Introduction

AI and knowledge management in evaluation and evidence synthesis is emerging as a critical topic for development and foreign policy organizations. From 24–26 March 2025, experts and practitioners from around the world, including the global south, gathered at Wilton Park to discuss how AI and knowledge management can help evaluation evidence become more accessible, inclusive, timely, and useful for decision-making.

The event was held in association with Global Affairs Canada, IOD Parc, and the Foreign, Commonwealth and Development Office. Participants explored how AI technologies, when combined with robust knowledge management systems and high-quality evidence synthesis, can help decision makers understand what works, what does not, and how to design more effective policies and programmes.

This case study is relevant for monitoring, evaluation, international development, and humanitarian learning because it frames AI not as a stand-alone tool, but as part of a wider evidence ecosystem involving knowledge management, equity, standards, human oversight, collaboration, funding, and evidence uptake.

Case Background

The Wilton Park discussion took place at a time when the volume of high-quality evaluation evidence in foreign policy and global development has grown significantly. Yet evidence synthesis remains inconsistent, often isolated across organizations, and not sufficiently connected to decision-making.

Participants saw AI and knowledge management as a combined opportunity to improve the efficiency of synthesis, preserve evidence in times of uncertainty, and help development actors make better use of existing knowledge under constrained budgets.

The discussions emphasized that AI should not be treated as a replacement for evaluation expertise. Instead, AI should be embedded in robust knowledge systems that help evidence users find, understand, verify, and apply relevant findings.

The Core Problem

The central problem is not simply the production of evidence. The development and foreign policy sector already produces significant evaluation evidence. The harder challenge is making that evidence discoverable, synthesizable, inclusive, and usable for decision makers.

Participants highlighted a gap between evidence production and evidence uptake. AI can help close that gap, but only if evidence is well managed, machine-readable, quality assured, and connected to the priorities of evidence users.

Opportunities for AI in Evaluation and Evidence Synthesis

Knowledge Management as the Foundation

Participants emphasized that effective knowledge management is a prerequisite for meaningful AI use. AI tools depend on the quality of the underlying evidence systems, including repositories, metadata, taxonomies, machine readability, and organizational cultures of evidence sharing.

Recommended knowledge management actions included developing a blueprint for internal processes, principles, systems, and partnerships; creating an organization-wide knowledge agenda; and building a cross-organizational maturity model to define success and track progress.

Risks and Challenges

Participants repeatedly cautioned that AI is not a silver bullet. They identified risks related to transparency, quality, tool applicability, skills and capability, data security, bias, resources, funding, environmental impact, labour conditions, and the conceptual framing of AI.

Key concerns included black-box systems, hallucination, loss of complexity, weak replicability, privacy risks, intellectual property concerns, uneven access to AI skills, and the possibility that AI tools may reproduce or deepen existing evidence inequalities.

Using AI to Address Data Inequalities

The discussion highlighted that AI could help broaden the evaluative evidence base, but only if equity is built into tool design and data governance. Participants warned that models trained mainly on dominant geographies, English-language sources, or narrow methodological traditions may marginalize evidence from the global south and from qualitative, indigenous, informal, or community-generated knowledge.

Strategies discussed included deeper global north and south partnerships, use of diverse training data, improved non-English language capability, open-access publication, minimum standards for non-English language data, and active methods for ensuring that AI models can understand local and indigenous knowledge.

Principles and Standards

Participants identified the need for principles and standards to build trust in AI-supported evaluation and evidence synthesis. High-level principles included transparency, accountability, fairness, inclusivity, data protection, privacy, validity, reliability, agility, collaboration, and a human rights-focused approach.

They also discussed whether the sector needs broad guidelines or more formal standards. Guidelines were seen as flexible and adaptable, while standards could establish clearer boundaries and help shape AI tools to meet evaluation community needs.

AI and KM for Evidence Uptake

A key message from the Wilton Park discussion was that AI and knowledge management are not ends in themselves. Their purpose is to help evidence be used. Participants noted that AI tools can free resources for dissemination, support evidence brokers, generate timely insights, and help policymakers access digestible information on what works.

The discussions also emphasized the importance of demand-side engagement. Co-generating evidence with governments and policymakers, understanding end-user needs, building relationships, and aligning incentives remain central to evidence uptake.

Evaluation Framework for AI and KM in Evidence Synthesis

EvalCommunity Academy users can adapt the following framework when assessing AI and knowledge management initiatives in evidence synthesis.

FAQ

Conclusion

This case study shows that AI and knowledge management can help development and foreign policy actors make better use of existing evaluation evidence. The opportunity is significant: faster synthesis, broader evidence access, more inclusive knowledge systems, and more timely insights for decision makers.

However, the Wilton Park discussion also makes clear that AI is not a substitute for evaluation quality, human judgement, or institutional collaboration. Responsible use requires strong knowledge management, inclusive evidence systems, principles and standards, human-in-the-loop quality assurance, and sustained investment.