12 NGOs Using AI Systems in Real-World Operations (2026)

Introduction

Across the humanitarian and development sector, Artificial Intelligence(AI) has moved beyond experimentation and into everyday operations. Large NGOs today manage complex, multi-country programmes under constant pressure, coordinating supply chains, responding to displacement and climate shocks, and delivering services at scale in environments where uncertainty is the norm rather than the exception.

In this context, AI is no longer a standalone innovation initiative. It is increasingly embedded within forecasting systems, data platforms, and decision-support tools that shape how organisations allocate resources, prioritise interventions, and prepare for crises before they escalate. Traditional manual processes and static reporting structures struggle to keep pace with the speed, scale, and coordination demands these organisations face.

In 2026, the NGOs that stand out are those running live, data-driven systems, from predictive analytics for food insecurity and population movement to satellite-based assessments and operational planning tools. For these organisations, AI functions as operational infrastructure, embedded within how work gets done at scale.

Why AI-Enabled Systems Matter in NGO Operations

AI, machine learning, and automation are increasingly central to how large NGOs operate in real-world conditions. Predictive models support early warning for food insecurity, displacement, disease outbreaks, and disaster impacts, enabling organisations to anticipate demand rather than react once crises escalate.

AI-enabled analytics inform prioritisation across logistics, response planning, and resource allocation, helping teams make decisions under pressure with incomplete and rapidly changing information. Machine learning also plays a critical role in integrating fragmented data, combining satellite imagery, administrative records, field data, and third-party sources into usable operational insight.

At the same time, data platforms and automation enable coordination across regions, partners, and agencies without proportional increases in manual effort. AI-supported systems reduce the gap between data collection, analysis, and action, which is critical in time-sensitive contexts. Rather than replacing human expertise, many of these deployments reflect a human-centred AI approach, where systems extend organisational capacity and support teams operating under sustained pressure.

Criteria for Inclusion

The organisations featured in this list were selected based on practical, execution-focused criteria aligned with large-scale NGO operations:

• Significant operational scope across regions, programmes, and partners
  
• Active use of AI, machine learning, or advanced data systems within operational workflows
  
• Deployment in live environments rather than conceptual tools or short-term pilots
  
• Clear relevance to decision-making, forecasting, coordination, or resource allocation
  
• Organisational complexity where data-driven systems materially affect outcomes

This list is not ranked. It reflects a cross-section of large NGOs and multilateral organisations where AI-enabled systems are already influencing how work gets done.

The NGOs Operating AI-Enabled Systems at Scale in 2026

The twelve organisations below represent some of the most operationally complex NGOs in the world. From global food logistics and displacement response to agricultural systems and humanitarian coordination, each organisation applies AI, machine learning, or advanced data systems to support decision-making in real-world environments.

Together, they illustrate how AI has become part of the operational backbone of large NGOs, embedded in systems that must function reliably under uncertainty, scale, and constant change.

1. World Food Programme (WFP)

The World Food Programme operates one of the largest humanitarian logistics and food assistance systems globally, delivering food and cash-based support across conflict zones, climate-affected regions, and fragile economies. Its operations span procurement, warehousing, transportation, and last-mile distribution under severe time and resource constraints.

WFP has developed and deployed AI-enabled and machine-learning-based systems for food insecurity forecasting, supply-chain optimisation, and early warning. These systems support decisions around stock positioning, response planning, and crisis anticipation, embedding data-driven intelligence directly into large-scale humanitarian operations.

Established: 1961
Headquarters: Rome, Italy

2. UNHCR (United Nations High Commissioner for Refugees)

UNHCR manages protection and assistance operations for displaced populations worldwide, including refugees, asylum seekers, and internally displaced persons. Its responsibilities include registration, shelter planning, service delivery, and coordination with governments and humanitarian partners.

Through predictive analytics initiatives focused on displacement forecasting, UNHCR applies machine-learning models to anticipate population movements and service demand. These systems support operational preparedness and capacity planning, particularly during large-scale displacement events.

Established: 1950
Headquarters: Geneva, Switzerland

3. UNICEF

UNICEF operates large, multi-country programmes focused on child health, nutrition, education, and emergency response, working closely with national systems and delivery partners.

To support these operations, UNICEF relies on large-scale data platforms and advanced analytics for programme targeting, supply planning, and risk identification. In selected areas, machine-learning techniques are applied to map vulnerability and inform planning decisions, complementing broader data-driven operational systems.

Established: 1946
Headquarters: New York, USA

4. International Committee of the Red Cross (ICRC)

The International Committee of the Red Cross operates in active conflict zones, providing humanitarian assistance, protection services, and support for affected populations under extreme access and security constraints.

ICRC uses advanced data analysis and satellite-based assessment tools to support damage assessment, situational awareness, and response prioritisation. These data systems help field teams make faster, more informed decisions in volatile environments where traditional information channels are limited.

Established: 1863
Headquarters: Geneva, Switzerland

5. International Federation of Red Cross and Red Crescent Societies (IFRC)

IFRC coordinates one of the world’s largest humanitarian networks, supporting disaster response, public health programmes, and resilience-building efforts through national Red Cross and Red Crescent societies.

The organisation operates global data platforms and analytics systems that support disaster forecasting, emergency response coordination, and operational planning. These systems help prioritise interventions and allocate resources across large-scale, multi-country humanitarian operations.

Established: 1919
Headquarters: Geneva, Switzerland

6. International Organization for Migration (IOM)

IOM manages complex migration operations worldwide, including displacement response, migration data coordination, and support to governments on mobility-related challenges.

IOM applies data science and predictive analytics to analyse migration flows, displacement risks, and mobility patterns. These systems support operational planning, emergency preparedness, and policy design in regions experiencing large-scale population movement.

Established: 1951
Headquarters: Geneva, Switzerland

7. Food and Agriculture Organization of the United Nations (FAO)

FAO works with governments and partners to support global food systems, agricultural productivity, and climate resilience across rural and agrarian economies.

FAO deploys data-driven platforms and machine-learning-enabled tools for crop monitoring, early warning, and food security analysis. These systems support decisions related to agricultural planning, climate risk management, and food system resilience at national and regional levels.

Established: 1945
Headquarters: Rome, Italy

8. Mercy Corps

Mercy Corps operates large-scale humanitarian and development programmes across fragile and conflict-affected regions, focusing on resilience, livelihoods, climate adaptation, and emergency response. The organisation works closely with local partners, governments, and communities to deliver programmes in environments shaped by volatility, resource constraints, and complex social dynamics.

To support these operations, Mercy Corps applies advanced data systems, analytics, and machine-learning-supported tools for crisis analysis, programme targeting, and adaptive management. Through initiatives such as predictive risk modelling, market and climate analysis, and data-driven monitoring systems, the organisation integrates AI-enabled insights into decision-making processes that inform where and how interventions are deployed at scale.

Established: 1979
Headquarters: Portland, Oregon, USA

9. BRAC

BRAC operates one of the largest NGO-led development programmes globally, spanning health, education, agriculture, financial inclusion, and social protection through extensive field networks.

BRAC uses data platforms and selective machine-learning applications to improve programme targeting, service delivery, and operational efficiency. These systems support decision-making across large beneficiary populations and diverse programme areas.

Established: 1972
Headquarters: Dhaka, Bangladesh

10. Save the Children

Save the Children delivers large-scale programmes in child health, nutrition, education, and protection across multiple regions and emergency contexts.

The organisation relies on advanced data systems and analytics to assess risk, monitor programme performance, and inform targeting decisions. These tools support operational planning and prioritisation across complex, resource-constrained environments.

Established: 1919
Headquarters: London, United Kingdom

11. GiveDirectly

GiveDirectly operates large-scale cash transfer programmes that deliver direct financial assistance to households living in extreme poverty, often across national and regional scales.

The organisation uses machine-learning models trained on satellite imagery and proxy indicators to identify eligible households and prioritise targeting. These models directly inform operational decisions related to beneficiary selection and programme rollout.

Established: 2009
Headquarters: New York, USA

12. Digital Green

Digital Green works with governments and development partners to support agricultural extension and farmer outreach at scale, particularly in low-income and rural regions.

The organisation deploys data-driven and machine-learning-enabled advisory systems to personalise recommendations, prioritise farmer engagement, and optimise outreach operations. These systems are embedded within large agricultural programmes rather than standalone pilots.

Established: 2008
Headquarters: New Delhi, India

Conclusion

In 2026, the role of AI in the NGO sector is no longer defined by aspiration or experimentation. The organisations highlighted in this list demonstrate how data-driven systems are already embedded within large-scale operations, supporting forecasting, coordination, targeting, and response across complex humanitarian and development contexts.

What distinguishes these NGOs is not the novelty of their technology, but the way AI-enabled systems are integrated into everyday decision-making. These are not isolated tools or pilot projects; they are operational systems running continuously in live environments.

As global instability, climate pressure, and resource constraints continue to intensify, NGOs that treat AI as operational infrastructure, rather than a side initiative, will be better positioned to adapt and respond effectively. For organisations exploring how applied AI can support complex, real-world operations, Omdena works with partners across sectors to design and deploy AI systems that function under the same constraints these NGOs navigate every day.