How the World Development Report 2026 Says AI Should Be Evaluated for Development

What Monitoring & Evaluation professionals can learn from the World Bank’s framework for evaluating AI performance, use and development outcomes.

Why this report matters to M&E professionals

The World Development Report 2026: The Promise of Artificial Intelligence examines how artificial intelligence affects development and how governments can shape its impacts.

For Monitoring & Evaluation professionals, one of the most relevant parts is Spotlight 6: “Evaluating and Monitoring the Performance of AI Solutions.”

The report explains that realizing the potential benefits of AI requires more than developing or deploying a technically capable model. Organizations need to define objectives, establish performance requirements, validate the model in context, monitor how people use the resulting tool, and assess whether the intervention produces the intended outcomes.

Adopt, Adapt and Advance

The World Development Report organizes approaches to AI around three pathways: adopting AI, adapting AI and advancing AI.

Adopt AI

Organizations use existing AI technologies and applications for identified needs.

Adapt AI

Organizations customize or adapt existing AI technologies to local data, workflows and needs.

Advance AI

Organizations develop more advanced AI capabilities, including complex AI systems where there is a strong case and sufficient capacity.

The report connects these pathways to the capabilities available in a particular country or government agency. It emphasizes the importance of matching the technology to what an organization can realistically sustain.

What is the four-step AI evaluation framework?

Spotlight 6 presents four evaluation steps that build on one another. The framework is described as an introductory way for organizations to anticipate risks, identify potential failure points and establish good practices when using AI.

Define objectives and minimum performance requirements.
Clarify what the AI or machine-learning model is intended to achieve and establish the relevant performance benchmark.
Validate performance with real-world data.
Test whether the model performs adequately using real-world inputs from the context in which it will be deployed.
Monitor human-machine interaction.
Examine whether users can access the tool and whether they use it in the intended manner, including issues such as take-up, adherence, access barriers and misuse.
Assess development outcomes.
Determine whether the AI-supported intervention achieves the desired outcomes, avoids harm and remains cost-effective.

The report presents these steps as connected stages rather than as a single technical test of model accuracy.

Why does contextual validation matter?

The report emphasizes that an AI model should be validated using real-world inputs and independently created validation data.

This matters because a model can perform differently when it is deployed in a context that differs from the conditions in which it was developed or tested.

The report gives model validation a specific role in establishing whether an AI system produces accurate outputs against an appropriate performance benchmark.

For M&E professionals, this connects AI evaluation with familiar concerns about context, benchmarks, data quality and validity.

Why monitor how people use AI?

The report distinguishes model performance from what happens when people actually use an AI-based tool.

For deployed AI tools, the report identifies intermediate outcomes that can be monitored, including:

Take-up and adherence
Access barriers
Manipulation or misuse

The report also discusses indicators such as user expectations, trust and satisfaction, usage analytics, manual overrides, server downtime and help-desk requests as examples of information that can help organizations understand human-machine interaction and operational performance.

This is important for M&E because a technically functional AI system does not automatically mean that users will access it, trust it or follow its recommendations.

How do you move from AI performance to development outcomes?

The report’s final evaluation stage asks whether an AI-based intervention produces the desired development outcomes.

The framework considers effects on beneficiaries as well as effects on nonbeneficiaries, including operators of the AI tool. It also includes cost-effectiveness.

This distinction is important for M&E practice:

Model performance does not by itself demonstrate development impact.
AI tool usage does not by itself demonstrate development impact.
Development outcomes need to be assessed separately.
Potential harm and effects beyond intended beneficiaries also need consideration.
Cost-effectiveness is part of the report’s evaluation framework.

These points are an application of the World Bank’s evaluation framework to M&E practice; they should not be interpreted as additional findings presented separately by the report.

How should AI use match organizational readiness?

Chapter 8 of the report examines governments as users of AI and links the appropriate AI strategy to an agency’s level of digital readiness.

Early digital government

The report describes this stage as having limited digital capability, partially paper-based systems, and weak implementation and oversight. Adopting AI is identified as the priority starting point, while adaptation can be pursued through targeted pilots. Advancing AI is not recommended at this stage.

Connected digital government

This stage involves moderate digital capability, established administrative systems, functioning e-government platforms and some capacity for oversight and model use. Adoption is feasible, while adapting AI is identified as the priority starting point.

Advanced digital government

This stage is characterized by broad digital skills, interoperable and well-governed data ecosystems, mature platforms and robust legal, institutional and technical controls. Adoption and adaptation are feasible, while advancing AI is described as appropriate only for selective strategic investment.

The report’s broader message is that AI deployment should be matched to the capacity of the organization to operate, monitor and sustain the technology.

Why do data and skills matter for AI evaluation?

The report identifies data quality, human capital, systems infrastructure and governance capacity as important foundations for government AI use.

It also discusses the importance of making public administrative data AI-ready. In developing countries, public data may be fragmented or remain in paper formats, while relevant data in local languages can also be limited.

For M&E teams, this reinforces a familiar principle: the quality of an AI-supported analytical process depends partly on the quality and suitability of the underlying evidence and data.

The report also emphasizes staff capacity. Government agencies need people who can understand the objectives of an AI application, define operational requirements, test the system on government tasks, monitor it and use its outputs appropriately.

What does this mean for M&E teams?

The World Development Report’s framework can be translated into a practical sequence for an M&E team considering an AI-supported workflow.

Define the problem.
What specific evaluation, monitoring or programme problem is the AI intended to address?
Define the expected performance.
What minimum performance level would make the tool useful for the intended task?
Validate in context.
Test the system using relevant real-world data and operating conditions.
Monitor actual use.
Track whether users can access the tool and whether they use it as intended.
Look for unintended effects.
Consider effects on beneficiaries, nonbeneficiaries and people operating the AI-supported system.
Assess development outcomes.
Examine whether the intervention produces the intended results.
Consider cost-effectiveness.
Assess the resources required in relation to the outcomes achieved.

AI evaluation checklist for M&E professionals

Before introducing an AI tool into an M&E workflow, ask:

Purpose: What specific problem is the AI intended to address?
Performance: What benchmark will be used?
Context: Has the system been validated with relevant real-world data?
Access: Can intended users access the tool?
Use: Are users using the tool as intended?
Barriers: Are there access or implementation barriers?
Misuse: Could the tool be manipulated or misused?
Outcomes: What development outcome should change?
Unintended effects: Could there be effects on nonbeneficiaries or operators?
Cost-effectiveness: Are the outcomes worth the resources required?

How was AI used in the World Development Report 2026?

The report includes an AI Disclosure Statement describing how AI was used during its preparation.

According to the statement, Claude Sonnet 4.6 and 5, DeepSeek Chat, GPT-5 mini, Gemini 1.5 Flash, and Opus 4.6 and 4.8 were used for coding and labeling unstructured data.

The World Bank’s internal mAI interface using ChatGPT 5 and Copilot was used to improve clarity and shorten text in earlier drafts.

The report states that all uses and outputs of AI were closely monitored by the team and that all analytical judgments, policy interpretations and conclusions were developed, reviewed and verified by the author team.

Frequently asked questions

What are the four steps for evaluating AI in the World Development Report 2026?

The report identifies four connected steps: define objectives and minimum performance requirements; validate the model with real-world data; monitor human-machine interaction; and assess development outcomes, including potential harm and cost-effectiveness.

Why does AI need to be validated in context?

The report emphasizes validation with real-world inputs from the local deployment context because model performance needs to be assessed against the conditions in which the AI application will actually operate.

What should organizations monitor after deploying AI?

The report highlights take-up and adherence, access barriers, manipulation or misuse, and discusses additional indicators such as usage analytics, manual overrides, user expectations, trust, satisfaction, server downtime and help-desk requests.

Does AI model performance demonstrate development impact?

No. The report separates model validation, human-machine interaction and development outcomes. The final stage asks whether the AI-supported intervention achieves the desired outcomes, avoids harm and remains cost-effective.

What is the Adopt, Adapt and Advance framework?

The report uses these three pathways to describe different approaches to AI: adopting existing AI, adapting AI to local needs and contexts, and advancing AI capabilities where the necessary capacity and rationale exist.

Why is this relevant to Monitoring & Evaluation?

The framework connects AI performance with contextual validation, actual use, development outcomes, potential harm and cost-effectiveness, making it directly relevant to evaluation and monitoring of AI-supported interventions.

The Promise of Artificial Intelligence – A case study from World Bank