The Independent Evaluation Group’s AI Strategy
The Independent Evaluation Group's AI Strategy: A Journey of Discovery
A Journey of Discovery: Background and Context
The Independent Evaluation Group (IEG) has been on a long journey of discovery to leverage data science and artificial intelligence for evaluation practice. The adoption of this AI strategy marks an important milestone on that longer trajectory rather than a point of departure. Since at least 2018, IEG has been experimenting with AI techniques in evaluation and knowledge brokering, building on years of trial and error, exploration, and collective learning across a range of applications.
Foundational principle:
"The use of data science and AI must be anchored in — not become a substitute for — the fundamental principles of high-quality evaluation. The foundational building blocks of evaluation—rigorous design, depth of stakeholder engagement, ethical responsibilities, and robust theories—remain at the heart of what it takes to deliver impactful evaluative evidence."
The strategy was developed as the fields of international development and evaluation struggled to keep pace with the speed of AI development. The World Bank Group has been leading the adoption of AI-enabled solutions to development challenges. IEG has benefited from these early and large investments and has positioned itself at the forefront of AI adoption among peer evaluation functions. The organization has worked closely with the Information and Technology Solutions department to customize tools specifically for evaluation practice.
Strategy Framework: Vision, Objectives and Enablers
Vision Statement
"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."
Apply AI to evaluation and validation practice
Apply AI to leverage IEG knowledge
Attaining these objectives requires efforts grouped into four supporting building blocks: governance, staffing and capacity, data and IT ecosystems, and partnerships. All of these are grounded in values and principles that inform decisions and actions during implementation.
Core Values and Guiding Principles
IEG will use AI to elevate the quality, rigor, and transparency of evaluation practice. Thorough testing for accuracy, recall, and biases is at the core of practice. The organization stands by the principles of rigor and sees AI use as an enabler.
Guided by IEG's quality and transparency standards, AI will enable fast-tracked data analysis and synthesis while upholding evaluative rigor. The organization prioritizes use cases where efficiency gains can be achieved without compromising quality.
IEG actively engages across the World Bank Group to promote the thoughtful and responsible use of AI for evaluation practice and outcome management. It also serves as a leader in advancing innovative evaluative practices globally.
As part of the Bank Group, IEG aligns with institutional AI policies. Where possible, it leverages Bank Group-provided technologies; when necessary, it develops independent solutions to meet its evaluation needs while maintaining methodological integrity.
IEG encourages experimentation at all levels and embraces a culture of continuous learning in the use of AI. Lessons from experimentation are used to adapt and improve approaches and scale successful innovations where appropriate.
IEG abides by the Bank Group's responsible AI use guidance and strives to apply a safe, secure, humane, inclusive, and environmentally friendly approach to AI use. Ethical challenges are reviewed iteratively as they arise from experiments and use cases.
The Maturity Model: A Roadmap for Progress
To help self-evaluate progress and perform health checks on strategy implementation, IEG has adopted a maturity framework with five levels. Although adoption and practices vary across the organization, the goal is to move toward institutionalized use in the next three to five years. Informed by a stocktaking exercise, IEG currently stands at Level 2 (Early adoption) in FY26 and strives to reach Level 3 (Emerging practice) in the short term (FY27-28).
AI in Evaluation Practice: Concrete Examples
IEG has already demonstrated successful AI applications across the evaluation cycle. The strategy documents numerous examples drawn from actual evaluations:
Data capture and collection
The evaluations on Doing Business and undernutrition used AI to identify Bank Group multisector portfolios. Building on these early experiences, IEG developed an AI-enabled portfolio identification application called irr. The epidemic and pandemic preparedness evaluation used a retrieval-augmented generation model to extract core information from a wide portfolio of interventions.
Data analytics
The COVID-19 evaluation used decision tree analysis to identify drivers of successful implementation. The gender strategy evaluation used a classification model to track gender gaps over time. The jobs and labor market reform evaluation used an LLM to classify outcome indicators and build a synthetic pre-evaluation portfolio. The procurement evaluation applied cluster analysis to categorize country contexts in relation to performance.
Visualization, triangulation and interpretation
The biodiversity evaluation used geospatial analysis to answer key evaluation questions and generated powerful visualizations including maps that ease interpretation. The water resource management evaluation used AI to create an effective visualization communicating a complex water resource management cycle. The undernutrition evaluation offers an interactive dashboard for evidence visualization.
The sweet spot: A hybrid human-AI approach
IEG's sweet spot lies in a hybrid space where AI tools are used in a controlled, targeted way to complement or enhance conventional evaluation methods. This hybrid middle-ground approach broadens the practical utility of AI and mitigates many of the risks associated with relying on AI solutions or full automation. As AI automates some routine evaluation tasks, IEG will elevate the strategic role of evaluators as designers, interpreters, and stewards of quality. Evaluators will take on new roles as strategic leaders and methodological experts, contextual integrators, AI workflow architects, and quality stewards.
AI in Validation Practice
The validation of self-evaluations is a critical activity for IEG. The organization 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, starting with Implementation Completion and Results Report Reviews. Early experimentation has included using AI for scoping (understanding Bank Group engagement), summarizing meetings and project documents, editing validation text, and drafting sections of validation documents.
Governance: A Novel Peer Governance Model
Given that AI in evaluation is a new and technically complex area that changes rapidly, and given the inherent asymmetry of information between evaluation leadership teams and the use of AI in design and implementation, IEG has introduced a peer governance model (or collective self-governance) with two main mechanisms.
AI Review Board (AIRB)
A cross-functional peer body with periodically rotating members representing data scientists and analysts, evaluation team leaders, managers, Methods Advisory Function team members, and data, knowledge, and learning staff. The AIRB develops quality standards, a code of conduct, a risk framework, and prohibited use cases. It also runs the IEG AI innovation fund and develops safe feedback and escalation channels.
AI Community of Excellence
A voluntary group of IEG staff with specialized technical skills who lead continuous experimentation and scale-up of AI use. Members commit three staff weeks per fiscal year. The Community maintains a repository of use cases, runs a help desk for AI implementation, reviews prompts and workflows, and liaises with the Task Team Leader Community of Practice.
Key Risks and Mitigation Strategies
The World Bank's overall risk framework for AI focuses on legal, ethical, security and privacy, validity and accuracy, and accountability and traceability risks. The strategy translates these into specific risks for evaluation practice with corresponding mitigation measures:
AI Tool Ecosystem
IEG's AI tool ecosystem is designed for interoperability, security, and scalability. The organization follows the principle "leverage first, build when needed," prioritizing existing off-the-shelf and Bank Group-developed tools while reserving custom development for unique IEG use cases.
Staffing, Capacity and Resources
The success of the AI strategy hinges on IEG's ability to empower evaluators and knowledge brokers to integrate AI into their workflows. IEG currently has four data scientists (centralized) and ten analysts (decentralized). The strategy calls for several key actions:
- Adopting a role-specific competency framework as a basis for workforce development plans for evaluators, knowledge brokers, and the leadership team to ensure a baseline of AI literacy for all IEG staff.
- Leveraging existing training resources available through the Bank Group, particularly the Data Talent Board offerings, rather than developing customized technical training.
- Hiring specialized expertise in software engineering, computer science, and data science for technical quality assurance as needs arise.
- Establishing an AI innovation fund as a contingency mechanism that evaluation teams can tap into when their AI experimentation is costlier than expected when evaluation budgets are set.
- Creating soft and hard commitments and incentives within IEG for the responsible use of AI at scale, fostering a culture of practical ethics in AI experimentation.
Key Takeaways for Evaluators and M&E Professionals
Peer governance as a model
The AI Review Board and Community of Excellence offer a replicable model for collective self-governance of AI in evaluation functions. This approach balances the need for innovation with appropriate oversight, recognizing that traditional hierarchical governance may not fit the rapid evolution of AI.
Human-in-the-loop mandate
The strategy embeds expert review and human checkpoints at key milestones. AI augments rather than replaces evaluator judgment and methodological rigor. This is not optional but mandatory, with formal sign-off required on deliverables.
Maturity framework for progress tracking
The five-level maturity model provides a practical tool for evaluation offices to assess their own AI adoption progress. It helps organizations understand where they stand, where they want to go, and what investments are needed to get there.
Risk-based tiered quality control
The strategy establishes a tiered approach where lower-risk applications require limited oversight, while higher-risk analytical applications require rigorous expert-driven reviews. This scalable approach can be adapted by any evaluation office regardless of size or resources.
Frequently Asked Questions
What is the "sweet spot" for AI use in evaluation according to IEG?
The sweet spot lies in a hybrid space where AI tools are used in a controlled, targeted way to complement or enhance conventional evaluation methods — not full automation. This hybrid middle-ground approach broadens AI's practical utility while mitigating many of the risks associated with relying on AI solutions alone.
How does IEG govern AI use given its complexity and rapid change?
IEG uses a novel peer governance model with two mechanisms: the AI Review Board (a cross-functional body that develops quality standards and a risk framework) and the AI Community of Excellence (a voluntary group of technical staff who lead experimentation and knowledge sharing). This approach remains nimble while providing appropriate oversight.
What AI tools does IEG use or plan to use?
IEG leverages a blend of publicly available tools (ChatGPT, Claude), Bank Group-provisioned tools (Microsoft Copilot), Bank Group-customized tools (mAI, Bank Group GPT, ChatIFC), and IEG-custom applications (irr for portfolio identification). The guiding principle is "leverage first, build when needed."
What is the timeline for IEG's AI maturity journey?
Currently at Level 2 (Early adoption) in FY26, IEG aims to reach Level 3 (Emerging practice) in FY27-28, Level 4 (Expanding practice) in FY29-30, and Level 5 (Institutionalized use) beyond FY30. The maturity model is revisited periodically to keep pace with technological developments.
Why This Matters for the Evaluation Community
The IEG AI Strategy represents one of the first comprehensive frameworks for AI adoption in an independent evaluation function. For evaluators worldwide, it offers several valuable resources:
A maturity model for assessing AI adoption progress
A peer governance model balancing innovation and oversight
Concrete, documented AI use cases across the evaluation cycle
A risk-based tiered quality control framework
As IEG states, "The use of data science and AI must be anchored in the fundamental principles of high-quality evaluation." This strategy provides a roadmap for achieving that balance while maintaining the core values of evaluation practice.
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