Summary
For emerging market economies, AI cuts both ways. It could unlock step-change productivity gains in agriculture, healthcare and education, or deepen dependency on foreign platforms, displace labour in key outsourcing sectors, and widen global inequality. The difference comes down to whether countries adopt AI passively or invest deliberately in local ecosystems, workforce development and sensible regulation.
Artificial intelligence gets described as the next general-purpose technology, on a par with electricity or the internet. Its effects won't land evenly. For emerging market economies it represents something more double-edged: a chance to leapfrog stages of development, alongside a real risk of entrenching the inequalities that already exist.
The question isn't whether AI reshapes these economies. It's how, and who ends up benefiting.
A New Engine for Productivity
Many emerging markets carry low productivity across agriculture, manufacturing and public services. AI could produce genuine step changes in all three.
- Agriculture: crop monitoring, predictive weather models and precision farming raise yields while cutting input costs.
- Healthcare: AI-assisted diagnostics help offset shortages of doctors, particularly in rural areas.
- Education: personalised learning systems widen access to good instruction where teacher supply is thin.
Where infrastructure gaps have historically capped growth, this allows leapfrogging, much as mobile phones let countries skip the expense of building out landline networks.
Labour Markets: Disruption and Augmentation
The labour picture is more ambiguous, and probably more consequential.
The risk is concentrated displacement. Many emerging economies depend heavily on business process outsourcing, customer support and routine manufacturing. Those are precisely the sectors most exposed to automation. If global firms swap outsourced human labour for AI systems, countries such as India, the Philippines and Kenya face serious job losses in the sectors they built around.
The opportunity is complementarity. AI also augments workers: letting small businesses operate more efficiently, opening digital marketplaces to informal workers, and raising the productivity of mid-skill roles rather than eliminating them.
Which effect dominates depends almost entirely on education systems and the capacity to reskill at speed.
The Data and Infrastructure Divide
AI needs high-quality data, compute infrastructure and reliable connectivity. Many emerging markets are behind on all three.
That creates a dependency risk. Models get built in developed economies, local firms rely on foreign platforms, and the value accrues to global technology companies rather than to the countries using the tools.
Without investment in local ecosystems, emerging economies become consumers of AI rather than producers of it, and capture only a fraction of the economic upside.
Government Capacity and Public Services
Used well, AI could meaningfully strengthen state capacity: fraud detection in tax systems, better targeting of social welfare programmes, urban planning and traffic management.
The governance risks are equally real. Weak regulatory frameworks invite misuse, surveillance in particular. Limited technical expertise produces poor procurement decisions that lock in bad systems for years. And bias baked into imported systems often transfers badly to local contexts.
AI can strengthen institutions, but only where institutions are already strong enough to govern it.
Financial Inclusion
Financial access is among the most promising applications. AI systems can assess creditworthiness from alternative data such as mobile usage and transaction history, extend lending to underbanked populations, and support new fintech platforms.
That could unlock entrepreneurship at scale in regions where conventional banking infrastructure is thin. The same models, designed carelessly, will reinforce existing bias or exclude vulnerable people entirely. This one depends heavily on monitoring.
Geopolitics and Technological Sovereignty
AI is increasingly bound up with global power. Emerging markets face a strategic choice between aligning with US-based ecosystems, adopting Chinese infrastructure and standards, or building independent capability.
That decision shapes data governance, long-term economic dependencies, and domestic innovation capacity for decades. Countries that invest early in talent, research and infrastructure keep far more strategic autonomy than those that don't.
A Fork in the Road
AI won't reduce global inequality on its own. Without deliberate policy and investment it will amplify it.
Passive adoption means reliance on foreign systems, job displacement without reskilling, and limited value capture at home.
Strategic integration means investing in local ecosystems, developing the workforce, regulating with some competence, and pointing AI at local problems that actually matter.
Which path gets taken determines whether AI drives economic convergence or widens the gap.