In developing economies, the discourse surrounding Artificial Intelligence (AI) has shifted significantly from the existential concerns of advanced nations. Instead of grappling primarily with frontier risks like job displacement or existential threats, the focus is firmly on how to ensure that AI technologies are deployed to genuinely benefit their citizens. This requires not only the thoughtful design of AI tools that can integrate with and operate within existing government structures but, crucially, substantial investment in the state capacity necessary to effectively implement and leverage these powerful technologies.
The challenge of modernizing public services is a pervasive one in low- and middle-income countries. Many nations strive to deliver essential services to their populations, yet often operate with legacy systems that hinder efficiency and accessibility. The result can be a bureaucratic maze that leaves citizens struggling to access even basic provisions. Imagine a scenario where a mother undertakes a day-long journey to a rural clinic, only to be turned away because she lacks a specific form obtainable only from an office in a distant town. Or consider a farmer, eligible for crucial financial support, who abandons the process after navigating a series of discouraging queues and unhelpful encounters. Artificial Intelligence, when strategically implemented, possesses the transformative potential to bridge these systemic gaps, extending the reach and efficacy of a capable state.
The stakes for these nations are exceptionally high. AI presents a dual-edged sword: it could serve as a powerful equalizer, democratizing access to information and services, or it could exacerbate existing inequalities, widening the chasm between those who benefit from technological advancements and those who are left behind. While the potential contributions of AI to vital sectors like agriculture, healthcare, and education are immense, a stark reality check comes from the World Bank’s World Development Report 2026. This report estimates that the productivity gains from AI could be more than twice as substantial in advanced economies compared to developing ones, assuming current trends persist. This projected divergence underscores that the future trajectory of AI’s impact is not predetermined by the technology itself, but by the deliberate decisions made today by developers, investors, and governments regarding its creation, funding, and deployment.
The Crucial Element: State Capacity
Kristalina Georgieva, the Managing Director of the International Monetary Fund (IMF), articulated a key prerequisite for AI adoption during the IMF-World Bank Annual Meetings in October. She highlighted the importance of foundational elements such as electricity access, robust digital connectivity, and a workforce equipped with relevant skills. However, for those with decades of experience in development finance, it is becoming increasingly clear that while these factors are undeniably important, they are not the primary barriers to successful AI integration. The most significant hurdle, above all else, lies in building the fundamental state capacity required to effectively harness these sophisticated tools.
In contrast to the debates in advanced economies, which have largely revolved around safety protocols, cybersecurity threats, and the specter of widespread job displacement, the pressing question for developing economies is far more grounded: how can AI be made to serve the practical needs of their citizens? This necessitates AI tools that are not only technically proficient but also culturally and linguistically relevant. They must be designed to operate in local languages, trained on representative local data, and developed with a deep understanding of local realities and constraints. Furthermore, the successful deployment of AI requires a symbiotic relationship with investment in the institutions and individuals on the front lines of public service. These are the people who possess an intimate understanding of where AI can deliver tangible benefits and who can ensure its responsible and effective application. A significant portion of this critical groundwork falls to the technology companies developing these tools and the financiers supporting their endeavors.
Beyond Tool Development: Integration and Infrastructure
Even the most sophisticated AI tools will struggle to yield meaningful results if the governments tasked with their implementation lack the necessary capacity. Building this capacity involves more than simply adopting new software; it demands the strategic integration of AI systems into existing government programs and workflows. A compelling example of this approach is MahaVISTAAR, a free AI-driven advisory service developed by the Indian state of Maharashtra for its agricultural sector. This initiative empowers farmers to seek advice on crop management, weather patterns, and pest control through user-friendly interfaces accessible via app, voice call, or chat, all in their local dialect. The program’s success is remarkable: over 740,000 farmers have already enrolled, with an additional 70,000 joining each month. Critically, the cost to the government for reaching each farmer through this AI service is less than 18 cents, a fraction of the expense associated with traditional in-person outreach. The success of MahaVISTAAR lies in its conceptualization as an extension of existing public infrastructure for agricultural advisory services, rather than an isolated, standalone application.
This principle of building integrated public infrastructure was evident during a recent visit to Pakistan, where a provincial government in Punjab is actively cultivating this capacity, viewing AI as a public good. Punjab has established a dedicated government office focused on AI, fostering collaborations with local researchers to develop critical public-health applications. These include the creation of local-language tools designed to equip frontline health workers with the timely and accurate guidance needed to support mothers and children effectively.
AI tools intended for public service must be designed to function seamlessly within the existing governmental framework, not merely be adapted to it. This means they should be engineered to operate on public systems and utilize public data, adhering strictly to the data-sharing agreements and security protocols that national policymakers have established. Crucially, these tools must also be affordable and sustainable for governments to procure and maintain over the long term.
Essential Pillars for AI Capacity Building
Based on extensive experience in development, three core elements are indispensable for building robust AI capacity within developing nations:
1. Strategic Leadership and Institutional Frameworks
Governments require visionary leaders capable of identifying the most impactful use cases for AI within their specific contexts. This strategic vision must be underpinned by strong institutional ownership and effective cross-ministry coordination. An AI strategy is not merely a technological blueprint; it demands a clearly defined budget, detailed implementation timelines, and the backing of empowered ministries. Furthermore, a mechanism for ensuring binding decisions across different agencies is essential to prevent the proliferation of fragmented, overlapping projects. Such fragmentation can lead to duplication of effort, suboptimal outcomes, and the erosion of already limited institutional capabilities. The establishment of a national AI steering committee or a dedicated AI task force, comprising representatives from key ministries and technical experts, could serve as a valuable step in this direction. This body would be responsible for setting national AI priorities, overseeing funding allocation, and ensuring alignment with broader development goals.
2. Cultivating a Skilled Civil Service
A critical deficit exists in the number of civil servants possessing the requisite AI expertise to effectively commission, acquire, integrate, evaluate, and regulate these complex systems. Addressing this requires the development of robust talent pipelines that channel individuals with specialized technical skills—distinct from general digital literacy—into government roles. While even wealthy nations are grappling with the challenge of meeting the demand for AI talent, developing countries can ill afford to overlook this imperative. Initiatives such as government-sponsored training programs in AI and data science, partnerships with universities for specialized curricula, and the creation of attractive career paths for AI professionals within the public sector are vital. Furthermore, fostering a culture of continuous learning and professional development within government ministries will be crucial to keeping pace with the rapid advancements in AI technology.
3. Integrated Digital Public Infrastructure
The foundation for effective AI deployment rests on the establishment of integrated digital public infrastructure. This includes robust e-government service delivery platforms, secure digital identity systems, efficient digital payment mechanisms, and a secure data exchange layer that facilitates interoperability between different government systems. Weak digitization and fragmented, siloed vertical systems represent direct constraints on AI performance, scalability, and safety. Once this foundational infrastructure is in place, AI can, in turn, play a transformative role in enhancing the security and efficiency of government systems themselves. AI-powered tools can be instrumental in detecting fraudulent activities, identifying cyber threats, and recognizing anomalous patterns that might otherwise go unnoticed. For instance, implementing a national digital ID system can streamline access to various government services, reducing the need for physical paperwork and minimizing opportunities for fraud. Similarly, a secure data exchange layer can enable ministries to share anonymized data for policy analysis and service improvement, while maintaining citizen privacy.
The Path Forward: Choices and Consequences
The creation of the necessary AI tools may, in some respects, be the more straightforward component of this complex undertaking. However, the ultimate success of AI in reaching and benefiting the most vulnerable populations—whether it’s the mother seeking healthcare or the farmer requiring agricultural advice—will be determined less by the relentless pace of technological innovation and more by the deliberate choices governments make today. If policymakers and leaders in developing economies prioritize building state capacity, fostering digital infrastructure, and ensuring AI is designed and deployed with local needs at its core, then AI can indeed become a powerful engine for progress. It can empower nations to fulfill their most fundamental and important duty: to serve their citizens effectively, equitably, and efficiently in the 21st century. The World Bank’s ongoing work in supporting digital transformation and capacity building across various developing nations, often in collaboration with national governments and international partners, represents a significant commitment to navigating this complex landscape. These efforts, spanning policy advice, technical assistance, and financial support, aim to lay the groundwork for a future where AI truly serves as a force for inclusive development.
