AI and Global Food Security: A Focus on Food Systems

In July 2026, the CSIS Global Food and Water Security Program convened the fourth in a series of roundtable discussions to better understand the emerging benefits—and potential risks—of AI applications for global food security. The first three roundtables focused on AI applications for precision agriculture, food security early warning systems, and crop breeding, respectively. The July convening, which addressed AI-enabled applications for food systems broadly, brought together representatives from a range of companies, research institutes, nonprofit organizations, international organizations, and U.S. government agencies. The following issues were explored, illuminating the state of AI integration across food systems today and providing an outlook on its potential impacts in the future.

Food systems encompass the web of people, systems, and organizations spanning operations that start before the farm and extend beyond the plate. Elements of food systems include farmers; producers of inputs (such as fertilizers, pesticides, and herbicides) and technologies (such as irrigation, mechanization, and field sensors) used by farmers; those who process and distribute food; consumers; and those who manage food and related waste products. Today, food systems around the world face increasingly frequent and complex challenges that not only hinder the realization of global food security but threaten the economic stability of billions whose livelihoods depend on food systems. In July 2026, the Global Food Price Index reached its highest level in three and a half years, driven by the convergence of economic disruptions related to the Iran war and Strait of Hormuz closure as well as escalating conflict in the Black Sea. At the same time, the ongoing El Niño, now predicted to be the strongest on record, threatens to increase rates of acute hunger in many regions.

While food systems are affected by these shocks and stresses, they also contribute to shocks and stresses; for example, food systems account for a significant proportion of global freshwater consumption and about one-third of all human-generated greenhouse gas emissions, among other environmental impacts. The challenge for policymakers is to provide enough healthy, affordable food for a growing global population while reducing the impacts of food systems and building food-system resilience to shocks and stresses. As such, technological advances that help any facet of the food system become more efficient, sustainable, and resilient confer important benefits for the security of the entire food supply chain. To this end, advances in AI-enabled technologies offer a broad scope of new opportunities that can improve today’s food systems operations.

AI Opportunities for Global Food Systems

AI is already being applied across food systems at local, regional, national, and global levels. While the individual AI models and tools being developed are unique, many of them share a common goal of turning complex signals into better decisionmaking for food system actors. This can be achieved through a combination of procedures that help to optimize operations, generate novel inference, predict outcomes, and create more accessible information platforms.

AI supports transformative innovations for the earliest stages of food systems. Before fields are sown, AI-enabled tools can accelerate research and development in crop science by increasing access to information systems and improving the biological tools used by researchers to produce improved crop varieties. AI is also informing farmers’ decisions when planting begins. As previous CSIS analysis described, AI-assisted market forecasting tools and agricultural advisory tools can help farmers manage risks throughout a growing season, anticipate crop prices, and plan what crops to grow and when to sell them. Once crops are in the ground, AI-enabled precision agriculture tools then give farmers real-time, field-level guidance through satellite imagery, sensor data, and machine learning to inform irrigation, fertilizer applications, and pest management decisions.

Beyond the field, technology developers are embedding AI in the logistics that move food from farm to market. Emerging applications can forecast agricultural commodity prices and predict supply and demand imbalances, giving traders, policymakers, and food companies earlier warning of potential shortages or gluts. AI is also supporting innovations in food processing, where computer vision and machine learning tools are used to sort and grade produce, optimize processing lines, detect contamination, and improve food safety monitoring, thereby helping reduce spoilage and catch defects earlier in the production chain. Food scientists are also bolstering the discovery and development of novel, sustainable foods—such as alternative proteins—through the increased integration of AI tools.

At the retail level, supermarket owners are using AI-driven demand forecasting and inventory management systems to better match orders to consumer demand, reducing both overordering and understocking across complex food supply chains. These same demand forecasting, logistics, and inventory tools can also help reduce food waste by reducing overordering of food and deploying AI-supported systems to inform dynamic discounting programs that mark down food nearing its expiration date, incentivizing purchase before it is wasted. Once food and its packing materials are discarded, AI can play a role in sustainable processing and reuse. By leveraging AI applications for material science, the production of novel bio-based materials can help diminish the environmental impact of food waste.

In addition to its applications for commercial supply chains, AI is helping to improve the way that humanitarian organizations deliver food assistance. The World Food Programme (WFP) SCOUT tool, utilized across WFP operations in Africa, improves the timing of purchases and the selection of storage locations. Over 18 months of activity, it has saved approximately $6.2 million, which WFP estimates is “enough to provide a month of life-saving food assistance for 300,000 people,” and it is projected to generate up to $25 million in annual savings. The joint WFP-REACH initiative AF-PULSE platform uses geospatial AI techniques to track real-time risks to food supply chains in Afghanistan and inform alternative routes for aid workers. In the United States, the U.S. Department of Agriculture has approved AI integration in the eligibility and benefits administration of the SNAP program, while states like Maryland have secured grants to improve AI’s use for reducing administrative barriers and improving access to SNAP benefits. U.S. food banks, too, are beginning to adopt tools for demand forecasting, logistics, and donor engagement to stretch limited resources further.

Challenges and Solutions

Given the complex nature of food systems and the regulatory environments that govern them, policy that enables effective AI solutions should aim to balance the needs of local, national, and global actors. Managing trade-offs between data access, information safety, user trust, and the speed of innovation is a core tension that policymakers and technology developers must continuously reconcile as AI applications proliferate across agricultural production, food processing, logistics, markets, and humanitarian response.

Scaling Proven Applications

AI-enabled tools are often built to address a single, well-defined use case within a particular context. Designing applications that address these individual needs effectively while benefitting the broader food systems in which they function is a challenge, complicated by the fact that an effective AI-enabled tool is often highly localized, making it difficult to transpose its application to different contexts for similar use cases. As AI is increasingly applied for individual use cases, it becomes more important to ensure that the way AI is applied within a food system meets standards for data accuracy, availability, and safety more broadly.

A related challenge lies in moving proven applications beyond the single-organization pilot phase and integrating them into the institutions, infrastructure, and workflows that govern food systems. Scaling any solution within a complex system is not a simple matter of deploying the same AI-enabled tools or models in other locations, but adapting them to local environmental, economic, demographic, technological, and institutional conditions while maintaining sufficient consistency to enable interoperability and learning across different contexts. This challenge is particularly acute in humanitarian and food-insecure contexts where organizations may have limited resources to maintain AI-enabled solutions after an initial grant or project ends.

Effective adoption requires financing for data collection and maintenance, technical and local staffing, computing infrastructure, training, and iterative model improvement. Conventional short-term project funding models are often ill-suited to these recurring costs or the need for longer-term planning. Inclusive financing models will need to account for the longer time horizons needed to build trusted data systems and institutional capacity, especially for applications where there is little immediate commercial return, to offset the costs of developing and maintaining these capabilities.

Facilitating Data Access

Meeting standards for data accuracy, availability, and safety requires validated, interoperable data, yet data sharing at this scale raises its own set of challenges. Stakeholders across food systems, including model developers, producers, companies, and governments, hold legitimate concerns over data privacy, intellectual property, commercial confidentiality, and strategic advantage. For instance, farmers may be reluctant to share operational data that could be used against their commercial interests, while countries may be cautious about sharing agricultural data that reveals vulnerabilities in national food security or advantages better-resourced foreign firms and institutions. These concerns are not simple technical barriers, but core design constraints that must be incorporated into data governance frameworks. Effective data-sharing agreements will therefore require clear standards around ownership, access, permitted uses, and attribution, as well as the incentives that make participation worthwhile for those contributing data. 

Developing Trust

Finally, trust is a critical prerequisite for effective AI adoption, and one that must be deliberately cultivated. Foundational to this notion is the need to maintain a “human in the loop” approach for AI-assisted operations and decisionmaking, reinforcing the fact that AI should be used to enhance human capacity, not replace it. Producers and institutions are more likely to share data and integrate AI into decisionmaking processes when they have confidence in the organizations and governance arrangements involved. However, organizations do not always face immediate incentives to invest in building trust and developing relationships, as the benefits are diffuse and difficult to capture. Policymakers can address this gap by creating clear incentives for transparency, validation, and responsible deployment. The objective should not be to create a single global model for AI applications across each stage of the food system, but to establish the conditions under which different actors have the capacity and incentive to develop systems that stakeholders can trust.

Zane Swanson is deputy director of the Global Food and Water Security Program at the Center for Strategic and International Studies (CSIS) in Washington, D.C. Emma Curtis is a research associate in the Global Food and Water Program at CSIS. Caitlin Wesh is director of the Global Food and Water Program at CSIS.