Reinventing Development Data: How AI Is Reshaping Statistical Capacity in Asia-Pacific

ADB’s brief argues that AI and big data can dramatically strengthen statistical systems in developing Asian economies, enabling faster, more granular, and more cost-efficient insights for policymaking. But without strong foundations, reliable surveys, digital infrastructure, skilled staff, and interoperable governance frameworks, these innovations risk remaining isolated pilots rather than transformative tools.

Drawing on research from global institutions including the International Monetary Fund, the United Nations Statistics Division, the OECD, and ADB’s own Economic Research and Development Impact Department, the Asian Development Bank (ADB) argues that developing economies stand at a historic juncture: artificial intelligence and big data can radically strengthen national statistical systems, if countries invest in the foundations required for responsible digital transformation. In an era of tight budgets and mounting climate, social, and economic pressures, the brief emphasizes that accurate, timely statistics are indispensable for good governance. Yet progress across Asia and the Pacific has been sluggish. The region’s Statistical Capacity Index rose only modestly from 65.1 to 67.1 over fifteen years, concealing stark disparities between countries approaching global best practice and those still far behind. This uneven terrain is appearing just as digital data, from satellites to mobile phones, explodes in volume, offering new possibilities for understanding development challenges with greater speed and precision than ever before.

The Rise of Machine Intelligence in Official Statistics

AI is reshaping the statistical landscape, particularly through machine learning and deep learning models capable of processing vast amounts of unstructured data. Unlike generative AI, which produces content autonomously, these models rely on human supervision and validation to classify satellite images, monitor mobility, detect anomalies, or predict agricultural output. But their potential is unevenly distributed. Most developing member countries (DMCs) score between 0.35 and 0.54 on the AI Preparedness Index, well below high-income economies, hampered by weak digital infrastructure, limited broadband, insufficient legal safeguards, and gaps in technical capacity. Bridging these divides could require as much as $200 billion in regional digital infrastructure investment, underscoring the scale of the challenge ahead.

Real-World Innovations: From Farms to Floodplains

Across the region, ADB-supported pilots showcase how AI and big data can solve persistent development bottlenecks. In agriculture, a sector vital for rural livelihoods, machine-learning techniques paired with satellite imagery are correcting long-standing biases in crop measurement in Vietnam, Georgia, and the Cook Islands. In the People’s Republic of China, AI-driven analysis of two decades of satellite images now produces agricultural GDP estimates at a one-kilometer resolution, helping governments assess climate impacts and regional disparities. Real-time population grids in the Philippines and Thailand, generated through geospatial data, strengthen disaster response and poverty monitoring. GPS evidence has revealed how floods displaced communities in Thailand, while automated ship-tracking data quantified the ripple effects of the 2021 Suez Canal blockage, which forced nearly 20 vessels bound for Asia onto longer routes, delaying shipping by over a week. These cases illustrate how modern data ecosystems can provide insights that traditional surveys alone could never produce.

Building Skills and Overcoming Barriers

To ensure these innovations are not isolated experiments, ADB pairs its research with extensive training, user-friendly tools, and open-source resources. It has published handbooks on remote sensing and machine-learning-enabled road quality monitoring, created GitHub repositories for maritime analytics, and launched dashboards that visualize tourism and mobility trends using anonymized GPS information. Workshops across Southeast Asia and the Pacific give national statisticians hands-on experience in applying these methods in their own contexts. Yet structural constraints persist. High-resolution satellite imagery remains prohibitively expensive, pushing ADB to develop a super-resolution model that enhances free, lower-resolution data, an approach now piloted in Vietnam and the Philippines. Many countries also struggle to adopt digital survey tools like computer-assisted personal interviewing, despite their ability to reduce error and speed up data processing. Weak legal frameworks inhibit access to private-sector data, even though partnerships, such as ADB’s collaboration with Alibaba, have demonstrated the value of platform data in analyzing plastic waste, small business resilience, and pandemic-era consumer behavior.

Stronger Foundations for a Data-Driven Future

The brief stresses that AI can expand but not replace the foundations of official statistics. Reliable censuses, household surveys, administrative records, and business registers provide the “ground truth” needed to train and validate AI models. Investments in interoperability, especially through Statistical Data and Metadata eXchange (SDMX) standards, remain crucial for ensuring that data systems can integrate new sources smoothly. ADB highlights Mongolia and Uzbekistan as examples where systemic modernization, supported by institutions like Statistics Korea, has created durable governance frameworks, integrated administrative databases, and advanced satellite accounts that position these countries for the AI era. Yet more than 60% of DMCs still cannot fully fund their national statistical plans, reflecting deep gaps in domestic prioritization. The brief concludes that while technology is advancing rapidly, the future of AI-powered statistics will depend above all on political leadership, sustained investment, and a commitment to integrating digital advances into national systems in ways that strengthen, rather than bypass, traditional statistical foundations.