The World Bank's private sector arm says AI value in emerging markets will come from local connectivity, data, skills, and institutions, not from importing foreign models as finished goods.
The World Bank's private-sector arm is publishing a sequenced playbook with a blunt message for emerging-market governments and investors: AI value will not come from importing foreign models as finished goods, but from building the local ecosystems of connectivity, data, skills, and institutional connective tissue that make AI actually work in a country.
The International Finance Corporation's 2026 report "Accelerating Artificial Intelligence Investment in Emerging Markets" frames the choice as a three-horizon decision: short-term productivity from local adoption, medium-term gains from building domestic ecosystems, and long-term systemic returns from global diffusion. The shift that makes the timing urgent is agentic AI (systems that plan and execute multi-step tasks with little human help) moving from concept into deployment just as the window for building domestic AI infrastructure is still open.
Emerging markets face a closing window on two fronts. AI development remains highly concentrated in a few high-income economies, and the commoditization of models and infrastructure is accelerating, meaning the competitive edge for emerging markets will not come from owning the latest model. It will come from the surrounding operating environment.
The report identifies four layers that define that environment. Hard infrastructure: connectivity, data centers, high-performance computing, and edge devices. Soft infrastructure: skills programs, accelerators, research hubs, and AI communities. Digital public infrastructure: shared systems for identity, payments, and data exchange. And AI building blocks: foundational models, MLOps platforms (the tooling for deploying and maintaining AI in production), and data tools, including both proprietary and open-weight approaches that lower costs and increase local control.
The vertical case is where the ecosystem argument becomes concrete. The report highlights fintech credit scoring in Africa and agritech yield prediction in South America as sector-specific AI where defined tasks, large local datasets, and clear monetization paths already exist. These are not general-purpose AI plays; they are narrow, workflow-shaped solutions built on local data and institutional knowledge that imported models rarely have.
The ANI wire's summary of the report also notes the IFC's warnings against a naive investment posture. Fragmented markets and low purchasing power complicate monetization. The handful of global players that dominate model development are not waiting. And as models themselves commoditize, value capture shifts downstream to integration, data, and domain expertise, precisely the layers where emerging-market operators can build a defensible position.
This is why the ecosystem frame is not semantic. If an emerging-market policymaker or investor funds a single model-deployment project, they are funding a finished good that depreciates as the underlying model becomes a commodity utility. If they fund skills pipelines, digital public infrastructure, and sector-specific data assets, they are funding the substrate that every subsequent AI deployment will sit on, regardless of which model is in fashion.
Capital rebalancing toward ecosystem build-out is the next test. The IFC is positioning this report as a handbook for investment decisions; whether development banks, sovereign funds, and private investors rebalance from model imports toward ecosystem build-out is the watch item through the rest of 2026 and into 2027.