The AI Leap: How Emerging Market Economies Could Be Transformed—For Better and Worse

Artificial intelligence is often framed as the next general-purpose technology—on par with electricity or the internet. But its global impact won't be evenly distributed. For emerging market economies (EMEs), AI represents something more complex: a simultaneous opportunity to leapfrog stages of development and a risk of deepening existing inequalities.

The question isn't whether AI will reshape these economies—it's how, and who benefits.

1. A New Engine for Productivity Growth

Many emerging markets struggle with low productivity across key sectors like agriculture, manufacturing, and public services. AI has the potential to unlock step-change improvements:

• Agriculture: AI-driven crop monitoring, predictive weather models, and precision farming can significantly increase yields while reducing input costs.
• Healthcare: Diagnostic tools powered by AI can help address shortages of doctors, particularly in rural areas.
• Education: Personalized learning systems can scale access to high-quality instruction where teacher supply is limited.

In countries where infrastructure gaps have historically slowed growth, AI could enable leapfrogging—similar to how mobile phones bypassed landline expansion.

2. Labor Markets: Disruption vs. Augmentation

The labor implications are more ambiguous—and potentially more consequential.

Risk: Job Displacement in Key Sectors

Many EMEs rely heavily on:

• Business Process Outsourcing (BPO)
• Customer support services
• Routine manufacturing

These are precisely the sectors most exposed to automation through AI. If global firms replace outsourced human labor with AI systems, countries like India, the Philippines, and Kenya could face significant job losses.

Opportunity: Human-AI Complementarity

At the same time, AI can augment workers:

• Enabling small businesses to operate more efficiently
• Allowing informal workers to access new digital marketplaces
• Increasing productivity of mid-skill roles rather than eliminating them

The outcome will depend heavily on education systems and reskilling capacity.

3. The Data and Infrastructure Divide

AI systems require:

• High-quality data
• Compute infrastructure
• Reliable internet access

Many emerging markets lag in all three.

This creates a risk of AI dependency, where:

• Models are built in developed economies
• Local firms rely on foreign platforms
• Value accrues primarily to global tech companies

Without investment in local AI ecosystems, EMEs may become consumers rather than producers of AI technology—capturing only a fraction of its economic value.

4. Government Capacity and Public Services

AI could dramatically improve state capacity in emerging markets—if deployed effectively.

Potential applications include:

• Fraud detection in tax systems
• Targeting of social welfare programs
• Urban planning and traffic optimization

However, there are also governance risks:

• Weak regulatory frameworks may lead to misuse (e.g., surveillance)
• Limited technical expertise can result in poor procurement decisions
• Bias in imported AI systems may not translate well to local contexts

In short, AI can strengthen institutions—but only if institutions are strong enough to govern it.

5. Financial Inclusion and Entrepreneurship

One of the most promising areas is financial access.

AI-powered systems can:

• Assess creditworthiness using alternative data (e.g., mobile usage, transaction history)
• Expand lending to underbanked populations
• Enable new fintech platforms

This could unlock entrepreneurship at scale, especially in regions where traditional banking infrastructure is weak.

At the same time, poorly designed models could reinforce bias or exclude vulnerable populations if not carefully monitored.

6. Geopolitics and Technological Sovereignty

AI is increasingly tied to global power dynamics.

Emerging markets face strategic choices:

• Align with U.S.-based AI ecosystems?
• Adopt Chinese AI infrastructure and standards?
• Build independent capabilities?

These decisions will shape:

• Data governance
• Economic dependencies
• Long-term innovation capacity

Countries that invest early in AI talent, research, and infrastructure will have far greater strategic autonomy.

The Bottom Line: A Fork in the Road

AI will not automatically reduce global inequality. In fact, without intentional policy and investment, it could amplify it.

Emerging market economies face a fork in the road:

Path 1: Passive Adoption

• Reliance on foreign AI systems
• Job displacement without reskilling
• Limited value capture

Path 2: Strategic Integration

• Investment in local AI ecosystems
• Workforce development and education
• Smart regulation and governance
• AI applied to local, high-impact problems

The difference between these paths will determine whether AI becomes a tool for economic convergence—or divergence.