The World Bank Group has released a report arguing that artificial intelligence is transitioning from experimental tools to a general-purpose technology with the potential to reshape production, productivity, and economic growth. However, the benefits in emerging markets will hinge on building sustainable local ecosystems rather than merely importing models.
AI Evolution and Adoption
The report notes that AI is evolving rapidly from traditional pattern-recognition systems to generative AI that creates content, and further to emerging agentic AI capable of planning and executing multi-step tasks with minimal human intervention. Adoption is spreading faster than previous technology waves, generating strong global demand. In emerging markets, AI presents an opportunity to leapfrog constraints in education, healthcare, and finance, particularly where defined tasks and large datasets exist.
Concentration and Investment Needs
Development remains highly concentrated in a few high-income economies. According to the report, for emerging markets to capture value, investment decisions must look beyond model-centric hype and assess the full operating environment, including digital infrastructure, data, skills, and the actors that connect them. The report highlights that AI-enabling elements include hard infrastructure such as 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. Both proprietary and open-source/open-weight approaches are highlighted as ways to lower costs and increase local control.
AI-Enabled Solutions and Impact Horizons
AI-enabled elements are vertical AI solutions built for specific sectors, ranging from AI-enhanced versions of existing software to AI-native firms. Examples include fintech credit scoring in Africa and agritech yield prediction in South America. The report outlines three impact horizons:
- Short to medium term: Direct gains from local adoption, including productivity improvements, cost efficiency, and better service delivery.
- Medium to long term: Benefits from building domestic ecosystems, such as jobs, skills, exports, and stronger institutions.
- Long term: Systemic gains from global diffusion, leading to new industries, occupations, and scientific discoveries.
Key Challenges and Recommendations
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 states that 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.
