Rethinking Qualitative Evidence in the Age of AI: Decentralizing Decision-Making in MEL

Organizations collect vast amounts of qualitative data from the field; yet much of it is used only for storytelling rather than decision making, often remaining limited to program officers and managers. As teams experiment with AI tools to analyze transcripts and field notes, unstructured data often produces weak or unreliable insights. Using a MEL case study, this session demonstrates how structuring qualitative evidence enables responsible AI-supported analysis while preserving context and rigor. It also explores when AI strengthens evaluation, when human judgement should take precedence, and how to make evidence visible across teams for decentralized learning and decision-making.