Introduction
In the rapidly evolving field of engineering in AI, it is easy to get caught up in the technical breakthroughs of large language models and frontier systems. However, a much broader conversation is happening regarding how these AI development tools are deployed in the real world—specifically in the realm of international development.
How does artificial intelligence reshape institutions, alleviate (or exacerbate) poverty, and transform the global south?
Based on a compelling analysis by the Development Intelligence Lab, we can examine the intersection of AI and development cooperation through three distinct lenses: efficiency, effectiveness, and long-term development trajectories. Here is a closer look at how AI tools are shaping the practice of development cooperation.
1. AI for Efficiency: Accelerating Development Practice
For most practitioners, AI first arrives as a productivity tool. In resource-constrained environments, the ability to automate routine tasks is highly appealing. AI chatbots and summarization tools are being used to draft donor reports, generate monitoring, evaluation, and learning (MEL) frameworks, and act as digital note-takers during extensive coordination meetings.
Because these tools seamlessly integrate into existing workflows, they offer immediate, visible gains. However, equating efficiency with genuine progress carries risks:
- Entrenching Poor Processes: Automating a flawed or overly bureaucratic system just makes a bad process run faster; it does not fix the underlying incentives.
- Uneven Distribution: Data shows that the benefits of AI efficiency are heavily skewed toward higher-income, well-educated groups. If AI only saves time for privileged international organizations, it risks reinforcing existing inequalities.
Efficiency is only valuable if the time saved is reinvested into tackling complex, on-the-ground challenges that organizations usually lack the bandwidth to address.
2. AI for Effectiveness: Improving Project Impact
While efficiency deals with how development cooperation operates, effectiveness looks at what it achieves. AI is making its mark here in two primary ways: by being embedded into existing programs (like AI-powered health diagnostics or agricultural forecasting) and by becoming a core focus of development programs itself (such as funding digital public infrastructure).
Research indicates that AI does not magically fix broken systems. Instead, it makes capable, disciplined systems significantly more effective. However, the integration of AI introduces a fascinating political tension. If a local national ministry can draft policy briefs using well-engineered AI prompts, the traditional reliance on highly paid foreign consultants becomes difficult to justify.
The “Sovereign AI” Push There is a significant catch: most frontier AI models are trained predominantly on Western, non-local data. This introduces a subtle re-importation of foreign biases, cultural assumptions, and political norms into local decision-making. To combat this, a growing movement for “Sovereign AI” has emerged. Developing nations are increasingly pushing to build or fine-tune their own models based on local languages and institutional contexts, ensuring that the productivity gains of AI do not result in a new technological dependency loop.
3. AI as a Force Shaping Trajectories: The Long-Term View
The most critical lens looks beyond individual tools and programs to ask a macroeconomic question: How is AI shaping the long-term trajectories of global poverty, prosperity, and power?
AI capabilities—including compute, talent, and data—are highly concentrated in a few global tech hubs. Consequently, the economic and productivity gains are disproportionately benefiting nations already at the technological frontier. According to IMF analyses, while 50% of jobs in advanced economies are exposed to AI integration, only 25% are exposed in emerging economies, signaling a widening global productivity gap.
Furthermore, AI’s role in governance and stability cannot be ignored. While democratic states grapple with slow regulatory cycles to ensure accountability, authoritarian regimes can adopt AI rapidly, often leveraging it for surveillance rather than human development.
Conclusion
The international development community cannot afford to be passive consumers of AI development tools. While it is natural to start by adopting AI for basic efficiency gains, stalling at that stage would be a mistake. By the time the long-term trajectory issues—like infrastructure dependencies and deep technological inequality—become undeniable, the foundational choices will have already been made.
Development actors must build AI fluency, engage directly with the technology companies building these frontier models, and act as aggressive agenda-setters. Only by actively participating in AI’s global rollout can we ensure that the engineering of AI serves human development and equitable prosperity, rather than simply widening the gap.
FAQs
1. What does "AI for efficiency" mean in international development?
AI for efficiency refers to the use of AI tools (like chatbots and note-takers) to speed up administrative tasks, such as drafting donor reports, creating evaluation frameworks, and analyzing data. The goal is to make the daily operations of development cooperation faster and cheaper.
2. How does AI improve the effectiveness of development programs?
AI improves effectiveness by being embedded into specific sector programs, such as healthcare or agriculture, to enhance service delivery and decision-making. It is most effective when integrated into systems that are already capable and governed by strong policy and institutional coordination.
3. What is "Sovereign AI" and why is it important?
Sovereign AI refers to countries or regions building and adapting their own AI models trained on local data, languages, and cultural norms. It is crucial because most global frontier models are trained on Western data, which can introduce foreign biases and create a new form of technological dependency for developing nations.
4. How can the development community influence the future of AI?
The development community can influence AI by building technical fluency, engaging directly with major technology firms, and advocating for development values (such as equity, local capability, and human rights) in the rooms where global AI standards and infrastructures are being designed.
