Artificial intelligence and digitalization are reshaping how evidence is generated, analyzed, and used. Across the monitoring and evaluation (M&E) profession, practitioners encounter a growing volume of AI-related tools, guidance, training, and organizational policies. Some of this is useful. But the sheer volume and diversity of the landscape – spanning technologies from satellite imagery analysis to large language models, and audiences from individual practitioners to those responsible for strengthening national M&E systems – has made it difficult for professionals and institutions to determine what is relevant, reliable, and appropriate for their specific circumstances.
This paper offers a framework to help M&E and evaluation professionals and institutions navigate this complexity. It proposes a way of thinking – a set of complementary perspectives that help practitioners and institutions ask better questions about if, where, how, and under what conditions AI and digital tools may have a role in their work.
