Recent decades have seen a significant uptick in the amount of high-quality evaluation evidence in the foreign policy and global development sphere. However, current efforts to synthesise this evidence are inconsistent and often take place in isolation, while the use of evidence to inform decision making remains low. AI technologies underpinned by effective KM approaches have the potential to facilitate a step-change by improving efficiency, enhancing the quality of evidence synthesis, and ensuring that decision makers can find, understand and use evidence in an effective and timely manner.
“How do we re-think development?”
Recognising the moment. In the context of reduced global development budgets, the use of AI and KM in evaluation and evidence synthesis – together with increasing cross-organisational co-operation – was viewed as essential to maximising the impact and value for money of interventions. Participants also recognised the critical function of AI and KM in guarding against evidence loss in times of geopolitical uncertainty.
Improving efficiency. Incorporating AI and KM into evaluation and evidence synthesis has the potential to lead to large-scale efficiency and productivity gains. Utilised effectively, complimentary AI tools and robust KM practices can bring together vast amounts of information quickly and cheaply, allowing researchers to plug gaps in the existing evidence synthesis landscape, answer emerging research questions responsively, and maximise the prospect of evidence being used by decision makers.
Doing things differently. AI was situated as having the capacity to engender a shift in the way development is approached. New technologies offer the opportunity to leverage knowledge of what works to alleviate poverty and improve lives at both a global and a local level, placing data and insights at the heart of global development. At the same time, AI tools have the power to put agency into the hands of local communities – particularly in the global south – by facilitating the inclusion of a broader range of evidence in synthesis products to ensure local relevance, widen access to evidence, and enable local policymakers to make evidence-informed decisions. Developing the agenda. Although AI tools in combination with effective KM practices hold significant potential, their use in this field remains in its early stages. Participants highlighted the value of bringing together experts from evaluation, KM, and AI alongside representatives from a range of bilateral and multilateral organisations to discuss the possibilities and challenges of incorporating AI and KM into evidence synthesis. They also underscored the importance of ongoing collaboration across the sector – through networks including the Evidence Synthesis Infrastructure Collaborative, the Multi-Donor Learning Partnership, and the Global SDG Synthesis Coalition – to ensure the fair and effective application of AI as the technology continues to develop.