Billions of people worldwide are malnourished. Despite our best efforts, the cost of a healthy diet and food insecurity continue heading in the wrong direction. Without significant intervention, this crisis will persist—but we believe artificial intelligence (AI) represents a powerful new tool to help change that trajectory.

At GAIN, we’re no strangers to the challenges that limit our impact: identifying the right populations to target, making decisions with incomplete data, localising interventions across diverse contexts, understanding complex supply chains, and verifying compliance with fortification standards. These obstacles slow our progress toward a world where everyone can access healthy diets.

Meanwhile, AI adoption has surged from niche experimentation to mainstream deployment. Today, 78% of companies use AI in at least one business function—up from 55% just a year earlier. The development sector has seen transformative technologies before: mobile money and text messaging revolutionised how we deliver resources and information to remote populations. AI represents the next wave of innovation—one we must harness thoughtfully and strategically.

Why AI? Why Now?

At GAIN, we’ve been thinking critically over the past few months about what this means for our work. With funding from the Gates Foundation, we conducted a comprehensive assessment of AI use in food and nutrition programming through literature reviews, expert interviews, and case studies. What we found is both promising and sobering.

Leading organisations are already pioneering sophisticated applications. The World Food Programme’s HungerMap LIVE uses machine learning to forecast food security trends across 90+ countries. Digital Green’sAI-powered Farmer. Chat provides context-specific agricultural advice to farmers, achieving 24% income increases for participating farmers. Private-sector actors use AI to automate regulatory compliance checking and predict mycotoxin outbreaks.

Yet significant gaps remain. Nutrition data exists in silos with minimal interoperability. Ensuring the quality and safety of fortified foods—a critical part of improving micronutrient intake—still relies heavily on manual testing, slowing down monitoring and response efforts. We lack the personalised interventions at scale that commercial platforms routinely deliver. And perhaps most critically, most AI development occurs in high-income countries, risking solutions that widen rather than narrow nutrition disparities.

Our Approach: Three Strategic Pillars

We designed our newly launched AI strategy to respond to these challenges and leverage these opportunities. It focuses on three interconnected objectives—improving how we design, deliver, and scale nutrition programmes:

**Design: **AI can help us overcome programmatic challenges by enabling better data integration and analysis. Imagine using AI to identify micronutrient deficiency hotspots or discovering novel vehicles for food fortification through pattern recognition in consumption data.

Delivery: AI can streamline operations and personalise interventions. We’re exploring rapid quality assurance testing using computer vision to analyse fortification compliance in real-time, and developing chatbots that deliver tailored nutrition advice through platforms people already use, like WhatsApp.

**Scale: **AI can manage complexity as programmes expand, from automating the identification of high-potential areas for expansion to tracking advocacy impact through analysis of media and parliamentary records.

We’ll pursue these objectives through three approaches: innovation (creating entirely new models), reinvention (fundamentally transforming existing programmes), and optimisation (refining current approaches for greater efficiency).

Promising Opportunities Ahead

Several potential high-impact applications stand out. For example, AI-powered chatbots can make information, advice, and training more accessible across our programme areas—from regulatory compliance guidance for fortification businesses to nutrition advice for youth or company employees. We’re exploring predictive analytics for markets and supply chains to anticipate disruptions and identify food safety hotspots. And we’re considering how social listening can be used to understand consumer sentiment and trends in real-time at unprecedented scale and lower cost.

For our food fortification work, quality assurance represents an immediate priority. Testing currently takes days or weeks and is expensive, yet regulatory capacity is often limited. AI-powered diagnostics using computer vision and predictive analytics could transform compliance monitoring. In our work with nutritious food enterprises, we will seek opportunities to integrate AI training for small and medium businesses while developing tools to make regulatory data more accessible.

Leading Responsibly

We recognise both the risks and the potential of AI. To guide its responsible use, we have developed an AI framework that sets out the organisational and operational guardrails for deployment—defining how decisions are made, what standards apply, and how risks are managed. Complementing this, practical guidelines for staff translate these principles into day-to-day practice. The framework draws on UNESCO’s ethics guidelines and WHO’s health AI guidance. We’ll require transparency from AI providers, ensure human oversight of automated decisions, and prioritise solutions that work in low-resource settings. Our commitment is to build inclusive AI that narrows rather than widens nutrition disparities.

Critically, we’ll only use AI where it’s the right tool for the task and avoid custom solutions when off-the-shelf options suffice. We’ll remain cognisant of costs—financial, environmental, and social—and ensure the benefits justify them.

What Success Looks Like

Our objective is simple but ambitious: cultivating an organisational culture where AI is not a technological add-on but a strategic enabler of greater nutrition impact. Over the next year, we’ll develop metrics to track AI integration across our portfolio and assess whether these tools actually improve outcomes—from programme effectiveness to operational efficiency.

This transformation requires strong partnerships. We’re working with governments to unlock critical nutrition data, with technology providers to access cutting-edge capabilities, and with research institutions to ensure ethical implementation. To all these partners, we bring our deep expertise in nutrition programming, understanding of local food systems, and extensive in-country networks. The development sector stands at an inflection point. The foundational investments we make now in data infrastructure, capacity building, and responsible AI governance will determine whether AI becomes a transformative force for nutrition equity or another technology that primarily benefits the already advantaged.

We invite partners and practitioners to join us in developing and deploying transparent, equitable, and evidence-based AI tools that can reduce the burden of malnutrition among the most vulnerable. Together, we can turn AI’s promise into tangible progress toward a world free from malnutrition.

***This blog draws on ***GAIN’s new Strategy for Harnessing Artificial Intelligence in Programmes.

Download GAIN’s AI Strategy