World Bank: Build local AI ecosystems in emerging markets for growth

AI is becoming a general-purpose technology, but its benefits for emerging markets hinge on building local ecosystems, not just importing models, a new World Bank report finds. Success requires investment in infrastructure, skills, and data.

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.

The Path to AI Adoption in Emerging Markets

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.

Core Components of the AI Ecosystem

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.

Projected Impact Horizons

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.

Navigating Challenges and a Path Forward

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.

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