Every policy decision starts with a question, yet today obtaining an answer can take weeks. Policymakers at ministries of agriculture and finance need to test critical scenarios, such as the impact on food security in Ethiopia if fertilizer prices increase by 20%, and receive evidence-based responses quickly. In practice, however, these models require technical experts to run, turning each analysis into a process that takes days or weeks. Because exploring multiple scenarios remains difficult, decisions are frequently delayed, reactive, and hard to justify with solid evidence.

The Solution

Policy AI is an AI-powered policy intelligence platform that makes complex food, land, and water decisions faster, clearer, and evidence-driven for governments and global partners. It connects data, models, and AI insights into one interactive platform, moving policy analysis from a slow, static, specialist-driven workflow to a dynamic, interactive one. When a policymaker asks a question in natural language, the AI interactively facilitates scenario design and automatically runs the relevant models within minutes, translating the output into clear policy insights that update instantly as the user adjusts the scenario.

The Emerging Policy AI Ecosystem

The Policy AI platform is evolving into a unified analytical ecosystem designed to inform decisions across local, national, and global scales. By combining active, field-tested tools with specialized conceptual models, this expanding suite bridges local economic impacts with global trade, livestock systems, and governance dynamics.

Existing Pilots: Currently piloted in Ethiopia, Kenya, Malawi, and Nigeria, RIAPA AI serves as a national economywide policy simulator within the platform. It integrates household surveys, World Bank poverty data, national statistics, and IFPRI RIAPA datasets to model policy impacts on local economies and food security.

Conceptual Stage: Complementing RIAPA AI, several modules are currently in the conceptual stage to expand the platform's analytical reach. Globe AI focuses on global trade system modeling using datasets from GTAP, FAOSTAT, the World Bank, UN Comtrade, and the IMF; Livestock AI models agricultural systems by drawing on ILRI livestock databases, FAOSTAT, national agriculture ministries, and WOAH; and Political Economy Diagnostics assesses governance and policy contexts through government reports, World Bank governance indicators, OECD datasets, and CGIAR policy research.

Event Objectives

This event will bring together global leaders, researchers, and partners to officially introduce Policy AI and chart its future evolution. it is a hybrid event jointly organized by the CGIAR Digital Transformation Accelerator and CGIAR Policy Innovations,
hosted by the International Affairs Office in the United Arab Emirates. 

Through interactive demonstrations and strategic collaboration, the event aims to achieve two core objectives:

1. Launch and showcase — Publicly launch Policy AI and demonstrate RIAPA.AI to global and UAE audiences,
capturing structured stakeholder feedback to shape the development roadmap.

2. Pitch to proposal — Surface new ideas from CGIAR researchers and partners and develop them into a funding-ready proposal to expand Policy AI.