Why is artificial intelligence important for developing countries?

Author : Pavan Kumar | Published On : 25 Apr 2026

Most conversations about AI happen in boardrooms in San Francisco, London, or Singapore. That is a problem. Because the places where AI could matter most where it could bridge the largest gaps in healthcare, education, agriculture, and governance are rarely at the center of those conversations. If you are a policymaker, entrepreneur, or development professional in a developing country, you are probably watching from the outside and wondering: 

It is. And by the end of this piece, you will understand exactly where the importance of artificial intelligence is greatest, what it can realistically do in low-resource contexts, and where the genuine risks lie.

Why Do I Think About This Differently Than Most?

I have spent years working in AI and data science, building systems that help organizations make better decisions with imperfect data. What I have noticed is that the problems AI solves best are not the ones technology-rich countries face. They are the ones developing countries face every day.

Incomplete records. Scarce specialists. Overloaded public systems. Decisions made on gut feeling because no one has the data infrastructure to do otherwise.

AI, at its core, is pattern recognition at scale. And those patterns exist whether you are in Nairobi, Dhaka, or São Paulo. The question is not whether AI works there. The question is how to deploy it thoughtfully.

Step 1: Understand What AI Actually Does in the Real World

• Machine learning: Identifies patterns in historical data and uses them to predict future outcomes. A bank in Ghana can use this to assess creditworthiness without requiring physical documentation.

• Natural language processing: allows machines to understand and generate human language. A health chatbot in rural India can answer questions in Hindi, Tamil, or Bengali  24 hours a day, at virtually zero marginal cost.

• Computer vision: Allows machines to interpret images. AI systems can now screen chest X-rays for tuberculosis with accuracy comparable to a trained radiologist.

• Predictive analytics: uses data to forecast what is likely to happen. Governments can anticipate disease outbreaks, crop failures, or infrastructure stress before they become crises.

Step 2: Recognize Where AI Provides the Highest Return

1) Healthcare:

AI diagnostic tools can screen for conditions like diabetic retinopathy, malaria, and tuberculosis using images from mobile phones. These tools require no specialist on site. They work in clinics that have a smartphone and an internet connection. The IMF noted in 2024 that nearly 40% of global employment is exposed to AI but in healthcare, that exposure is mostly about augmenting undertrained workers, not replacing them.

2) Agriculture:

An AI-powered WhatsApp chatbot called Darli, developed by Ghana-based Farmerline, offers farmers advice on pest management, crop rotation, and fertilizer in 27 languages including 20 African languages. Since its launch in early 2024, it has supported over 110,000 farmers across Ghana and Kenya. That is not a pilot program. That is scale, achieved with existing mobile infrastructure.

3) Education:

AI tutoring systems can adapt to a student's level in real time. In regions where teacher-to-student ratios are stretched thin, this matters. A student in rural Indonesia does not need to wait for a qualified math teacher to arrive. They can get personalized instruction on a tablet.

4) Financial Inclusion:

Credit scoring through machine learning applications has allowed fintech companies across Sub-Saharan Africa and Southeast Asia to serve customers that traditional banks turned away. With no formal credit history, alternative data mobile payments, utility bills, behavioral patterns can predict repayment probability with reasonable accuracy.

5) Public Service Delivery

AI in government means smarter resource allocation. Predictive analytics can help identify which communities are at highest risk for disease or food insecurity before emergencies happen. This is data-driven decision making at a societal level, and it is already working in several African and South Asian countries.

Step 3: Be Honest About the Constraints

• Data infrastructure: AI needs data to learn. Many developing countries have incomplete, fragmented, or inaccessible data systems. Building those systems is not optional, it is a prerequisite.

• Electricity and connectivity: Generative AI applications are compute-intensive. Without reliable power and internet access, cloud-based AI tools are inaccessible. Local or edge-computing solutions can help, but they require investment.

• Skilled talent:Machine learning engineers, data scientists, and AI product managers are scarce everywhere. In developing countries, the competition for this talent with multinational companies is severe. Building domestic AI capability takes time.

• Governance and ethics: AI systems trained on data from one context often perform poorly in another. A credit model trained on U.S. consumer data will not transfer well to Kenya. Governments need to understand AI ethics and challenge algorithmic bias, data privacy, and accountability before deploying these systems at scale.

Step 4: Focus on the Leapfrog Opportunity

Developing countries do not have to replicate the AI journey of wealthy nations. They can skip several steps entirely.

Countries that lack legacy systems in healthcare records, financial infrastructure, or government databases have a strange advantage: they can build AI-native systems from the ground up. No legacy to replace. No bureaucracy of existing platforms to navigate.

Research by Erik Brynjolfsson's team found that generative AI increased call center agent productivity by 14%, with the gains especially benefiting entry-level, lower-skilled workers. That pattern AI benefiting less experienced workers more is highly relevant in developing country contexts, where the workforce is often younger and has less formal training.

This is the role of artificial intelligence that most development economists underemphasize. It is not just automation. It is a capability multiplier for people who previously had no access to expert guidance.

Step 5: Build AI Readiness Systematically