AI investment in emerging markets must go beyond models to ecosystems: Report
New Delhi, July 5
Artificial intelligence is moving from experimental tools to a general-purpose technology that could reshape production, productivity and economic growth, but its benefits in emerging markets will depend on building sustainable local ecosystems, not just importing models, according to a new report by World Bank Group.
The report argued that AI is evolving rapidly from traditional pattern-recognition systems, to generative AI that creates content, to emerging agentic AI that can plan and execute multi-step tasks with little human help. Because adoption is spreading faster than previous tech waves, demand is strong worldwide. In emerging markets, AI offers a chance to leapfrog constraints in education, healthcare and finance, where defined tasks and large data sets exist.
However, development remains highly concentrated in a few high-income economies. For emerging markets to capture value, the authors say investment decisions must look beyond model-centric hype and assess the full operating environment: digital infrastructure, data, skills, and the actors that connect them.
AI-enabling elements include hard infrastructure like connectivity, data centers, high-performance computing and edge devices; soft infrastructure like skills programs, accelerators, research hubs and AI communities; digital public infrastructure for identity, payments and data exchange; and AI building blocks such as foundational models, MLOps platforms and data tools. The report highlights both proprietary and open-source/open-weight approaches as ways to lower costs and increase local control.
AI-enabled elements are vertical AI solutions built for specific sectors. These range from AI-enhanced versions of existing software to AI-native firms built from the ground up, including fintech credit scoring in Africa and agritech yield prediction in South America.
The report outlined three impact horizons. Short to medium term, direct gains from local adoption productivity, cost efficiency, better service delivery. Medium to long term: benefits from building domestic ecosystems, jobs, skills, exports and stronger institutions. Long term: systemic gains from global diffusion, new industries, occupations and scientific discoveries.
Key challenges flagged include fragmented markets and low purchasing power that complicate monetization, concentration among a few global players, and rapid commoditization of models and infrastructure. The handbook recommends validating product-market fit early, investing in local adaptation, and using open tools where appropriate.
The report said sustainable AI in emerging markets requires coordination across governments, businesses, investors, communities and entrepreneurs. With the right foundations, AI can support long-term economic transformation tailored to local needs and capacities.
— ANI
Reader Comments
As a teacher in a rural school, I see the potential for AI in education—but only if we have basic digital infrastructure first. We still struggle with intermittent electricity and internet. Before we talk about "ecosystems," maybe we need to fix the basics. That said, the report's focus on leapfrogging gives me hope. 😊
Interesting but overly optimistic. Emerging markets have tried "leapfrogging" before with mobile phones, and while it worked partially, the gap with developed nations remains vast. The report correctly identifies concentration risk among a few global players—but how do we realistically compete with Big Tech's billions?
The "three impact horizons" framework is practical. But the biggest challenge in India is monetization—our market is vast but fragmented, with low purchasing power. Agritech and fintech are promising, but we need public-private partnerships to make AI accessible to small farmers and small businesses. Otherwise, it'll remain an urban elite tool.
Open-source models are the way forward for India. We have the talent and the data—look at what IITs and startups are doing with open-weight models. But we need more government investment in compute infrastructure and data centers. Why should we pay licensing fees to Silicon Valley when we can build our own? 💪
The report's focus on "ecosystems" is important but a bit vague. What concrete steps are needed? For example, India's UPI is a great example of digital public infrastructure that enabled fintech innovation. Can we replicate that model for AI?
We welcome thoughtful discussions from our readers. Please keep comments respectful and on-topic.