The Current Column

Organisational learning

How AI can help development organisations learn

Roll, Michael / Lindsey Moore
The Current Column (2026)

Bonn: German Institute of Development and Sustainability (IDOS), The Current Column of 4 May 2026

Bonn, 04 May 2026. Under certain conditions, AI can help development organisations become learning organisations. Experiences from USAID shortly before the organisation’s closure are insightful.

Artificial intelligence (AI) presents numerous challenges and opportunities for development cooperation. This column focuses on how AI can help development organisations become learning organisations. Whether it does so, however, depends on how AI-supported analysis and learning are embedded in organisations. Paradoxically, the US development agency USAID, which was closed in July 2025, is an interesting example of how and what can be learned for development cooperation with AI – and what its limits may be.

USAID’s aborted AI learning initiative

After many years as a USAID staff member, the second author of this column left the organisation in 2021 to study how development organisations can learn from their own experiences more systematically. This work resulted in the Development Evidence Large Language Model (DELLM), an AI system developed specifically for development cooperation. USAID became one of the first clients. To train DELLM, experts first manually coded countless text segments from the approximately 100,000 USAID evaluation reports, spanning more than 60 years. Increasingly automated but guided and supervised by experts, the model learned to ‘read’ these reports and distinguish between sectors, cooperation approaches, outcomes, positive and negative lessons and other aspects.

Once training was complete, USAID staff had the organisation’s vast institutional knowledge at their fingertips. DELLM could be used for operational planning as well as for organisational learning. In terms of operational planning, staff were now able to use the model to access subject-specific and regional insights from over six decades within minutes. Previously, each of these queries would have required months of research and incurred high costs. Due to the sudden closure of USAID in 2025, however, the model could not be further used for organisational learning. As long as evaluation reports were still internally accessible, DELLM was instead used to analyse and preserve insights from the organisation’s past. The results from this search for the most important overall success factors of USAID interventions are insightful in themselves. But they also suggest why translating AI-supported analysis into organisation learning is not straightforward.

Success factors for development cooperation

The search for the most important overarching success factors of USAID development interventions yielded the following insights: (1) Decision-making should take place as closely to the ground as possible in order to receive rapid implementation feedback and be able to adjust course. (2) Reforms should be practical, i.e. build on existing systems and seek to adapt them. (3) Effective solutions should be rapidly embedded locally so that they continue to work even after the end of funding. (4) Reform processes should be led by local actors rather than merely involving them in a consultative capacity. Finally, (5) collaboration with the middle tier is crucial – that is, with partners who are responsible for the practical implementation of measures. What do these five lessons tell us about the potential of AI-supported organisational learning for development cooperation and about its limits?

First, these insights are not new to development effectiveness experts. At the same time, approaches to development cooperation that are based on them are still rarely used. One example are approaches to adaptive development cooperation. Despite having been successfully implemented in some cases, including in German development cooperation, they remain exceptions. At the political level, systematic organisational learning is hampered by development policy’s focus on furthering national interests and by accountability concerns. But it is also held back by the structures and incentives within development ministries and agencies. Organisational learning is unlikely to happen unless evidence-based work is valued and encouraged, generalist career tracks are complemented by specialist ones, objectives and shortcomings are openly discussed and the organisation’s culture supports all that. 

Embedding AI-supported learning for development

AI has the potential to provide access to an organisation’s institutional knowledge in a comprehensive, intuitive and needs-based manner. This is particularly useful for operational planning. But in terms of organisational learning and reform, the specific advantages of AI and the necessary enabling conditions should be kept in mind. First, like for all AI-tools, the quality of their output is only as good as the quality of the underlying data and their training. Second, to translate insights into organisational learning, they have to be embedded in an open, self-critical and evidence-based organisational culture. Third, while AI-supported insights may not be entirely new, together with other sources of knowledge such as development research, they can serve to review and complement existing knowledge. In a conducive organisational environment, AI can therefore be an important tool among others to help transform development organisations into learning organisations and increase their effectiveness and efficiency.

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Dr Michael Roll is a sociologist and senior researcher in the research department “Transformation of Political (Dis-)Order” at the German Institute of Development and Sustainability (IDOS) in Bonn.

Lindsey Moore is the CEO and Founder of DevelopMetrics and adjunct professor of AI and Policy at Georgetown University. Her work focuses on responsible artificial intelligence, evidence synthesis, and knowledge management for international development.