal year. Responsibilities include:

• Establishing and maintaining a repository of use cases
• Running a help desk for AI implementation
• Identifying promising experiments within ongoing evaluations
• Reviewing prompts, workflows, and model performance
• Codeveloping guidance on use cases and prompting techniques

Key Risks Identified

The strategy explicitly identifies several categories of risk:

• Strategic and operational risks: inconsistent use, wasted resources, “shadow AI,” overreliance on automation
• Quality and credibility risks: lack of transparency, “black box” models, model degradation, inadequate human oversight
• Ethics and legal risks: bias, environmental impact, privacy violations, lack of accountability pathways

9. Staffing, Capacity Building, and Skills Transformation

The strategy recognizes that success hinges on empowering evaluators with new skills. Currently, IEG has 4 data scientists (centralized) and 10 analysts (decentralized), but AI literacy among leadership and task team leaders is low.

Key capacity-building initiatives include:

• Adopting a role-specific competency framework for AI use
• Leveraging existing Bank Group training resources (Data Talent Board, Knowledge Management Talent Board)
• Curating customized training for IEG-specific needs
• Promoting experiential, on-the-job, continuous learning
• Establishing an AI innovation fund to support experimentation
• Creating incentives for responsible AI use at scale

10. Data and Information Technology Ecosystems

The strategy emphasizes that robust, complete, and up-to-date data systems are a necessary condition for advanced AI use. IEG is revising its data strategy to fully align with AI requirements.

The AI tool ecosystem includes four categories:

Guiding principles for the technology ecosystem include: leverage first, build when needed; intentional experimentation; strategic partnerships; continuous skill development; and architectural flexibility.

11. Partnerships and Strategic Engagement

The strategy adopts an engagement spectrum with six levels: information sharing, networking, coordination, cooperation, collaboration, and partnership. IEG will engage selectively based on decision criteria including alignment with strategy objectives and availability of staff bandwidth.

Key partnership areas include:

• Evaluation Cooperation Group (ECG) AI Working Group
• World Bank Information and Technology Solutions (ITS)
• Data Talent Board
• Knowledge and Learning Directorate
• Global Evaluation Initiative (GEI)
• Asian Development Bank’s EVA tool for collaborative knowledge sharing

12. Broader Implications for the M&E Sector

AI Is Changing the Role of Evaluators

The most important message is that AI will transform evaluator identities — from manual data processors to strategic thinkers, AI supervisors, interpreters of evidence, and ethical decision-makers.

AI Will Reshape the Entire Evaluation Cycle

AI is expected to influence evaluation design, data collection, analysis, synthesis, reporting, dissemination, and evidence use — transforming the full evaluation ecosystem, not just isolated tasks.

“Human-in-the-Loop” Is a Central Principle

The strategy explicitly rejects full automation. The preferred approach is AI-assisted evaluation with strong human oversight, expert review, ethical supervision, and methodological validation.

AI Could Enable Faster, Real-Time Evaluation

AI allows rapid synthesis of evidence, automated monitoring, faster document analysis, continuous portfolio scanning, and quicker production of insights — potentially enabling more dynamic, responsive evaluation systems.

AI Could Increase Demand for Evidence

Because evidence can be produced faster and synthesized more efficiently, organizations may demand more evaluations, leading to quicker donor reporting, faster adaptive management, and more frequent learning cycles.

Some Traditional Tasks May Decline

Routine tasks like manual coding, repetitive synthesis, document extraction, transcription, and basic classification work may gradually shrink. Junior evaluators will need stronger analytical and strategic skills.

AI Governance Will Become a Core M&E Competency

Future M&E systems will require AI risk frameworks, review boards, ethical compliance mechanisms, transparency protocols, documentation standards, and disclosure of AI usage in reports.

Development Contexts Create Special AI Challenges

Many AI models are trained on Western or non-representative datasets, creating risks in fragile states, low-income countries, conflict zones, and underrepresented populations. Biased AI systems may misinterpret local realities, and language limitations may exclude communities.

AI Will Likely Increase Multidisciplinary Teams

Future M&E teams will increasingly bring together evaluators, data scientists, AI specialists, software engineers, geospatial analysts, and IT professionals.

AI Could Democratize Access to Evaluation Knowledge

AI tools could make complex evaluation findings easier for policymakers, local NGOs, communities, governments, and field staff to understand and use, potentially increasing the practical impact of evidence.

13. Key Benefits and Risks of AI in M&E

14. Ethical Responsibilities and Prohibited Practices

The strategy explicitly prohibits several AI use cases:

• Causing or exacerbating harm to social, cultural, economic, natural, or political environments
• Promoting bias, discrimination, or stigmatization of individuals or groups
• Preventing humans from overriding AI-generated decisions
• Actively hiding the use of AI
• Purposefully manipulative or deceptive techniques
• Inferring emotions of humans (for example, clients or counterparts)

Staff are also required to follow strict guidelines for responsible AI use, including not uploading restricted information to publicly available tools, being cautious about inaccurate or biased outputs, and disclosing AI use in official work.

Main Reference and Original Source

Primary source: World Bank Independent Evaluation Group. (2025). Artificial Intelligence Strategy. Independent Evaluation Group, World Bank.
Download: Full PDF available here
Related resource: World Bank Group Guidelines on Responsible Use of Artificial Intelligence

Resources for Further Learning