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The promise of AI For developing economies, AI presents a dual promise. First, it offers the potential to leapfrog traditional development pathways. In agriculture, AI-powered precision farming tools are improving crop yields while reducing water and fertilizer use. In infrastructure, predictive analytics can optimize maintenance of roads, ports and energy systems, reducing costs and increasing resilience. In finance, AI-driven credit scoring is expanding access to capital for small and medium-sized enterprises, particularly women- and youth-led businesses that have historically been excluded from official financial systems. Second, AI can enhance public service delivery. From early disease detection to intelligent traffic management and targeted social protection programs, governments can use data-driven systems to allocate resources more effectively. The World Economic Forum estimates that government technology and digital public services could reduce government efficiency‑related costs by around 30 percent, unlocking up to US$5.8 trillion in global savings by 2034. For developing countries operating under tight fiscal constraints, such efficiency gains are transformative.
Yet we must also confront the risks with clarity and responsibility.
AI is not neutral. It reflects the data it is trained on, the infrastructure it depends upon and the governance frameworks that guide its use. Without inclusive data sets, we risk embedding bias into automated decision-making systems. Without robust digital infrastructure, entire populations may be excluded from the benefits of AI-driven growth. Additionally, as of 2026, approximately 2.2 billion people remain offline, limiting a large number of the population from the benefits of the digital era. Connectivity gaps translate directly into opportunity gaps. Moreover, the energy demands of AI systems are rising sharply. Data centers already consume about 1.5 percent of global electricity, some 415 TWh in 2024, and their demand is projected to more than double to roughly 945 TWh by 2030, slightly above Japan’s current electricity use. AI‑focused data centers are highly concentrated in a few regions, placing substantial pressure on local grids. This highlights a critical imperative: digital transformation must be tightly aligned with the energy transition, with greater investment in renewables, efficient data infrastructure and climate‑conscious innovation. There is also the question of employment and social stability. While AI can drive productivity gains, it may also disrupt labor markets. Structural changes driven by AI, automation and the green transition could create around 170 million new jobs and displace about 92 million existing roles by 2030, resulting in a net increase of roughly 78 million jobs globally, provided that reskilling and upskilling keep pace. For development institutions, this signals the urgency of investing not only in digital infrastructure, but in human capital. Digital literacy, data skills and adaptive learning systems must become central pillars of development strategies. The role of development institutions What, then, does this mean for multilateral development banks and international financial institutions? First, development finance must integrate digital transformation as a cross-cutting enabler. AI should not be treated as a standalone sector, but as a tool embedded across agriculture, energy, health, urban planning and financial inclusion. Project design must consider digital components from the outset, ensuring interoperability, data governance, and cybersecurity resilience.
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