Bridging the Evidence-Policy Gap with AI: Lessons from Benin and Francophone Africa

In many francophone African countries, high-quality evaluations are produced but remain underused in policy decisions, especially in sectors such as education, youth employment, agriculture, and climate resilience. At the same time, AI tools are rapidly entering administrations, parliaments, and NGOs, often without clear frameworks for responsible, evidence informed use. This session will explore how AI can help bridge or unintentionally widen the gap between evaluation evidence and public policy.
Drawing on recent experiences from Benin and the wider region, we will showcase practical use cases where AI supports evidence synthesis (rapid reviews, mapping of existing evaluations), policy friendly communication (briefs, visualizations, scenario summaries), and structured dialogue between researchers, policymakers, and civil society. We will also discuss risks for trust : over reliance on “black box” tools, bias in training data, and the temptation to use AI generated narratives without sufficient methodological scrutiny. The session targets three audiences : researchers seeking to integrate AI into rigorous evaluation workflows ; public officials aiming to strengthen evidence informed decision making; and NGOs working to translate evaluation findings into effective advocacy. Together, we will identify practical principles and safeguards for using AI to turn evaluations into credible, actionable inputs for policy, rather than just faster text.