📋 Tender Overview
The United Nations World Food Programme (WFP) has launched a new strategic partnership with the Directorate for Development Cooperation and Humanitarian Affairs of Luxembourg’s Ministry of Foreign Affairs, the Luxembourg Institute of Science and Technology (LIST) and the European Organization for Nuclear Research (CERN).
This strategic partnership aims to enhance WFP’s ability to make data-driven decisions in emergency responses, leading to more efficient food distribution, faster damage assessments and improved monitoring of cash-based assistance programmes. By combining WFP’s experience in humanitarian operations with CERN’s AI expertise and LIST’s Earth Observation capabilities, the partnership seeks to develop practical solutions to address food insecurity. These innovations will be used to streamline emergency response activities and contribute to building a foundation for a longer-term digital infrastructure capable of predictive analysis for humanitarian operations.
“This collaboration allows us to leverage responsible AI to transform our programmes, empowering our colleagues on the ground with cutting-edge tools to make data-driven decisions in real-time, especially when crises strike. Thanks to Luxembourg’s commitment to innovation, CERN’s technological expertise, and LIST’s Earth Observation capabilities, we’re setting a new standard for humanitarian response to save more lives and achieve food security for all,” said Rania Dagash-Kamara, Assistant Executive Director for Partnerships and Innovation at WFP.
CERN has a long tradition of excellence and over the years has developed strong AI capabilities and competencies to enable its cutting-edge scientific research programme,” remarked Enrica Porcari, Head of the IT Department at CERN. “This partnership confirms once more CERN’s role in fostering collaborations that address common challenges and contribute to transfer knowledge from science to society to create a positive impact for future generations.”
The partnership will focus on three main areas. First, LIST will adapt WFP’s SKAI platform – a tool developed with Google Research for rapid damage assessment – to expand its capabilities to assess and predict crop and infrastructure damage from climate disasters. Second, CERN will apply its technology to help WFP better monitor its cash-based assistance programmes by identifying irregularities in financial transactions and registration data, ensuring that funds are used effectively. Lastly, CERN will create a secure platform that enables multiple organizations to safely share and process sensitive data, supporting a wide range of humanitarian efforts.
“LIST is honoured to take part in this strategic collaboration with the World Food Programme and CERN in AI for Earth Observation (EO) and crop modelling to support humanitarian aid agencies”, said Lucien Hoffmann, Science Director at LIST. "LIST brings its recognized expertise in integrating Earth observation data with in-situ measured data, land surface models and satellite and terrestrial communication services in order to provide evidence-based decision support tools in near real time for one of humanity’s greatest challenges related to food security."
Xavier Bettel, Vice Prime Minister and Minister for Development Cooperation and Humanitarian Affairs, Ministry of Foreign and European Affairs, added, “In the face of multiple, intersecting emergencies and disasters that are plunging the world into a deepening global hunger crisis, rethinking our approaches to dealing with humanitarian crises has become a necessity. I am proud that Luxembourg's expertise, knowledge and leadership in promoting AI can make a significant contribution to this unique partnership, which aims to foster cutting-edge technologies to accelerate progress toward a world without hunger and enhance humanitarian action worldwide.”
The agreements with CERN and LIST, signed in 2023 and 2024 respectively, reflect WFP’s commitment to harness breakthrough innovations that will transform how humanitarian organizations respond to crises.
