Bridging AI and DPI for Long-term Development
Naveen Varshan Ilavarasan, DPI Specialist, UNDP
As countries advance their efforts towards designing and implementing digital systems that can deliver timely, large-scale public and private benefits, artificial intelligence (AI) and digital public infrastructure (DPI) have emerged as two critical enablers. Recognizing this potential, the G20 Troika of Brazil, India and South Africa — three major developing economies — emphasized that well-designed DPI, when augmented by AI, can transform lives and accelerate public outcomes. Though conceptually different, the intersections between AI and DPI are particularly evident through three key dimensions.
The foundational linkage between DPI and AI, where DPI provides the backbone that makes AI effective, scalable and relevant for people.
The real-world applications that demonstrate how AI and DPI together serve public needs across sectors.
The principles that help guide AI integration across DPI, underscoring the need for safeguards to ensure value for all.
In recent months, this evolving intersection has gained momentum, not just as a technological opportunity, but as a strategic importance. Nandan Nilekani, former Chairman of India’s Unique Identification Authority, has emphasized the potential of combining foundational digital public infrastructure with AI to drive scale, inclusion and public value. Others, including UCL’s Professor David Eaves and Sarosh Nagar, have highlighted the distinct trajectories of AI and DPI, as well as the possibilities for their convergence to strengthen public outcomes.
This leadership—alongside the focus of the G20 Troika countries highlighted earlier—has contributed to a growing recognition that aligning AI with DPI offers a powerful pathway to deliver meaningful public value, and that this alignment cannot be left to chance. Across countries where UNDP works, this is not merely a trend, but a deeper transformation that demands country leadership, homegrown innovation and a whole-of-society approach—including government, civil society, the private sector and the public themselves.
At the foundational level, DPI can provide the underlying data infrastructure—such as civil,functional or entity-based registries, non-personal data and open datasets—that enhance the application of AI in real-world contexts. When such high-quality, country-specific and consent-based data related to public services is available, AI models and applications can be trained in ways that reflect local needs and contexts.
Central to DPI is a focus on interoperability, allowing different systems—such as identity and healthcare, or payments and social protection—to connect and work together. This, in turn, unlocks the integration of diverse datasets, enriches the context for AI use, and enables the development of AI-powered services that draw on multiple systems.