CGIAR Accelerator for Digital Transformation

Digital Transformation will co-create inclusive solutions leveraging advancements in AI, machine learning, modeling and big data analytics – driving improved decision-making, policies, investments and broader adoption of digital tools for impactful outcomes towards food-land-water systems transformation. We aim to be a thought leader in digital and data science research across food, land, water, and climate systems, accelerating the delivery of research and digital services responsibly using data and tools such as AI and improving decision-making based on evidence and insights into complex systems.

Our work will support impacting 20+ partners with inclusive data driven management of food-land-water systems, encouraging AI tools for 60% of partners to accelerate innovation and influencing $10 m+ investment in digital solutions development. The challenges that we will address are;

• Interconnected and complex issues such as climate change, persistent inequalities and consumption patterns that demand strategic digital innovation
• Sparse data in the Global South which limits local adaptation of digital solutions
• Vulnerable populations which need tailored, inclusive digital solutions addressing poverty and inequality
• Collaboration and equity-focused approaches ensure inclusive, ethical digital innovation

Activity 5. Operation Research on AI-Accelerated Agricultural R&D

IWMI is leading one of five activities within a broader Google-funded initiative to establish an AI-powered agricultural research ecosystem. The overall project aims to accelerate CGIAR’s ability to develop and deploy innovative, data-driven approaches to enhance global food security amid the climate crisis. It focuses on creating AI-ready datasets, advanced machine learning models, federated AI development platforms, and capacity-building efforts.

IWMI’s specific activity is focused on assessing the actual demand for AI applications across CGIAR’s research ecosystem. By engaging with Science Programs, accelerators, and research teams, this work will inform the development of AI methodologies, governance frameworks, and training programs that support scientific discovery in food, land, and water systems.